Journal of Sustainability, Policy, and Practice
A peer-reviewed, open-access academic journal dedicated to advancing interdisciplinary research and critical dialogue on the complex relationships between sustainability, policy-making, and practical implementation. The journal provides a platform for scholars, practitioners, policymakers, and educators to explore innovative approaches, theoretical frameworks, and case-based studies that address global and local challenges in sustainable development.
Reframing Business Strategy through Data: A Review of Data-Driven Strategic Thinking
This review paper explores the evolving landscape of business strategy through the lens of data-driven strategic thinking. It examines how organizations are increasingly leveraging data analytics, machine learning, and other data-centric approaches to inform strategic decisions, gain competitive advantages, and adapt to dynamic market conditions. The paper provides a historical overview of the shift from traditional intuition-based strategy to data-informed strategy, highlighting key milestones and influential frameworks. It then delves into two core themes: (1) the application of data analytics for strategic insights, including market segmentation, customer behavior analysis, and competitive intelligence; and (2) the use of data-driven experimentation and learning to refine strategic choices and foster organizational agility. A comparative analysis of different data-driven strategy frameworks is presented, along with a discussion of the challenges associated with implementing and sustaining a data-driven strategic approach. Finally, the paper explores future perspectives, including the potential of artificial intelligence, blockchain, and other emerging technologies to further transform business strategy. The review synthesizes a wide range of academic literature and industry best practices, offering valuable insights for researchers and practitioners seeking to understand and implement data-driven strategic thinking.
Copyright (c) 2026 Journal of Sustainability, Policy, and Practice
System Architecture Design of Cloud Platforms for Large-Scale Data Processing
Cloud platforms have become essential for managing and processing large-scale datasets in various domains, including scientific research, finance, and social media. Designing robust and scalable system architectures for these platforms is a significant challenge, requiring careful consideration of factors such as data storage, computation, networking, and security. This research article presents a comprehensive overview of system architecture design principles for cloud platforms tailored for large-scale data processing. We explore various architectural patterns, including distributed storage systems, parallel processing frameworks, and resource management strategies. We delve into specific techniques for optimizing data locality, minimizing network latency, and ensuring data consistency across the platform. Furthermore, we investigate the impact of different hardware and software technologies on the performance and scalability of cloud platforms. To validate our proposed design principles and architectures, we present experimental results obtained from deploying and evaluating several prototype cloud platforms using publicly available datasets. These results demonstrate the effectiveness of our approach in achieving high throughput, low latency, and efficient resource utilization. Finally, this article compares and contrasts various state-of-the-art cloud platforms, highlighting their respective strengths and weaknesses. Based on our findings, we propose several research directions for future development in the area of cloud platform architecture for large-scale data processing.
Evaluation of Differential Privacy and Federated Learning for AI-Driven Customer Service Applications
The proliferation of artificial intelligence in customer service applications has heightened concerns about the protection of sensitive consumer data. This paper presents a comprehensive comparative evaluation of privacy-preserving techniques, specifically differential privacy and federated learning, within AI-driven customer service contexts. A multidimensional evaluation framework is proposed to assess these techniques across security robustness, model accuracy, computational efficiency, and algorithmic fairness. An experimental analysis of customer interaction datasets reveals that federated learning achieves 94.2% accuracy retention while maintaining privacy guarantees, whereas differential privacy mechanisms offer superior protection against membership inference attacks at a 12.3% accuracy trade-off. The findings provide actionable recommendations for enterprises seeking to balance data protection compliance with service quality optimization, contributing to the development of trustworthy AI systems aligned with regulatory requirements.
Privacy-Preserving Federated Learning for Collaborative Risk Monitoring Across Financial Institutions: Balancing Regulatory Compliance and Intelligence Sharing
Financial institutions today face growing pressure to balance data privacy protection with the sharing of risk intelligence across organizations. This paper offers an in-depth analysis of how privacy-preserving federated learning techniques can be applied to cross-institutional financial risk monitoring. At the core of the proposed framework is the integration of differential privacy mechanisms with federated averaging algorithms, enabling multiple financial institutions to collaboratively train fraud-detection models without exposing sensitive customer data. Experimental evaluations on synthetic financial transaction datasets show that the framework achieves 94.7% detection accuracy under a configured differential privacy budget (ε = 1.0), with privacy accounting across training rounds as described in Section 3.3. By applying the combined sparsification and quantization strategy, the total communication volume decreases by 97.2% relative to the uncompressed baseline, while retaining 98.9% of the baseline accuracy (Table 3). This research provides practical guidance for financial institutions seeking to adopt privacy-preserving collaborative analytics that meet regulatory requirements, such as the Gramm-Leach-Bliley Act.
Performance Benchmarking and Optimization Strategies for Depth Estimation Algorithms in Unstructured Environments
The deployment of depth estimation algorithms in autonomous robotic systems necessitates comprehensive performance evaluation beyond traditional accuracy metrics. This research establishes a standardized benchmarking framework that quantifies multidimensional trade-offs among estimation accuracy, inference latency, and computational resource consumption across diverse hardware configurations. Through a systematic evaluation of representative algorithms on GPU-accelerated platforms, we identify critical bottlenecks that affect real-time performance and propose data-driven optimization strategies. Our experimental analysis shows that algorithm-hardware matching decisions significantly impact operational efficiency, with throughput varying by roughly 3-4× across the evaluated configurations. The proposed framework enables developers to make informed deployment decisions based on quantitative performance profiles tailored to specific application requirements.
AI-Enhanced Healthcare Data Quality Governance: An Integrated Approach for Anomaly Detection and Integrity Verification
Healthcare data quality remains a critical challenge affecting clinical decision-making, patient safety, and operational efficiency across medical institutions. This paper presents an integrated approach for AI-enhanced healthcare data quality governance that combines rule-based anomaly detection, statistical scoring mechanisms, and temporal consistency verification. The proposed framework establishes hierarchical quality checkpoints across heterogeneous EHR tables and clinical documentation streams (and is extendable to multi-source settings), enabling real-time identification of data entry errors, logical conflicts, and distribution drift patterns. Through systematic evaluation on the MIMIC-III EHR dataset (53,423 ICU admissions; >50,000 ICU admission records) using proxy anomaly labels derived from rule violations and cross-field/temporal consistency checks (with controlled synthetic anomaly injections for robustness testing), our approach achieves 94.7% detection accuracy with a false-positive rate of 3.2%. The experimental results validate the effectiveness of the integrated governance methodology in maintaining data integrity across diverse clinical scenarios while providing interpretable evidence chains for healthcare practitioners.
Comparative Evaluation of Machine Learning Algorithms for Spectrophotometric Dental Shade Classification
Accurate dental shade matching is critical for achieving optimal esthetic outcomes in restorative dentistry. This study presents a comparative evaluation of machine learning algorithms for spectrophotometric dental shade classification, focusing on Support Vector Machine, Random Forest, and Extreme Learning Machine approaches. Spectral reflectance data from 1,280 standardized dental composite specimens (as controlled surrogates for shade-guide categories) across 16 VITA Classical shades were collected using a calibrated spectrophotometer. Feature extraction methods, including CIELAB coordinates, spectral coefficients, and principal component analysis, were systematically compared. Experimental results demonstrate that the Extreme Learning Machine achieved the highest classification accuracy of 97.8%; its mean ΔE00 was 1.42, and 89.3% of predictions fell below the clinical acceptability threshold of ΔE00 = 1.8, with a b coordinate RMSE of 2.14. Random Forest demonstrated superior robustness in edge-shade classification, achieving 94.2% accuracy. The findings provide practical guidance for selecting algorithms in industrial dental shade-matching applications.
Explainable Risk Stratification for Polypharmacy-Related Adverse Outcomes in Community-Dwelling Elderly: A Rule-Enhanced Machine Learning Approach
Polypharmacy among community-dwelling elderly populations presents substantial clinical challenges, including elevated risks of falls, delirium, and hospital readmission. This study proposes a hybrid rule-enhanced machine learning framework for explainable risk stratification without requiring specialized clinical systems. The methodology integrates rule-based screening using established pharmacological risk dictionaries with gradient boosting algorithms to generate interpretable probability estimates for adverse outcomes. Patient medication lists are standardized to generic nomenclature and mapped to sedative burden scores, anticholinergic indices, and drug-drug interaction matrices. The framework outputs 30-day and 90-day readmission risk probabilities alongside actionable clinical recommendations. Evaluation encompasses high-risk detection recall, false positive rates, SHAP-based feature contribution analysis, and subgroup fairness metrics across vulnerable populations including living-alone, minority, and LGBTQ+ elderly cohorts. Results demonstrate the potential for reproducible, transparent algorithmic approaches to enhance medication safety review in community care settings while supporting health equity objectives.
G-MATCH: Graph-Structured Memory with Interpretable Motif Matching for Future Node Affinity Prediction
Predicting future node affinity in dynamic graphs is essential for applications such as recommender systems. However, existing methods, including state-of-the-art approaches like NAVIS, rely on continuous vector representations that struggle to explicitly capture, reason over, and memorize discrete, structured interaction motifs, limiting interpretability and generalization. To address this, we introduce G-MATCH (Graph-structured Memory with Attentive Template Consensus for Heterogeneous interactions), a novel paradigm that reformulates a node's state as a dynamic, heterogeneous graph of learnable interaction motifs. G-MATCH incorporates four key innovations: (1) state evolution via graph matching and dynamic motif creation/pruning, (2) a global motif bank for cross-node knowledge transfer, (3) interpretable affinity prediction through motif attribution, and (4) optimization with a listwise ranking loss and structured regularization. Extensive experiments on future affinity (TGB) and converted link prediction datasets demonstrate that G-MATCH consistently outperforms all strong baselines, including NAVIS, achieving an average improvement of +4.2% in NDCG@10. The model also excels in few-shot and limited-information settings. Ablation studies confirm the critical role of each component, and case studies highlight its unique capability for explainable, motif-level reasoning. The transition from linear states to graph-structured memory marks a significant advance, enabling superior performance and unprecedented interpretability in modeling complex node interactions.
Dynamic Portfolio Construction in High-Frequency Markets Using Microstructure-Aware Deep Temporal Models
High-frequency financial markets exhibit rapidly changing liquidity and strong nonstationarity, which reduce the effectiveness of portfolio methods based on static return and covariance estimates. This study presents a microstructure-aware framework for dynamic portfolio allocation that maps high-frequency market states directly to portfolio weights. The model uses order-flow intensity, depth imbalance, and volume asymmetry as key inputs and combines recurrent and convolutional temporal components to capture both persistent dependencies and short-lived microstructure shocks. Portfolio weights are determined under a joint return–risk objective. The framework is evaluated on ten years of high-frequency U.S. equity data. Results show higher risk-adjusted performance than mean–variance and risk-parity benchmarks, with a 14%–22% improvement in Sharpe ratio and more stable drawdown behavior. These findings suggest that integrating market microstructure information into temporal allocation models can improve exposure management in intraday portfolio optimization.
Developing Multi-Dimensional Control Risk Assessment Models for Intelligent Financial Systems
This review paper explores the development of multi-dimensional control risk assessment models tailored for intelligent financial systems. The integration of artificial intelligence (AI) and machine learning (ML) technologies into financial operations has introduced new dimensions of risks beyond traditional models. This paper provides a comprehensive overview of existing control risk assessment methodologies, evaluating their applicability and limitations in the context of intelligent financial systems. The core themes addressed include data-driven risk assessment, algorithmic bias and fairness, and the challenges of model interpretability and explainability. Furthermore, this review examines the implications of regulatory compliance and ethical considerations within AI-driven financial environments. Critical analysis reveals the need for adaptive and dynamic risk assessment models capable of addressing emerging threats and vulnerabilities. The paper culminates by proposing future research directions focused on enhancing model robustness, improving real-time risk monitoring, and establishing standardized frameworks for control risk assessment in intelligent financial systems. The study synthesizes insights from diverse disciplines, including finance, computer science, and regulatory studies, to foster a holistic understanding of control risk management in the evolving landscape of intelligent finance. This review is essential for researchers, practitioners, and policymakers seeking to navigate the complexities of risk assessment in the era of AI-driven financial innovation.
AI-Driven Regulation and Risk Control Mechanisms for High-Frequency Financial Trading
This review paper examines the evolving landscape of high-frequency trading (HFT) and the emerging role of artificial intelligence (AI) in its regulation and risk control. HFT, characterized by its speed and complexity, poses unique challenges to market oversight and stability. We explore how AI-driven mechanisms are being developed and deployed to address these challenges, focusing on two core themes: AI for regulatory compliance and AI for risk management. We analyze the potential of AI to enhance surveillance, detect anomalies, and improve the overall resilience of financial markets. Furthermore, we compare existing approaches, highlight current challenges, and discuss future perspectives on the integration of AI in HFT regulation. This review considers the trade-offs between innovation and stability, as well as the ethical implications of AI-driven market oversight. Finally, it provides a direction for researchers and policymakers seeking to navigate the complexities of AI-enhanced financial ecosystems.
Research on Collaborative Governance Models in the Operation of Intelligent Logistics Systems
This research investigates collaborative governance models within intelligent logistics systems, focusing on how these models impact operational efficiency, adaptability, and resilience. The study examines various governance structures, including public-private partnerships, industry consortia, and platform-based ecosystems, analyzing their effectiveness in managing complex logistics networks. Key performance indicators such as delivery time, cost optimization, and environmental sustainability are used to evaluate the different models. Furthermore, the research explores the role of technology, data sharing, and trust in fostering successful collaboration among stakeholders. The findings contribute to a better understanding of how to design and implement collaborative governance models that enhance the performance of intelligent logistics systems and promote sustainable development. A mixed-methods approach, combining quantitative analysis of logistics data with qualitative case studies of successful and unsuccessful collaborations, is employed. The research aims to provide actionable insights for policymakers, logistics companies, and technology providers seeking to improve the governance and operation of intelligent logistics systems.
Causal Impact of Cashback Campaigns on Post-Marketing Default Behavior in Consumer Lending
This study examines how cashback marketing campaigns influence subsequent credit-risk outcomes. Using an AB testing framework implemented on a dataset of 640,000 loan applicants, the treatment group received a one-time cashback incentive, while the control group did not. Propensity-score weighting and double-machine-learning estimators were applied to isolate causal effects. Results show that campaign exposure increases short-term activation rates by 18.4% but also raises 90-day delinquency risk by 6.7 percentage points. Heterogeneity analysis reveals significantly larger default spillovers among new-to-bank customers (increase of 11.3 pp). A cost-risk decomposition suggests that while the campaign boosts revenue in the first month, cumulative losses surpass gains after 5.6 months. The findings highlight the risk implications of customer-acquisition incentives.
Federated Graph Learning for Cross-Institution Money-Laundering Detection on Heterogeneous Transaction Networks
This study proposes a federated graph-learning framework for detecting money-laundering activities across financial institutions without sharing raw customer data. Four banks participated in local training using transaction-graph structures containing 15.7 million nodes and 112.5 million edges. A global aggregation mechanism was used to synchronize encrypted model parameters. Compared with local graph models, the federated framework improved the average AUC from 0.84 to 0.90 and increased precision at 10% recall by 29.7%. Robustness tests showed stable performance even when one institution exhibited severe label imbalance. Differential-privacy noise prevented reconstruction of sensitive records under gradient-inversion tests. The results confirm that cross-institution modeling can significantly enhance detection accuracy while maintaining data confidentiality.
Residual Stress-Driven Fatigue Life Optimization of Medical Welded Structures via Coupled Thermo-Mechanical Modeling
The study investigates the influence of welding-induced residual stress on the fatigue life of medical welded structures using a coupled thermo-mechanical finite element framework. A moving double-ellipsoidal heat source model was employed to simulate the welding process of stainless steel medical components. Residual stress fields were extracted and mapped into a fatigue damage model based on stress-life (S-N) curves and rainflow cycle counting. Numerical simulations were conducted on 36 welding parameter combinations, covering heat inputs from 0.8 to 1.6 kJ/mm. The predicted peak tensile residual stress ranged from 210 to 385 MPa, leading to fatigue life variations of up to 3.4 times under identical external loading. The proposed optimization strategy achieved an average fatigue life improvement of 27.6% compared with baseline welding parameters, demonstrating its effectiveness for life enhancement design in medical devices.
Place-Based Divergence in Sustainable Textile Material Selection: A Comparative Study of China, Japan, Italy, and Finland
The global textile industry faces an imperative shift towards sustainability, yet practices diverge significantly across regions, particularly in the choice of materials. This study investigates how and why sustainable textile material selection varies between distinct socio-industrial contexts. Employing a comparative case study methodology, it analyzes China, Japan, Italy, and Finland through systematic coding of secondary data. Findings reveal four distinct, coherent material selection logics: trade-off decision-making in China driven by scalability, authenticity logic in Japan rooted in craft tradition, value maintenance logic in Italy centered on quality equivalence, and lifecycle judgment logic in Finland premised on ecological innovation. Synthesizing these, the study proposes a Place-based Framework, conceptualizing material selection as a contextualized judgment process shaped by the interaction of six dimensions: resource ecology, craft-technology pathways, industrial logic, cultural values, design decision-making, and sustainability orientation. Furthermore, an exploratory collaboration model derived from the framework demonstrates varied complementarity and conflict risks across regional pairs. This research provides a structured lens to understand regional pathways in sustainable transition and offers practical insights for fostering cross-regional collaboration based on complementary logics rather than standardized solutions.
Short-Term Stock Market Trend Prediction Driven by Artificial Intelligence - A Comprehensive Model Based on Large-Scale Multi-Source Data
Short-term stock market trend prediction plays an important role in financial analysis and investment decision-making, yet it remains a challenging task due to market volatility and complex influencing factors. From a business data analytics perspective, this study investigates an artificial intelligence-driven framework for short-term stock market trend prediction based on large-scale multi-source financial data. The proposed approach integrates historical market data and constructed analytical features to capture short-term market dynamics and generate directional trend signals. Rather than focusing on point price prediction, the framework adopts a classification-based strategy to support next-day trend assessment. Model performance is evaluated using publicly available financial market data, and the results demonstrate that the proposed framework is capable of providing stable and interpretable prediction outcomes. In addition, the study discusses the practical application of the proposed framework within real-world financial analysis workflows. The results indicate that artificial intelligence techniques, when combined with structured feature construction, can serve as effective auxiliary tools for short-term market analysis. This research contributes to the application-oriented exploration of artificial intelligence methods in financial data analysis and provides practical insights for business-oriented market prediction tasks.
Applications of Artificial Intelligence Techniques in Medical Drug Discovery and Precision Treatment
Artificial intelligence (AI) has revolutionized various facets of medical drug discovery and precision treatment. This review paper provides a comprehensive overview of AI techniques applied in these domains, encompassing historical developments, core applications, comparative analyses, existing challenges, and future perspectives. The review begins with a historical overview of AI in medicine, charting its evolution from expert systems to deep learning. Core applications are then explored, including AI-driven drug target identification, de novo drug design, prediction of drug efficacy and toxicity, and patient stratification for precision treatment. Specific AI techniques such as machine learning, deep learning, and natural language processing are examined in the context of each application. A critical comparison of different AI approaches highlights their strengths and limitations. The review also addresses challenges in the field, such as data biases, lack of interpretability, and regulatory hurdles. Finally, future directions are discussed, emphasizing the potential of AI to transform drug discovery and personalized medicine. This review aims to serve as essential reference for researchers and practitioners in the intersection of AI and medicine, inspiring future advancements in the field.
Structural Exclusion and Employment Barriers for Individuals with Mental Disabilities: A Comparative Welfare Regime Analysis
This study explores the employment challenges faced by mentally disabled individuals, with a focus on structural barriers, welfare policies, and empowerment strategies. Mental disabilities, encompassing conditions such as depression, schizophrenia, and ADHD, significantly impact individuals' ability to secure and maintain employment due to societal stigma, discriminatory hiring practices, and insufficient workplace accommodations. Drawing on theoretical frameworks such as Critical Disability Theory, the Social Model of Disability, and the Capability Approach, the literature review highlights how systemic exclusion is perpetuated by cultural norms, regional disparities, and policy gaps in countries like China. A comparative analysis of welfare regimes reveals that social democratic models, such as those in Nordic countries, offer valuable insights into integrating mental health services with employment support, while corporatist and liberal regimes provide lessons on employment quotas and anti-discrimination laws, respectively. Logistic regression analysis further demonstrates how personal connections and witnessing successful employment influence societal attitudes toward mentally disabled individuals, emphasizing the need for targeted interventions to foster empathy, reduce stigma, and promote workplace inclusion. By addressing structural inequalities and implementing evidence-based strategies, this research provides actionable insights to improve employment opportunities and social inclusion for mentally disabled individuals, contributing to a more equitable and inclusive society.
Detecting Disclosure Discrepancies in SEC Filings: A Deep Learning Approach for Regulatory Compliance Verification
The accuracy of financial disclosures filed with the Securities and Exchange Commission (SEC) remains fundamental to maintaining market integrity and investor confidence. This research presents a comprehensive deep learning approach for automated detection of disclosure discrepancies in SEC filings, specifically targeting 10-K and 10-Q annual reports and XBRL-tagged financial statements. Our methodology employs a hybrid architecture combining deep learning classification models with rule-based validation frameworks. The core innovation lies in a Transformer-based discrepancy classifier that processes cross-period text alignments to distinguish substantive changes from routine modifications, achieving 94.3% accuracy on 3,200 expert-labeled disclosure pairs. This classifier integrates with XBRL validation rule engines and intelligent accounting standards checklists to identify numerical contradictions, formatting irregularities, and narrative inconsistencies across 10-K annual reports, 10-Q quarterly reports, and XBRL-tagged financial statements. Experimental validation using 2,847 SEC filings from publicly traded companies demonstrates detection accuracy of 94.3% for cross-period discrepancies and 91.7% for XBRL tagging errors, significantly outperforming traditional rule-based validation tools. The practical implementation reduces manual review time by 67% while maintaining high precision in identifying material misstatements requiring correction before filing.
Style Genes: Leveraging Generative AI for Artwork Authentication through Artistic Style Consistency Analysis
The proliferation of sophisticated art forgeries poses mounting challenges for authentication practices in today's art market. This paper introduces a novel framework that leverages generative artificial intelligence to verify artwork authenticity by analyzing artistic style consistency. We conceptualize artist-specific stylistic signatures as "style genes" and employ fine-tuned diffusion models to extract and analyze these inherent characteristics. This approach combines prompt engineering techniques with inverse style-matching protocols to assess whether questioned artworks align with the stylistic fingerprints of attributed artists. Experimental validation on master Chinese ink paintings (including Qi Baishi, Xu Beihong, and Zhang Daqian) and Western oil paintings (including Picasso, Monet, and Van Gogh) demonstrates superior performance compared to traditional methods, achieving 94.3% accuracy in forgery detection while maintaining interpretability through multimodal contextual analysis (integrating visual, textual, and historical data).
A Comparative Study of NER Methods for Ownership Structure Extraction from M&A Due Diligence Documents
Cross-border mergers and acquisitions require efficient extraction of ownership structures from due diligence documentation. This study compares named-entity recognition methodologies for extracting equity structures from corporate governance documents. We construct an annotated dataset from authentic materials and evaluate six NER approaches spanning traditional sequence labeling (CRF, BiLSTM-CRF), general-purpose transformers (BERT, RoBERTa), and domain-adapted models (FinBERT-MRC, Legal-BERT). Legal-BERT achieves an overall F1 score of 87.3% while encountering challenges in multilingual entity names and nested ownership structures. Error analysis reveals three primary failure modes-cross-lingual recognition ambiguities, percentage-quantity confusion, and challenges in representing complex structures-providing actionable guidance for implementing automated equity analysis systems in time-sensitive M&A transactions.
Anatomy-Aware Contrastive Pre-training: Leveraging Spatial Consistency for Label-Efficient Medical Image Diagnosis Across Multi-Modal Imaging
Medical image analysis faces persistent challenges in acquiring expert annotations due to high costs and specialized expertise requirements. Self-supervised learning offers a promising solution by learning representations from unlabeled data. This paper introduces an anatomy-aware contrastive pre-training framework that exploits spatial consistency and anatomical structure priors inherent in medical images. The proposed approach integrates contrastive learning with anatomical constraints, enabling effective knowledge transfer across CT, MRI, and X-ray modalities. Through comprehensive experiments on multiple diagnostic tasks, the framework demonstrates superior label efficiency, achieving competitive performance with only 10% of labeled data compared to fully supervised baselines. The cross-modal evaluation reveals consistent improvements of 8.3% in classification accuracy and 6.7% in segmentation Dice scores. These results validate the effectiveness of incorporating anatomical priors into self-supervised learning pipelines for medical imaging applications.
Optimization of Anomaly Detection Algorithms for Consumer Credit Default Rates Based on Time-Series Feature Extraction
Consumer credit default rates exhibit complex temporal dynamics that challenge traditional risk monitoring frameworks. This research develops an optimization methodology for anomaly detection algorithms through advanced time-series feature extraction techniques applied to consumer credit default patterns. The proposed framework integrates statistical feature engineering with adaptive machine learning algorithms to identify aberrant default rate behaviors across multiple temporal scales. Experimental validation employs Federal Reserve consumer credit data spanning 2010-2024, encompassing credit card charge-offs, delinquency transitions, and macroeconomic indicators. The optimization strategy incorporates dynamic threshold adjustment mechanisms coupled with ensemble-based feature selection to enhance detection sensitivity while minimizing false positive rates. Comparative analysis demonstrates that the optimized Isolation Forest algorithm achieves 87.3% detection accuracy with a 0.923 AUC-ROC score, outperforming baseline methods by 18.7% in early warning capability. The framework successfully identified 92% of significant default rate mutations with an average lead time of 3.2 months before traditional statistical control charts. Implementation of adaptive feature weighting reduces computational complexity by 34% while maintaining detection performance. These findings establish a robust analytical foundation for real-time consumer credit risk surveillance systems.
Comparative Analysis of Pre-Trained Language Models for Medical Document Classification and Priority-Based Workflow Routing
Medical document processing in healthcare systems faces significant challenges due to the exponential growth in data volume and the complexity of clinical terminology. This paper presents a comprehensive comparative analysis of pre-trained language models for medical document classification and priority-based workflow routing. We evaluate BioBERT, ClinicalBERT, and base BERT models through systematic fine-tuning on diverse medical document types, including clinical notes, diagnostic reports, and insurance claims. Our multi-task learning architecture simultaneously performs document classification and priority scoring, achieving 94.7% classification accuracy and an AUC-ROC of 0.928 for urgency detection. The proposed approach reduces per-document handling time by 99.9%, cutting average manual review from 4.3 minutes per document to 0.31 seconds, while maintaining high accuracy across heterogeneous medical texts. Experimental results on 45,000 annotated medical documents demonstrate that domain-adapted models outperform general-purpose transformers by 8.3 percentage points. The integration of shared representation learning with task-specific output layers enables efficient workflow optimization, allowing for the processing of documents at 0.31 seconds per item with GPU acceleration. These findings provide actionable insights for healthcare organizations implementing automated document management systems.
Copyright (c) 2025 Journal of Sustainability, Policy, and Practice
Market-Oriented Perspectives on Dynamic Pricing Decisions under Limited Inventory Conditions
This review paper investigates the convergence of Unmanned Aerial Vehicle (UAV) technology, semantic segmentation algorithms, and real-time task scheduling on embedded RISC-V platforms. UAVs are increasingly utilized in diverse applications, necessitating efficient onboard processing for tasks such as object detection, environmental mapping, and autonomous navigation. Semantic segmentation, a crucial computer vision technique, enables pixel-level understanding of UAV-captured imagery. However, the computational demands of semantic segmentation algorithms pose a challenge for resource-constrained embedded systems. The RISC-V architecture, an open-source instruction set architecture (ISA), offers a promising solution for developing energy-efficient and customizable hardware platforms for UAVs. This paper provides a comprehensive overview of the current state-of-the-art in UAV semantic segmentation, real-time task scheduling methodologies, and the utilization of RISC-V platforms in this domain. We examine various semantic segmentation algorithms optimized for embedded deployment, focusing on their accuracy, computational complexity, and memory footprint. We also explore different real-time task scheduling techniques employed to manage the execution of semantic segmentation and other critical tasks on UAVs, considering factors such as latency, jitter, and resource utilization. Furthermore, we analyze the advantages and challenges of leveraging RISC-V processors for UAV applications, highlighting their potential for customization, energy efficiency, and security. Finally, we identify key research gaps and future directions in this rapidly evolving field, emphasizing the need for developing novel hardware-software co-design methodologies to enable robust and efficient UAV semantic segmentation on embedded RISC-V platforms. This review contributes to a deeper understanding of the opportunities and challenges in deploying advanced computer vision algorithms on UAVs, facilitating the development of intelligent and autonomous UAV systems.
Robustness Evaluation of AI Security Monitoring Algorithms in Multi-Dimensional Data Flow Environments
AI security monitoring algorithms are increasingly deployed to detect malicious activities within complex, multi-dimensional data flow environments. Ensuring the robustness of these algorithms against adversarial attacks and noisy data is crucial for maintaining system integrity. This review paper provides a comprehensive overview of techniques for evaluating the robustness of AI-based security monitoring algorithms specifically designed for multi-dimensional data flow environments. We begin by outlining the challenges associated with securing these environments and the role of AI in enhancing security monitoring capabilities. We then delve into a historical overview of robustness evaluation methods, highlighting their evolution and limitations. The core of the paper focuses on two key themes: adversarial robustness and data quality robustness. Adversarial robustness explores techniques for assessing and improving the resilience of algorithms against adversarial examples, while data quality robustness examines the impact of noisy, incomplete, or biased data on algorithm performance. We critically compare existing evaluation methodologies, emphasizing their strengths, weaknesses, and applicability to different types of AI algorithms and data flow environments. Further, we discuss the prominent challenges in ensuring robustness, such as scalability, transferability, and the need for adaptive evaluation techniques. The review concludes by outlining future research directions, including the development of more robust algorithms, advanced evaluation frameworks, and techniques for explainable robustness. This review will provide researchers and practitioners with a valuable resource for understanding the state-of-the-art in robustness evaluation and for guiding future efforts in developing more secure and reliable AI-based security monitoring systems.
Interactive Effects of Keto Diet and High-Fiber Intervention on Energy Metabolism in Patients with Diabetes and Obesity
This study investigated how ketogenic and high-fiber diets, alone and together, affect energy use and blood sugar control in adults with obesity and type 2 diabetes. Sixty participants completed three eight-week diet phases-a ketogenic diet, a high-fiber diet, and a combined plan-using a randomized crossover design. Energy use was measured by indirect calorimetry, and blood sugar was tracked with HbA1c and continuous glucose monitoring. The ketogenic diet raised fat oxidation from 32% to 61% (p < 0.001), while the high-fiber diet increased the thermic effect of food from 6.8% to 9.1% (p = 0.01). The combined diet led to the greatest drop in HbA1c (−1.6 ± 0.4%, p < 0.001) and a 22% decrease in blood sugar swings. These findings show that low-carbohydrate and high-fiber diets improve metabolism through different but complementary ways-one by increasing fat use, the other by raising thermogenesis. Using both diets together may help improve energy balance and glucose control in people with type 2 diabetes, though longer studies are needed to test lasting effects.
Performance Optimization and Cycle Life Study of Mechanically and Electrically Self-Repairable Interface Materials
This study focuses on the fabrication and evaluation of a self-repairing conductive polymer composite suitable for flexible electronic applications. The composite consists of a polyurethane matrix embedded with microcapsules filled with healing agents, enabling automatic recovery after mechanical damage. Electrical and mechanical testing demonstrated that the material could recover 92% of its original conductivity and 85% of its tensile strength over ten damage-repair cycles. Scanning electron microscopy images verified the closure of cracks and the retention of material continuity. Compared with conventional non-healing conductive films, this design significantly improves durability and operational stability. These results support the potential application of this material in wearable electronics and tactile interface devices. Future work should address long-term environmental resistance and scalable production techniques.