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.
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.
Copyright (c) 2026 Journal of Sustainability, Policy, and Practice
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.
High Electron Affinity as a Determinant of Oxidizing Strength in Layered Birnessite
Birnessite, a layered manganese oxide, has strong oxidation ability, but the main factor that controls this activity is still unclear. In this study, twelve natural and synthetic birnessite samples with different interlayer cations and Mn (III)/Mn (IV) ratios were prepared to examine how electron affinity affects oxidation. The electron affinity measured by ultraviolet photoelectron spectroscopy ranged from 5.6 to 6.1 eV and changed with interlayer composition. Oxidation tests using Fe (II) and As (III) followed first-order kinetics, with rate constants between 0.013 and 0.072 min⁻¹ for Fe (II) and between 0.006 and 0.041 min⁻¹ for As (III). A clear linear relation (R² > 0.85) was found between electron affinity and oxidation rate, showing that higher electron affinity increases oxidation ability. X-ray photoelectron spectroscopy showed that part of Mn (IV) was reduced to Mn (II) and that surface hydroxyl groups increased after reaction, indicating surface changes that reduced activity over time. Density functional theory calculations supported these results by linking conduction-band position with electron affinity. The findings show that electron affinity is a useful parameter for predicting the oxidation behavior of birnessite and can help in developing manganese oxide catalysts for environmental oxidation and pollutant control.
Cross-Modal Attack Detection and Adaptive Reconstruction Method Based on Uncertainty Estimation
As multimodal fusion applications integrating visual, speech, and language models become widespread in critical domains such as healthcare, transportation, and national defense, the vulnerability of these models to cross-modal adversarial attacks poses a significant threat to system security. Traditional detection methods are typically confined to single-modal signal analysis, struggling to capture subtle inconsistencies across multi-source information. This paper proposes an uncertainty-based cross-modal attack detection and adaptive reconstruction method, aiming to achieve real-time detection and repair through joint modeling of multi-modal consistency. The approach embeds a Bayesian inference module within the Transformer fusion layer to estimate joint uncertainty across modalities, enabling dynamic monitoring of semantic consistency. Upon detecting anomalous uncertainty distributions, the system automatically activates a lightweight reconstruction subnetwork. This subnetwork regenerates perturbed features based on cross-modal correlations, thereby repairing compromised regions. Experiments conducted on the COCO-Multimodal QA and AVSpeech datasets demonstrate that this method improves detection accuracy by 34% and 29% against FGSM and PGD attacks, respectively. Post-attack repair increases model accuracy by 22% with less than 6% increase in inference latency. The findings demonstrate that uncertainty-driven modal consistency estimation effectively enhances the security and reliability of multimodal learning systems in real-world scenarios. This research provides a deployable defense mechanism for multimodal AI systems, applicable to defense surveillance, autonomous driving, and medical image analysis. It aligns with the technical development direction of the U.S. Department of Defense's AI Security Assurance Program and holds practical significance for strengthening the security of critical national AI infrastructure.
Dynamic Pricing with Product Returns: Integrating Optimization Models and Market-Oriented Development Strategies
Dynamic pricing under inventory constraints has traditionally been examined through optimization-based models that emphasize short-term revenue maximization. However, the increasing prevalence of product return mechanisms in contemporary markets introduces additional behavioral and structural complexities that challenge the effectiveness of purely analytical pricing approaches. This integrative review examines dynamic pricing in return-intensive environments by synthesizing insights from operations management, market-oriented development, data-driven demand analysis, and mechanism-oriented system thinking. The study highlights how product returns function as an intermediate structure linking pricing decisions, demand realization, and inventory dynamics, thereby reshaping the feedback mechanisms of pricing systems. By moving beyond static demand assumptions and exogenous return representations, the paper reframes dynamic pricing as a system-guiding intervention that operates indirectly through consumer expectations, return policies, and market perceptions. An integrative conceptual framework is proposed to bridge optimization rigor with strategic and behavioral realism. The review contributes to the literature by emphasizing long-term stability, adaptability, and strategic coherence in pricing system design, and it offers managerial insights for coordinating pricing and return policies in complex and uncertain market environments.
Carbon Reduction Pathways for Manufacturing Enterprises Under Digital Monitoring Systems
This study examines how digital monitoring systems, including IoT sensing and automated carbon dashboards, support carbon reduction in manufacturing enterprises. The analysis focuses on 62 factories across electronics, machining, and chemical industries. Results show that real-time carbon tracking reduced energy waste by 12%, while automated equipment scheduling achieved an additional 9% reduction. Process-level carbon mapping also helped companies detect high-emission segments and optimize production planning. The findings demonstrate that digital tools significantly enhance carbon visibility and enable data-driven sustainability strategies.
Impact of Corporate ESG Programs on Carbon Performance and Employee Engagement
This research evaluates the impact of corporate ESG (Environmental, Social, and Governance) programs on carbon performance and internal engagement. Survey and carbon-data analysis from 3,000 employees in 12 multinational companies show that firms with active ESG programs achieved a 15% improvement in carbon reduction efforts and a 22% increase in employee participation in sustainability tasks. The study highlights that employee awareness, training, and communication mechanisms play key roles in strengthening corporate sustainability outcomes.
Life-Cycle Carbon Assessment for Green Supply Chains in the FMCG Sectora
This study investigates life-cycle carbon emissions in fast-moving consumer goods (FMCG) supply chains, covering raw material procurement, packaging, transport, and retail distribution. Data from 18 large FMCG brands show that packaging contributes 42% of total emissions, while long-distance logistics accounts for 28%. The study models three reduction scenarios-low-carbon materials, route optimization, and shared distribution centers-and finds that a combined strategy can reduce end-to-end emissions by up to 31%. The results provide practical guidance for companies building greener supply chains.
Integrating AI, Data Analytics, and Policy Insights for Sustainable Urban and Economic Development
Sustainable urban and economic development increasingly relies on the integration of artificial intelligence (AI), data analytics, and policy insights. This paper provides a comprehensive review of the conceptual frameworks, technological applications, and governance models that enable cities to achieve efficiency, resilience, and environmental sustainability. We examine the interdependence between urban systems and economic systems, the role of multimodal data and AI-driven analytics in mobility, energy, and infrastructure management, and the transformative potential of digital twins for real-time simulation and risk reduction. Furthermore, we discuss data-driven economic strategies, industrial transformation, and policy frameworks that align technological innovation with ethical, social, and institutional considerations. The study concludes by highlighting key challenges, future research directions, and the necessity of holistic governance to ensure equitable, adaptive, and sustainable urban and economic development.
Semi-Supervised Feature Selection with Bias Mitigation for SME Credit Assessment Using Alternative Data
Credit scoring for small and medium enterprises (SMEs) faces a fundamental challenge: assessing creditworthiness when traditional financial data is unavailable. This paper presents a semi-supervised feature selection framework that addresses this challenge by leveraging alternative data sources, ranging from transaction patterns to behavioral signals. We develop a graph-based approach that reduces the requirement for labeled data by 70% while improving the area under the curve (AUC) from 0.836 to 0.871, a 4.2 percentage point increase (~5% relative) compared to the best supervised baseline. The framework integrates bias mitigation techniques, which reduce the approval-rate gap by 78.9% while maintaining stable default rates across groups, without compromising model performance. Experiments on 111,579 SME loan applications across three geographic regions demonstrate that the approach scales efficiently with O (n log n) complexity and can process 500,000 applications in approximately two hours (≈131 minutes for 500k records). The practical implications are significant: financial institutions can now assess credit risk for businesses previously considered "unscorable" due to the absence of traditional credit history. This framework facilitates broader access to capital for millions of SMEs, particularly in developing economies where formal financial records are limited.
Reliability Assessment and Adaptive Fusion Algorithm for Multi-Sensor Data in Autonomous Driving under Adverse Weather Conditions
Atmospheric disturbances impose systematic degradation on multi-sensor perception systems in autonomous vehicles, necessitating a fundamental rethinking of sensor fusion strategies. This study presents a comprehensive reliability assessment framework combined with an adaptive fusion algorithm designed to mitigate sensor-specific performance deterioration under adverse meteorological conditions. Using an empirical dataset encompassing 10,000 hours of vehicle operation, we establish quantitative correlations between atmospheric parameters and measurement uncertainty across heterogeneous sensor modalities. Real-time trustworthiness estimation is achieved through a dynamic scoring mechanism that integrates environmental context with temporal performance evolution. The proposed adaptive fusion algorithm performs reliability-weighted integration through probabilistic decision modeling, optimizing sensor data combination while minimizing perception errors. Experimental validation demonstrates that the method improves object detection accuracy by 22.9 percentage points compared with majority-vote fusion (AP@0.5) and by 18.4 percentage points compared with fixed-weight fusion in heavy rain (AP@0.5). Furthermore, the false positive rate is reduced by 66-73% relative to the fixed-weight and majority-vote baselines, respectively. These improvements directly enhance collision avoidance performance, substantially advancing the safety and robustness of autonomous vehicles operating in challenging environmental conditions.
Real-Time Fraud Risk Scoring through Behavioral Sequence Analysis: An Explainable Approach for online Transaction Security
Payment fraud continues to evolve, resulting in global losses of approximately $33.83 billion last year alone. We developed a system capable of highly effective fraud detection by analyzing spending behavior over time rather than focusing solely on individual transactions. While fraudsters can imitate a single purchase, it is far more difficult for them to replicate an entire behavioral history. Our key innovation lies in a hierarchical architecture that combines modified gated recurrent units (GRUs) with gradient boosting methods, allowing each component to perform its specialized role. The proposed system achieves an area under the precision-recall curve (AUPRC) of 0.876 and a Recall@1%FPR of 0.834 on the IEEE-CIS dataset comprising 590,540 transactions. In production-level stress tests, it sustains throughput up to 12,000 transactions per second under peak load conditions (with an average daily volume of approximately 3.4 million across partner institutions) and maintains a median latency of 47.3 ms (95% CI: [45.8, 48.9] ms; p95: 67.2 ms), remaining consistently below the 50 ms operational threshold. The system's core mechanism involves a temporal-gap-aware gating module within the modified GRU encoder that captures irregular intervals between purchases, effectively distinguishing genuine consumer behavior from fraudulent activity. This encoder is integrated with an ensemble of LightGBM, XGBoost, and Random Forest models, enabling robust voting-based decision fusion. To balance interpretability and performance, the system employs selective explainability-providing gradient-based attributions and counterfactual explanations only for the 5% of transactions flagged as high-risk-thereby ensuring regulatory transparency without compromising speed. Extensive validation using real banking datasets from three financial institutions confirmed its reliability and practicality. While not flawless, the system demonstrates sufficient robustness and interpretability for large-scale deployment-an outcome that ultimately matters more than theoretical optimality when millions of dollars are at stake each day.