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.
AI-Based Sentiment Analysis for Stock Market Prediction: A Systematic Literature Review
The integration of artificial intelligence and natural language processing into financial market prediction has attracted significant research attention over the past decade. This review systematically examines the landscape of AI-driven sentiment analysis techniques applied to stock market movement prediction, covering 87 studies published between 2011 and 2024. A taxonomic framework is proposed to categorize existing approaches along four dimensions: data sources, sentiment extraction techniques, correlation modeling strategies, and evaluation metrics. Quantitative comparisons across lexicon-based, machine learning, deep learning, and large language model paradigms reveal that transformer-based models achieve the highest directional accuracy (up to 65.28%) on standard benchmarks, while lexicon-based methods retain advantages in computational efficiency and interpretability. Key challenges including data noise, temporal decay, and cross-market generalizability are critically assessed, alongside emerging trends in multimodal fusion and explainable AI for financial sentiment.
Copyright (c) 2026 Journal of Sustainability, Policy, and Practice
Trustworthy Artificial Intelligence in Financial Decision-Making: A Systematic Review of Explainability, Fairness, and Accountability
The rapid adoption of artificial intelligence (AI) in financial services has introduced critical concerns regarding the transparency, equity, and governance of algorithmic decision-making. This paper presents a systematic review of 43 peer-reviewed studies published between 2018 and 2024, examining three core dimensions of trustworthy AI in finance: explainability, fairness, and accountability. The review synthesizes findings across credit scoring, fraud detection, risk management, and algorithmic trading domains. Results indicate that post-hoc explainability methods such as SHAP and LIME dominate current implementations, while fairness-aware approaches remain underexplored relative to performance optimization. A persistent trade-off between predictive accuracy and fairness is documented across multiple application contexts. This paper contributes a structured analytical framework and identifies gaps that warrant future investigation under evolving regulatory mandates including the EU AI Act.
Empirical Evaluation of Constraint Discovery Techniques for Business Logic Consistency Verification in Payroll Data Migration
Enterprise payroll system migrations demand rigorous verification that business logic---salary calculations, grade-based rules, and overtime policies---is faithfully preserved in the target environment. Manual testing remains the prevailing practice, yet it scales poorly and is prone to human oversight. Automated constraint discovery offers a promising alternative by extracting latent business rules directly from source data and applying them as verification checks on migrated records. This paper presents an empirical comparative evaluation of three constraint discovery families---functional dependencies (FDs), conditional functional dependencies (CFDs), and denial constraints (DCs)---for detecting business logic inconsistencies introduced during payroll data migration. Experiments are conducted on two real-world datasets: the NYC Citywide Payroll Data (approximately 600,000 records per fiscal year, 17 attributes) and the IBM HR Analytics dataset (1,470 records, 35 attributes). A controlled error injection methodology simulating three categories of migration inconsistencies at four injection rates (1%, 3%, 5%, 10%) is employed to construct ground truth labels. Results indicate that CFD-based discovery achieves the most favorable precision-recall balance (F1 = 0.778 at a 5% error rate on NYC data), while DC discovery attains the highest recall (0.81) at the cost of reduced precision. FD discovery, though computationally efficient, exhibits limited recall due to its inability to encode context-sensitive rules. These findings provide actionable guidance for practitioners selecting automated verification strategies in enterprise data migration projects.
A Comparative Analysis of Attention Mechanisms in Vision Transformers for Fine-Grained Image Recognition
Fine-grained image recognition demands the ability to distinguish visually similar subcategories by capturing subtle, localized discriminative features. Vision Transformers (ViTs) have emerged as strong contenders in this domain, yet their diverse attention mechanisms yield different inductive biases that remain insufficiently compared under unified conditions. This study presents a controlled empirical evaluation of seven representative attention mechanisms on three fine-grained benchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft. All methods are trained with identical preprocessing, augmentation, optimization, and ImageNet-21K pretrained weights at 448×448 resolution. The evaluation spans classification accuracy, computational cost measured through FLOPs, parameter counts, and throughput, as well as attention interpretability quantified through part-localization precision. Results indicate that deformable attention achieves the highest accuracy across all three benchmarks, with a moderate advantage of 1.0--1.5 percentage points over the global self-attention baseline. Window-based and cross-shaped mechanisms offer favorable accuracy-efficiency tradeoffs, while class-attention decoupling improves parameter efficiency without sacrificing competitive accuracy. A strong rank correlation (Spearman ρ = 0.93) between localization precision and classification accuracy supports the interpretation that spatial selectivity is a key driver of fine-grained recognition quality. These findings provide actionable guidance for selecting attention strategies under varying computational budgets.
Comparative Evaluation of Automated Approaches for Legal Aid Document Generation: Template-Based, Rule-Based, and LLM-Based Methods
The accessibility of legal services remains a critical challenge in the United States, with over 80% of low-income individuals unable to obtain necessary civil legal assistance. This study presents a systematic comparative evaluation of three automated document generation approaches for legal aid applications: template-based document generation, rule-based conditional generation, and large language model (LLM)-based intelligent drafting. The study compiled and anonymized N=247 housing-related legal aid cases spanning eviction defense, security deposit claims, and repair request letters, drawn from publicly available eviction records in the Atlanta metropolitan area and supplemented by open legal datasets including Multi-LexSum and LegalBench. The evaluation framework assessed four dimensions: legal element completeness (92.3% for templates, 94.7% for rules, 96.1% for LLMs), linguistic accuracy (88.5%, 91.2%, 94.8%), jurisdictional compliance (95.1%, 93.4%, 89.7%), and practitioner usability scores (7.2/10, 8.1/10, 8.9/10). The findings reveal distinct performance trade-offs: template methods excel in standard cases with high efficiency but limited flexibility; rule-based approaches handle moderate complexity at increased maintenance costs; and LLM methods demonstrate superior adaptability in non-standard scenarios that require rigorous post-processing validation mechanisms.
Fairness-Constrained Temporal Feature Learning Algorithm for Cross-Population Glucose Prediction and Adherence Risk Stratification Using Continuous Glucose Monitoring Data
Continuous glucose monitoring (CGM) data provides granular temporal information that enables individualized diabetes management, yet existing glucose prediction algorithms are predominantly trained and evaluated on small, homogeneous cohorts, raising concerns about cross-population generalizability and demographic equity. This study proposes a fairness-constrained temporal feature learning algorithm that integrates patch-based Transformer encoding with adversarial debiasing to improve cross-population glucose prediction accuracy and adherence risk stratification. Using four publicly available CGM benchmark datasets encompassing 243 participants across diverse diabetes types and demographic backgrounds, we evaluate the proposed approach against five baseline algorithms across 30-minute and 60-minute prediction horizons. Subgroup-stratified analysis reveals that the fairness-constrained approach reduces the maximum inter-group RMSE disparity from 4.83 mg/dL to 1.97 mg/dL at the 30-minute horizon while maintaining competitive overall prediction accuracy (RMSE: 18.26 mg/dL). CGM wear-time gap features extracted by the temporal encoder achieve an AUC of 0.817 for 7-day adherence risk prediction. These findings demonstrate that incorporating fairness constraints into CGM temporal feature learning can mitigate demographic performance disparities without substantial accuracy trade-offs, supporting more equitable data-driven diabetes care aligned with national chronic disease reduction strategies.
Comparative Empirical Evaluation of Hallucination Mitigation Strategies in LLM-Based Text Generation
Large language models (LLMs) have achieved remarkable performance across natural language tasks, yet their tendency to generate factually incorrect content --- commonly termed hallucination --- remains a critical barrier to deployment in high-stakes domains. Two dominant families of mitigation strategies have emerged: retrieval-augmented generation (RAG) approaches that ground outputs in external knowledge, and prompting-based approaches that leverage self-verification without external retrieval. While both families have demonstrated promising results individually, no systematic comparative evaluation exists across standardized benchmarks under unified conditions. This paper presents a comparative empirical analysis of hallucination mitigation strategies spanning four RAG variants (Naive RAG, Self-RAG, Corrective RAG, FLARE) and three prompting-based methods (Chain-of-Verification, self-consistency decoding, self-contradiction detection) evaluated on five public benchmarks: TruthfulQA, HaluEval, FActScore, FELM, and RAGBench. Drawing exclusively from published experimental results, the analysis reveals that advanced RAG strategies achieve 10--25 percentage-point improvements in factual precision over naive baselines, while prompting-based methods offer competitive performance on reasoning-intensive tasks without retrieval infrastructure. Task-dependent performance patterns emerge: knowledge-intensive factoid tasks favor retrieval augmentation, whereas logical consistency tasks benefit from self-verification prompting. A practical decision matrix is derived to guide practitioners in selecting appropriate strategies based on task characteristics and resource constraints.
Comparative Evaluation of Graph Neural Networks for Cross-Market Risk Contagion Path Identification in Multi-Layer Financial Networks
Cross-market risk contagion---the propagation of financial distress across asset classes such as equities, bonds, derivatives, and foreign exchange---poses a persistent challenge for systemic risk monitoring. While graph neural networks (GNNs) have demonstrated strong performance in single-market financial risk tasks, their effectiveness for identifying contagion paths across interconnected multi-layer financial networks remains insufficiently explored. This paper presents a comparative empirical evaluation of four GNN architectures---GCN, GAT, GraphSAGE, and EvolveGCN---alongside a spatial-temporal graph convolutional baseline (ASTGCN) and three traditional econometric methods, applied to multi-layer financial networks constructed from U.S. equity, bond, derivatives, and foreign exchange market data spanning January 2018 to December 2023. The evaluation covers contagion path identification accuracy against realized stress episodes, node vulnerability ranking precision, the incremental benefit of multi-layer network construction over single-layer alternatives, sensitivity to edge-density thresholds, and a feature-matched comparison that isolates the contribution of graph-based nonlinear aggregation from that of richer input features. Results indicate that temporal-aware GNN architectures achieve moderate yet consistent gains in contagion detection, with ASTGCN attaining the highest AUC-ROC of 0.841 (95% CI: [0.823, 0.858]) on multi-layer networks. Multi-layer construction yields a 7.9 percentage-point improvement in AUC-ROC over the best single-layer configuration (95% CI: [5.4, 10.3] pp, p < 0.001). Macro-financial factors---particularly the VIX and credit spreads---emerge as the most informative features for vulnerability scoring. A case study on the March 2020 COVID-19 market crash validates the practical relevance of the identified contagion paths.
Feature Weight Optimization in Machine Learning Classifiers for Conflict Escalation Early Warning: Evidence from Diplomatic Signals and News Text
Early warning of armed conflict escalation remains a central challenge at the intersection of computational social science and national security analytics. Existing machine learning pipelines for conflict prediction typically treat all input features with equal or heuristically assigned weights, overlooking the differential informativeness of diplomatic signals versus macroeconomic indicators across varying escalation phases. This paper proposes a structured feature weight optimization framework integrating diplomatic statement tone data from GDELT/CAMEO event coding with news-derived LDA topic features. Two baseline classifiers---Random Forest and Gradient Boosting---are compared under standard and optimized weighting conditions. SHAP-based interpretability analysis quantifies the marginal contribution of each feature group to escalation-onset prediction. Experiments on a longitudinal country-month panel (2010--2023, N = 25,074) demonstrate that the proposed weighting strategy improves AUC-ROC by 5.2 percentage points over unweighted baselines while reducing false alarm rates by 11.2%, offering actionable guidance for intelligence analysts prioritizing early escalation indicators across heterogeneous data streams.
A Comparative Study of Spatiotemporal Clustering and Classification Approaches for Security Incident Risk Assessment in UN Peacekeeping Operations
This study presents a comparative analysis of spatiotemporal clustering and classification approaches applied to security incident risk assessment in United Nations peacekeeping operations. Drawing on 61,274 conflict event records from the Armed Conflict Location and Event Data Project (ACLED) spanning 2010 to 2024 across four active mission areas---MINUSCA, MONUSCO, UNMISS, and UNIFIL---and supplemented by geospatial features from PRIO-GRID, this research evaluates three spatial clustering methods (ST-DBSCAN, Kernel Density Estimation, and Getis-Ord Gi*) for hotspot identification and examines temporal periodicity through STL decomposition and autocorrelation analysis. The study compares Random Forest, XGBoost, and SVM classifiers under natural class imbalance using SMOTE, ADASYN, and SMOTE-Tomek resampling strategies. Results indicate that ST-DBSCAN demonstrates superior flexibility in delineating irregular hotspot boundaries, while XGBoost paired with SMOTE yields the highest Macro F1-Score (0.716) and Matthews Correlation Coefficient (0.683) among all configurations. These findings provide methodological references for data-driven risk assessment in peacekeeping contexts.
Comparative Evaluation of Gradient Compression Strategies for Communication-Efficient Federated Learning in Multi-Hospital Medical Image Classification
Federated learning enables privacy-preserving collaborative training across hospitals, yet the communication overhead of exchanging model parameters remains a critical deployment bottleneck. While gradient compression techniques have been extensively studied in distributed training, their effectiveness under the heterogeneous data distributions characteristic of multi-hospital settings is not well understood. This paper presents a controlled empirical comparison of six gradient compression strategies --- stochastic quantization (QSGD), ternary quantization (TernGrad), sign-based compression (signSGD), Top-K sparsification, Random-K sparsification, and a hybrid sparsification-quantization approach --- applied to federated medical image classification. Experiments are conducted on Fed-ISIC2019 with six natural hospital centers and PathMNIST with synthetic non-IID partitioning across five clients. Results indicate that Top-K sparsification with error feedback achieves the strongest accuracy--communication tradeoff, retaining 97.8% of the 200-round baseline accuracy at nominal 100× compression on Fed-ISIC2019. Multi-bit quantization methods remain more stable as data heterogeneity increases. Sign-based compression, evaluated under a different aggregation protocol (majority vote) than the other methods, degrades substantially under natural non-IID conditions. The hybrid approach performs strongly in the low-budget regime but introduces additional implementation complexity. Communication savings are reported as analytical estimates based on nominal compression ratios; protocol-level overhead would moderately reduce actual savings in deployment. These findings provide evidence-based guidance for healthcare institutions selecting compression strategies for bandwidth-constrained federated learning deployments.
Evaluating BLIP-2, LLaVA, and GPT-4V for Accessible Artwork Description Generation in Virtual Museums: A Comparative Study Based on WCAG-Aligned Evaluation and User Satisfaction
Virtual museums have expanded cultural access beyond physical boundaries, yet people with visual impairments remain excluded from artwork experiences due to insufficient image descriptions. While vision-language approaches offer the potential to automate accessible content generation, their effectiveness in art-specific contexts has not been rigorously assessed. This study presents a comparative empirical evaluation of three representative vision-language approaches available as of early 2024 --- BLIP-2, LLaVA, and GPT-4V --- for generating accessible artwork descriptions in virtual museum environments. Using a curated evaluation set of 250 artworks spanning six genres from the SemArt dataset, we compare descriptions produced under baseline and art-optimized prompt conditions. Evaluation combines automated captioning metrics (BLEU-1, BLEU-4, METEOR, ROUGE-L, CIDEr-D), a WCAG 2.1-aligned evaluation rubric scored by trained accessibility evaluators, and a user study with 18 blind and low vision participants. Results indicate that GPT-4V with art-optimized prompts achieves the highest CIDEr-D score (0.476) and WCAG sufficiency rating (3.87/5.00), while all three approaches exhibit notable performance degradation on abstract artworks. User preference data and qualitative feedback suggest that contextual richness, in addition to factual accuracy, may play an important role in shaping satisfaction among visually impaired users. These findings provide practical guidance for virtual museum developers seeking to deploy AI-generated accessible content at scale.
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.
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.
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.
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.
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.