Journal Name: Computer Fraud and Security ISSN: 1361-3723 Journal E-mail: editor@computerfraudsecurity.com Paper Publication Fee: 1500 USD Welcome to the Computer Fraud & Security Journal (CFSJ), your premier source for insights and research in the rapidly evolving fields of cybersecurity, fraud prevention, and digital security. Our Mission At CFSJ, our mission is to provide a comprehensive platform that bridges the gap between academic research and practical application in the world of computer fraud & security. We are dedicated to advancing the understanding of cybersecurity threats, fraud prevention techniques, and the technologies that protect digital assets. Our goal is to empower researchers, practitioners, and decision-makers with the knowledge and tools they need to combat cyber threats effectively. What We Do CFSJ is a leading peer-reviewed journal that publishes cutting-edge research and analysis on various aspects of computer fraud & security. Our content includes: Original Research Articles: In-depth studies and innovative research findings that push the boundaries of knowledge in cybersecurity and fraud prevention. Review Articles: Comprehensive overviews of current trends, challenges, and advancements in the field. Case Studies: Real-world applications and analyses of cybersecurity incidents and fraud cases. Technical Notes: Short reports on emerging technologies, methodologies, and tools in the domain of computer security. Our Editorial Team Our editorial board comprises experts from academia, industry, and government sectors, each bringing a wealth of knowledge and experience to the table. Our reviewers are specialists in their fields, ensuring that the content we publish is of the highest quality and relevance. Why Choose Us? Expertise: Our contributors are recognized leaders in cybersecurity and fraud prevention, providing authoritative and up-to-date information. Quality: We uphold rigorous standards for research and review, ensuring that every article published meets our high-quality benchmarks. Impact: By disseminating valuable research and insights, we aim to influence both policy and practice in the field of computer security. Get Involved We invite researchers, practitioners, and professionals to contribute to our journal, share their expertise, and engage with our community. Whether you are interested in submitting your research, reviewing articles, or simply staying informed about the latest developments, CFSJ is your go-to resource for all things related to computer fraud & security.
Epistemic Side-effect Effect of Moral Decision-making in Autonomous Driving
The Epistemic Side-Effect Effect (ESEE) influences how people perceive the reasonableness of ethical decisions made by autonomous driving and the extent to which the driving system is informed of adverse outcomes. When autonomous driving faces complex adverse outcomes, it must deal with challenges related to knowledge and responsibility attribution. In order to minimise the impact of ESEE, more advanced probabilistic assessment models of adverse outcomes need to be built into the algorithmic system to overcome the knowledge attribution problem, and transparency requirements and responsibility tracking mechanisms need to be established to ensure that responsibilities are clearly defined after an accident to address the responsibility attribution problem.
Economic Growth and Its Dynamics based on Resource Allocation
Global economic growth is deeply mired in weakness and has become the world’s most pressing challenge. From the perspective of resource allocation, economic growth prerequisites include factor utilization efficiency and factor combinations that align with technical-economic relationships between production factors constituting products. The driving forces of economic growth stem from population growth and optimal resource allocation in open economies under the dual pressures of resource scarcity for human survival and development, along with technological expansion. The root causes of cyclical economic growth lie in the alienation of prices, technology, and capital. This paper's conclusions provide insights for breaking through the constraints of cyclical economic growth and achieving high-quality sustainable economic development.
Designing Identity with AI: Leveraging Visual Communication and Deep Learning for National Cultural Brand Development
This meta-analysis explores AI-generated visual communications and deep learning in national cultural branding and examines whether AI-generated designs can effectively produce culturally relevant and engaging brand identities. The study reveals that AI-generated designs have a mean accuracy rate in recreating cultural patterns at 88.5% and perform better than traditional means in public interactions, with click-through rates being 50% higher and user interaction longer. AI-generated designs score lower in terms of cultural authenticity (7.2/10) than human-designed elements (8.6/10) due to limitations in interpreting subtle cultural contexts and evoking emotions. Techniques such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) have great potential, with GANs being robust in visual appeal and CNNs in pattern detection. The findings emphasize that AI's computational power can be blended with human knowledge to ensure cultural authenticity and public acceptance. This study concludes that AI can aid in cultural branding, but a balanced approach combining technology and human imagination can be used to generate effective and powerful national identities.
Leveraging Machine Learning for Enhanced English Written Translation: Semantic Precision and Contextual Adaptation
This meta-analysis explores the application of machine learning to expand the capabilities of written language translation of the English language with a focus on semantic accuracy and contextual adjustment. By consolidating 68 industry reports and peer-reviewed research, the study compares performance indicators such as BLEU scores, METEOR scores, and failure error rates. Findings indicate that transformer models outperform older statistical methods with a 2.5 improvement in translation accuracy and a 73% failure reduction in ambiguous phrases. Reinforcement learning-enhanced models also increase the optimization of legibility and fluency with human evaluation scores of over 90%. Efficiency in computations also increases with a 67% improvement in latency compared to older methods. Despite such advancements, concerns of bias, ethics, and sustainability exist. Future research must address such concerns as it also explores hybrid methods and multimodal integration of AI. The study presents a data-driven model for optimizing AI-based translation systems, validating the potential of such systems to increase global communication and language technology.
Research and Evaluation Analysis of Sliding Mode Control based on Linear Matrix Inequality for Two-Wheeled Self-Balancing Robot
The dynamic model of a two-wheeled self-balancing robot (TWSBR) has the characteristics of multivariable, strong coupling, nonlinear, and unstable control performance. In this paper, the mathematical model of the motor is combined with the Lagrangian equation of motion, and the dynamic model of the TWSBR based on voltage as the control input is re-established. Based on this dynamic model, a novel sliding mode control method based on linear matrix inequality (LMI-SMC) optimization is designed. The stability of TWSBR is proved using Lyapunov’s theorem. Finally, the sliding mode control method and the stability of TWSBR are verified through simulation. A detailed comparison with other typical control methods is carried out. The simulation results show that the sliding mode controller reduces the steady-state error, suppresses the chatting, and is robust.
An Efficient Post-Processing Method for SSL-PUF in MEC Security Authentication
Computation offloading is a key technology in mobile edge computing (MEC) that addresses the performance and energy constraints faced by mobile devices when handling computationally intensive tasks. Identity authentication for computation offloading is a critical issue as it ensures data security and user identity legitimate verification. Semiconductor superlattice physical unclonable functions (SSL-PUFs) are unique physical characteristics based on semiconductor superlattice materials, which can be used for secure authentication and encrypted communication in edge computing with wide applications in security authentication. However, the adoption of SSL-PUF in computation offloading for MEC applications faces two practical challenges: insufficient alignment accuracy of response signals and poor stability of SSL-PUF response signals. To address these two issues, an efficient post-processing algorithm specifically designed for SSL-PUF has been proposed. This algorithm consists of two steps. The first step involves aligning the re-sponse signals of SSL-PUF using a sequence alignment algorithm based on preset sequence, which significantly reduces the intra-chip Hamming distance of SSL-PUF. Then, a data fusion algorithm combining time majority voting mechanism is used to filter out erroneous response data, thereby improving the accuracy of SSL-PUF response signals. Experimental results demonstrate that after applying the proposed post-processing algorithm, the randomness of the signals remains largely unaffected. The maximum bit error rate of SSL-PUF response signals is reduced by 34.33%, and the average intra-chip Hamming distance decreases from 12% to 4.9%. The reliability of SSL-PUF is significantly enhanced, making it promising for secure identity authentication in mobile edge computing.
Semantic-Conditional Network for Micro-Video Summarization
The goal of video summarization is to extract key information from a raw video so that long videos can be interpreted in a short time without losing much semantic information. Previous methods primarily consider the diversity and representation of the obtained summary without paying sufficient attention to the semantic information of the resulting frame set, especially when generating summaries motivated by user queries. In this paper, we break the conventions in conditional video summarization and propose a new model to accept user queries semantically, namely Semantic-Conditional Network (SC-Net). Technically, for each video, we first search the semantically relevant video frames via a cross-modal retrieval model to convey the comprehensive semantic information in the user query. The rich semantics are further regarded as semantic prior to trigger the optimization of the summarization network, which produces summaries in a diverse and representative way. Furthermore, a novel one-stage training strategy optimizes the time complexity from polynomial to linear. Extensive experiments on publicly available datasets demonstrate promising results compared with state-of-the-art methods.
Research on Underwater Optical Image Target Detection and Network Security Protection Method based on Deep Learning
This paper studies underwater optical image target detection and network security protection methods based on deep learning. The YOLOv7-based method is used to solve the color distortion and blur caused by the scattering and absorption of light. By replacing the ELAN structure in the backbone network with the NewDSCLayer structure, the ELAN structure in the detection head module with the GSCLayer structure, and introducing the SimAM attention mechanism, the detection accuracy of overlapping targets and small targets is effectively improved, and the detection speed is also improved. In addition, this paper also designs an adaptive network security protection strategy based on deep reinforcement learning, which can identify multiple network threats through behavior analysis and feature matching, and dynamically adjust defense measures. Experiments have shown that the algorithm proposed in this paper has higher accuracy, faster response speed and higher system stability, and can provide efficient protection for network security.
Optimization of Rescue Team Scheduling under High Altitude Earthquakes Based on Reinforcement Learning
In the early stages of high-altitude earthquake disasters, efficient rescue team scheduling is critical to minimize casualties and optimize resource utilization. This study proposes HRLPPO, a hierarchical reinforcement learning framework combining Proximal Policy Optimization (PPO) with task stratification, to address the dynamic allocation of rescue teams under complex constraints. The framework divides rescue tasks into a high-level strategy for selecting teams and disaster sites, and a low-level strategy for determining personnel dispatch quantities. Key innovations include integrating high-altitude compatibility constraints, minimizing dispatch costs via distance-aware reward functions, and enabling rapid decisions through pre-trained policies.A custom reinforcement learning environment was designed to simulate real-world scenarios, incorporating rescue team capabilities, site demands, and geographical constraints. Experiments using data from the 2022 Luding earthquake in Sichuan demonstrated HRLPPO’s superiority over traditional methods (e.g., Genetic Algorithm, Ant Colony Optimization). Results showed 18.5% lower dispatch costs, 95% faster decision times (0.43s vs. 487.43s for 10-team scenarios), and 99.79% rescue satisfaction rates under both sufficient and insufficient high-altitude team conditions. The model’s robustness was further validated in large-scale scenarios (40 teams, 20 sites), achieving 81.68% overall satisfaction despite resource shortages.This work provides a novel decision-making tool for emergency management, enhancing rescue efficiency in high-altitude regions. Future efforts will integrate GIS platforms for real-time disaster response.
Residual Network with Multi-scale Information Fusion for Apple Fungal Infection Classification
Black rot, Cedar rust, and Scab are three common apple diseases caused by fungal infections. The infected areas not only impair the function of the current parts but also spread to healthy regions of the plant, necessitating early detection in production. Addressing the challenge of subtle lesion features, we have designed a deep learning model that integrates multi-scale information fusion by combining wavelet transform with a residual network, and introducing a multi-channel Collaborative Attention mechanism. In comparisons with similar models, our proposed model achieved the best results, with a classification accuracy of 98.1% for various lesions. It demonstrated excellent stability in data detection imaging under various conditions. This model provides a new perspective for apple lesion detection and may potentially be analogously applied to other types of detection in the future.
Research on the Construction of Higher Education Ecosystem Model under the Empowerment Development Strategy of Higher Education
The construction of a higher education ecosystem model is a crucial research direction for enhancing the quality and management efficiency of higher education. Based on the empowerment development strategy of higher education, this study adopts graph neural networks (GNNs) combined with genetic algorithms (GAs) for system modeling and optimization. A complex dynamic system model encompassing multiple elements such as teachers, students, courses, and teaching resources is constructed. Experimental results demonstrate that GA-GNN exhibits outstanding performance across multiple key indicators. In the robustness analysis, the performance score of GA-GNN gradually increases from an initial 0.509 to 0.891 after 800 iterations, showcasing a consistent and stable improvement trend. In terms of generalization ability, GA-GNN achieves a score of 0.895 on the university performance dataset and 0.942 in the direction of curriculum design improvement, indicating its broad adaptability across different datasets and application scenarios. Furthermore, GA-GNN also performs exceptionally well in convergence, with an initial convergence score of 0.440 and reaching 0.950 after 800 iterations, far surpassing the performance of other algorithms.In summary, GA-GNN demonstrates wide applicability and excellent performance in higher education management, possessing efficient modeling capabilities and application value within the complex and dynamic higher education ecosystem.
Comparing Prehistoric Subsistence Practices Revealed by Pottery Residue Analysis Across Asia
Asia, as a cradle of human civilization, saw its prehistoric inhabitants develop diverse subsistence patterns due to varying geographical environments, climatic conditions, and resource distributions. Traditional archaeological methods, while offering some insights into these patterns, are limited in revealing detailed and cultural aspects. In recent years, the analysis of pottery residues has emerged as a new approach, gaining increasing attention. As an essential part of prehistoric life, pottery residues can directly reflect important information such as food sources, processing methods, and trade interactions. This study systematically classified and summarized 103 published analyses (including more than 240 sites) of prehistoric pottery residues across Asia, spanning from the early Neolithic to the Iron Age. Through statistical charts and comparative analysis, the research uncovered significant diversity in subsistence strategies among different regions: East Asia relied heavily on plant resources, while also utilizing aquatic and animal resources; West Asia was dominated by animal fats, with frequent occurrences of dairy products and beeswax, suggesting early animal husbandry and trade activities; Central Asia showed a clear dependence on ruminant animals and horse milk; and North Asia focused on marine resources. The results indicate that the subsistence practices of prehistoric people in Asia were influenced by multiple factors, with unique adaptive strategies emerging in different regions. These differences not only reflect the prehistoric populations' ability to utilize and adapt to their natural environments but also laid the foundation for subsequent cultural exchanges. Additionally, the study highlights the importance of interdisciplinary approaches and recommends integrating faunal and floral archaeological data with isotope studies in future research to achieve a more comprehensive understanding of ancient human subsistence and fill existing research gaps.
A New Algorithm for High Dynamic Range Image Denoising based on Generative Adversarial Networks
In this study, a new algorithm for high dynamic range image denoising based on generative adversarial network is successfully designed and implemented, and a series of innovative and practical research results are achieved in the field of image denoising. In terms of algorithm design, the structure of the generative adversarial network is innovatively optimised, which enhances the ability of retaining image details, judges the authenticity of the generated image more effectively, and improves the adversarial effect of the generative adversarial network. In terms of loss function design, a multi-loss function optimisation strategy that integrates the consideration of adversarial loss, content loss and structural loss is proposed to make the generated denoised image visually similar to the real noise-free image through the adversarial game between the generator and the discriminator, reasonably adjusting the weight of each loss term to achieve effective constraints on the generator and improve the quality of the denoised image. Compared with traditional denoising algorithms such as mean filtering, median filtering and Gaussian filtering, this algorithm has significant advantages in objective evaluation indexes such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), which can remove noise more effectively while retaining the details and structural information of the image in a better way, and the visual effect of the denoised image is significantly improved. The denoising time of a single image in the testing stage is short, and the memory consumption is within the acceptable range, which can meet the needs of practical applications.
The Mediating Role of Communication Satisfaction between Leader's Communication and Turnover Intention
Purpose: This study is the first to examine the effect of leadership communication on employee turnover intention in microenterprises, as well as the mediating role of communication satisfaction in the relationship between leader's communication and turnover intention. Methods: This study employed a 2 stages cross-sectional design to collect 436 valid responses by using simple random sampling technic from workers at microenterprises in Sichuan Province, China. The data was analyzed using SmartPLS 4.0 for structural equation modelling and SPSS 20.0 for descriptive statistics. Results: The results showed that there were significant negative correlations between leadership communication competence, leadership communication style, leadership communication frequency and employee turnover intention; there was a mediating effect of communication satisfaction between leadership communication competence, leadership communication style, leadership communication frequency and employee turnover intention, and it was partially mediated. Practical implication: This finding suggests that in microenterprises, organizations must recognize the importance of leadership communication on employee turnover intention. Managers in microenterprises need to build good leadership-membership relationships with their employees through appropriate, frequent, and effective communication. Focusing on effective leadership communication, rather than solely on monetary compensation, offers a strategic way to manage operating costs. Originality/value: This study confirms leader-member exchange theory and extends the applicability of lmx theory, and also highlights communication satisfaction as a key factor in relationship quality. It points out how employees' subjective experience of communication quality translates into behavioural responses. This study provides a deeper understanding of LMX theory in explaining employee attitudes and behaviours
Asia, as a cradle of human civilization, saw its prehistoric inhabitants develop diverse subsistence patterns due to varying geographical environments, climatic conditions, and resource distributions. Traditional archaeological methods, while offering some insights into these patterns, are limited in revealing detailed and cultural aspects. In recent years, the analysis of pottery residues has emerged as a new approach, gaining increasing attention. As an essential part of prehistoric life, pottery residues can directly reflect important information such as food sources, processing methods, and trade interactions.
Application of Artificial Intelligence in Online Fraud Detection: Research on Intelligent Protection Systems based on College Students' Cybersecurity Education
The increasing reliance on digital transactions has exposed college students to heightened fraud risks, necessitating advanced cybersecurity solutions. This meta-analysis examines the effectiveness of Artificial Intelligence (AI)-driven fraud detection systems and their role in protecting college students from online fraud. Findings reveal that deep learning models achieve up to 96.8% accuracy, significantly outperforming traditional rule-based fraud detection methods. AI-driven fraud detection also reduces false positives, enhances response times, and increases user engagement with security alerts. However, regression analysis indicates a strong inverse correlation (r = -0.91) between cybersecurity awareness and fraud incidents, highlighting the critical need for cybersecurity education to complement AI fraud prevention strategies. The study emphasizes the importance of integrating AI-based security frameworks with cybersecurity training programs to enhance digital safety for students. Future efforts should focus on refining AI models and increasing student awareness to mitigate fraud risks effectively.
Application of Deep Learning in Multi-Style Dance and Music Matching Choreography
In recent years, with the development of the digital era, music to dance generation research has received extensive attention from industry and academia, and has become one of the basic tasks in the cross-modal field, which can be widely used in entertainment, education, virtual and other fields, and has a good application prospect. Based on deep learning, the choreography system is proposed in this paper. In this paper, a novel automatic music choreography system is proposed, aiming to realize efficient matching and generation of music and dance movements through deep learning models. In the study, a bidirectional cyclic gating unit is used as the core network structure, and multi-temporal modeling is carried out for the timing characteristics of the dance movements. Two standard motion capture datasets, Laban-16 and Laban-48, were used in the experiments to verify the effectiveness of the proposed algorithm. The accuracy of the model in continuous motion recognition is significantly improved on the Laban-16 dataset, with a recognition rate of 72.79%, demonstrating strong generative ability and innovation, and the recognition rate on the Laban-48 dataset is 68.92%. In addition, the experimental results show that the multi-temporal-based algorithm outperforms the traditional joint features in motion recognition performance, verifying the effectiveness of feature selection.
Diversity Analysis of Flora in Secondary Growth of Forest in Fuyang, Hangzhou
On the basis of a comprehensive survey and data collation, the floristic characteristics of secondary forests in Fuyang District, Hangzhou were studied, and the level of biodiversity in the area was assessed by investigating the plant species, their numbers and their distribution. The results showed that a total of 267 species of vascular plants belonging to 77 families and 165 genera were identified within the geographical scope of this study. In terms of specifics, ferns included 6 families, 6 genera and 8 species, while gymnosperms covered 6 families, 11 genera and 12 species, and angiosperms contained 65 families, 148 genera and 247 species. In the classification of families, 20 families contained only a single species and 42 genera also contained only a single species. In terms of family composition, Lauraceae and Rosaceae were prominent with 20 and 21 species, respectively. In the classification of genera, Holly (Ilex) was the most species-rich genus with 10 species count. The ferns in the study area are mainly characterized by a cosmopolitan distribution, an Old World tropical distribution, and an interrupted distribution between tropical Asia and tropical America. Seed plants have a diverse range of distribution areas, mainly in the tropics and temperate zones. The main vegetation types in the study area include warm coniferous forests, evergreen deciduous broadleaf mixed forests, evergreen broadleaf forests, bamboo forests, and scrub grasslands.
Detection of Anomalies in Iot Networks with an Efficientnet-Dicenet Fusion Model
With the rapid expansion in the number of devices on the Internet of Things (IoT), it has become quite important to have robust anomaly detection methods to save the IOT networks from growing security vulnerabilities. The EfficientNet-DiCENet fusion model is introduced, which combines the compound scaling of EfficientNet with the depthwise convolutions of DiCENet for further improving anomaly detection in IoT networks. A meta-analysis was used to compare traditional ML and DL methods. The results indicate that the proposed model reaches 98.6% accuracy, with a false positive rate of 3.2%, which exceeds the performance of the existing models in terms of detection performance and computational efficiency. The model also presents low energy consumption (3.7 mJ) to enable real-time, resource-constrained IoT devices. This research contributes to IoT security by presenting an optimized, scalable, and efficient anomaly detection framework. Future work will focus on enhancing adversarial robustness and real-world deployment validation.
Enhancing Service Innovation Through Ai-Based Prediction Models in Digital Transformation
Service innovation has dramatically changed digital transformation by integrating artificial intelligence (AI) based prediction models. This study explores a wide range of studies and computational experiments to determine the effect produced by AI-EfficientNet, DiCENet, LSTM, and transformer-based architecture on predictive accuracy and orientation of the service. The results indicate that transformer-based models surpass traditional machine learning methods with an accuracy of 96.2% and a minimum mean squared error (MSE) of 0.007. The most important features in service innovation are the customer purchase history and real-time demand data. Fortunately, there remains a computational inefficiency problem, and thus, an optimal necessity still exists for model pruning and quantization. This highlights the interdependent role of responsible deployment of AI in the context of the tradeoff between accuracy and ethical and computational concerns. This study provides useful ideas to businesses and politicians on adopting AI and its applications in enhancing decision-making and improving the efficiency of providing a service in the context of digital transformation.
Risk Score Estimation and Features Ranking Using Regression based on Deep Learning Models
This study explores the effectiveness of regression-based deep learning models for risk score estimation and feature ranking, comparing them with traditional machine learning approaches. A comprehensive meta-analysis and computational experiments were conducted on different datasets to assess the model performance. The study indicates that deep learning models, especially transformer-based architectures, can predict more accurately (R² = 0.95 and mean squared error (MSE) = 0.007), compared to more tried and traditional methods such as linear regression and random forest. Transformer models also achieve more refined and interpretable feature rankings, which help decision-making in high-risk domains such as finance and healthcare. While such advantages exist, computational efficiency and interpretability remain challenging and require further optimization techniques. This research demonstrates the likelihood of deep learning in risk assessment and reinforces the need for future enhancements to increase the scalability and real-world applicability.
A Hybrid Deep Learning Framework for Real-Time Fault Diagnosis and Prediction of Elevator Systems
With the rapid rise of the number of elevators in China, the connection between elevators and daily production and life is more and more close. How to ensure the normal operation of elevators and timely diagnosis and early warning of faults has become the focus of the government and relevant scholars. In this context, this paper monitors and warns the corresponding faults in the main components of traction elevator (control system, mechanical system), and visually displays the monitoring and early warning results. The main research contents are as follows: analyzes the typical faults of the elevator, mainly analyzes the main characteristics of broken wire defects based on the basic type of wire rope defects; expounds the basic principle of magnetic leakage detection and the basic design idea of the excitation device; designs an elevator fault detection and early warning system. The system collects the vibration acceleration signal of the elevator car through the sensor and uses Ethernet to transmit the data flow processing engine using Fume, Kafka and Flink, aggregate and distribute the data to MySOL and database; builds the visual interface based on InteliJ IDEA development tool to realize the real-time monitoring and fault warning of elevator operation, providing the basis for fault diagnosis and preventive maintenance of the elevator.
Adaptive Threshold Cloud Detection Method based on Gaofen-1 Remote Sensing image
Aiming at the problem of fewer bands of Gaofen-1 satellite sensors, which makes it difficult to realize accurate cloud localization under the restriction of limited band information, an adaptive cloud detection method based on four bands is proposed. The method combines the physical properties of clouds, adopts the improved Haze Optimized Transform (HOT) index for the radiometrically calibrated Gaofen-1 remote sensing images, selects three sub-positional values of the histogram for threshold segmentation, and introduces the maximum interclass variance method based on this method, which takes the maximum variance value as the threshold of the average reflectance index for image segmentation, and combines the visible band ratio and the cloud in the blue wave with the cloud in the blue wave. Based on this method, the maximum inter-class variance method is introduced, and the maximum variance value is used as the threshold value of the average reflectance index for image segmentation, and at the same time, combining with the visible band ratio and the optical properties of the cloud in the blue band, the final cloud mask is obtained by multiple corrections through the cascade of multi-class indicators. The experimental results show that the proposed method makes the average accuracy of cloud recognition as high as 94.52% and the average precision rate as high as 98.97%, which improves the traditional threshold cloud detection accuracy and at the same time, it can be used to prepare labeled data to provide objective validation sets for remote sensing imagery in the process of exploring the field of deep learning.
Enhancing Landslide Prediction: Mobile Edge Computing in Rainfall-Triggered Remote Sensing
Landslides pose significant risks to infrastructure, human lives, and ecosystems, particularly in regions prone to heavy rainfall. Traditional prediction models rely on centralized cloud-based processing, which often suffers from latency and inefficiencies in real-time hazard assessment. This study explores the integration of Mobile Edge Computing (MEC) with remote sensing technologies to enhance the accuracy and timeliness of rainfall-triggered landslide prediction. By leveraging MEC’s localized processing capabilities, real-time sensor data—such as satellite imagery, ground-based precipitation measurements, and soil moisture indices—can be analyzed closer to the source, reducing transmission delays and improving predictive performance. We propose an adaptive framework that utilizes machine learning algorithms at the edge to assess landslide susceptibility dynamically. Comparative analysis with conventional cloud-based models demonstrates improved response times and predictive accuracy. The findings highlight MEC's potential in transforming landslide early warning systems, offering a scalable and efficient solution for disaster risk mitigation in vulnerable regions.
Mobile Edge Computing in Enterprise Management: The Road to Sustainable Business Practice
In the era of digital transformation, enterprises are increasingly leveraging emerging technologies to enhance operational efficiency and sustainability. Mobile Edge Computing (MEC) has emerged as a pivotal technology, enabling real-time data processing at the network edge, reducing latency, and optimizing resource utilization. This paper explores the role of MEC in enterprise management, focusing on its potential to drive sustainable business practices. By decentralizing computing power and minimizing reliance on centralized data centers, MEC contributes to energy efficiency, reduced carbon footprints, and improved system resilience. Additionally, MEC facilitates intelligent decision-making through edge AI, enhances cybersecurity, and supports seamless IoT integration. This study provides a comprehensive analysis of MEC’s applications in enterprise management, highlighting its impact on sustainability, cost reduction, and overall business agility. The findings underscore the necessity for organizations to adopt MEC as a strategic enabler of sustainable and competitive enterprise ecosystems.
Enhancing Enterprise Financial Management through Mobile Edge Computing: A Real-Time Financial Analysis Approach
In an era of rapid digital transformation, enterprises require agile and intelligent financial management solutions to ensure real-time decision-making and operational efficiency. Mobile Edge Computing (MEC) offers a decentralized computing paradigm that brings data processing closer to the source, minimizing latency and enhancing computational efficiency. This paper explores the integration of MEC into enterprise financial management to enable real-time financial analysis, risk assessment, and decision support. By leveraging MEC’s low-latency processing capabilities, businesses can enhance financial forecasting, fraud detection, and resource allocation with unprecedented speed and accuracy. The proposed framework incorporates cloud-edge collaboration, AI-driven analytics, and secure data transmission to optimize financial workflows while maintaining data integrity and compliance. Through case studies and experimental results, we demonstrate the efficacy of MEC in streamlining enterprise financial operations. This study highlights the transformative potential of MEC in reshaping financial management strategies, providing enterprises with a competitive edge in a data-driven economy.
Smart Animation Cities: Mobile Edge Computing and Big Data Analytics for Enhancing Interactive Animated Scenes and Characters
The integration of Mobile Edge Computing (MEC) and Big Data Analytics is revolutionizing the development of interactive animated scenes and characters in smart animation cities. By leveraging MEC, computational tasks are processed closer to end-users, reducing latency and enhancing real-time responsiveness in animated environments. Meanwhile, Big Data Analytics facilitates dynamic content generation, behavior prediction, and personalized interactions, improving the realism and interactivity of animated characters. This paper explores how these technologies contribute to more immersive and intelligent animation systems, enabling real-time adaptation to user inputs and environmental changes. Case studies and experimental results demonstrate the impact of edge-based architectures in optimizing animation performance, reducing bandwidth consumption, and delivering seamless experiences in smart animation ecosystems. The findings highlight the potential of MEC and Big Data Analytics in shaping the future of next-generation interactive animation.
The mediating role of Communication Satisfaction between Leader's Communication and Turnover Intention
Tingfeng Zhou, Nanxi Liu, Qiaoling Li, Fengxia Chen
Performance Analysis of Novel Dry-Water Powders for Fire Scenarios in Thermal Insulation Materials
To mitigate the casualties and property damage caused by sudden fires in thermal insulation materials, this study employs an integrated approach comprising process optimization, laboratory experiments, and fire scene simulations. The research focuses on the performance of novel dry-water powders under combustion conditions of insulation materials. Specifically, dry-water powders were synthesized using effective fire-extinguishing components such as Mg(OH)2, Al(OH)3, APP, along with the surfactant methyl hydrogen silicone oil and the gelling agent Gellan gum, all subjected to modification treatments. The results indicate that the optimal process parameters for preparing dry-water powders are a stirring speed of 24,000 rpm, a stirring time of 5 seconds, and a SiO2 ratio of 9:100. The Al(OH)3 gel dry-water powder exhibited good flowability, with an outflow rate of 3.846 g·s-1 and a repose angle of 27.5°. The novel dry-water powders had low bulk densities, ranging from 0.312 to 0.333 g·mL-1, with the APP gel dry-water powder having the highest bulk density of 0.333 g·mL-1. The majority of the dry-water powder particles were within the size range of 1.3 to 250 μm, with the Mg(OH)2 gel dry-water powder exhibiting the smallest particle size, where d10 was 15.323 μm, d50 was 55.922 μm, and d90 was 216.172 μm. In flame tests conducted on EPS, XPS, and PU insulation materials, the APP gel dry-water powder, Mg(OH)2 gel dry-water powder, and Al(OH)3 gel dry-water powder all exhibited superior fire-extinguishing performance compared to ABC dry powder. Among them, the APP gel dry-water powder demonstrated particularly notable effectiveness in extinguishing fires in EPS and XPS, while the Al(OH)3 gel dry-water powder also performed well in PU flames. Moreover, the flowability and stability of all three gel dry-water powders were significantly enhanced, effectively reducing the challenges associated with powder filling and simultaneously improving their fire-extinguishing capabilities.
Personality Traits and Risk Investment Decisions: An Examination Based on Microdata from Chinese Household Finance
Introduction: As the fundamental unit of socio-economic activities, the effectiveness of households' asset portfolios is crucial for steady wealth growth and effective risk management, forming the cornerstone of a robust financial system and economic development. Utilizing data from the China Household Finance Survey, this study explores the significance of personality traits on the effectiveness of Chinese households' financial asset portfolios from a cognitive perspective. Objectives: The primary objective of this study is to analyze the impact of personality traits on the effectiveness of Chinese households' financial asset portfolios. It aims to investigate how non-neurotic, openness, and agreeableness personality traits influence cognitive abilities and, consequently, portfolio performance. Additionally, the study seeks to explore the heterogeneity of these effects across rural and urban households, regional differences, and income levels. Methods: Drawing on cognitive psychology and personality economics, this study employs a quantitative approach using data from the China Household Finance Survey. It conducts in-depth analysis through regression models to examine the relationship between personality traits and portfolio effectiveness. The research focuses on key personality dimensions, including non-neuroticism, openness, and agreeableness, and their correlation with cognitive abilities and investment behaviors. Results: The results reveal a significant correlation between personality traits and cognitive abilities. Specifically, non-neurotic personality traits contribute positively to enhancing individuals' cognitive abilities, leading to more effective portfolios. An increase in household heads' openness to experience is associated with improved portfolio effectiveness. Conversely, the development of agreeableness traits is detrimental to enhancing portfolio effectiveness, with notable heterogeneity across rural and urban households, regions (eastern, central, and western), and income levels. Mechanism analysis suggests that extroversion fosters deeper cognitive abilities, enhancing risk-taking willingness and portfolio effectiveness, while agreeableness leads to superficial cognitive abilities, increasing conformity in investment decisions and reducing portfolio effectiveness. Conclusions: This study offers a novel perspective on the factors influencing household financial decisions from the viewpoint of personality economics. By examining the relationship between personality traits and the effectiveness of financial asset portfolios, it contributes significantly to a comprehensive understanding of household investment behaviors. The findings highlight the importance of considering personality dimensions in financial decision-making and investment strategies, suggesting potential interventions to improve portfolio effectiveness and financial well-being.
Computational Blockchain Framework for Quality Assessment in Sharing Economy-Based Supply Chain Management
Due to the complexity of global supply chains and the sharing economy, inefficiencies in traditional quality assessment methods are drawn out, mainly using manual inspections, centralized oversight, and paper-based tracking. In the case of these outdated approaches, they are likely to commit errors, fraud, and regulatory noncompliance [1]. This paper explores blockchain technology as a solution to supply chain transparency, automation, and quality assurance problems. The tamperproof records of Blockchain allow the transactions recorded to be tamperproof. At the same time, smart contracts ensure that the agreements governing the transactions are automated and have continuous and self-enforcing compliance. Adding blockchain technology to the Internet of Things (IoT) even further helps in the real-time monitoring of product conditions and reduces the rate of defect and fraud. This study shows how Blockchain significantly improves the traditional methods of quality assessment using comparative analysis, resulting in almost 2 times fewer defects (up to 67%), less than 90% of supplier compliance, and 60% less audit expense in major industrial sectors. Nevertheless, problems, including high deployment cost and scalability issues, still exist. Nonetheless, the study outlines how Blockchain paves the path to transformative supply chain management, not management, in a traditional supply chain context and a decentralized sharing economy. The results of this study can be used to discuss further the opportunity to incorporate emerging technologies in helping suppliers be accountable for fulfilling orders on time and care in how they handle customer data while also increasing the efficiency of the supply chain.
Financial Risk Management Enhanced by AI: Predictive Accuracy and Application Scenarios
Artificial Intelligence has transformed financial risk management through more prediction accuracy and real-time decision-making. Whereas traditional risk management models struggle to change with the markets' volatility, AI-driven models, such as machine learning or natural language processing models, approach such problems using data to offer insight into risk assessment and optimized investments. This paper will apply AI in financial risk management using recent market data such as stock indices, foreign exchange rates, commodity prices, and bond yields. The study shows that AI improves market forecasting, determines where the financial risks are, and lessens the uncertainties caused by the interpretation of historical patterns and real-time market fluctuation. The results show that AI-powered models enhance predictions of risks about stock market volatility, changes in currency rates, and movements of commodity prices. Moreover, AI-powered sentiment analysis tools offer investors a way to understand investor behavior and trends in macroeconomic scenarios. But with all the challenges like data privacy concerns, regulatory compliance, and something to do with model interpretability. The focus of this study is to predict how AI can revolutionize the financial risk management process, and we highlight the determinants for the safe application of such valuable technology in the financial sector. As the future unfolds, AI-driven AI-driven risk assessment models, such as explainable AI models and quantitative computing, will continue to be improved, making it simply unavoidable for financial institutions and investors to rely on such AI-driven risk assessment models.
Optimizing Cross-Border E-Commerce Logistics with Ai: Empirical Evidence from International Trade Platforms
This study explores the effect of artificial intelligence (AI) in logistics optimization for cross-border e-commerce. The research through empirical data analysis confirms that AI effectively reduces costs, improves delivery speed, and improves supply chain efficiency and customs clearance. AI-powered technologies like route optimization, demand forecasting, and warehouse automation significantly improve the operations of logistics companies. However, AI has faced challenges such as high implementation costs and regulatory hurdles, yet it offers potential revolutionary opportunities for international trade. This study points out the necessity for the combination of stakeholders to develop an efficient, adaptive, and robust logistics system with accompanying AI technology globally.
Insulator Overheating Infrared Image Detection Method Based on Improved YOLOv8
Insulators are an important part of transmission lines, insulator deterioration, failure and other problems are one of the main causes of transmission line failure, and insulator deterioration, failure is often accompanied by insulators appear abnormal temperature rise, so there is a need to carry out a rapid assessment of the insulator heating condition, and can be monitored in real time at the edge end of the algorithmic approach. In this paper, to address the above problems, the improved YOLOv8 algorithm is used to improve the YOLOv8 algorithm for the insulator infrared image of the overheating region of the target recognition approach. Firstly, the YOLOv8 target detection algorithm is improved by introducing the CBAM attention mechanism, i.e., by introducing the channel and spatial attention module in the CNN (Convolutional Neural Network) to improve the perceptual ability of the overall model and improve the performance without increasing the network complexity. Then the deformable convolution DCNv3 is introduced to generate learnable offsets to improve the traditional fixed convolution operation, which realizes nonlinear sampling of the input feature maps during the convolution process and improves the robustness and accuracy of the algorithm. Finally, the WioU loss function is introduced to optimize the network, which is experimentally verified to achieve an inference accuracy of 87.5% mAP at the edge end of the device to meet the identification of abnormal temperature rise of insulators at the edge end, and the comparative test proves the effectiveness and superiority of the algorithm proposed in this paper through ablation.
Research on Vocabulary Optimization of Multilingual Models for Low-Resource Languages
To enhance the performance of multilingual models on low-resource languages, particularly in downstream tasks such as sentiment analysis, a framework for vocabulary expansion is proposed. This framework selects low-frequency but informative words using Zipf’s Law and optimizes the vocabulary with weighted entropy analysis. Experimental results show improvements in accuracy and macro F1 scores by 3.85% and 5.22% respectively, particularly in tasks involving Hindi product reviews and Hindi-English code-switching. However, the study also notes limitations including performance fluctuations in intermediate stages of vocabulary expansion and a need for further exploration of the strategy’s applicability to other NLP tasks. Despite these issues, the proposed framework provides a valuable method for enhancing the representation of low-resource languages in multilingual models.
The Evaluation Method of Electric Scooter Design Solutions
To tackle the challenges of uncertainty and subjectivity in evaluating electric scooter modeling schemes, this study introduces an innovative decision-making framework that integrates the Social Network Analysis-Group Decision Making (SNA-GDM) model with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Initially, a comprehensive evaluation index system is established, followed by the determination of index weights to facilitate the selection of the optimal scheme. Analysis of practical cases demonstrates the proposed approach's ability to overcome the inherent limitations of traditional evaluation techniques. The validation of the optimal electric scooter modeling scheme provides valuable insights for future research, development, and design endeavors in this field. The integrated evaluation method, which combines SNA, GDM, and TOPSIS, exhibits significant advantages and practical applicability. By addressing the drawbacks of the conventional TOPSIS method, such as data sensitivity and subjectivity, and enhancing the objectivity and accuracy of the expert group evaluation through the introduction of the SNA-GDM model and consensus adjustment mechanism, this method represents a substantial improvement in the evaluation of electric scooter modeling schemes.
The Correlation Study of Mathematics Self-Concept and Learning Strategies of Junior High School: Based on the Analysis of Compound Multiple Mediation Effect based on the Analysis of Compound Multiple Mediation Effect
Since the 21st century, educators have realized the essential role of mathematics in the education of teenagers. Government and many of them have made great efforts to improve students’ abilities to solve mathematics problems, but many students in secondary school still feel stressful and fearful because of their poor mathematics grades. Many mathematics scholars blame to the mathematics itself, like mathematical symbols and special language, which they think cannot be well-acquired by less talented mathematics learners. When the mathematics problems become complicated, some students will lose confidence, interest and finally the motivation to learn.
Integrated Control Strategy of Variable Coefficient Virtual Inertia for Doubly-Fed Wind Turbines with Primary Frequency Regulation
Wind power generation has emerged as one of the most scalable renewable energy sources. However, the decoupling of wind turbine speed from grid frequency in doubly-fed induction generators (DFIGs) while tracking maximum power leads to a lack of frequency regulation capability in wind power systems. To address this issue, a comprehensive control strategy is proposed, integrating the variable virtual inertia control of DFIGs with primary frequency control, enabling the wind turbines to achieve comprehensive control over system frequency and smooth out frequency fluctuations. This control mechanism employs a dynamic factor based on frequency deviation, allowing for a rapid increase in electromagnetic power during significant frequency drops, effectively supporting grid frequency. Through an in-depth analysis of the primary frequency control capabilities of wind turbines, the pitch angle control has been improved, resulting in a control strategy that allows for flexible adjustment of the static regulation coefficient of wind turbines. Simulation results validate the feasibility of the proposed frequency control strategy.
YU Da, WANG YanYue, YAN YuXin, LIN Xu, WANG WeiHua
Weakly Supervised Traffic Identification Method based on Fast Detection of Angle Outliers
Current information and communication systems are facing network attacks with strong unknown characteristics, and existing network attack detection methods often fail to effectively detect unknown attacks. To address this limitation, this paper develops a novel weakly supervised traffic detection method named CTFABOD, designed to enhance the detection of unknown network attacks in contemporary information and communication systems. CTFABOD method uses generative adversarial network to enhance the performance of fast detection of angle outliers, to achieve precise detection of unknown network attacks. This paper tests the CTFABOD model on the classical NSL-KDD dataset, selects four classic weakly supervised models as comparison models, and uses AUC and precision as the evaluation indicators. The experimental results show that CTFABOD method has achieved the highest AUC and precision scores on the NSL-KDD dataset, and increased the precision score by 10.31%. This study highlights the effectiveness of combining angle-based outlier detection with generative adversarial network in improving the detection of unknown network attacks and suggests promising applications in various high-dimensional data analysis tasks.
Construction and Application of Financial Risk Early Warning System for Logistics Enterprises based on Big Data
With the rapid development of the logistics industry, financial risks have become increasingly complex and dynamic, necessitating an efficient and intelligent early warning system. This study constructs a financial risk early warning system for logistics enterprises based on big data technology. By integrating multi-source financial data, operational metrics, and external economic indicators, the system employs machine learning algorithms and data mining techniques to assess and predict potential risks. A risk evaluation model is developed using key financial indicators such as liquidity, solvency, profitability, and operational efficiency. The system utilizes real-time data processing and predictive analytics to provide timely warnings, enabling enterprises to take proactive measures to mitigate financial crises. Empirical analysis demonstrates that the proposed system improves risk identification accuracy and enhances financial decision-making. The findings suggest that big data-driven financial risk management can significantly enhance the stability and resilience of logistics enterprises in a highly competitive market.
Design and Implementation of Vocal Music Creation and Arrangement System Aided by Artificial Intelligence
With the rapid advancement of artificial intelligence (AI), its application in music creation and arrangement has gained significant attention. This paper presents the design and implementation of a vocal music creation and arrangement system aided by AI. The system integrates deep learning models, natural language processing, and digital signal processing techniques to generate and arrange vocal melodies based on user input. It employs recurrent neural networks (RNNs) and transformer-based architectures to analyze musical patterns, harmonization, and vocal synthesis, ensuring a seamless and coherent musical composition.
Discussion on Intelligent Mode of Capital Flow Management of Logistics Enterprises in the Era of Big Data
In the era of big data, logistics enterprises face new challenges and opportunities in capital flow management. Traditional financial management models are becoming inadequate in handling the complexities of real-time transactions, dynamic supply chains, and multi-source data streams. This paper explores the intelligent mode of capital flow management, integrating big data analytics, artificial intelligence (AI), and blockchain technology to enhance financial decision-making, risk control, and efficiency. Key components such as automated cash flow forecasting, intelligent risk assessment, and digital payment systems are analyzed. The study highlights how intelligent capital management improves liquidity, reduces financial risks, and enhances the overall operational efficiency of logistics enterprises. Finally, future trends and challenges in the adoption of intelligent financial management systems are discussed.
Performance Movement Analysis of Musicians via Image Processing
Analyzing the movement of musicians during performance provides valuable insights into technique, ergonomics, and artistic expression. This study presents a novel approach to Performance Movement Analysis of Musicians using image processing techniques. The proposed system captures and processes video data to track body posture, hand positioning, and instrument interaction in real-time. Key methods include motion tracking, optical flow analysis, and pose estimation to assess movement efficiency and detect potential strain or inefficiencies in playing techniques. The system also enables quantitative evaluation of performance dynamics, offering applications in music education, injury prevention, and performance optimization. Results demonstrate the effectiveness of image processing in capturing fine motor movements and providing actionable feedback for musicians. Future work includes integrating machine learning models to enhance movement classification and personalization.
Automated Auditing Based on Machine Learning: Model Construction and Empirical Analysis
This study explores how machine learning (ML) brings automation to auditing as a way to boost accuracy as well as efficiency and detect fraud. An evaluation of financial anomaly detection was conducted using decision trees, random forests, XGBoost, and neural networks as the tested machine learning models. The analytical models evaluated Neural Networks for the most accurate predictions, although XGBoost and Random Forest offered optimal accuracy versus computational performance. The selected financial indicators consisted of Total Revenue and Net Profit Margin, which proved instrumental for assessment purposes. The implementation of these benefits requires solving issues regarding interpretability along with bias concerns and compliance regulations. The research exhibits how machine learning technologies can transform auditing by demonstrating the necessity to advance these technological systems for better audit transparency and reliability in financial inspections.
A Post-Structuralist Semiotic Interpretation of Flower Windows in Rural Homestay Design in Yangshuo
In contemporary rural homestay design, the flower window, a traditional Chinese architectural decorative symbol, is extensively employed. The significance of it as a cultural symbol has undergone profound alterations in the development of contemporary society. This paper takes the application of flower windows in contemporary rural homestay design in Yangshuo and the customer experiences as the research object. Through on-site case studies and investigations of customer emotional experiences, by applying Roland Barthes’ structuralism and post-structuralist semiotics theory, it attempts to interpret the changes in form and content of the flower window as a traditional cultural symbol in homestay design and further analyze its implicit connotative function for the experiencers. It is expected to offer references for the cultural inheritance in homestay design.
Research on the Impact of the Implementation of New Asset Management Regulations on Commercial Credit Supply from the Perspective of Financial Supervision
This article explores the influence of the new regulations on the supply of commercial credit to enterprises and its mechanism based on the information of China's non-financial public corporations from 2015-2022, using the generalized difference model (GDD). The findings show that the new regulations on credit regulation have a remarkable effect on the supply of commercial credit by suppressing the financialization of firms, and the effectiveness is more prominent among firms with a greater degree of initial period of financialization. Mechanism tests indicate that reducing business risks and easing financing constraints are two important transmission paths of the new regulations on commercial credit supply. Further analysis shows that the policy effect is mainly reflected in the short-term credit and accounts receivable and notes receivable tools within one year, and significantly improves the performance of the main business of enterprises. The results verify the theoretical logic of financial regulatory policies to serve the real economy by optimizing resource allocation and reducing systemic risks, and provide new empirical evidence for understanding the interaction between financial supervision and micro-enterprise behavior. This paper has important implications for the regulatory authorities to optimize the policy framework and enterprises to adjust the capital allocation strategy, and the future research can be further extended to the long-term supply chain impact of regulatory synergies and commercial credit.
Research on "Dynamic and Multi-Dimensional" Digital Art Teaching Design Based on the Concept of Maker in the Era of Digital Media
The purpose of this study is to explore the "dynamic and multi-dimensional" digital art teaching design based on the concept of maker in the era of digital media, and to analyze its theoretical basis, practical strategies and teaching effects, so as to promote the deep integration of art education and modern information technology, and cultivate students' innovation ability and interdisciplinary literacy. Methods: The study used a combination of theoretical analysis and practical cases to verify the effectiveness of the "dynamic and multi-dimensional" teaching mode through the teaching practice of the experimental school affiliated to the Central Academy of Fine Arts. Results: Digital art teaching based on the concept of maker can significantly stimulate students' interest in learning, improve their innovation ability and interdisciplinary literacy. Students have accumulated rich practical experience in project-based learning and teamwork, and their hands-on ability and teamwork ability have been significantly enhanced. However, in the process of teaching implementation, there are also challenges such as insufficient equipment stability and teachers' digital technology capabilities to be improved. Conclusion: With the rapid development of digital media technology, digital art teaching should develop in the direction of optimization of technical facilities, professional development of teachers, updating of curriculum content, deepening of interdisciplinary integration and expansion of international vision. In the future, it is necessary to further strengthen hardware support and teacher training, promote the internationalization process of digital art education, and lay the foundation for cultivating innovative talents.
Resistance Spot Welding Quality Evaluation System based on Improved Neural Network Model
Resistance spot welding is an important branch of welding technology, the quality of its solder joints directly determines the safety and stability of equipment or system. However, resistance spot welding is a complex process of electrical, thermal, magnetic, force, phase change and other multi-factor coupling, and the quality of solder joints is easily affected by many factors, resulting in low detection accuracy, high equipment cost and poor versatility. Therefore, a quality voltage evaluation system of resistance spot welding based on improved BP neural network algorithm is proposed in this paper to realize real-time monitoring of solder joint quality. The BP neural network algorithm is improved to provide training data and algorithm validation data. The characteristic parameters of electrode voltage are selected as the input end of the neural network. The sampling of characteristic parameters is completed through the detection platform. Finally, the sampling points were used as the training set to train the improved BP neural network algorithm, and the neural network model was obtained and tested. The accuracy of the predicted solder joint quality was as high as 94%, which verified the accuracy of the resistance spot welding quality evaluation method.
An Exploration of the Integrated Degree of the Digital Economy with the Culture and Tourism Industry from the Perspective of Input-output Table
The digital economy has become an essential "catalyst" for leading the upgrading and high-quality sustainable development of the culture and tourism industry. It plays a crucial role in enhancing the added value of culture and tourism products, improving tourists' tourism experience, expanding marketing channels for culture and tourism products, analyzing tourists' tourism consumption preferences. In recent years, Ministry of Culture and Tourism of the People’s Republic of China has successively issued lots of highly effective policies aimed at acceleratinh the integration of the digital economy with the culture and tourism sectors. In order to comprehensively comprehend the integration and technological economic relations between digital economy, culture, and tourism industry in China, the paper adopts the analysis method of input-output table, carefully utilizes the data of input-output table of 2015, 2017, 2018 and 2020, and compels the input-output table of four sectors, namely culture related industries, tourism related industries, digital economy industry and other industries, and accurately calculates the correlation coefficient of each industry, courageously explore the technical and economic relations between the digital economy and the culture and tourism industry. The results affirmatively imply that the culture and tourism industry has relatively strong correlation and integration basis, but it has not reached a fine integration state at present; the digital economy has a high penetration ability for the integrated development of China's digital economy with the culture and tourism industry. The cultural industry has obvious advantages promoting the integrated development of digital culture and tourism; the digital economy enables the integrated development of the culture and tourism industry by means of direct and indirect effects, and the indirect effect is greater than the direct effect. The article is of practical significance for gaining a comprehensive understanding of the importance of integrating the digital economy with the culture and tourism sector. It further refines the strategies and recommendations for this integration, thereby better guiding the formulation of integrated development policies, promoting the digital transformation of the culture and tourism industry, and informing investment decisions regarding digital infrastructure and financing within this sector.
A Study on the Impact of Academic Entrepreneurship on Basic Research
The impact of academic entrepreneurship on basic research output has been widely debated, with disagreements arising from difficulties in collecting micro-level data and the limitations of traditional regression methods in addressing confounding factors beyond the "entrepreneurial identity" of academic entrepreneurs. To tackle these issues, we developed a large, multi-dimensional database of 13,762 science and engineering faculty members from 17 top universities in Shaanxi Province using computer-based data crawling techniques. By matching data on faculty research outputs and academic entrepreneurship activities, we applied propensity score matching (PSM) to examine the causal relationship between academic entrepreneurship and basic research output. Our findings indicate that academic entrepreneurship positively impacts the number of papers published annually, exploration of new knowledge, patent applications, and patent citations. These effects vary among faculty with different characteristics, suggesting a virtuous cycle between academic entrepreneurship and basic research. To foster scientific self-reliance, universities should encourage academic entrepreneurship, providing tailored support based on individual characteristics.
Empirical Study on the Dynamic Interaction between China's Digital Industry Development and Economic Growth: Evidence from Panel Data Using the EWM-PVAR Approach
This research explores the mutual influence between China's digital industry expansion and its economic growth, employing an EWM-PVAR model based on panel data analysis. The rapid growth of China's digital economy significantly impacts global resource allocation and reshapes industrial structures, driving national economic development. Utilizing datasets sourced from the National Bureau of Statistics of China and the China Urban Statistical Yearbook, the study evaluates digital industry development through eight indicators, including telecommunications volume, internet penetration, and IT sector employment. Analytical methods include the ADF unit root test, Johansen cointegration test, and Granger causality test to investigate the dynamics between digital industry development (LNDID) and economic growth (LNGDP). Results indicate a notable bidirectional causality between digital industry development and economic growth. Further analysis using impulse response functions demonstrates that the digital industry's short-term effect on economic growth is significantly positive, though this impact diminishes and ultimately reverses into a negative influence in the long run. Variance decomposition reveals a declining explanatory capacity of digital industry growth on overall economic performance over time, whereas the economic growth itself progressively gains a stronger explanatory role. The findings emphasize the interdependent relationship between digital industry development and economic growth, highlighting the necessity for coordinated strategies to achieve sustainable economic advancement.
Innovative Teaching Methods and Ideological Education Integration in Circuit Course Reform: A Comprehensive Approach
This paper focuses on the teaching reform of the circuit course. On the one hand, it elaborates in detail the practical process and remarkable achievements of applying multiple innovative teaching methods such as concrete analogies, interesting homophones, and innovative mnemonics in the circuit course and deeply integrating ideological and political education. On the other hand, it deeply analyzes the connotation, implementation steps and application results of the "One Thought, Two Guidance, Three Imitation" teaching mode. Both teaching explorations are committed to solving the inherent problems of traditional circuit teaching, effectively improving students' learning effects and comprehensive qualities, and providing extremely valuable experiences and ideas for the teaching reform of the circuit course and related majors.
Cultural Sensitivity and Emotional Intelligence in Teaching English to Diverse Learners
This study examines emotional intelligence (EI) and teacher culture-sensitivity as predictors of students' achievement in the English language from multicultural cultural backgrounds. A mixed-method design included quantitative questionnaires from 20 English teachers and 200 students, student interviews, and classroom observations. Teachers also reacted to valid EI measures as well as culture-sensitivity measures, and students' English proficiency (via pre-course and post-course tests), as well as engagement (via feedback questionnaires), were monitored over one school term. Via multiple regressions, both EI and culture-sensitized pedagogies of teachers were statistical, positive predictors of students' gains in language proficiency, together predicting approximately 45% variability in learning outcomes. Furthermore, student interviews and classroom observations corroborated quantitative findings, with high EI and culture-sensitized teachers creating more positive, engaging classroom atmospheres that supported student participation as well as confidence in English. The study highlights teacher EI, as well as culture-sensitized pedagogies, as central to supporting second language learning in a multicultural student population. Training teachers in EI development as well as in culture-sensitized pedagogies is recommended as a way forward to enhance English pedagogy. The study is a contribution to a developing body of research on teacher socio-emotional as well as intercultural competence in second language pedagogy.
From Emotions to Expressions: Integrating Emotional Intelligence in English Pronunciation and Fluency Training
This study sought to investigate the impact that the incorporation of emotional intelligence (EI) activities into English pronunciation and fluency training can have on the speaking ability of English as a Foreign Language (EFL) learners. A quasi-experimental pre-test/post-test control group design with 60 intermediate EFL learners in experimental and control groups was conducted. The experimental group was presented with a pronunciation and fluency training course supplemented with EI activities for 8 weeks, while an identical training course with no EI incorporation was conducted with the control group. Participants' pronunciation accuracy and speaking fluency were measured pre-and post-training with standardized oral tests, and EI level was measured with a valid instrument. The statistical analysis (independent-samples t-test) indicated that the experimental group outperformed the control group in pronunciation accuracy and fluency in the post-test. Furthermore, the Pearson correlation analysis confirmed that learners' EI level is positively related to pronunciation and fluency improvement. This implies that EI incorporation into pronunciation and fluency training can be effective in improving learners' oral production. In sum, the study highlights the central role of emotional aspects in second language acquisition and supports incorporating EI-based activities into the EFL curriculum to enhance speakers' competence.
Numerical Investigation of Concrete and Helical Pile-Supported Embankments Under Railway Traffic Loading
Pile structures increase embankment stability and settlement control. Recent research has focused on helical piles' enhanced compressive and tensile performance, rapid installation, and absence of concrete problems. This numerical study compares helical and concrete pile-supported embankments. A three-dimensional finite element model was developed using ABAQUS software to evaluate soft soil embankments with equal pile length and diameter. The numerical model was verified by field and lab measurements. Helical piles decrease embankment, soft soil settling, and control lateral displacement better than concrete piles. Furthermore, the distribution of axial forces along the embedded depth demonstrates that helical piles exhibit higher bearing capacity and function in a frictional manner. The inclusion of a pile cap was also examined, revealing that it enhances load distribution, reduces vertical settlement by 19%, and improves stress transfer efficiency. However, adding additional helical blades to the pile body does not significantly improve embankment performance and primarily affects the load transfer mechanism at specific spacing ratios.
Research on Effective Data Transmission of Internet of Things Gateway based on Dynamic Routing and Edge Computing Technology
This research explores the effective data transmission mechanisms for Internet of Things (IoT) gateways, focusing on dynamic routing and edge computing technologies. With the rapid growth of IoT systems, efficient data transmission has become critical in managing the large volumes of data generated by devices. The study investigates how dynamic routing protocols can optimize data flow between IoT devices and the cloud, minimizing latency and enhancing network reliability. Furthermore, the integration of edge computing is examined to process data closer to the source, reducing the dependency on centralized cloud systems and improving response times. Using simulation models and real-world experimentation, the research evaluates various routing algorithms and edge computing architectures, proposing an optimal framework for IoT data transmission. The findings suggest that dynamic routing and edge computing significantly improve network performance, particularly in scenarios requiring low latency and high throughput. This research provides valuable insights into designing robust IoT networks that can handle the complex data demands of modern applications, including smart cities, industrial automation, and healthcare.
Research on the Blended Teaching Mode of Medical English under the "1+X" Certificate System: Based on the Theory of Needs Analysis
Based on the needs analysis theory, this study investigates the effectiveness of the blended teaching mode for Medical English under the "1+X" certificate system. The "1+X" certificate system, which combines specialized vocational training with additional certification options, has gained popularity in medical education, yet the integration of Medical English within this framework remains underexplored. The research employs needs analysis theory to assess the specific linguistic needs of medical students, identifying key areas for instructional improvement. Data was collected through surveys and interviews with medical students and instructors, focusing on their experiences and challenges in acquiring Medical English proficiency. The study analyzes how a blend of traditional face-to-face teaching methods with online learning platforms can better address these needs, providing a more flexible and personalized learning environment. Blending these teaching modes can enhance students' engagement, promote self-directed learning, and improve their mastery of essential medical terminology. Additionally, using digital tools and resources allows for more interactive and practical applications, contributing to the development of a comprehensive and adaptable curriculum. This research offers valuable insights into optimizing Medical English instruction within the "1+X" system, proposing a more responsive and effective educational approach for future medical professionals.
The Mediating Effect of Language Anxiety on the Relationship between Cultural Sensitivity, Emotional Intelligence, and English Proficiency among Multicultural Language Learners
In today's globalized world, English learning is a significant medium of intercultural communication and international interactions in cultural, economic, and political domains. Therefore, the current research explored the mediating effect of language anxiety in the relation between cultural sensitivity, emotional intelligence, and English language achievement among multicultural language learners. The research was based on a correlational design using Structural Equation Modeling (SEM). Participants were 300 multicultural language learners who were selected through cluster sampling procedures. Data were collected by administering questionnaires of cultural sensitivity, emotional intelligence, language anxiety, and learners' final-term English grades. Pearson's correlation coefficient and SEM analyses were conducted using SPSS 24 and Amos 23 software. Results indicated that cultural sensitivity and emotional intelligence directly and significantly contributed to learning English. Moreover, these constructs indirectly affected learners' language achievement through the mediating effect of language anxiety. The research concludes that the development of cultural sensitivity and emotional intelligence can be an effective method for reducing language anxiety and subsequently enhancing English learning among multicultural learners.
On A Special S-Act Over Several Kinds of Semigroups
In this paper, we study some properties of a special S-acts D(S) = (S × S)S over several kinds of semigroups. First, we establish relationships between the diag- onal S-act D(S) of the semigroup S and the ﬂatness properties of the diagonal act- s D(M(S; Λ; P)) of the Rees matrix semigroup M(S; Λ; P). Then, we show that for semigroups S with local units by (W F)/ of diagonal S-acts.Finally, we ascertain the direct product (S × T, *) is a semigroup when (S, ·) and (T, O) are two semigroups with operation and give ﬂatness properties of diagonal (S × T, *)-act.
Social Media Network Rumor Theme Mining and Evolutionary Analysis based on DTM Models
Mining social media network rumors and analyzing topic popularity and evolution is crucial for accurately understanding and guiding public opinion development and predicting rumor trends. Using Weibo rumor text data as the analysis sample, this study employs the dynamic topic models (DTMs) to extract topics from rumors across different periods. The optimal number of topics for the DTM model is determined using consistency indicators, while topic popularity is assessed using topic intensity. The analysis covers four aspects: hot topics, discourse characteristics, topic popularity, and evolution patterns. The study identifies five major categories of hot topics: international events, local tourism, school education, social security, and epidemic prevention and control. In terms of discourse characteristics, rumors exhibit four main features: colloquialism, emotionalism, vagueness, and multimodality. Regarding topic popularity, epidemic prevention and control topics show a high level of popularity, while local tourism and school education topics display a gradually increasing trend. In topic evolution, topics of international events remain relatively independent, whereas the other four categories are closely related. This study provides valuable insights for government efforts to manage public opinion and curb the spread of rumors.
Research on the Corrosion Impact of DC Grounding Electrode Current on Surrounding Tower Grounding Systems
With the widespread application of high-voltage direct current (HVDC) transmission technology, the corrosion of grounding systems in transmission towers caused by the ground current of DC grounding electrodes has become increasingly significant. Currently, existing research, both domestically and internationally, has not fully considered the impact of non-uniform soil conditions on the distribution of DC currents in towers. To address this gap, this study employs the CDEGS simulation software to construct uniform and non-uniform soil models, systematically analyzing the influence of soil resistivity, soil layer thickness, and different grounding electrode configurations on the distribution of DC currents and corrosion characteristics in tower grounding systems. The results reveal that under uniform soil conditions, the ground current of the tower closest to the grounding electrode increases with soil resistivity and exhibits significant spatial attenuation characteristics. In horizontally layered soil models, when the upper soil layer has lower resistivity, the ground current decreases with increasing upper layer thickness, whereas the opposite trend is observed when the upper layer has higher resistivity. As the upper soil resistivity gradually exceeds that of the lower layer, the ground current tends to saturate, while changes in lower layer resistivity have a more pronounced impact on current distribution. Furthermore, the current density distribution varies significantly among different grounding electrode configurations. Type C grounding electrodes exhibit the lowest localized corrosion, while Type B electrodes suffer the most severe corrosion at the ends of their rays. These findings provide technical support for the optimized design and corrosion protection strategies of transmission tower grounding systems, enhancing their operational safety and reliability.
Simulation Analysis of Impulse Characteristics of Grounding Electrode in High Altitude Area
In order to study the effect of the structure and material of the grounding electrode on the impulse grounding resistance in the soil condition of high altitude area, the soil model of a high altitude area was measured on site, and the influence of the structure and material of the grounding electrode on the impulse characteristics of the grounding electrode was studied by simulation analysis method. The results show that the impulse grounding resistance is the lowest when the Angle of ray is 45°. With the increase of the total length of the grounding electrode, the impulse grounding resistance decreases with the increase of the number of ray roots, and the effective impulse length exists. The graphite type grounding electrode has better performance than the traditional metal grounding electrode, and the graphite based flexible grounding electrode can be preferred in the area of high soil resistivity. The addition of vertical grounding electrode can effectively reduce the impulse grounding resistance of horizontal grounding electrode, and the highest resistance reduction rate can reach 31.73%. The research results can provide some guidance for the design of grounding electrode in high altitude area.
The Role of Dong Character in School Education and Social Fields
Since the implementation in the 1950s, the Dong character has experienced “ two ups and two downs” in its development. Over the past sixty years, it has made significant contributions and played an irreplaceable role in areas such as literacy education, bilingual training, bilingual teaching, inheritance of national cultural heritage, enrichment of Dong texts and reading materials, development of Dong music, promotion of radio, film and television. And with the promotion and use of Dong character, the Dong language itself has also developed and gained new vitality.
A Lightweight Network Intrusion Detection System Based on Temporal Convolutional Networks and Attention Mechanisms
The proliferation of Internet of Things (IoT) devices has led to a significant increase in the number and complexity of cyberattacks, posing severe challenges to global cybersecurity and exposing the limitations of traditional signature-based intrusion detection systems (IDS) in addressing unknown or evolving threats. To tackle this issue, we propose TCNSE, a network intrusion detection model that integrates a Temporal Convolutional Network (TCN) with a Squeeze-and- Excitation (SE) module. The TCN effectively captures temporal dependencies in network traffic through dilated convolutions, while the SE module enhances feature representation by modeling inter-channel relationships. Our model maintains high detection accuracy with low computational complexity, making it suitable for deployment on resource-constrained IoT devices. We applied our model to the CIC-IDS 2018 dataset, implementing efficient data preprocessing and feature selection techniques that reduced the number of features from over 80 to 20. We also addressed class imbalance using hybrid sampling methods and Focal Loss. Experimental results demonstrate that the TCNSE model outperforms existing advanced intrusion detection models, achieving key performance metrics of 98.4% accuracy, 99.4% precision, 98.7% recall, and a 99.1% F1 score.
An Innovative Anti-Tampering Encryption Method for Enhancing Privacy Protection in Power Big Data
Power big data encompasses vast amounts of private information. Traditional encryption algorithms often exhibit longer computation times and greater resource demands when handling large-scale data. This not only heightens the risk of private information decryption but also diminishes encryption efficiency. To address these challenges, we propose a novel tamper-proof encryption method for power big data based on national security algorithms. Initially, we conduct an analysis of the composition, operational principles, and advantages of various national security algorithms. Subsequently, we select the Advanced Encryption Standard (AES) algorithm and enhance its performance through expansion and optimization. Finally, the improved AES algorithm is employed to achieve tamper-proof encryption of power big data privacy information, utilizing operations such as round transformation, byte substitution, and row matrix manipulation. Experimental results demonstrate that the proposed encryption method significantly reduces the likelihood of private information decryption while enhancing encryption efficiency. These findings underscore the effectiveness of our approach in achieving robust tamper-proof encryption for power big data.
Data Security Risk Assessment and Control based on Improved Apriori and NSGA-II
With the rapid development of information technology, the prominence of net work data security risks is escalating. Accurately evaluating these risks and devising effective control strategies have emerged as urgent imperatives. To address this challenge, this study introduces an enhanced version of the Apriori algorithm to discern correlations within data and perform corresponding risk assessments. Additionally, it employs the elitist non-dominated sorting genetic algorithm for data security risk control. The findings demonstrate that the probability of occurrence of primary risk factors, as calculated using the enhanced Apriori, exceeds 0.7, with approximately 75% confidence in the association between each factor and medium to high risk. These calculations align closely with the ”main network security risks” outlined in the unit’s 2021 network security work summary. Furthermore, the distribution of Pareto optimal solution sets derived from the multi-objective evolutionary algorithm based on decomposition exhibits non-uniformity, whereas those obtained from the elitist non-dominated sorting genetic algorithm manifest diverse and evenly distributed outcomes. Moreover, considering risk state values and control costs, this study adopts a third approach to mitigate security risks concerning application systems and data. This strategy yields a risk level of only 0.386 and incurs a cost consumption of merely 2,404,800 yuan. The proposed data security risk assessment and control strategy demonstrate strong feasibility, effectively enhancing the value of data utilization and delivering practical benefits to enterprises or organizations.
Triggering, Organizing, and Meaning: A Study of Rural Mass Incident Occurrence
In the process of rural group events, the trigger event ignites the fuse of rural group events; the weak organization, the network of acquaintance relations, and the spatial environment of gathering in rural areas cross the dilemma of China's collective action constraints, which contributes to the aggregation of members of farmers' groups; and when the farmers attribute the reasons for the damage of their interests to the deprivation of the grassroots government, the collective identities within the farmers' groups are activated, and in the members' communication and interaction, implicit infection in the development and escalation of rural group events. Introduction: "Modernity brings stability, while the process of modernization brings turmoil" （Huntington,1989.p.38).The comprehensive transformation of Chinese society after the reform and opening up has led to a rupture in the social structure, the accentuation of social contradictions, and an increase in the number of rural mass incidents. How do rural mass incidents occur? What factors have contributed to their occurrence? Is there a certain regularity? Understanding the occurrence of rural mass incidents is of great significance to improving grassroots governance, revitalizing the countryside and creating a beautiful landscape. Objectives: The purpose of this paper is to study the occurrence of rural mass incidents.What factors contribute to the occurrence of rural mass incidents? Conclusions: In short, in the process of rural group events, the triggering event ignites the fuse of rural group events; the weak organization, acquaintance relationship network, and spatial environment of gathering in the countryside cross the dilemma of collective action constraints in China, which contributes to the aggregation of members of farmers' groups; and the construction of meanings activates the collective identities of farmers' groups, and in the exchanges and interactions of members and the implied infections of members, the rural group events evolve and escalate. The construction of meanings activates the collective identity of farmers' groups, and in the exchanges and interactions of members and the implied infections of members, the rural group events evolve and escalate.
Research on Mobile Learning Platform Interface Design Basedonvisual Attention Features of College Students
Eye-tracking technology was applied to investigate the visual experience characteristics and the factors influencing college students' visual attention in the mobile learning platform interface. It also aimed to summarize the visual experience patterns and design insights for platform interface development. Methods: Using head-mounted eye-tracking technology, 28 images from 6 categories of typical interface elements of the CGTN learning platform were selected as test samples. The subjects' eye movement data were recorded as they browsed the interface. Results: Significant differences were observed in attention time, frequency of attention, visual attention rate, and recall rate across different regions and subjects (P &lt; 0.05). Conclusion: The analysis of factors influencing visual attention in platform interface design revealed that color, text, and typography significantly impact visual experience, while secondary areas and layout also contribute substantially to visual communication. The design of color and text elements, alongside innovative typography, can effectively boost the visual attention of college students and improve the platform's ability to communicate information.
Research on Sliding Mode Control Strategy for DAB Based on Single-Phase Shift Modulation
Introduction: With the rapid growth of the global economy, the issue of fuel resource shortages has attracted widespread attention. Countries are accelerating the transition from traditional energy sources to clean electricity. In this process, efficient energy transmission and storage technologies have become key challenges. The Dual Active Bridge (DAB) converter, due to its symmetric structure, soft-switching characteristics, and flexible control capabilities, has become a research hotspot for isolated DC-DC converters. However, systemic design strategies such as optimizing inductor current stress and extending the soft-switching range still require improvement. Additionally, traditional PI control has obvious deficiencies in dynamic response and robustness.. Objectives: Optimizing Performance of DAB Converters; Enhancing Dynamic Performance with New Control Strategy. Methods: Control Strategy Improvement: Based on the full-bridge DAB single-phase phase-shift control, establish a dynamic model of the output voltage and propose a sliding mode control strategy incorporating an integral term.Simulation and Verification: Build a system model using MATLAB/Simulink and compare the dynamic response characteristics of sliding mode control with traditional PI control.Hardware Implementation: Complete the DAB system parameter design and key component selection, and develop hardware circuits to verify the theoretical feasibility. Results: Compared to traditional PI control, the sliding mode control reduces the overshoot by 42% and the steady-state error by 68% during load transients.The proposed strategy extends the soft-switching range of the DAB to full-load conditions, and the peak inductor current decreases by 25%.Hardware experimental results show that the system efficiency reaches 96.2% at rated power, validating the effectiveness of the control strategy. Conclusions: The sliding mode control strategy proposed in this paper significantly improves the dynamic performance and robustness of the Dual Active Bridge (DAB) converter, addressing the key drawbacks of traditional PI control. Through simulation and hardware experiments, this approach demonstrates superiority in efficiency optimization and steady-state accuracy, providing theoretical support and practical engineering reference for high-reliability power conversion systems. Future research could further explore multi-objective collaborative optimization strategies and improvements in wide-range input/output adaptability
Construction and Practice of a Business Academic Writing Competency Development Model Empowered by Artificial Intelligence
This research explores the construction and practical application of a business academic writing competency model empowered by artificial intelligence. The study investigates how AI technologies can enhance writing instruction, feedback processes, and assessment in business education contexts. Through an analysis of current approaches, technological capabilities, and pedagogical frameworks, we develop a comprehensive model that integrates AI tools at various stages of the writing development process. The case study demonstrates significant improvements in students' writing competencies, particularly in structural and mechanical aspects, while also fostering increased engagement and metacognitive awareness. While the model shows promise in providing consistent, immediate feedback and enabling greater scalability of instruction, challenges remain in developing higher-order critical thinking skills and ensuring appropriate student engagement with AI feedback. The research provides practical guidance for educators and institutions seeking to implement AI-powered writing instruction while highlighting areas for future development and research.
Multi-objective Optimization of Eco-tourism Routes Integrating Ecological Value: A Study based on Ant Colony Algorithm
This paper introduces a new multi-objective model for eco-tourism route planning that incorporates the assessment of the ecological significance with the standard goals of tourist preference and route length. The paper proposes a new approach to the ACO algorithm to derive Pareto-optimal solution for the eco-tourism problem with multiple objectives. The algorithm uses multiple matrices of pheromones for tracking different goals and objectives and also includes the ecological value through a compounded index that considers aspects of the relative importance of biodiversities, the sensitivity of ecosystems, connectivity of habitats, and other ecological values. Experimental outcomes show that the proposed MOP is 58% more diverse than the SOP in terms of solutions and 43% less sensitive to parameters than the SOP. Pareto-optimal solutions show that high ecological value routes are less attractive as they have 45% fewer attractions but have 75% less negative impact on the environment than high satisfaction routes. The model also determines appropriate size and orientation of routes varying by seasons, which indicates the requirement of appropriate management strategies. The results indicate that intelligent route optimization can produce reasonable amount of ecological gains at the expense of slight tourist satisfaction loss, providing valuable support to the eco-tourism managers in the sustainable destination management.
A Comprehensive Analysis of AI-Powered Wearable Devices for Real-Time Health Monitoring and Performance Metrics in Elite Sports
This in-depth review discusses the combination of artificial intelligence (AI) and wearable sensor technology in elite sports, exploring how these technologies revolutionize tracking and enhancing athlete performance. The paper starts by chronologically describing the development of wearables in sports from their beginnings as simple tracking devices to current advanced AI-based systems, followed by a discussion of existing technologies such as sensor types, processing, and implementation methods. Important applications are examined through health monitoring (cardiovascular health, sleep, risk of injury) and performance analysis (technical skill assessment, monitoring of training load). The key challenges are delineated in the review, e.g., data validity, privacy, and integration, but important future directions are pinpointed, e.g., next-generation wearables and complementarity with other technology. The paper concludes with pragmatic suggestions for the integration of such systems into elite sport programs, highlighting the need for interdisciplinary collaboration, ethical management, and user-centered design to maximize the potential of AI-driven wearables in sport.
Optimizing Cross-Border E-Commerce Logistics with Artificial Intelligence: Empirical Evidence from International Trade Platforms
This in-depth report discusses how artificial intelligence is revolutionizing cross-border e-commerce logistics. It explores new technologies reshaping global trade. Through an exploration of real data from global trade platforms, the research shows how AI technologies like machine learning, predictive analytics, and smart tracking systems improve logistics. This renders it more efficient, less costly, and provides improved customer experiences. The study states that AI-driven solutions help with more precise demand forecasting, route optimization, tracking of inventory, and customs clearance. This grants businesses new abilities to handle complicated cross-border trade situations. The research utilizes case studies of major corporations like Alibaba, DHL, and Maersk to illustrate the potential contribution of AI in rendering supply chains intelligent and responsive. The report emphasizes the importance of technology in the future of global trade.
From Oral Heritage to Digital Code: Philosophical Reflections on the Reconstruction of Traditional Choral Teaching through Artificial Intelligence
The intersection of artificial intelligence and choral traditions is transforming how we learn and preserve music. This critical study explores the deep connection between emerging technology and cultural transmission, revealing how AI can document, learn, and even reimagine musical heritage. By analyzing the interplay among machine learning, bodily knowledge, and traditional pedagogies, the research reveals the intricate processes of using emerging technologies in music pedagogy. The study proves that AI has tremendous potential to understand complex performance properties, maintain distinctive musical heritage, and create adaptive learning experiences. However, it also explores the ethical issues involved in using technology closely, outlining the need for maintaining cultural values and human capabilities. The research proposes a model of collaboration that views AI as a tool to enhance, not replace, traditional musical transmission, ultimately invoking an integrationist position in terms of both technological potential and cultural authenticity.
A Data-Driven Approach for Mining Truck User Requirements from Online Reviews
The accurate identification of user requirements is essential for successful product design. However, existing methods for extracting user needs from online reviews often face challenges due to fragmented content and implicit expressions. To address these limitations, this study proposes a comprehensive framework for mining truck user requirements from online reviews. The process begins with the collection of online reviews to form an initial dataset, which is manually filtered for relevance and processed using NLP techniques to construct a domain-specific lexicon. We leverage K-fold cross-validation and LSTM neural networks to optimize text classification accuracy. Subsequently, sentiment analysis and quantification are per-formed on both explicit and implicit sentence structures, enabling the development of a requirement prioritization model that integrates IPA, the KANO model, and the DEMATEL method to quantify inter-requirement relationships. The results demonstrate that our approach effectively transforms unstructured user feedback into actionable product design insights, provides a reliable solution for manufacturers to improve truck design and development. This research not only advances the field of user requirement mining but also provides a practical tool for data-driven decision-making in the automotive industry.
Exploring the Behavioral Intention of Higher Education Students Using Interactive Films for Online Learning
With the continuous popularization of online learning in higher education, interactive films, as digital media that integrate non-linear narrative and user decision-making mechanisms, are expected to create a personalized and immersive learning experience in online education. This study constructs and validates a theoretical model integrating perceived usefulness, perceived ease of use, autonomy, competence, and relatedness based on the Technology Acceptance Model (TAM) and Basic Psychological Needs Theory (BPNT), to explore the influence on students' behavioral intentions regarding online learning through interactive films. The study distributed paper questionnaires to university students from six higher education institutions in Chongqing and employed Structural Equation Modeling (SEM) for empirical testing of the relationships among variables. The results indicate that, apart from association, all other factors significantly influence behavioral intentions, confirming the applicability of the integrated TAM and BPNT model in this context. In the future, efforts should be made to enhance its usefulness and ease of use at the instructional design level to meet students' autonomy and competence needs for a better user experience. This includes improving tool availability and motivation support to expand its potential applications in online education.
Quantum-Resistant Zero-Knowledge Proof Blockchain Electronic Voting System
Following the emergence of the COVID-19 pandemic, electronic voting has gradually become an inseparable part of people's lives. However, it has also raised a series of severe privacy and trust challenges. The immutable and publicly transparent characteristics of blockchain are a perfect fit for the development of electronic voting systems, effectively eliminating voters' concerns about ballot tampering.At the same time, zero-knowledge proofs enable the prover to show they possess certain information to the verifier, without disclosing the actual details. It is important to note that with the rapid development of quantum technology, traditional cryptographic schemes face unprecedented security threats. To address this challenge, We present a quantum-resistant blockchain solution for electronic voting, incorporating zero-knowledge proofs. Compared to conventional elliptic curve-based zero-knowledge proof schemes, our proposed solution is based on RLWE, ensuring voter privacy, and uses BFV fully homomorphic encryption technology to implement a blockchain-based electronic voting protocol, ensuring the system’s high availability, security, and anonymous voting. Security analysis and performance testing, along with comparisons to existing similar solutions, show that our scheme has advantages in terms of security and robustness, making it highly practical.
Emergence of Rich Patterns in a Discrete System with Migration and Diffusion
This study employs the pattern formation of a discrete system with migration and diffusion. By analyzing the stability of fixed points and flip and Neimark-Sacker bifurcations, we demonstrate the non-spatial form of the system can generate complex dynamical behaviors, including periodic, quasiperiodic and chaotic orbits. Numerical simulations reveal the emergence of rich patterns which show different ways of transitions, especially on the routes to chaos with the variation of bifurcation parameter. We find the migration in the discrete system enhances spatial coupling and reshapes pattern stability. This work connects bifurcation with spatiotemporal dynamics, revealing how migration-diffusion interaction drives pattern self-organization.
Quantitative Research on China’s PV Industry Policy based on PMC-index Model
Photovoltaic industry is an important new energy industry, and photovoltaic industry policy is an important part of the photovoltaic industry. To analyze the structure and evolution of China’s photovoltaic industrial policies and scientifically evaluate China’s photovoltaic industrial policies, this paper analyzed the texts of China’s photovoltaic industrial policies from 2007 to 2023. Firstly, the structure and evolution of PV industrial policies were analyzed based on the division of PV industrial instruments. Then, the PMC-index model was established to evaluate six national PV industrial policies. The results show that: in terms of policy structure and evolution, there is a gap in the proportion of various policy instruments, and the structure of photovoltaic industry policies has changed; In terms of policy evaluation, among the six national-level photovoltaic industry policies, one policy was assessed as excellent, four policies were assessed as acceptable, and one policy was assessed as poor. The quality of the six policies and the degree of response of the six policies to the photovoltaic industry are above an acceptable level. There are shortcomings in the policies, which are embodied in insufficient incentive measures, lack of coordination of policies in multiple periods, and the scope of policies needs to be further expanded. Based on the research of this paper, the government should pay attention to promoting the construction of photovoltaic projects, strengthening technological innovation in the photovoltaic industry, and expanding the application range of photovoltaic power generation when formulating the photovoltaic industry policies.
Research on Key Core Technology Identification based on Patent Semantic Analysis: A Case of the Biological breeding field
To achieve high-quality development, China must break through key core technologies. Accurately identifying key core technologies is crucial for the government or enterprises to optimize their research and development layout and actively seize the technological high ground. This study used LDA semantic analysis to classify patents in the field of biology, and selected topics in the field of biological breeding based on topic keywords; Conduct a competitive analysis of two key core technologies: gene editing technology and plant transgenic technology. It is recommended to closely monitor the latest research and development progress in these two technologies by the world’s leading seed companies. Jointly with relevant entities, focus on tackling key core technologies, strengthen high-level open cooperation with domestic and international entities, accelerate the integration of informatics with biology, and promote the breakthrough of key core technologies in the field of biological breeding. These actions aim to provide decision support for the high-quality development of China’s biological breeding sector.
Empowering ConvNeXt for Precision Classification of Industrial Surface Defects: A Comprehensive Approach with Multi-Scale Fusion
The industrial sector demands high precision in the classification of surface defects to ensure product quality. Traditional visual inspection methods, limited by their inconsistency and inability to scale, necessitate an advanced solution for defect detection and classification. This research introduces an enhanced ConvNeXt architecture that integrates deformable convolutions, attention mechanisms, and a multi-scale fusion approach to address the complex nature of defect imagery in industrial settings. Firstly, deformable convolutions are employed to provide the model with the flexibility to adapt to the varied and irregular shapes of surface defects. Unlike standard convolutions, these allow the network to modify its receptive field dynamically, enhancing its ability to capture crucial textural and geometric nuances. This adaptation significantly boosts the model’s accuracy in feature extraction from complex industrial surfaces. Secondly, to refine the focus within these enhanced feature maps, an attention mechanism is integrated. This mechanism prioritizes the most informative parts of the image, thus directing computational resources towards areas with potential defects. By doing so, it not only improves the model’s efficiency but also its effectiveness in recognizing subtle yet critical defect features that might otherwise be overlooked. Thirdly, the multi-scale fusion strategy is implemented to harmonize and leverage information across different scales and resolutions. This aspect of the model ensures comprehensive coverage and consistent performance across varying sizes and types of defects. It effectively aggregates the detailed local features captured by deformable convolutions and the prioritized global features enhanced by attention mechanisms, providing a robust classification output. Experimental results on diverse industrial datasets have demonstrated that the proposed model substantially outperforms existing methods in terms of both accuracy and reliability. The integration of these three advanced techniques—deformable convolutions, attention mechanisms, and multi-scale fusion—creates a synergistic effect that significantly elevates the capability of ConvNeXt for precise classification of industrial surface defects. This study not only proves the feasibility of enhancing a sophisticated architecture like ConvNeXt for industrial applications but also sets a new standard for automated defect classification systems, combining deep learning innovation with practical, impactful industrial use.
Dynamic Time Series Modeling Method based on Bayesian Learning and Gray Forecasting
As real-life time series data are usually complex, the traditional dynamic time series model cannot achieve satisfactory prediction results. In this paper, we combine gray prediction with dynamic time series to construct a dynamic time series model based on gray prediction to improve the prediction accuracy of dynamic time series. Aiming at the missing data problem often faced in the process of dynamic time series modeling, we design the missing data recovery algorithm for dynamic time series based on Bayesian learning, and model the missing data prediction problem in dynamic time series as a multi-sparse vector recovery problem based on the theory of compressed sensing. Carrying out dynamic time series prediction simulation experiments, the residual sum of squares, residual median error, and average relative error of the dynamic time series model based on gray prediction in this paper are 0.616, 0.307, and 0.297, respectively, which are lower than those of the comparative traditional GM(1,1) model, and the time series analysis model. And in the missing data recovery simulation experiments, this paper's algorithm has a smaller RMSE at any data missing rate, and the RSME value of this paper's algorithm remains lower than 0.2 when the data missing rate reaches the highest 95%.
Research on Usability Evaluation of Self-Service Terminal in Chinese General Hospitals Using Grounded Theory And AHP
Objective: To examine the usability of self-service terminals in Chinese hospitals, focusing on user experience issues related to human-machine interaction within the hospital information system (HIS). Method: Utilizing grounded theory, this study generated and encoded qualitative data concerning user interaction needs with hospital self-service terminals. The Analytic Hierarchy Process (AHP) was employed to determine the importance weights of factors influencing user experience, enhancing the framework’s completeness. Results: This research identified critical usability factors such as Effectiveness, Learnability, Universality, Practicability, and Security. These factors, comprising 2-3 sub-factors each, totaling 12, significantly influence the user experience with hospital self-service terminals. An ELUPS model, a user-centered usability evaluation system, was developed to sustainably enhance HIS integration. Conclusion: This study advances a comprehensive methodological approach for constructing usability evaluation systems through grounded theory. The findings offer theoretical guidance for the development of tangible self-service applications in hospital HIS and provide a theoretical framework for ensuring sustainable user access to self-service terminals.
Resource Oriented Architecture for Cloud and big data with Resource Workflow Management to Support Secure by Design Operation
Introduction: The advent of cloud computing and big data technologies has revolutionized how data is stored, processed, and analyzed. However, the rapid expansion of these domains has introduced significant challenges in resource management, particularly in achieving high-performance computation Objectives: Resource-Oriented Architecture (ROA), a paradigm rooted in efficient allocation, monitoring, and utilization of distributed resources, has emerged as a promising solution for addressing these challenges. This study delves into the application of ROA for cloud and big data management and computation, focusing on optimizing resource allocation, ensuring scalability, and enhancing computational efficiency. Methods: The research develops the methods of task/job identification by utilizing the metadata which can use the cloud and big data workflow components to determine resource allocation. The identification process carried out on a task/job. The cloud and big data workflow components consist of task management, resource capacity management, communication and transition, and interaction. Results: This paper underscores the potential of Resource-Oriented Architecture as a transformative approach for cloud and big data management. By enabling precise, scalable, and efficient resource management, ROA addresses the key challenges associated with high-performance computation in these domains. Conclusions: By focusing on dynamic resource allocation, task management, and efficient data processing, ROA enables organizations to optimize their use of cloud resources while handling the immense volume, variety, and velocity of big data.
Research on Ai-Driven Liquidity Risk Prediction and Dynamic Management in the Banking Sector
This study presents a meta-analysis of artificial intelligence (AI) applications in predicting and dynamically managing liquidity risk within the commercial banking sector. Drawing from the empirical evidence from peer-reviewed journals, industry reports, and case studies from the years 2015-2025, this research compares the performance of AI models (Long Short-Term Memory (LSTM), XGBoost, and Deep Q-Networks (DQN)) with traditional forecasting methods (ARIMA and Historical Simulation models). Results show that AI models always beat the traditional approach on forecasting accuracy (up to 99.3%), always better on root mean square error (RMSE), and predict with a much shorter lag. Real-world implementations in institutions like JPMorgan Chase and China Construction Bank reveal operational gains in transaction processing speed, cost efficiency, and early warning capabilities. The study further tests AI performance under stressful situations, such as the COVID-19 pandemic and regional banking turbulence, showing that it was resilient. Despite obvious benefits, challenges in legacy systems, ambiguity in the regulation, and interpretability of models in terms of accuracy act as barriers to widespread adoption. In conclusion, this paper states that AI provides a transformative edge for proactive, data-driven liquidity risk management, and banks should, therefore, invest in AI integration, which should complement any transparency and regulatory compliance issues.
Multi-view Graph Learning based Progression Modeling of Parkinson’s Disease Using Whole-blood Transcriptomics Data
In human disease modeling, blood transcriptomics data has played a crucial role in revealing regulatory abnormality. As patterns underlying blood transcriptomics data are subtle, it is insufficient to rely on temporal expression features to predict future stages of brain diseases. Spatial features have encoded meaningful information about gene-level and cell-level interactions. Progression of neurological disorder lasts 10-15 years, indicating that disease-specific gene graphs are changing. In order to employ dynamic spatial features, this work proposes a novel dynamic spatiotemporal graph learning (DST-GNN) to conduct disease progression prediction. The DST-GNN method aims to integrate expression patterns and dynamic gene graphs. Validation experiments about benchmark whole-blood RNA-seq datasets from the AMP-PD platform have demonstrated the effectiveness and advantages of the DST-GNN.
Enhanced Identity Recognition Post-Cosmetic Surgery Using Convolutional Neural Networks and Extreme Learning Machine
Facial recognition systems face significant challenges in accurately identifying individuals who have undergone cosmetic surgery, as these procedures often result in substantial alterations to facial features. This study introduces a novel approach combining Convolutional Neural Networks (CNN) for feature extraction and Extreme Learning Machine (ELM) for classification, specifically designed to enhance identity recognition after cosmetic surgeries. The method was evaluated using the IIITD Plastic Surgery Face Database, which includes images from both local and global surgeries. Preprocessing techniques, such as histogram equalization and noise reduction, were employed to improve image quality and ensure robust feature extraction. The experimental results demonstrate that the proposed method significantly outperforms traditional CNN-based approaches, achieving recognition rates above 95% across both local and global surgeries. Notably, ear surgery (otoplasty) achieved 98.84% accuracy, and eyelid surgery (blepharoplasty) reached 98.20% accuracy. The Extreme Learning Machine (ELM) component played a crucial role in reducing overfitting and improving generalization, making the system highly efficient for handling large datasets. These findings highlight the effectiveness of the proposed method in clinical, legal, and security applications, where accurate post-surgery identity recognition is critical. The ELM-based system offers a reliable and efficient solution for identity verification in complex post-surgical scenarios, demonstrating its potential for broader application in real-world settings.
A Joint Extraction Framework for Cyber Threat Intelligence Triplets Based on Knowledge Enhancement and Multi-task Optimization
Triplet extraction from unstructured text is a fundamental task in the construction of cyber threat intelligence (CTI) knowledge graphs. However, traditional pipeline approaches often suffer from redundant relation prediction, entity overlap, complex contextual dependencies, and limited domain knowledge. This paper proposes a joint triplet extraction framework tailored to CTI scenarios. Our model integrates a knowledge enhancement module, potential relation prediction, relation-specific entity labeling, and a global triplet validation module. The knowledge enhancement module enriches threat texts using an external knowledge base, improving semantic understanding of security-related terms. The potential relation prediction module filters out invalid relations, while the dual BIO-based labeling mechanism addresses overlapping entities. The final validation module scores and selects the most valid triplets. We constructed a CTI-specific knowledge base from MITRE ATT&CK and other public sources, and evaluated our method on two datasets: HACKER and RE-DNRTI. Experimental results show our method outperforms strong baselines such as PRGC and OD-RTE in both precision and F1 score, particularly in noisy and complex scenarios. Ablation and sensitivity experiments demonstrate the importance of each module and the robustness of the overall framework. This research contributes a reliable and interpretable method for high-quality CTI triplet extraction.
Neural Network Optimization Algorithm for Concrete Crack Image Recognition
Concrete structure is one of the most common materials in modern architecture and infrastructure construction. However, cracks will inevitably appear in the use of concrete structures, which is an important factor affecting the safety and service life of structures. Traditional crack analysis relies on manual participation, which reduces work efficiency and also has human influence. Based on the development of artificial intelligence, image recognition with neural network has become a desirable solution. In order to solve the problem of concrete crack image recognition, an optimization scheme using neural network is proposed. By introducing advanced neural network model and combining with image processing technology, the algorithm realizes the automatic recognition and classification of concrete cracks. In the process of algorithm design, the structure of neural network is optimized, including the network structure, overfitting and the selection of additional modules, so as to improve the recognition and classification accuracy. In addition, aiming at the imbalance of data sets in practical applications, data enhancement technology is introduced to effectively improve the generalization ability of the model. The experimental results show that the proposed neural network optimization algorithm has achieved remarkable performance improvement in the task of concrete crack image recognition.
From Logs to Intelligence: Leveraging Data Science for Service Account Monitoring
Service accounts (non-human credentials used to facilitate automation, system integrations, and machine-to-machine communication) play a vital role in today’s enterprise and cloud infrastructures. Given their often-elevated privileges and broad access scopes, these accounts have become high-value targets for cyber adversaries. However, conventional security monitoring tools frequently fall short in identifying misuse or lateral movement involving service accounts, largely due to their distinct and complex usage patterns. This paper presents a data-driven framework that leverages machine learning, big data analytics, and real-time anomaly detection to analyze multi-source log data and uncover suspicious or malicious service account behavior. Emphasis is placed on hybrid environments that span cloud and on-premises systems. We also examine key operational challenges, including model drift, scarcity of labeled data, and regulatory compliance. The proposed approach offers actionable insights and outlines strategies for integrating intelligent service account monitoring into broader security operations and incident response workflows.
Security Awareness for IS-Supporters on Telegram
The so-called Islamic State (IS) has always had a strong online presence. The purpose of most IS propaganda is to shape perceptions and polarise the support of their audience, but there are also other forms of communication from the group and its supporters. In this paper, we present an analysis of the content from two IS-aligned media outlet on Telegram that specialize in cybersecurity, privacy, and encrypted communications to assist IS supporters with security awareness. We have analyzed content for a period of 60 days (from February 9th to April 9th 2022) on the first media and 180 days (from July 15th 2024 to Jnuary 15th 2025) on the second media and categorized the type of content into four different categories. Most of the communication in the channel is about cyber security awareness and tools for secure communication. However, almost 30% of the conversations are about how to secure the community, avoid detection and maintain a presence online.
A Study of the Impact of Industrial Robot Application on Carbon Emissions
Technological advancements, particularly artificial intelligence, are deeply integrating with social and economic systems, making this trend irreversible. Industrial robots, born from this revolution, are reshaping industries and driving economic development towards environmental sustainability. AI has seeped into the fabric of society and economy, transforming industrial robots from mere tools to pivotal agents in promoting green, low-carbon economic transitions. Leveraging panel data from 2006 to 2019 across various industrial sectors, the empirical study reveals that industrial robots are instrumental in reducing carbon emissions and promoting a low-carbon operational framework within the industrial economy, with effects that are sustainable over the long haul. Mechanism tests indicate that human-machine coordination and energy efficiency are the primary mediating factors contributing to the emission reduction effect of industrial robots. Mechanism tests indicate that human-machine coordination and energy efficiency are the primary mediating factors contributing to the emission reduction effect of industrial robots. Heterogeneity analysis demonstrates that industrial robots exhibit a pronounced emission reduction effect in high-emission industries, low-technology industries, and smaller-scale industries. This research offers empirical support for achieving the "dual carbon" goals and provides valuable insights for policy development.
Guohui Lan, Yudie Zhang, Yashu Chen, Xiaona Ma
Analysis of the Influence of Impedance Characteristics of Traction Network on the Stability of Vehicle Energy Storage System and Compensation Strategy
With the development of traction power supply system of rail transit in the direction of high efficiency and intelligence, the role of vehicle energy storage system in improving energy utilization rate and mitigating power grid impact has become increasingly prominent. However, the interaction between the impedance characteristics of the traction network and the control dynamics of the energy storage system is easy to cause low frequency oscillation and resonant instability, which restricts the stable operation of the energy storage system. In this paper, the coupling stability of traction network and energy storage system is the core of the research. Firstly, the frequency domain impedance model of traction network is established considering the parameter distribution characteristics, and the validity of the model is verified by the measured data. Through multi-time scale coupling analysis, the influence mechanism of impedance characteristics on the control bandwidth and modal damping of the energy storage system is revealed. It is found that the negative damping effect of traction network impedance is the key factor leading to the resonant frequency deviation of the system. To solve these problems, an active damping control strategy based on virtual impedance injection is proposed, and a collaborative compensation framework combining online impedance identification and model prediction is designed. Simulation and hardware-in-the-loop experiments show that the proposed strategy can effectively suppress system oscillation and improve the stability of the energy storage system in the wide band domain. The research results provide theoretical support for the engineering design of vehicle energy storage system, and have important reference value for the optimization of intelligent traction power supply system.
Visual Recognition Method for Intelligent Picking of Special-Shaped Fruits and Vegetables based on Multimodal Fusion
With the acceleration of agricultural intelligentization process, intelligent picking of heteromorphic fruits and vegetables has become a key research direction in the field of agricultural automation. Traditional visual recognition technology has bottlenecks such as low recognition accuracy and weak robustness when dealing with the complex morphology and variable environment of heterogeneous fruits and vegetables. In this paper, we propose a multimodal visual recognition method that fuses visible, depth and near-infrared images, and deeply exploits the features of multi-source data through innovative data fusion strategies and deep learning algorithms. The experimental results show that the accuracy of this method is 92.3% in the recognition of shaped fruits and vegetables in complex scenes, which is more than 20% higher than that of the single-modal method, which effectively solves the core technical problems of intelligent picking of shaped fruits and vegetables, and provides important technical support for the intelligent upgrading of agriculture.
Research on Intelligent Identification and Prevention System of Intelligent Agricultural Pests and Diseases based on Computer Vision
This study focuses on the intelligent identification and prevention system of pests and diseases in smart agriculture based on computer vision, and systematically describes its principle, construction and application effect through multi-dimensional data support. The study shows that the system can significantly improve the monitoring efficiency and control accuracy of pests and diseases, and is effective in reducing the use of pesticides and economic losses, providing key technical support for the development of smart agriculture. Meanwhile, the challenges faced by the system are analyzed in depth, and the future development trend is outlooked to provide reference for the subsequent research and practice in this field.
Design and Interaction Optimization of Smart Hand Rehabilitation Assistive Devices for Stroke Patients
Long term disability following a stroke is complicated by hand dysfunction which is a major contributor and is a significant impediment to patients' independence, and quality of life. In this meta-analysis, the study analyzes the effects of design and interaction optimization in smart hand rehabilitation assistive devices for stroke patients. The analyses of a random effects model to systematically reviewed and analyzed 26 peer-reviewed studies published between 2015 and 2025 were, in total, conducted. The devices included robotic exoskeletons, wearable smart gloves, EMG based tools, and AI-driven adaptive systems. Results indicate that smart devices have a major impact on hand function and AI-based systems showed the highest effect size (SMD = 0.92). Gamified and interactive elements (tasks, real-time feedback, and adaptive interface) improved the motor learning and patient engagement and improved therapy duration and faster recovery timeline. Not only this but the combination of intelligent systems in the rehabilitation process reduced therapist contact hours and reimbursements expenditure as a whole, resulting on average a saving of $1,524 per patient in six weeks. Although differences in device types and patient demographics were observed, no differences were found in the evidence for the superiority of (optimized) smart rehabilitation technologies over conventional methods. These findings demonstrate that computational design coupled with real-time interaction and personalized feedback enables faster post-stroke recovery. Based on the work presented, the study concludes with a call for longitudinal studies with an interdisciplinary perspective to evolve intelligent rehabilitation solutions.
Enhancing Blockchain Security: A Novel Algorithmic Approach to Preventing 51% Attacks
Many studies have shown that blockchain is not as secure as it was thought to be despite its components and operations involving many algorithms. Therefore, researchers searched and found many novel algorithms to enhance blockchain security. This study aimed to enhance blockchain security using a novel algorithmic approach to prevent at least 51% of attacks, with three objectives. The novel algorithm was termed Dynamic Node Contribution (DNC), which dynamically adjusts mining power distribution based on node reliability and historical contribution to the network. The DNC algorithm employs a reputation system, where each node earns a reputation score based on its mining history, transaction validation accuracy, and consistency. Comparing the baseline tests and tests after the introduction of DNC into the system, overall, it can be concluded that the DNC algorithm has the potential to fortify blockchain networks against 51% of attacks while maintaining efficient operation with an effective reputation-base system. The algorithm's capability to mitigate the risk of such attacks shows enhanced overall network robustness. The slight increase in transaction confirmation time, averaging 14.3 seconds compared to 12.5 seconds without DNC, can be attributed to the dynamic adjustments made by the algorithm to improve security. This increase is outweighed by increased security.
Zero Trust for AI: Enabling Secure Workload Execution and Data Protection
This work seeks to determine how integrating Zero Trust Architecture (ZTA) with artificial intelligence (AI) systems in retail can help strengthen data security, keep up with regulations and execute tasks more securely. It looks at the risks in using AI, for example, poisoning of AI models, data being leaked and adversarial attacks and suggests a method focused on checking each user’s identity, providing them with access only when needed and continuously checking the system. Research has revealed that adopting Zero Trust security lowers risks, adds trust to AI systems, and supports secure operations for customers. This paper advises implementing strong governance, targeted AI-based security and following compliance standards for a secure and ongoing switch to digital in the retail industry.
Emotiplayground Design of Interactive Assistive Toys for Emotional Communication Disorders in Autistic Children
This research is about the development of EmotiPlayground, an interactive toy that is designed to help children with autism spectrum disorder (ASD) develop emotional and social skills. An increasing number of autism cases globally makes it necessary to have effective interventions. Principles of emotional interaction design and Internet of Things (IoT) technology are used in EmotiPlayground to develop a customized experience to deal with the specific difficulties of children with ASD. As the toy is designed with a friendly bear design and color-coded emotion cards, it is the toy that allows children to explore and express their feelings. By using a user-based design (survey and interview with parents and educators), the toy is assured to conquer the specific user needs. The research shows how assistive technology could help promote emotional growth and family relationships to make Autism a more inclusive experience for children.
Bayesian Prioritization in Product Strategy: Embedding Predictive Analytics into Agile Decision-Making
In dynamic product environments, widely used methods for prioritization often don’t take into account uncertainty and earlier context. A Bayesian predictive analytics model is proposed in this paper for sorting backlog tasks, making use of feature usage data, customer division and rates of installation success. With probabilistic reasoning, the model improves objectivity and follows the company’s strategic direction. We explain how Bayesian inference helps us make decisions using available data and reduces bias when planning sprints. Trials with agile teams have confirmed that they see better organizational planning, stakeholder collaboration and final product results. The results support using data and context in decision-making by product teams today.
Improved Range-Doppler Algorithm for SAR Imaging Based on Phase-Coded Signals
Traditional synthetic aperture radar (SAR) imaging typically employs linear frequency modulation (LFM) signals, but such signals have simple structures, are vulnerable to interference, and often require increased bandwidth to improve range resolution, resulting in low spectral efficiency. In complex electromagnetic environments requiring multiple target balancing, this approach may waste bandwidth resources. This research proposes an improved range-Doppler algorithm imaging scheme using phase-coded signals for SAR echo signals. The study applies dual quadrature phase-shift keying (QPSK) processing to LFM signals and further enhances system performance through adaptive phase coding strategies, improved range cell migration correction methods, and novel Doppler compensation mechanisms. The improved algorithm has been extensively tested in simulated environments, demonstrating significant enhancements in anti-interference capability, imaging quality, and Doppler tolerance, with peak sidelobe ratio (PSLR) improvement of approximately 22% and integrated sidelobe ratio (ISLR) improvement of approximately 22%. This method has important value in applications requiring high-quality imaging and anti-interference capabilities, such as military reconnaissance and disaster monitoring.
A Novel Garbage Detection Algorithm for Unmanned Surface Vehicles based on Reparameterized Model and Dual Path Feature Fusion
With the growing garbage flowing into the ocean through inland rivers, garbage detection and cleanup has become an urgent and necessary task for the safety of the entire ecosystem. Considering the expensive labor cost, Unmanned Surface Vehicle(USV) has been accepted as an important intelligent robot in river management field, and camera is widely adopted as a cost-effective way compared to other sensors like radar and laser for USVs in garbage detection. However, garbage detection based on vision is often affected by factors, such as the small size of distant targets, surface glare, and interference from other floating objects. It is a conflicting issue to achieve a higher detection accuracy with limited resources. To solve these problems, a novel detection algorithm is put forward for USV in garbage cleanup in the paper. Specifically, Dual Block Module(DBM) and QarepC3 modules are proposed based on the reparameterized model and dual-path feature fusion approach. EMIOU and Joint Attention Module(JAM) are constructed according to the characteristics of floating garbage. Subsequent to comprehensive evaluations, the proposed network exhibits not only high detection accuracy and computational efficiency but also robust performance in the complex environments of inland river. Moreover, it outperforms state-of-the-art networks in surface garbage detection for USV in the experiments.
Self-Improved Jellyfish Optimization for Extending Coverage in WSN
Recently, the vast advantages in networking technology paves its way in various arena. In WSN, the network consists of spatially distributed sensor nodes with base stations. In real-time, the sensors in WSN monitors physical and environmental conditions including temperature, pressure, etc. The sensor nodes in the network are functioned as router and originator. In WSNs nodal coverage problem is one of the major problem. This issue limited the sensing coverage of nodes while monitoring or tracking specific conditions. That’s why this work is intended to solve this issue using coverage optimization. This work is supposed to propose a novel efficient node coverage model for WSN using 2D distance evaluation in accordance with weighted Minkowski distance. Consequently, the optimal positioning of sensor nodes is determined by proposed SIJSO algorithm. Here, the proposed SIJSO algorithm for optimal positioning of sensor nodes is an implemented version of traditional JSO algorithm. The traditional JSO algorithm is a nature-based metaheuristic algorithm which mimics the behavioral aspects of jellyfish. In the conclusion, this proposed work proved its efficiency through various analysis.
Agentic AI-Powered Automation: A New Paradigm for Healthcare Workflow Optimization
The approach to AI-driven healthcare automation, which is being introduced using agentic AI, is a revolutionary one that uses self-directing and context-sensitive agents with decision-making abilities assigned to them so that they can carry out the activities. This paper is devoted to the idea of the improvement of workflow efficiency, accuracy, and scale in clinical and administrative settings offered by multi-agent systems. Using case studies of experimental systems and frameworks (such as HIPAA-compliant agents and triage based on the use of LLMs), we can evaluate the usefulness of real-time decision-making assistance and teamwork among lunar AI. Among the key metrics, one may speak of faster prediction accuracy, decreased latency, and transparency. We should consider that our findings indicate that agentic AI sets a paradigmatic change towards intelligent, adaptive healthcare processes that are efficient and compliant to safety and regulations guidelines.
Human-AI Collaboration Models in Presales: Designing the Augmented Pre-Sales Engineer
As explained in this paper, we present the concept of the so-called Augmented Pre-Sales Engineer where the role of AI would be to augment rather than replace a human in enterprise presales jobs. It suggests three aspects to organize presales work, according to cognitive load and AI-readiness, in parallel, sequential, and shared-control models. The paper also illustrates that concern by pitting a case study at a medium-size SaaS company and reports a decrease in turnaround time associated with the receipt of an RFP by 30 percent and an increase in solution engineer satisfaction. They find that trust, feedback loops, and ethical integration are among the important applications enabling human AI synergy. The present work proposes a tractable, well-organized roadmap to adopt AI and bring it to sales practices involving high-level tasks without reducing human expertise.
Securing Scalable AI Workloads in the Cloud: Architectural Considerations for Data Privacy and Governance
The fast growth of AI workloads in the cloud has brought forth complex issues in the form of data privacy, governance, and secure scale. In this paper, we introduce a holistic architectural solution to AI system security throughout the whole lifecycle, including data ingestion, training, inference and deployment.
A Multi-Time-Scale Source-Load-Storage Collaborative Optimization Scheduling Model with Source-Load Matching Degree
In the context of a high proportion of new energy sources accessing distribution networks, the uncertainty surrounding new energy sources and loads puts pressure on distribution networks to reduce peak demand. For medium and low voltage distribution networks with a high proportion of new energy sources, this paper proposes a multi-time-scale active distribution network scheduling model that considers flexible load scheduling. Firstly, based on information entropy theory, a source-load matching index is proposed to measure the degree to which the load matches the new energy output. A regulation plan is then formulated for the day-ahead energy storage system and flexible load to optimise the day-ahead load of the distribution network. Secondly, during the intraday phase, the output of gas turbine units, flexible loads and energy storage is optimised to minimise dispatching costs and achieve an optimal operating scenario for fully absorbing new energy. Finally, the economic cost optimisation of the multi-time scale source-load-storage collaborative optimal scheduling model is verified using the IEEE-33 node, and the model is solved using the CPLEX and particle swarm optimisation algorithms. The effectiveness of the proposed model was verified.
Cnn-Based Intelligent Recognition and Digital Dissemination of Rural Cultural Symbols in Anhui, China
In rural areas, cultural communication is essential to human contact and societal development. As a crucial component of China's socialist building and revolutionary past, red culture has great significance and a broad impact. The study begins by outlining the significance of regional symbol identification and the context of its use in cultural heritage. Historic city landscape planning is essential to promoting regional identities and spreading cultural value in the face of rapid advancements in heritage preservation and cultural tourist integration. It has been difficult for scholars to define the link between the two, however. The rural cultural elements and dialect characteristics of various locations are reflected in several research on dialect maps in terms of spiritual civilization. In the context of material civilization, several academics have focused on the regional variability of changes in land use and behavior. The limits of the present conventional design approaches are also discussed, along with the need to introduce new technology to enhance them. Using the well-known historic Rural Cultural city of Anhui, China, as an example, this study introduces a multi-label deep learning approach to explore cultural perceptions in tourism heritage settings. (1) A framework including artifacts, production, traditional music and living culture was constructed utilizing social media big data and an enhanced ResNet-50 model, combining ArcGIS spatial analysis and diversity indexes. (2) The main component of heritage landscapes is artifact culture, which has a "material-dominated, intangible-weak" structure; (3) the intensity of rural cultural perception is unevenly distributed, with core areas demonstrating higher recognition and diversity; (4) diversity indices indicate that specialized locations reveal marked Rural Cultural singularity, while comprehensive venues present stronger Rural Cultural balance, suggesting a need for enhanced integration across locations. The multi-label classification model's accuracy of 92.35% was shown by the findings, confirming its potential. This work expands the application of multi-label deep learning in tourist heritage studies and offers helpful suggestions for international historic sites that cope with mass tourism.
Zero-Shot Clinical Trial Design Using Large Language Models: A Data-Driven Approach to Protocol Generation and Cohort Identification
This project examines the innovative use of Large Language Models (LLMs) in designing zero-shot clinical trials in oncology. The method permits autocomplete of the entire trial protocol based on limited information-name of the drug, mechanism, indication, and phase- without speciality training-related tasks. The context supplied by historical Sanofi-financed clinical trial shows relative background to what the model yields. It involves the generation of protocols, the extraction of inclusion/exclusion criteria, the design of pseudo-SQL queries to find the cohort of patients, and the creation of regulatory submission documents. The research showed that LLMs are capable of independently and effectively generating quality trial documentation. This zero-shot approach has the potential to save time and cost of initiating a trial and increase inclusivity by appropriately choosing cohorts solely based on characteristics of real-world data.
Balancing Privacy and Protection: Ethical Challenges in Cyber Surveillance for Public Safety
The growth of digital surveillance tools to address what is an ever changing set of public safety issues has brought forth acute ethical and legal questions. As states put in place greater surveillance measures in response to terror, cyber crime and pandemics the balance between national security requirements and individual privacy has tipped. This paper looks at the ethical issues presented by cyber surveillance which is put forward in the name of public protection. We draw from literature in the fields of cyber security, data ethics and legal studies to look at the basic principles of privacy, proportionality and accountability in surveillance policy. Also we do a comparative study of surveillance structures in the U.S., E.U. and China which present different regulatory approaches and what that means for civil liberties. The paper puts forth a rights based model for governance of cyber surveillance which includes transparency, democratic oversight, and ethical impact assessment. We put forth that it is possible to protect public safety without at the same time violating fundamental rights and we call for integrated policy solutions which at the same time achieve security goals and meet constitutional and human rights responsibilities.
Mohit sharma, Aprajita seth, Ravi Prakash Singh
Cluster Partitioning Method for Distribution Networks with Distributed Photovoltaics Considering Voltage Stability
To address the voltage stability degradation and increased topological complexity caused by the growing penetration of distributed photovoltaic systems in distribution networks, this paper proposes a voltage-stability-oriented cluster partitioning method. First, a comprehensive cluster partitioning index is established, considering both structural and functional aspects of the distribution network framework. Structurally, modularity metrics are used to quantify network aggregation characteristics, while functionally, dynamic evaluations integrate voltage stability indices and source-load matching indicators. Second, to overcome premature convergence in existing clustering algorithms, an improved coati optimization algorithm is proposed. This algorithm employs chaotic mapping for uniform population distribution, introduces adaptive escape operators to balance global exploration and local exploitation and incorporates optical opposition-based learning to enhance its ability to escape local optima. Finally, simulations on the IEEE 33-bus system demonstrate that the proposed comprehensive clustering index improves voltage stability by 46.79%, 49.54% and 47.83% compared to single indices, respectively. The improved clustering algorithm reduces computation time by 62.79% and 65.22% compared to the traditional coati algorithm and moth-flame optimization algorithm, while achieving 97.72% of centralized voltage control effectiveness with an 88.84% faster response time, verifying the effectiveness of the proposed method.
The Study on Mesh Optimization and Computational Efficiency Balance in Magnetic Network Modeling of Magnetic-Saturated Controlled Reactor
In response to the dynamic reactive power demand surge caused by new energy grid integration, Magnetic-Saturated Controlled Reactor (MSCR) has become an ideal compensation device by virtue of its fast response and continuous regulation capability, but its modelling complexity and computational efficiency need to be solved urgently. This paper takes MSCR as the research object and focuses on the optimisation strategy of mesh dissections for magnetic network models, seeking a balance between computational accuracy and solution efficiency, and verifying the validity of the model through an experimental platform. Firstly, the magnetic leakage effect is considered and the magnetic flux tube equivalence method is adopted to establish the MSCR magnetic network model, and the validity of the model is verified by the measured data. Through the differential grid dissection of the magnetic valve region, it is revealed that the computational efficiency of the magnetic network model is about 100 times higher than that of the finite element method, and the error converges with the grid encryption; under the full-load condition, the local grid encryption of the magnetic valve section can significantly reduce the error, while the sparse mesh error is already very small in the case of no-load, and the optimization method based on the load-grid correlation is further proposed to realize the optimization of no-load and full-load regions by dynamically adjusting the mesh density. By dynamically adjusting the mesh density to achieve sparsification in the no-load region and local encryption in the full-load region, the relative variation of the calculation results under full working conditions is controlled within 1%, which effectively reduces the impact of load fluctuations on the calculation accuracy. This method realizes the dynamic balance between accuracy and efficiency in the modeling of complex electromagnetic devices, and provides theoretical support for the optimization of MSCR function and the engineering promotion of magnetic network model. The research results not only provide theoretical support for the engineering design of MSCR, but also provide important technical support for the system-level simulation of other electromagnetic devices, as well as the real-time simulation and safe and stable operation of smart grid.
Mingxing Tian, Yuexin Li, Xun Zhu, Ying Wang
Impedance characteristic analysis and control strategy research of single-phase LLCL grid-connected inverter under weak grid conditions
Grid-connected inverters are core components for integrating clean and renewable energy technologies into the power grid and play a vital role in enhancing the stability and efficiency of the entire power system. During long-distance power transmission in grid-connected generation systems, the presence of line impedance often results in weak grid characteristics, which can trigger resonance and harmonic amplification, thereby affecting the stable operation of the grid. This paper focuses on a single-phase LLCL grid-connected inverter. First, a mathematical model of the LLCL inverter is established considering the impedance characteristics of a weak grid. By analyzing the amplitude and phase characteristics, the influence mechanism of weak grid impedance on the inverter system is revealed. The study finds that the inherent resonance peak and frequency shift of the LLCL inverter are key factors contributing to increased harmonic content in the system. To address these issues, a hybrid damping method combining active and passive damping is proposed to suppress the resonance peak. Furthermore, a composite current control strategy is designed, which parallelly integrates quasi-proportional resonant control and repetitive control, aiming to enhance the inverter’s adaptability to grid frequency deviation. The results of this study provide important theoretical support for the engineering design of renewable energy grid integration and offer valuable insights into the optimization of grid-connected inverter systems.
AI in Network Security Enhancing DDoS Attack Detection and Mitigation through Machine Learning and Deep Learning
The increase in popularity of the Distributed Denial of Service (DDoS) attacks poses significant problems for network security. The purpose of the present research is to explore how Artificial Intelligence (AI) contributes to the prevention and detection of these attacks using machine learning and deep learning platforms. In particular, the Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Random Forest (RF) models are used to determine their effectiveness in detecting and distinguishing healthy and malicious network traffic. The evaluation of accuracy, precision, recall, and F1-score of the employed models is determined through training and testing the two widely familiar datasets, CIC-DDoS2019 and NSL-KDD. The results revealed that SVM and LSTM models have promising results with a high level of precision score, but without a recall feature, which is important for DDOS attacks. However, the evidence strengthens the idea of using AI-supported applications in real-time threat detection and alleviation. This is mainly because they are more responsive than the current methods. Future research must improve the memory of models and test combined forms of AI to improve DDoS defense mechanisms.
Digital Twin Governance: IoT-Driven Real-Time Regulatory Auditing in Smart Hospital Architectures
In this paper, we attribute a new framework of Digital Twin Governance (DTG) where regulatory auditing is conducted in the same manner as in Smart hospital with the help of IoT and edge technologies used to be a reality in real time. Old healthcare audit systems are retrograde and mix up with exhibiting lack of ability to intercept the contravention of policy at an early enough stage thus causing a threat of damage and nonconformance. This gap will be occupied by our contribution that allows marrying digital twin models with explainability engines, powered by AI, rule engines to drive the workflow guiding the compliance with regulatory requirements, and blockchain logging where the most critical services in secure administration are offered. DTG was implemented in a designed mimicked smart hospital structure in which more than 5,000 virtual patients and various IoMT gadgets were utilized along with edge-cloud orchestration. Empirical findings confirmed that there was reduction in breach detection latency as the value arrived at 83 minutes to 0.32 minutes, and time of auditor review also reduced that is 17 minutes to 3.4 minutes per incident. The predictive audit based on an ensemble model distinguished a compliance violation rate at the rate of 95.4% within the abilities of an ensemble model outperforming the rates of a baseline model such as XGBoost or MLP. In the range of scale-out hospital workloads, the system revealed sub-60ms latencies regarding alerts. The above findings demonstrate that DTG is plausible and effective as far as offering proactive, elastic, and transparent audit is concerned. The architecture not only automates compliance activities but it also increases the trust between the stakeholders since the audit trail is blockchain anchored. The paper ends by providing architectural details, measures of scalability, and implications of governance should be adopted in implementing the digital twin-led auditing system in the healthcare sector in a real-life scenario.
Incident Intelligence in Telecom: A Framework for Real-Time Production Defect Triage and P0 Resolution
The rising intricacy of the telecom platforms drives the need of the intelligent, automated incident response systems. The current paper describes a real-time incident intelligence platform that was implemented into Charter Communications Mobile 2 ecosystem. Using Kafka-based ingestion of logs, the machine learning logic of chooser responder, and RCA pipelines that are automatic with Splunk and Datadog, this framework will lower the mean time to detect, assign, and resolve P0 incidents considerably. The deployment in the real world illustrates the better levels of keeping to the SLA, automation of the triage and resilience of the systems. The architecture will combine well-organized playbooks and feedback loops to permit continuous learning. The findings indicate that these structures can be the framework to provide a model of scalable, intelligent triage of production defects in a telco-grade application.
Precision Pulse: AI-Driven Micro-segmentation for Optimized Retail Customer Engagement
In this digital saturation environment, which weakens the performance of conventional mass communication, retailers should find more intelligent, less fat, and more situationally aware ways of communicating with customers. The current paper presents the development of a novel communication framework utilizing artificial intelligence and micro-segmentation approached called Precision Pulse that is based on reinforcement learning and dynamic real-time consumer behaviour analysis to provide the hyper-personalized outreach and target it across various channels comprising email, push notification, SMS, and in-app data. A fully automated, machine learning clustering approach with the help of reinforced learning, in a feedback loop, allows the system to continuously improve the customer segmentation and individualize content strategies against the behavioural signals that can change quite dramatically. Built-in content producers and responsive deliveries logic decrease message fatigue levels and increase the responsiveness and save message growth and redundancies in the context of the sustainability agenda to match campaign performance to the sustainability objectives by cutting down on irrelevant digital messaging. Severe A/B testing activity in multi-brands environment showed substantial growth in cluster correctness, engagement and opt-out decrease. It was substantially more effective than the traditional segmentation models, with the Silhouette Score of 0.88, and the segment-based revenue may grow by 39.9%. Besides, Precision Pulse improved customer retention by 35% and decreased volume of messages to 24% further confirming its strategic potential. The scalable roadmap developed in this research provides a reference framework on how to approach engagement responsibly and efficiently in the modern retail world where precision, flexibilities and ethical AI will lead towards the development of relevant, enduring customer relationships.
Security-Integrated Test Framework for FedRAMP-Ready Cloud Applications
As the modern SaaS architecture has complex and dynamic nature, it is a challenge to ensure FedRAMP compliance related to the cloud-native environments. The paper entails a framework of a security-integrated test automation that can be used to test important FedRAMP controls namely access management, enforcement of encryption, and audit logging accross multicloud environments. The framework takes advantage of the Policy-as-Code principles and reinforcements IaC scanners, such as the tfsec and Regula, the admission controllers in the Kubernetes area, such as the Gatekeeper and behavior monitoring based on SIEM-compatible logs. The CI/CD workflows include tests that can support the continuous security between the code and the runtime. The Terraform and Kubernetes configurations were deployed on the AWS, Azure, and GCP platforms through implementation of policies before and after the deployment on the platforms. Its performance indicates significant increase in the policy detection rates (up to 98 percent) and the speed at which it mitigates (less than 6 minutes) and very low false positive rates. It was also a portable framework, which was demonstrated to work on such DevOps platforms as GitHub Actions, Jenkins, and Azure DevOps. This will automate security checks and checks, and integrate those with current development pipelines, decreasing manual work, problems of compliance drifting as well as aligning the cloud development with the strict FedRAMP requirements. The framework proposed therefore amounts to a feasible, manageable and policy-based ready solution to cloud applications facing governments bridging the disparity between agility in operation and government security regulations.
Observability as a Service in Retail: A Strategic Framework for Enabling Digital Transformation
Retail market is rapidly moving towards digital transformation due to the changing needs of customers and due to the omnichannel services. In this paper the concept of Observability as a Service (OaaS) has been presented as a strategic facility, which gives real-time, multi-layer visibility of applications, infrastructure and customer-facing systems. A conceptual framework is SP proposed, which is a combination of business telemetry to detect problems proactive and optimize business performances. Case-based information shows the increased agility of the operations, the customer experience as well as business continuity. The study shows how (OaaS) in the IT System resiliency and responsiveness leads to the emergence of scalable, secure, and innovative ready retail infrastructures through quantitative analysis and system level analyses.
Intelligent Metadata-Driven Automation for eCTD 4.0 Lifecycle Management Using Explainable AI (XAI)
Regulatory submissions require both transparency and automation in the era of digital transformation to meet evolving compliance standards. This paper discusses the application of Explainable AI (XAI) on automated intelligent metadata-driven pharmaceutical regulatory submission lifecycle management of eCTD 4.0. The research is based on an explanatory research design and an analysis of secondary data in the form of industry reports, regulatory guides on XAI, as well as case studies to understand how XAI can increase transparency, traceability, and compliance. It is observed through literature and data visualisations that XAI enhances efficient learning of models, the reliability of white-box models, and serves high accountability challenges, as is the case. The examples of case studies of GlaxoSmithKline and AstraZeneca prove impressive improvements in speed of submissions, lowering the error rates, and boosting audits. The challenges involve bias, data privacy and interpretability of the variance among the users. This paper suggests bias-detecting, human-based and role-specific explanations of XAI models in an attempt to establish trust and regulatory conformity. All in all, various links on how to combine XAI and metadata automation demonstrate the necessity of future-ready, transparent and efficient eCTD 4.0 life cycle management.
The Impact of Teenagers' Digital Footprints: Examining How Data Breaches Trigger Anxiety and Depression and Solution to the Problem
The youths' actions within the digital platforms have been defined as digital footprints, which are crucial in facilitating analysis and understanding of their behaviour. Extensive digital footprints among the youths have exposed them to increased vulnerability to mental health. This scenario is explained by increased emotional response among the youths, which presents a unique challenge, especially due to their lower resilience than adults. The strong intersection between digital privacy violations and teenage mental health creates a complex relationship that requires the implementation of a multifacetedmultifaceted intervention that addresses the associated gaps. Interventions such as increased digital literacy, mental health support, and innovative technology interventions are crucial practices recommended to address issues related to digital breaches and teenagers' mental health. Future studies should consider the limitations of this research, which include rapid technological innovation and challenges isolating data breaches from other stressors of mental health among teenagers. The purpose of this systematic review study is to evaluate the link between data breaches and increasing cases of anxiety and depression among the teenagers. Also, the study will provide recommendations such as opportunities for digital literacy, access to mental health programs, policy interventions and improved technology innovations.
The Applications of VR/AR in Healthcare, Focusing on Advancements in Surgical Training and Preoperative Planning, Aiming to Improve Patient Outcomes and Healthcare Education
Virtual Reality (VR) and Augmented Reality (AR) technologies propel the healthcare system into forward-looking surgical training, preoperative planning, and more groundbreaking applications. These immersive technologies enable simulations and interactivity that enhance medical education while vastly diminishing surgical errors and improving patient results. Traditional training often wrestles with ethical dilemmas, expensive price tags, or a lack of individualized approach. VR and AR are the solutions for the above challenges as they are scalable, cost-efficient, and highly customizable. The range of their applications goes further in a patient's mind by including mental health therapy, such as virtual aspects that treat PTSD and anxiety, and healthcare marketing by creating patient experiences that are informative and engaging. This study seeks to understand how VR and AR have been integrated into such areas, drawing evidence from other recent literature and quantitative data to evaluate their effectiveness as transformational influences. These results indicate how they democratize quality care in society by breaking geographical boundaries and cutting costs. They have, however, also enhanced the accuracy of procedures and promoted patient involvement and novel tools for learning for healthcare professionals. By representing VR and AR's scalability and cost-effectiveness, this research demonstrates that the favorable future extension of healthcare towards better outcomes and greater accessibility for all depends on making them seminal and indispensable.
The Velocity Mandate: A Framework for Accelerating Hyper-Personalized Content in Financial Services with Generative AI, AEM, and Java
The financial services industry stands at a critical juncture, defined by a fundamental and widening paradox. On one side, client expectations, forged in the crucible of seamless digital experiences offered by technology giants and agile FinTech innovators, have irrevocably shifted. By embracing this integrated framework of technology, process, and governance, financial services institutions can break free from the constraints of their legacy content supply chains. They can close the gap between client expectations and operational reality, transforming their ability to communicate with a new level of speed, relevance, and personalization. This is the velocity mandate a strategic imperative for any institution that aims to compete and thrive in the new era of intelligent, client-centric finance.
Surendra B Konathala, Portfolio Manageri
AI-Based Should-Cost Modeling for Customized Automotive Motors
The automotive industry in the electric vehicles (EV) sector has been moving towards customization and small-batch production to respond to consumers' demand for custom vehicles. The way current cost modeling fits into the situation of tailored or small-volume automotive motors complicates the cost estimation and cannot be surmounted using traditional methods since the difficulty lies in the complex and variable parts of the automotive application, which traditional methods of handling cannot alleviate. This paper aims to evaluate the use of Artificial Intelligence (AI) and Machine Learning (ML) to model the costs of automotive motors and assess their ability to predict cost variability and risk introduced by existing complexities. The study also examines these approaches' accuracy, efficiency, and scalability relative to traditional cost modeling techniques. The authors of this study offer a direction toward getting a future cost modeling accuracy that is neutral to the underlying code. They analyze how AI can perceive and defeat challenges related to the supply chain, such as disruptions and volatility of prices, among others. All corresponding results demonstrate that AI models outperform conventional methods in terms of accuracy, efficiency, and scalability, particularly for large datasets and motor designs that contain variables. Individuals and companies may use AI models to make predictions (and thus improve decisions) about cost fluctuations. At the end of the paper, AI-based collaborative cost modeling, motor design optimization, and predictive analytics of commodity price fluctuations are viewed as future research recommendations. These advancements bring forth several benefits, which further aid in improving cost estimation and production processes, making them more efficient and less expensive in the automotive industry.
MongoDB as the Backbone for Modern Mobile and Web Applications
As a leading NoSQL database, MongoDB provides a creative remedy to the problem of huge and data-intensive mobile web apps. As opposed to relational databases, MongoDB is a flexible document-based architecture that is designed to store semi-structured and unstructured data efficiently, and thus, MongoDB is suitable for modern applications that demand high availability, scalability, and flexibility. This paper examines some of the key features of MongoDB that make it useful in the face of growing demands that these industries are increasingly facing, such as support for real-time data processing, schema flexibility, and horizontal scalability provided by sharding and replication. It is highly suitable for these sectors for handling high-volume transactions and cloud-native integration, as it is very good for low latency and real-time update applications. Optimized MongoDB performance techniques, including indexing strategies and aggregation frameworks, also help it perform and retrieve data quickly. As mobile and web applications continue to grow, MongoDB’s position as an enabler of real-time decision-making and the creation of machine learning models stays strong as it moves toward the future of data-driven apps. The study also covers future considerations on MongoDB, such as trends in NoSQL databases, integration with cloud-native architectures, AI, and data privacy and security that are becoming a priority to comply with global regulations. The results of this paper are presented through concrete case studies of performance, scalability, and operational efficiency improvements in the practical application of MongoDB.
Kubernetes in the Cloud: Implementing EKS Clusters for Scalable and Resilient Applications
Kubernetes is an open-source container orchestration platform, and it is now the industry standard for managing containerized applications in cloud environments. Since businesses constantly adopt cloud-native architectures, Kubernetes helps efficiently perform containerized workload deployments, scaling, and management. Amazon Web Services (AWS) provides Elastic Kubernetes Service (EKS) to simplify the setup and operations of Kubernetes clusters. With EKS, organizations can benefit from Kubernetes by abdicating responsibility for maintaining the infrastructure. This means they can have a good scale and exceptional resilience with seamless integrations with the other AWS services. This article explores the process of utilizing EKS clusters to build scalable and resilient cloud-native applications. The book explains how Kubernetes and EKS can be leveraged to optimize cloud infrastructure and how businesses can use these technologies. The main features of Kubernetes are auto scaling, self-healing and resource management, and they can be extended with EKS. Additionally, articles show examples and case studies on how EKS facilitates enterprises to scale applications easily, especially during peak demand, to ensure maximum application availability. It also covers the inherent challenges to managing EKS clusters, security best practices, and how to integrate AI and machine learning to enhance performance. Lastly, the article discusses what’s to come regarding cloud nativity and Kubernetes and how the position of EKS will likely change as cloud infrastructure continues to shift.
Copyright (c) 2025 Computer Fraud and Security
Deploying Azure AD Federation with SAML for Secure Enterprise SaaS Integration
This study discusses the implementation of Azure AD Federation, utilizing SAML as an effective method for securing enterprise SaaS integrations. As the use of cloud-based Software as a Service (SaaS) applications continues to grow, enterprises are finding it challenging to control, secure, and scale access to users. Identity federation with Azure AD, combined with SAML-based identity management, provides a single, centralized identity management system that simplifies access and enhances security across numerous cloud-based applications. This paper explores a practical example of the use of Azure AD Federation together with SAML, challenges faced, best practices, and the effect on performance and security. The main findings include significant improvements in authentication times (down by 70%), throughput (up by 33%), and failure rates (down by more than 80%). Moreover, the Multi-Factor Authentication (MFA) that is being adopted by 90% of users, along with the use of RSA-2048 encryption on SAML assertions, has enhanced security. The study discusses the importance of ongoing optimization in identity management systems and trends that are emerging, such as AI-driven threat detection and decentralized identity management systems. SAML-based Azure Active Directory Federation serves as a scalable, efficient, and secure solution for managing identity and connecting enterprise SaaS. This study reflects its role as a key part of the infrastructure of the new enterprise IT environment.
Automated Incident Response Using AI-Based Decision Trees
The emergence of high-level cyber threats, including polymorphic ransomware and APTs, needs quicker and more effective intrusion response capabilities in the field of cybersecurity. Manual systems are also traditionally slow, with a median response time of 15-30 minutes, making organizations prone to fast attacks. This study examines the application of AI-based decision tree implementation to validate the effectiveness of decision trees in automating incident classification, prioritization, and response. The research seeks to increase the speed and accuracy of incident detection and analysis through a machine learning model, such as Classification and Regression Trees (CART), in cutting the time to respond (MTTR) by at least 40% and achieving more than 95% detection rates of typical threats such as phishing and malware. The process involves training decision trees on features such as IP reputation, domain flux, and attempted failed logins, and then adding these models to existing incident response mechanisms, including SIEM and SOAR. The findings indicate that there is a substantial decrease in the time taken to respond (40-60%), and false positives are reduced by 15-25% relative to an ordinary system. The automation also decreased manual interaction by 80%, enhancing efficiency among analysts. The study explains how AI decision trees can be used to optimize the incident response process because of operational advantages, such as reducing costs and improving the security posture. Future studies should be directed to the further development of the model to be able to cope with more intricate threats and also to ensure that automated systems are ethical and transparent in their decision-making.
AI-Augmented Testing: GitHub Copilot for JUnit/Mockito Generation
In recent years, AI has reached a revolutionary stage of impact in the software testing sphere, particularly in the context of unit test generation using AI. One of the brightest examples of this trend is that of GitHub Copilot. This is an artificial intelligence-based tool using machine learning and natural language processing that can automatically generate JUnit and Mockito test cases based on the current code’s overview. The questionnaire of meaning checks the capacity of Copilot to replace the traditional testing methods with the creation of precise and exhaustive tests, resulting in a simultaneous increase in the productivity of the developers. The research compares AI-generated and human-written test suites based on various open-source Java projects by taking into consideration such key performance metrics as code coverage, the time taken for execution, and what in a language should be the defect detection. Empirical evidence shows that AI-generated tests cover 75% of the code base, which is greater than manually written tests 60%, It is said to take 40% less time to write a test. However, Copilot is found to be weak in terms of complex business logic and handling the cases of boundary conditions, as well as signaling the need for human developers to rectify them afterward, incorporating active transformation services. The results are promising for the effectiveness of AI-based instruments to enhance the speed of the testing process. However, they still emphasize that human intervention is imperative to ensure the quality and integrity of the products of the test generation. This paper can augment existing literature examining the topic of artificial intelligence in software testing and reinforce the fundamental idea that AI-enhanced tools can transform testing processes in significant ways, creating long-term value for both developers and the software industry as a whole.
Peer Benchmarking Systems for RIA Performance Evaluation in Investment Technology
RIAs need to establish thorough portfolio analysis methods to preserve client interests as well as improve business market standing. Modern investment technology frameworks support solutions developed in this paper to fulfill RIA performance evaluation standards. This framework applies various metrics that assess numerical and subjective parameters to deliver thorough performance reports about different advisory businesses. Traditional benchmarking methods fail because they employ standard assessment tools which disregard how RIA businesses need to support client segments, their investment approaches and operational capacity. The peer grouping methodology addresses industry requirements through its structured process that divides RIAs into groups based on AUM values combined with client population information and service delivery aspects. The detailed explanation of normalization strategies creates both exact and unbiased assessment outputs. The document evaluates mandatory benchmarks that consist of risk-adjusted returns risk alongside metrics for client retention and operational efficiency as well as digital engagement standards. Realistic benchmarking examples show that firms using peer metrics facilitated better customer satisfaction together with improved advisor performance and strategic growth both for business and clients. This paper evaluates present data privacy regulations and their standardization requirements along with participant motivation mechanisms. Team evaluation methods incorporated into RIA performance evaluations yield better result transparency and establish common industry benchmarks. Strategic choices made by RIAs become more informed through this system thus leading to better client results as well as adherence to regulatory prerequisites. The paper offers recommendations for further research along with detailed guidance for organizations wishing to implement these systems in their practice.
Enterprise Digital Transformation in Financial Services: Emerging Trends and Technologies
Transformations in the use of artificial intelligence (AI), cloud computing, and blockchain are revolutionizing the direction of the financial services industry. The transformation improves operational efficiency, customer experience, and intra-stage decision-making, allowing financial institutions to remain competitive in this ever-changing market. As this is possible, personalized financial services such as tailor-made loan products, investment advice, and financial planning can be achieved via the evolution of algorithms based on artificial intelligence algorithm, specifically in machine learning and natural language processes. Functioning on a scalable and secure infrastructure, cloud computing makes rapid deployment and real-time data availability possible, which is needed for agile operations. By integrating AI and the Cloud, financial institutions have the means to provide the modern customer demand and the rigid requirements of banking. It is also because blockchain technology is a change driver that provides secure and transparent financial transactions. Specifically, this has a major impact on sectors including banking, insurance, and security trading as it opens up the possibility of streamlining cross-border payments, reducing payment fraud, and better customer verifications. In addition, smart contracts in the blockchain are claimed to facilitate faster, more secure securities market transactions with better regulatory oversight. AI and cloud technologies will remain a force that changes the customer experience, and researchers see omnichannel services, real-time support, complex predictions of customer behavior, and predictive customer engagement. These technologies are pushing to be more efficient and secure and offer more personal products, forever changing how financial institutions conduct and relate to their customers. Organizations that provide financial services that endorse this innovation stand to gain in a more digital competitive landscape.
Automated Rollback Triggers in Jira: Linking Failed Deployments to Incident Management
This study examines automated rollback triggers integration with Jira with a focus on their usage for downtime reduction and incident handling optimization in case of failed deployments. Deployment failure is a common issue in DevOps, impacting system stability and business operations. Automated rollback triggers, connected with Jira incident handling software, enable an optimal mechanism for quickly reverting the system to stable conditions and thus preventing adverse deployment failure impacts. The research findings indicate that automated rollbacks can reduce system downtime by up to 40%, ensuring a quicker recovery and minimizing human error during recovery processes. Automated incident creation and tracking in Jira also saves up to 30% of time spent on incident resolution, ensuring better deployment handling. It further tracks development and operations team workflows through the configuration of Jira’s automated development and operations team workflows, allowing for higher priority work attention. Practical implication of research findings includes the use of automated rollback systems under the work of CI/CD pipelines, offering better overall deployment process efficiency, operations cost reduction, and better system uptime. Research offers practical and technological solutions under such applications, acting as informative content guiding towards business undertakings targeting over solicited fields’ simplification of deployment workflows and incident handling systems.
Streamlining and Automating Workflows in the Car Insurance Domain
The paper discusses how simplification and automation can revolutionize operations in the car insurance industry, where policy issuance, claims, and regulatory compliance are complex operations that manual processes have hitherto driven. Based on an analysis of industry trends and cross-industry best practices, it provides a systematic roadmap for implementing technologies such as artificial intelligence (AI), robotic process automation (RPA), cloud-based systems, telematics, and digital document management. These technologies minimize the time of the claim cycle, enhance the accuracy of pricing, and allow for detecting fraud in real-time, also contributing to increased customer engagement with 24/7 digital services. The paper emphasizes the importance of planning, sound data governance, and effective change management to ensure successful transformation, overcoming obstacles such as integrating with legacy systems, employee resistance to change, and data privacy risks. Measurable gains proven through case studies are in the form of quicker settlements and high percentage reductions in false claims. In the future, trends that could emerge include a blockchain-based approach to transparent claims, edge computing-based real-time analytics, and sustainable cloud practices, which can create a future of fully digital insurance ecosystems where minimal or no manual involvement is required. The results indicate that automation is not merely an upgrade in operations, but a strategic requirement for insurers in the long run to compete effectively, gain customer confidence, and maintain flexibility in a market that is increasingly technology-oriented.
Semantic Modeling and Text-to-SQL Pipelines in Snowflake for AI Retrieval
The paper will discuss the creation of Text-to-SQL pipelines and a semantic model in Snowflake to simplify the acquisition of AI-based data retrieval by businesses. The system can convert natural language queries into SQL queries using Snowflake Cortex, enabling users to access data without technical skills. Semantic modeling, hybrid search, and RAG are adopted together in the approach in a bid to enhance accuracy and relevance. We refer to pipeline design, workflow, performance, and governance, and demonstrate how the queries based on AI can be run quickly, securely, and in compliance. Real-life examples of financial, operational, and customer analytics indicate that timely decision-making and reduced manual labor can be achieved. This article puts Snowflake into focus as a powerful solution with respect to AI-based enterprise analytics.
Formal Verification for AI-Assisted Code Changes in Regulated Environments
Use of AI in code generation is able to make software creation faster, and yet due to lack of controls, the possibility of making severe compliance and reliability issues increases in regulated and safety-critical fields like finance and medicine. We describe, in this paper, a proof-carrying pipeline incorporating proofs presented by Large Language Model (LLM) advice into static analysis, symbolic execution, bounded Model checking, and policy execution mechanisms to certify changes to code before it is merged. The system will block unsafe pattern automatically, generation of auditor friendly evidence packages, and fits with Continuous Integration/Continuous Deployment (CI/CD) worklifes so that the impacts on developer velocity are minimal. We measure the method of the two production-scale repositories, one one in fintech and another one in healthcare, in terms of defect reduction, verification efficiency and the effect on audit preparation. The outcome denotes that the relative decrease in incident of defects after the merge was reduced by 73-78% and the success rate that is evaluated by the verification is markedly elevated when put to the test in a solitary mode. The preparation time of audits was decreased by more than 69%, and gift boxes of proofs were prepared in a structured manner and machine-defensible, therefore, decreasing manual review processes. Prompt refinement using the reinforcement learning also enhanced the throughput in verification by minimizing the number of repetitive re-verifications involved. The results show how AI-based development could be secure and meet standards with the integration of thorough formal verification. Through the use of generative AI techniques and technologies, the outlined pipeline solves the two-fold problem of pushing the development speed, and maintaining correctness and regulatory compliance and provides a scalable template of how high-stakes software engineering can be done in the era of generative AI.
Architecting Cloud-Based CPQ Solutions for Healthcare Enterprise Transformation: A Framework for Medical Device Sales and Compliance
The complexity of the products, emerging provider needs, and exclusive regulatory systems have made it challenging to sell and configure medical devices in the healthcare medical device industry. Systems of medical imaging and surgical kits require significant customization meant to be adapted to the specific requirements of a clinical setting, which introduces complexity to configuration, which conventional methods fail to adapt to. Cloud Configure, Price, Quote (CPQ) has dramatic potential in transforming healthcare organizations, like the ability to configure medical devices with accuracy, efficiency, and in a compliant way. This architectural framework targets integration with Electronic Health Records, mechanisms of data flows, validating compliance, and security challenges, and gives advice on how to implement it by healthcare technology executives. The framework involves theoretical assumptions, architectural patterns, integration mechanisms, and implementation strategies that facilitate healthcare organizations in contemporary environments.
Automated Workflow Validation for Large Language Model Pipelines Using Python & Java
This paper explains an automated system for checking language model workflows. Large language models, also called LLMs, go through many pipeline steps. These steps include cleaning data, processing prompts, integrating models, and producing results. Doing all these checks by hand takes time and creates mistakes. Our solution uses Python and Java together for workflow validation. Python helps test data, quick automation, and smaller tasks. Java is strong for large systems, handling validation and enterprise workflows. The system includes automated unit tests and regression checks for accuracy. It also has built-in error detection to catch early problems. Logging and monitoring are added to track every step clearly. This allows teams to repeat and reproduce results whenever needed. The system also simulates edge cases for tougher testing situations. These simulations show how workflows behave under strange or unexpected user inputs. Together, Python and Java provide flexibility and strength in one framework. This makes the system fit for both small and large environments. The main benefit is reduced failures across complex pipelines using LLMs. Developers can save time and avoid mistakes with automated testing. Teams no longer need heavy manual checks for every pipeline step. The framework also helps speed up the development and integration of new workflows. This makes LLM projects more reliable and easier to deploy. The expected outcome is stronger performance in production and fewer pipeline errors. Overall, the system improves speed, trust, and safety when using language model workflows.
Security at Scale: Automating Vulnerability Triage and Risk-Based Patch Management in CI/CD Pipelines
The faster and more frequently used CI/CD pipelines to deliver software have led to an increase in vulnerabilities due to a lack of pace related to traditional manual triaging efforts. This report shows us an expandable framework combining both static and dynamic analysis runs, compound risk-scoring, and automated patch orchestration to simplify vulnerability management in CI/CD pipelines. It uses modular microservices that are deployed through Kubernetes and uses pre-merge hooks, post-build pipelines, and a message bus to isolate the scanners, scoring engines, and orchestration elements. Risk scoring complements CVSS values with up-time threat intelligence, asset criticality, and employs natural language inference in a machine-learned layer of prioritization to further improve urgency assessment. Patch implantation strategies integrate the canary and blue-green strategies with rollback to deliver resilience in case of failures. Well, empirical testing on a microservices-based application comprising 50 injected vulnerabilities showed that median triage time was reduced by 50% (120 to 60 minutes), 30% more vulnerabilities were patched (58% to 88%), and 20% fewer were found to be false positives. Rollback events were reduced to less than 3% compared to 5% in the manual workflow, and resource overhead was not within enterprise quotas. Engaging engineering and security teams in qualitative surveys found that the mental burden and satisfaction decreased, and 95% of them wanted to adopt it. The framework facilitates the multi-cloud and hybrid setups, improved integration of incident response, and explainable ML models in compliance audits. The following areas of development involve adaptive feedback-driven risk models, large-scale field tests, and ML explainability as a means of venerating self-healing CI/CD pipelines that can subordinate security automation to the needs of an organization.
Building Trustworthy Mobile Apps: Security and Ethical AI Considerations
Mobile applications have become fundamental features incorporating artificial intelligence features that present opportunities and responsibilities to developers. The article explores the delicate nexus between creative functionality and ethical application and deals with the natural conflicts between individualization and privacy protection. Detailed models of reliable mobile applications are introduced, discussing authentication schemes in biometric validation and contextual security and privacy-conserving schemes such as on-device processing and federated learning. The reading outlines mitigation methods of bias, explainable interfaces, and audit tools that would be critical in fair ways of deploying AI. Examples of industries in financial services, healthcare, retail and education provide examples of implementation strategies. Security-oriented development guidelines, ethical policies, diverse population testing, and open documentation give practical ways to take responsibility in development. The development of new privacy technology, regulation, standardization, and trust measurement procedures suggests the way forward in ensuring that users are confident in more advanced mobile AI ecosystems.
Unified AI-Driven Orchestration of Security Controls in Multi-Cloud Environments
Contemporary companies traverse increasingly complex digital environments defined by disparate cloud infrastructures, numerous service vendors, and continually changing risk vectors. Traditional security architectures enforce preventive, detective, and remediative controls as discrete layers, engendering operational inefficiencies, slowed incident reaction, and disparate visibility across multi-cloud environments. Modern security solutions are primarily reactive in nature, devoid of the intelligent orchestration features needed to consolidate disparate control classes into integrated defense techniques. Statistical data indicate that companies face high breach containment times and security incident rates due to tool fragmentation and the need for manual correlation. The suggested AI-based orchestration framework rectifies these fundamental constraints by bringing all three security control layers into one consolidated, adaptive platform. Behavioral intelligence frameworks normalize cross-platform telemetry to facilitate correlation of threat indicators between previously siloed security domains. Hybrid decision frameworks strike a balance between automation effectiveness and human acumen, remediating mundane threats automatically while escalating compound cases with contextualized intelligence. Integrated policy engines facilitate uniform security posture enforcement throughout heterogeneous cloud environments using intent-based translation interfaces. Sophisticated machine learning practices empower anomaly detection, predictive danger evaluation, and ongoing adaptation to evolving threat environments. The architecture conforms to standard cybersecurity protocols with the extension of fundamental principles through adaptive intelligence and automated coordination functions. Strategic implications show that organizations with unified orchestration realize quantifiable reductions in threat detection false positives, operational efficiency, and incident response variability across multi-cloud heterogeneous environments.
Cloud-Native Regulatory Compliance Platforms: Architectural Design and Cybersecurity Considerations
The increasing adoption of cloud-native technologies in regulatory environments demands robust compliance platforms that address both architectural scalability and cybersecurity requirements. This research presents a comprehensive framework for designing cloud-native regulatory compliance platforms with integrated cybersecurity considerations. We developed and evaluated a microservices-based architecture implementing Zero Trust security principles, achieving 99.97% availability with sub-50ms response times for compliance queries. Our platform processes over 10 million regulatory transactions daily while maintaining GDPR, SOX, and PCI-DSS compliance standards. The cybersecurity framework demonstrated 99.2% threat detection accuracy with automated remediation capabilities. This study contributes novel architectural patterns for regulatory compliance in cloud environments and provides quantitative evidence for the effectiveness of integrated security-by-design approaches in compliance platforms.
IT Systems in Pharmaceutical Operations: Ensuring Data Integrity, Compliance, and Public Safety
Pharmaceuticals have witnessed revolutionary information technology deployment evolution from simple administrative systems to complex infrastructures directly impacting public health outcomes and regulatory compliance. This systematic review discusses the various roles played by information technology systems throughout pharmaceutical operations, with a focus on data integrity mechanisms, regulatory compliance frameworks, enterprise architectural patterns, and integration of artificial intelligence for improved quality assurance. Pharmaceutical activities produce large volumes of data over drug lifecycles that cover clinical trials, manufacturing, distribution, and post-market monitoring, necessitating stringent technical controls such as automated timestamping, cryptographic authentication, and end-to-end audit trail functionalities guaranteeing ALCOA+ compliance principles. Global regulatory bodies have developed strict regulations covering electronic record integrity and computerized system validation, with more and more pharmaceutical manufacturers required to implement advanced governance frameworks. Modern challenges involve orchestrating advanced system integrations, providing consistent data quality across diverse platforms, ensuring strong cybersecurity controls, and regulatory compliance in multiple jurisdictions. The integration of sophisticated architectural patterns, such as self-healing integration meshes, data mesh architectural patterns, Zero Trust paradigms, and event-driven systems, solves these complexities along with real-time operational intelligence. Artificial intelligence solutions advance quality assurance in terms of document review automation, anomaly detection, predictive maintenance, and detection of pharmacovigilance signals. Innovative technologies such as blockchain for supply chain integrity, federated learning for collaborative analytics, digital twins for optimization of processes, and quantum computing for molecular simulation are breakthrough opportunities that will revolutionize pharmaceutical operations and enhance therapeutic development at the same time as upholding strict safety standards.
The Semantics of a Resource Request: Moving Beyond Cores and Gigabytes
This article challenges traditional resource request models in cluster computing that rely on abstract CPU core and memory specifications, particularly for modern AI/ML workloads. Current coarse-grained abstractions mask essential hardware characteristics and workload requirements, causing performance unpredictability, resource inefficiency, and service level violations. By examining the limitations of current models, the paper exposes performance non-fungibility issues and contention on unmanaged resources like memory bandwidth and cache hierarchies. A new multi-dimensional taxonomy of resource semantics encompasses hardware attributes, workload behaviors, and operational policies. Through analysis of schedulers from HPC, hyperscale, and open-source domains, the article shows how richer semantics are gradually being adopted. The research addresses opportunities and challenges of this semantic shift and explores future directions, including standardized Resource Description Languages and the application of Large Language Models for intelligent scheduling. The article advocates for semantically-aware schedulers capable of intelligent, topology-aware resource matching to enhance performance and efficiency in large-scale systems.
Evolution of Modern AI: A Technical Analysis of Next-Generation Frameworks
Contemporary artificial intelligence experiences a critical shift with the advent of complex architectural designs that move beyond conventional scaling solutions. Agent-based architectures transform system building by engaging specialized intelligence modules orchestrated through central routing mechanisms, allowing modular implementation where individual units can be updated separately without touching the rest of the system. The Mixture of Agents framework exhibits significant gains in performance over a variety of benchmarks while preserving computational efficiency via selective expert activation. Dynamic context management protocols solve inherent shortfalls in transformer-based models by instituting normative frameworks for the integration of external storage and memory buffer usage. Version context protocol allows systems to have coherent, lengthy interplay without overloading interest mechanisms the using superior retrieval and filtering mechanisms. Mixture of Experts architectures apply expert specialization to execute divide-and-conquer algorithms that engage only appropriate neural network elements depending on input properties, resulting in impressive computational efficiency improvements. Automated reasoning ability combines external APIs and computational frameworks, making language models advanced problem-solving systems with multi-step reasoning and real-time information integration. Reminiscence-augmented intelligence structures put in force continual storage solutions that allow information to be retained over protracted interaction intervals, with personalized reviews built on the usage of preserved consumer choices and historic context. These architectural advances together shape the idea of AI structures that aid human-like cognitive flexibility with computational efficiency and interpretability for a wide variety of utility domains.
AI-Augmented Authentication: Machine Learning Framework for Adaptive Fraud Detection in Enterprise Identity Systems
Static rule-based authentication cannot keep pace with adaptive, AI-driven cyber-fraud tactics that exploit behavioral and contextual vulnerabilities. This article proposes an AI-Augmented Authentication (AIAA) framework that applies supervised and unsupervised machine-learning models to enhance risk-based authentication decisions. Drawing on production-scale IAM datasets, the approach employs behavioral biometrics, device fingerprinting, and geo-velocity features to classify login attempts and predict session-level anomalies in real time. AIAA integrates seamlessly with identity orchestration platforms such as ForgeRock AM, providing explainable risk scores that trigger dynamic multi-factor challenges. Experimental evaluation demonstrates up to 60% reduction in phishing-related account takeovers and 30% faster fraud detection compared to rule engines. The article positions AI-augmented authentication as a cornerstone of future Zero Trust strategies for financial and healthcare enterprises.
Beyond Demographics: How Artificial Intelligence Redefines Customer Segmentation in Digital Marketing
This article examines the transformative impact of artificial intelligence on customer segmentation strategies in contemporary marketing practices. By leveraging advanced machine learning algorithms, businesses can now transcend traditional demographic segmentation to identify nuanced behavioral patterns, preference structures, and predictive purchase indicators. The article synthesizes empirical evidence from multiple industry sectors to demonstrate how AI-driven segmentation enables the development of hyper-targeted campaigns with significantly enhanced engagement metrics. Through analysis of technological frameworks, implementation challenges, and case studies, this article provides a comprehensive understanding of how predictive analytics can optimize customer lifecycle management, reduce churn, and identify cross-selling opportunities. The article indicates that organizations implementing AI-powered segmentation strategies achieve more personalized customer experiences while simultaneously improving operational efficiency. This article contributes to marketing literature by proposing an integrated framework for AI adoption in segmentation practices while addressing critical considerations in data governance, privacy, and ethical implementation.
End-to-End Encryption Strategies for Cloud-Hosted Enterprise Email Systems
Cloud vendors offer services across different levels, where each level creates different balances between how much control organizations keep, what operational tasks they handle, and how flexibly they can configure things. Picking the right level means understanding which delivery methods fit particular machine learning requirements and what technical capabilities the organization actually has. Ongoing difficulties appear in distributing cryptographic keys, supporting mobile devices, and maintaining audit records when organizations roll out encryption to large numbers of users. A structural design built for enterprise needs tackles these problems using hybrid encryption that mixes public-key and private-key operations. Testing shows these setups cause barely detectable slowdowns while greatly improving message confidentiality versus depending only on network-level protections. Zero-knowledge designs provide unusually strong security by stopping cloud operators from reading message contents in all situations, whether facing court orders or system break-ins. Enterprises using these designs meet regulatory demands across multiple legal territories while keeping operations smooth for workers spread across different places. Shifting from internal servers to cloud-based platforms completely changes what encryption needs to do, requiring protection while messages travel, sit in storage, and get processed. Encryption methods must juggle conflicting requirements: strong confidentiality protection, small performance costs, simple key management, and working with existing email programs. Successfully handling these competing priorities allows businesses to capture cloud computing cost savings while keeping necessary confidentiality shields for sensitive company communications.
Artificial Intelligence in Predictive Consumption and Billing Systems
Artificial intelligence has revolutionarily reshaped consumption prediction and billing processes for sectors based on usage-driven monetization schemes. State-of-the-art gadgets gaining knowledge of systems permit companies to research massive historical databases, detecting problematic temporal patterns and behavioral relationships that guide demand forecasts with by no means-earlier than-visible accuracy. Real-time tracking structures take advantage of allotted computing systems and adaptive getting to know strategies to display consumption streams in real time, dynamically modifying resource allocation and pricing models in accordance with moving demand patterns. Behavioral forecasting moves beyond passive prediction into active trend generation, with recommendation systems utilizing matrix factorization strategies and deep neural networks to predict customer preferences while actively influencing consumption choices through tailored recommendations. Automated billing driven by cognitive intelligence features eliminates human intervention in invoice creation, applying advanced rating logic to multidimensional usage data while ensuring accuracy through smart validation processes. Anomaly detection systems based on advanced isolation forest algorithms detect anomalous billing behavior that is fraud, system-related, or revenue leakage before financial effects occur. Utilization-based billing structures deal with computationally demanding situations via market-oriented cloud architectures that mix consumption metrics from dispensed assets, applying complicated pricing policies across temporal dimensions and service levels. Conversational agents powered by series-to-sequence neural architectures beautify customer support capabilities, automating responses to billing inquiries at the same time as maintaining natural conversation interactions that improve pleasure and reduce operational costs.
Generative AI for Claims Evidence Interpretation and Fraud Analysis
The insurance claims process generates vast volumes of unstructured evidence that present substantial challenges for human adjudicators to analyze comprehensively and consistently. Generative artificial intelligence has emerged as a transformative technology for automating evidence interpretation and fraud detection across the insurance industry. Large language models process narrative evidence from claims descriptions, witness statements, and medical records to extract key facts and identify inconsistencies. Vision transformer architectures analyze claims imagery, including property damage photographs and accident scene documentation, to detect manipulation and assess damage severity. Multimodal transformer architectures integrate textual and visual information simultaneously, enabling correlation between written descriptions and photographic evidence. Fraud detection employs supervised machine learning models trained on historical claims data, unsupervised anomaly detection systems, and behavioral pattern analysis. Generative AI systems reduce document review time substantially while improving fraud detection accuracy when augmenting traditional rule-based indicators. Synthetic data generation addresses data scarcity challenges by creating realistic fraudulent claim examples for training purposes. However, significant technical challenges persist, including hallucinations where models generate factually incorrect information, reduced generalizability in fine-tuned models, adversarial attacks, and bias risks. Explainability requirements demand transparent reasoning for fraud flagging decisions through attention mechanism visualization and feature importance measures. Insurance regulators are putting more pressure on transparency and auditability in automated claims decisions, which will require full documentation of the decision and testing for bias across demographic categories. Privacy issues also require the safeguarding of sensitive policyholder data. Success will require balancing the recent and transformative capabilities of ethics and governance, oversight by humans, and regulatory compliance, which will be necessary to ensure fairness and accuracy in claims processing.
Cross-Platform Health Data Harmonization: A Modular Framework for Scalable Mobile Wellness Ecosystems
The current mobile health ecosystem reflects substantial fragmentation throughout proprietary platforms, resulting in fundamental hurdles to complete fitness information utilization and clinical integration. Large technology businesses have constructed isolated health data architectures: Apple Health, Google Fit, and Samsung Health alone serve millions of end-users with minimal interoperability among them. Thousands of platform-specific applications have given way to data silos that fundamentally break holistic health monitoring and inhibit healthcare providers from accessing complete patient health profiles. Overcoming these challenges requires complex architecture solutions that include the unification of the data integration layer, modular software development kit design, and cross-platform implementation. Health data integration architectures need to harmonize these different schemas by implementing a systematic mapping protocol, an API standardization framework, and semantic interoperability mechanisms in line with the standards stipulated in Fast Healthcare Interoperability Resources. Modularity patterns allow the decomposition of monolithic wellness applications into independent deployable components, for example, authentication services, synchronization protocols, gamification frameworks, and notification systems. Cross-platform implementation methods balance the advantages of code reusability against the demands that platform-specific user experiences place on the architecture and yield quantifiable developer productivity gains through the centralization of business logic while allowing for compliance with native interface conventions. The architectural frameworks examined herein show promise for decreasing development overhead, simplifying maintenance procedures, and establishing consistency in feature implementation across heterogeneous mobile environments, thereby moving closer to seamless health data integration.
Automating Cyber Offense Simulations with Machine Learning: Advancements in AI-Augmented Penetration Testing
The rapid growth of digital infrastructure complexity has made conventional manual penetration testing methods insufficient for modern enterprise security testing. Machine learning technologies enable revolutionary possibilities for automating cyber offense simulations using self-learning, self-adaptive, and self-optimizing systems for exploitation strategies. Reinforcement learning agents acquire sophisticated capabilities in vulnerability chaining and defensive evasion from environmental interactions, discovering attack sequences that evade traditional rule-based automation. Neural network models learned from vulnerability data identify generalizable patterns between system configurations and exploitability attributes, facilitating probabilistic reasoning concerning defensive control efficacy. Generative adversarial networks generate new exploitation payloads that retain functional efficacy while exhibiting varied observable attributes to evade signature-based detection systems. Variational autoencoders support probabilistic models for defense-conscious payload optimization from continuous latent space representations. Integration of intelligent automation in penetration testing processes resolves scalability constraints, supports continuous security verification, and offers persistent adversarial emulation reflecting advanced threat actor capabilities. Real-world deployment demands safety-constrained architectures balancing autonomous behavior with organizational needs, regulatory compliance frameworks, and ethical guidelines informing responsible offensive security technology development. This intersection establishes a foundation for autonomous red teaming that actively detects sophisticated attack vectors within current distributed computing landscapes.
Personal Data Privacy and Societal Hazards in the Digital Age: A Comprehensive Analysis
This article examines the intricate relationship between personal data privacy and contemporary societal hazards in the digital age. It traces the historical evolution of personal data as a valuable economic asset, analyzes emergent threats including data breaches, algorithmic discrimination, biometric surveillance, and synthetic media manipulation, and evaluates regulatory frameworks across global jurisdictions. The article identifies critical implementation challenges in privacy governance, including jurisdictional complexity, enforcement limitations, and technological advancement outpacing policy development. It presents mitigation strategies spanning Privacy by Design principles, advanced cryptographic techniques, digital literacy initiatives, ethical AI frameworks, and innovative governance models. Through integrated analysis of technical, legal, and ethical dimensions, the article establishes privacy protection as fundamental for maintaining human dignity, democratic function, and equitable power distribution in digitally mediated societies.
Artificial Intelligence Integration in Financial Systems and Enterprise Automation: Technical Architecture and Implementation Frameworks
Enterprise financial systems face critical limitations due to rigid rule-based architectures lacking adaptive intelligence capabilities. Traditional automation frameworks struggle with increasing transaction volumes, complex regulatory requirements, and volatile market conditions. Legacy infrastructure separates data processing, decision logic, and execution layers without intelligent feedback mechanisms. The architectural constraints result in workflow inefficiencies, delayed risk detection, and forecasting models unable to adjust to dynamic market conditions. Addressing these limitations requires technical frameworks enabling seamless AI integration within existing operational infrastructure. The article presents comprehensive implementation architectures across three interconnected domains. Process mining techniques combined with reinforcement learning enable the discovery and optimization of workflow execution strategies. Unsupervised anomaly detection frameworks that employ extended isolation forests and autoencoder networks offer real-time risk assessment capabilities. Ensemble learning architectures incorporating gradient boosting and neural networks with uncertainty quantification deliver robust financial forecasting. Natural language processing techniques extract compliance rules from regulatory documents enabling automated validation through knowledge graph architectures. The technical contribution establishes modular design principles supporting incremental AI adoption while maintaining system reliability and regulatory compliance. Implementation considerations address data pipeline engineering, model governance frameworks, and operational monitoring requirements. The frameworks enable financial institutions to augment rather than replace established processes with intelligent capabilities adapting to evolving operational conditions.
Blockchain Integration with SAP for Secure and Transparent Supply Chain Tracking: A Framework for Trust and Traceability in Digital Enterprises
Supply chain transparency can be improved using blockchain technology with Enterprise Resource Planning systems like SAP to provide transformative potential. The ongoing issues of traceability, data validation, and multi-stakeholder trust in complicated networks of the world require new technical solutions, instead of classical centralized architectures. The proposed framework creates a full integration model that maintains the efficiency of the operational SAP and introduces the distributed trust systems of blockchain. The integration through specialized architecture elements, data syncing protocols, security aspects, and consensus system enables organizations to upgrade to existing enterprise investments without disruptive system upgrades. The importance is not only in technical implementation but in the basic business transformation, which opens the opportunities to have better compliance and collaboration with other organizations. The case implementations in the pharmaceutical and food sectors show that there may be significant improvements in regulatory compliance, provenance verification, and stakeholder trust, which leads to the high potential of adoption in various industries where transparent and tamper-resistant supply chain records are the order of the day.
Multi-Context Protocol Framework: A Paradigm Shift in Cybersecurity Architecture
The cybersecurity landscape today is characterized by advanced threats that take advantage of traditional security architectures operating in isolation. This article proposes the Multi-Context Protocol framework, a paradigm shift in cybersecurity architecture that systematically overcomes these limitations by integrating a wide range of contextual dimensions. Unlike traditional approaches that assess security signals in isolation, MCP introduces structured processes for collecting, weighting, fusing, and operationalizing heterogeneous contextual data around user identity, device posture, network attributes, application behavior, and temporal patterns into unified security assessments. The framework is made up of specialized architectural components working in concert: Context Providers are specialized security sensors across multiple dimensions; a Context Fusion Engine aggregates and analyzes multidimensional data; Policy Decision Points evaluate the security assessments against organizational policies; and Policy Enforcement Points execute the corresponding security controls. This enables an organization, through continuous feedback loops and adapting learning mechanisms, to improve threat detection capability, reduce false positives, introduce proportionate automated responses, and enhance overall cyber situational awareness in light of a changing threat landscape.
Agentic Commerce at Scale: A Reference Architecture for Enterprise Retail Systems
Autonomous shopping and operations through specialized agent networks represent the next evolution in retail, yet most implementations remain constrained to experimental deployments due to fundamental gaps in safety, governance, and enterprise integration. This article presents a comprehensive cloud-native reference architecture that enables production-grade agentic commerce across the retail technology stack. The architecture encompasses buyer-side agents for discovery, negotiation, and checkout operating at the edge and in cloud environments, alongside merchant-side agents for catalog intelligence, dynamic promotions, and service automation, all governed by policy-enforced guardrails. A novel orchestration layer provides model routing, tool access control, memory management, and multi-stage safety filtering, while an integrated observability plane tracks reliability, cost, and risk metrics aligned to service-level objectives. The framework addresses critical enterprise constraints, including PII protection, brand safety, regulatory compliance, and seamless integration with existing product information management, order management, inventory, and content systems. Three production case studies demonstrate practical deployment patterns for promotion negotiation, returns automation, and catalog question-answering, establishing validated pathways from experimental prototypes to enterprise-scale autonomous commerce platforms.
Digital Resilience and Public Safety: Why Secure Automation Matters Beyond IT
The growing reliance of contemporary society on digital infrastructure would render secure automation not only an engineering problem but also a primary societal issue of public safety concern, with potentially far-reaching consequences for national security, economic stability, and societal well-being. The automated systems in areas such as healthcare delivery, transportation networks, financial infrastructure, and energy utilities have continuous integration pipelines, orchestration platforms, and machine-driven control loops running at an unprecedented scale and complexity. Although automation provides significant efficiency and opens up new services that were previously unavailable, it also introduces systemic risks, such as software supply chain breaches, malconfigured pipelines during deployment, and unauthorized code editing, which can lead to cascading failures with catastrophic real-world impacts. The article explores the intersection of Systems Infrastructure and Automation Engineering with digital resilience, national security frameworks, and public welfare requirements, and proposes a general Digital Resilience Framework that can integrate secure DevOps and Infrastructure-as-Code practices into critical societal domains. The framework focuses on reliable automation based on zero-trust architectures, policy-driven verification, cryptographic provenance, and resilient orchestration of multi-cloud and hybrid systems. This article argues that secure automation serves as the foundational layer on which the trust of digital society in the general population should be built, through interdisciplinary studies that combine engineering principles, cybersecurity standards, socio-technological governance frameworks, and policy frameworks. The article ends with recommendations on how to apply the principles of safety engineering, the supply chain verification rules, the ongoing monitoring of compliance, and the collaboration between the different sectors into the national strategies of digital resiliency, where automation security level should be managed in the same manner as the traditional engineering fields, such as enhanced governance, transparency, and moral accountability.
The Future of Compliance-as-a-Service (CaaS) in Cloud Security
The concept of cloud computing has transformed the current operations of enterprises, which pose new difficulties in maintaining regulatory compliance in dynamic and volatile environments. These strategies combine to give rise to Compliance-as-a-Service (CaaS), which would convert the traditional point-in-time assessment models to continuous and automated compliance systems. The article discusses the current trajectory of CaaS in cloud security systems in terms of its technical underpinnings and the aspects to take into account during implementation. The move towards compliance-as-code practices, AI-based controls, and standardized control models allows organizations to integrate compliance into development lifecycle processes and ensure visibility into complex architectures in real-time. Although certain issues like trust, may affect the implementation of CaaS, data privacy issues, and integration issues, CaaS is bound to change compliance beyond a reactive liability to a strategic enabler of secure cloud innovation that will make organizations unafraid of adopting cloud technologies without losing their regulatory orientation in the growing, complex environment.
Secure Overlay Architectures for Hybrid Enterprise Connectivity Using Zero-Trust Principles
Enterprise computing has been radically changed towards cloud-based, hybrid, and distributed styles of architecture, which essentially highlight the incompetence of the traditional perimeter-based models of security, which are based on implicit trust inside the network boundaries. Zero-trust architectures address these vulnerabilities by abandoning the location-based assumptions of trust and imposing ongoing checks of identity, device posture, and contextual attributes to allow access to a given resource. Secure overlay architectures apply zero-trust to practice by using software-defined perimeters between authenticated users and authorized applications to provide logical isolation that lives regardless of the underlying network infrastructure. These frameworks apply application-level access controls and micro segmentation plans that prevent lateral movement and limit the effects of breaches to explicitly licensed resources. Organizations using zero-trust overlay networks have quantifiable security benefits, including significant decreases in successful attacks, accelerated threat identification and mitigation, and increased insight into access patterns across diverse infrastructure. The architecture does remarkably well in organizations that are in a hybrid or multi-cloud environment, distributed workforce, and are seeking to mitigate attack surface as well as maintain the efficiency of operations. Zero-trust overlay architectures mark a crucial step forward from perimeter-centric approaches to identity-centric schemes that provide uniform protection irrespective of network location or infrastructure type.
Reinventing Digital Infrastructure: A Blueprint for Modernizing Legacy Systems at National Scale
Legacy digital systems continue to anchor mission-critical national infrastructure across transportation, finance, healthcare, and citizen-service domains. While these platforms have been operationally successful for decades, they now present structural barriers to resilience, interoperability, and innovation. Their tightly coupled architectures, centralized deployments, and opaque integration paths create cascading failure conditions and impede rapid change. This article proposes a modernization blueprint designed for national-scale environments, emphasizing architectural decoupling, parallel transformation, and socio-technical governance. The framework integrates domain-driven decomposition, event-based coordination, modular service boundaries, and progressive interoperability strategies to reduce operational fragility. Governance alignment models ensure backward compatibility and stakeholder continuity while transformation occurs in incremental phases. The blueprint demonstrates measurable outcomes, including accelerated deployment frequency, improved reliability posture, and reduced dependency on aging technology stacks. This modernization foundation enables nations to scale public-facing capabilities, strengthen digital sovereignty, and support emerging economic and citizen-centric services. Unlike incremental upgrades, the proposed model positions modernization as a structured capability investment, enabling sustained innovation across evolving policy, demographic, and technology cycles.
From Shared Responsibility to Shared Fate: Redefining Cloud Security Paradigms in Multi-Cloud Environments
Cloud computing has become integral to the digital ecosystem, with projected revenues exceeding 2 trillion dollars by 2030. The traditional Shared Responsibility Model, which delineates security boundaries between cloud service providers and customers across IaaS, PaaS, and SaaS models, faces significant challenges. High-profile security breaches, including the 2019 Capital One and Toyota incidents, demonstrate the limitations of this framework. Customers often lack visibility into infrastructure layers, struggle with expertise gaps, and face complexity in multi-cloud environments. Industry projections indicate a 45% increase in focus on preventing cloud misconfigurations by 2026. The emerging Shared Fate Model addresses these challenges by shifting from delegated responsibilities to shared ownership, aligning incentives between providers and customers. This model incorporates continuous security monitoring, application validation frameworks, and collaborative threat detection. Artificial intelligence plays a crucial role in enabling this transition through automated security operations and predictive capabilities. The future trajectory points toward standardized shared control frameworks across cloud services, establishing consistent security postures and becoming the expected standard for critical applications.
The Evolution of Monitoring: From Reactive Alerts to Predictive Insights
Infrastructure monitoring has traditionally operated through reactive alerting mechanisms where predefined thresholds trigger notifications after system anomalies occur. Traditional monitoring frameworks demonstrate fundamental inadequacies when confronted with contemporary distributed architectures characterized by microservices, containerized workloads, and dynamic resource allocation patterns. Threshold-based alerting generates excessive false positive notifications while simultaneously missing subtle degradation patterns preceding critical failures. Manual​‍​‌‍​‍‌​‍​‌‍​‍‌ correlation of different alert streams leads to significant time delays in the identification of the root causes and thus, the extension of the incident resolution timelines beyond the acceptable service level boundaries. Modern observability demands require the telemetry of a complete set of metrics, distributed traces, and structured log events to empower sophisticated correlation analysis. Machine learning algorithms create behavioral standards from the historical operational data and thereby detect statistical anomalies, which are the main indication of emerging system degradation even before the end-users can be affected. Predictive monitoring systems use deep learning structures, time-series forecasting models, and correlation engines to discover failure precursors and causal chains that link infrastructure incidents to the resulting service impacts. The implementation of custom metric creation, intelligent alert suppression features, and enrichment pipelines that help in augmenting the notifications with contextual information, as well as automated remediation guidance are some of the implementation strategies. The shift from reactive to predictive monitoring is a major architectural change that is necessary for the retention of service reliability in the ever more complex cloud-native distributed computing environments.
Enterprise Resource Planning Systems in Real-Time Problem Resolution
Organizations have never had to manage such complexity as constantly evolving supply chains, demand, and technology. This has created a demand for enterprise resource planning platforms to become the foundation of the digital enterprise, providing real-time data, predictive analytics, and integrated automation to transform operational challenges into commercial opportunities. Meanwhile, in-memory computing architectures remove the latencies of database lookups and present business functions with a consistent view of data. Algorithmic decision systems deploy fairness and transparency approaches in order to create fairer outcomes without impacting performance. In demanding regulations in healthcare, financial services, and environmental sustainability, privacy-preserving technologies and compliance automation support use cases in public health emergencies, humanitarian crises, and disaster recovery, and sustainability, including dynamic coordination of suppliers, resource and financial optimization, monitoring emissions, and improving sector performance. The future affords autonomous supply chain systems that optimize efficiency and ethical constraint, democratized market access architectures that minimize entry barriers, and human-augmentation philosophies that favor decision support over replacement. Organizations extracting calculated advantage from these trends achieve resilience in their value creation pursuits and are beneficial, economic, environmental, and community-focused.
Latency vs. Privacy: A Theoretical Architecture for Pre-Warmed Trusted Execution Environments in Real-Time Bidding
Real-Time Bidding has a high set of temporal limitations since advertising platforms have to be fast and capable of responding within about 100 milliseconds to compete effectively in online auctions. With the development of privacy-first models, which require Trusted Execution Environments, there grows a basic system incompatibility, with standard instantiations of these secure computational models, where the size of the initialization sequences is orders of magnitude larger than the time required to run a single auction. The cold start phenomenon, including secure boot sequence and remote attestation protocol implementations, causes latencies that make on-demand provisioning incompatible with real-time bidding needs. The Pre-Warmed Enclave Pool architecture mitigates this problem by keeping a persistent collection of fully initialized and attested secure enclaves, practically separating the costly trust establishment steps in the context of a request and its important processing path. Projections based on mathematical modeling of the performance of recorded attributes of a commercial trusted execution platform show that this architectural style is capable of decreasing end-to-end processing latency to the point of achieving the required thresholds of auction participation. The resolution requires one to accept more infrastructure spending, which is a privacy tax, which is the costs that are necessitated by the need to over-provide resources to accommodate variable traffic patterns whilst maintaining sufficient service levels. According to this architectural blueprint, it is possible to combine privacy compliance with performance optimization, but in order to achieve both goals at the same cost, organizations must bear increased computational expenses to fulfill regulatory compliance and protect user information as the economic cost of regulatory compliance and user-data privacy.
Strategic Integration of PLM Systems for Manufacturing Location Optimization Under Regulatory and Environmental Constraints
Modern manufacturing organizations face unparalleled intricacy in the identification of optimal locations of production in light of converging pressures from protective tariffs, sustainability regulations, and geopolitical sourcing constraints that fundamentally reshape strategic decision frameworks. Traditional cost-optimization models fall short in their multidimensional nature of location choices, creating analytical gaps that expose organizations to regulatory vulnerabilities and competitive disadvantages. Product Lifecycle Management systems have evolved beyond engineering documentation platforms to function as comprehensive strategic intelligence frameworks, capable of integrating material specifications, supplier characteristics, environmental attributes, and regulatory requirements within unified data architectures. It is by synthesizing heterogeneous variables across financial, operational, and sustainability domains that PLM enables sophisticated scenario modeling, quantifying trade-offs between domestic and international manufacturing alternatives under dynamic market conditions. The article looks at how PLM's relational data structures facilitate the simultaneous consideration of tariff exposure, carbon intensity, supply chain resilience, and regulatory compliance requirements. Through integrating with enterprise resource planning and supply chain management platforms, PLM transforms fragmented information into actionable intelligence that supports strategic decisions about manufacturing location. Advanced implementations incorporating predictive analytics and machine learning capabilities enable organizations to forecast the impact of regulatory changes, sustainability mandates, and market volatility on long-term manufacturing economics, thereby elevating location decisions from tactical procurement choices to strategic imperatives aligned with corporate goals across financial performance, environmental stewardship, and operational resilience dimensions.
Federated Learning Architectures for Cross-Cloud Healthcare Data Integration: An Empirical Study on Privacy-Preserving EHR Harmonization
The ability to share Electronic Health Records across all healthcare providers that function within a siloed data landscape is still an issue for many healthcare providers today. Federated learning offers a new way to conduct shared machine-learning methods through the secure management of patient data that meets all regulatory compliance standards. Traditional centralized approaches require sensitive medical data relocation. This creates substantial security vulnerabilities. It also violates data protection regulations. The proposed architecture enables distributed model training across heterogeneous cloud platforms. Individual patient records remain unexposed throughout the process. Healthcare organizations maintain complete data sovereignty. They simultaneously contribute to shared predictive models. A few examples of ways to protect patient data are through differential privacy, secure contract aggregation, and homomorphic encryption. These protect against various attack vectors targeting patient information. Cross-cloud deployment addresses technical complexities. These arise from diverse infrastructure platforms, authentication systems, and network configurations. Electronic Health Record harmonization transforms disparate clinical data formats. It creates standardized representations suitable for machine learning applications. Empirical validation demonstrates something important. Federated approaches achieve comparable accuracy to centralized baselines. They maintain strict privacy guarantees simultaneously. Communication efficiency optimizations reduce bandwidth requirements. This makes deployment feasible across institutions with limited network capacity. Computational overhead measurements quantify the processing costs. These are associated with cryptographic protections. Scalability testing confirms that system performance improves consistently. This happens as additional healthcare organizations join federated networks. The framework establishes foundational principles. These support deploying privacy-preserving collaborative learning in clinical environments. The deployment satisfies HIPAA and GDPR regulatory mandates.
Multi-Modal Sensor Fusion for Autonomous Vehicles: Advancing Road Safety Through AI-Driven Perception and Ethical Integration
Multi-modal sensor fusion has become a foundational component of perception systems for autonomous vehicles, enabling robust environmental understanding across diverse and safety-critical operating conditions. This article presents a narrative review of camera-, LiDAR-, radar-, and ultrasonic-based perception architectures, with emphasis on their complementary sensing characteristics, fusion strategies, and system-level trade-offs. Existing literature on probabilistic, learning-based, and track-level fusion approaches is synthesized to highlight how redundancy and cross-modal validation improve reliability under adverse weather, occlusion, and high-speed scenarios. Beyond technical architectures, this review examines emerging work on explainable artificial intelligence for autonomous perception, including methods for decision traceability, post-incident analysis, and regulatory accountability. Fairness-aware training practices and dataset diversity considerations are also discussed in the context of equitable protection for vulnerable road users across varying environmental and demographic conditions. Finally, the article surveys broader system-level implications of advanced perception, including environmental efficiency, economic impact, and future directions in cooperative and networked perception. By integrating technical, ethical, and societal perspectives, this review provides a structured reference for researchers, engineers, and policymakers engaged in the design and deployment of responsible autonomous vehicle perception systems.
Cloud-Native Architectures and Financial Inclusion: A Systematic Analysis of Technological Pathways to Equitable Banking Systems
The global financial landscape experiences a profound transformation as cloud-native architectures emerge as catalytic mechanisms for extending banking services to underserved populations. Approximately 1.4 billion adults worldwide remain excluded from formal financial systems, perpetuating economic marginalization, particularly pronounced in emerging markets. Cloud-native technologies—encompassing microservices architectures, API-driven ecosystems, and distributed computing resources—fundamentally reimagine financial service delivery by dramatically reducing infrastructure costs, accelerating innovation cycles, and enabling scalable digital-first banking models. Microservices decompose monolithic banking systems into independently deployable components, facilitating granular scaling and rapid product development while reducing operational expenses. API ecosystems establish interoperable frameworks wherein diverse financial actors seamlessly exchange value, exemplified by India's Unified Payments Interface, which processes billions of monthly transactions through standardized protocols. Zero-trust security architectures protect vulnerable populations through continuous authentication, automated encryption, and AI-driven fraud detection, democratizing enterprise-grade security across mass-market financial services. Beyond individual access, cloud-native financial systems catalyze societal transformation through transparent government payment systems that reduce corruption, open banking frameworks that enhance consumer autonomy, and data-driven governance mechanisms that inform evidence-based policymaking. The convergence of these technological capabilities with inclusive design principles creates pathways for constructing equitable financial ecosystems that align innovation with social justice, transforming financial inclusion from an aspirational goal into an achievable infrastructure for global economic development.
Secure API Gateway Strategies for Financial Institutions Using OAuth2 and Identity Federation
Financial​‍​‌‍​‍‌​‍​‌‍​‍‌ institutions are confronted with numerous challenges in maintaining the security of their distributed API ecosystems, as their digital banking platforms continue to evolve on cloud-native microservices architectures. The security measures based on the traditional perimeter fail to provide solutions to issues that arise due to open banking mandates, partner integrations, and multi-channel customer engagement platforms. The existence of fragmented identity systems, static credential vulnerabilities, and overlapping regulatory compliance requirements unveils considerable security holes in the protection of sensitive financial data during API transactions. This paper offers a holistic security model that encompasses OAuth 2.0 authorization protocols, OpenID Connect identity layers, and federation mechanisms to achieve the unification of API gateway architectures. Token-based access control eliminates the use of static credentials with the help of time-limited authorization tokens that carry cryptographically verified claims and detailed permission scopes. Centralized gateway enforcement consolidates security functions, including TLS termination, token validation, threat detection, and policy-based routing across distributed service meshes. Identity federation protocols enable trust establishment with external partner organizations while maintaining organizational autonomy over authentication policies. Context-aware authorization mechanisms adjust security requirements based on transaction risk profiles, geographic locations, and behavioral patterns. Implementation strategies address operational security concerns through automated cryptographic key rotation, least-privilege scope design, comprehensive security monitoring, and regulatory-compliant consent management frameworks. The framework supports strong customer authentication requirements, real-time payment systems, and artificial intelligence-enabled financial services while maintaining scalability for high-volume transaction processing environments.
Advances in Graph Databases for Financial Fraud Detection
Monetary fraud detection systems increasingly war with the complexity and sophistication of modern fraudulent schemes exploiting complex networks of entities and transactions. Traditional relational database architectures show insufficient performance while analyzing multi-hop relationships and identifying coordinated fraudulent activities across interconnected accounts. Criminal organizations deliberately structure operations to exploit limitations of conventional detection systems. Graph database technologies address the critical need for systems capable of representing and analyzing complex entity relationships in real-time operational environments. Specialized graph algorithms, including community detection, centrality measures, and pathfinding techniques, reveal hidden fraud patterns such as money laundering networks and collusion rings invisible to traditional detection methods. The integration of graph databases with machine learning models, particularly Graph Neural Networks, enables enhanced predictive capabilities through graph-based feature engineering and embedding techniques. Financial institutions require systems that process massive datasets while maintaining query performance for real-time fraud intervention. Distributed graph processing architectures and optimization strategies solve fundamental scalability challenges inherent in processing billions of transactions daily. The vertex-cut partitioning addresses power-law degree distributions typical in financial networks. Multi-level caching architectures and adaptive query execution enable sub-second response times for complex pattern matching across distributed deployments. The convergence of graph databases, superior algorithms, and gadget mastering represents a paradigm shift in fraud detection abilities for contemporary financial systems.