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.
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.
Copyright (c) 2026 Journal of Sustainability, Policy, and Practice
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.
Copyright (c) 2025 Journal of Sustainability, Policy, and Practice
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.
Effectiveness Evaluation of Adaptive Difficulty Adjustment Algorithms with Multimodal Feedback for Social Skills Training in Children with Autism Spectrum Disorder
Children diagnosed with autism spectrum disorder display substantial heterogeneity in social communication skills, necessitating intervention strategies that dynamically adapt to individual developmental trajectories. We propose a hierarchical reinforcement learning framework that integrates multimodal behavioral streams to guide difficulty progression in therapeutic social scenarios. Skill development is modeled as a constrained Markov decision process, with difficulty vectors d in D evolving according to composite performance signals p(t) and engagement indicators e(t), where the optimization objective J = E [sum over t of gamma ^ t * (r_skill (s_t, a_t) + lambda * r_engage (s_t, a_t))] balances immediate skill gains against sustained participation. Three synchronized channels-facial landmarks tracked via 68-point models sampled at 120 Hz, acoustic features represented by 13-dimensional MFCCs, and skeletal configurations captured across 25 anatomical joints-are processed through temporal convolutional networks. Attention-weighted aggregation f_fusion = sum over i of alpha_i * phi_i (x_i) allows each modality-specific encoder phi_i to contribute proportionally to its instantaneous reliability. Clinical trials involving 124 participants (ages 6-14, ADOS-2 scores 12.4 ± 3.2) demonstrate a 42.3% acceleration in competency acquisition compared with therapist-directed baselines (hierarchical model coefficient beta_time×condition = 1.42, SE = 0.18, t (1984) = 7.89, p < 0.001). Transition prediction between difficulty states achieves 87.4% accuracy. Power-law retention analysis indicates reduced forgetting in the adaptive framework (b_adaptive = 0.084 versus b_control = 0.162), with 78.4% of acquired competencies maintained at a 12-week follow-up.
Deep Learning-Based Prediction Technology for Communication Effects of Animated Character Facial Expressions
The increasing demand for engaging animated content requires advanced mechanisms to predict communication effectiveness prior to production completion. This study introduces a deep learning framework designed to forecast audience engagement through automated analysis of animated characters' facial expressions. The methodology combines convolutional neural networks for precise facial feature extraction with transformer architectures for temporal prediction of communication impact. By processing visual expression data and correlating it with audience response patterns, the framework achieves 87.3% accuracy in predicting viral potential across a range of animation styles. Compared to traditional content evaluation methods, this approach significantly reduces production iteration cycles by 42% while preserving creative authenticity. Experimental validation using 15,000 animated sequences from commercial productions confirms the framework's effectiveness in predicting audience emotional resonance and content shareability across diverse demographic groups.
Performance Evaluation Methods in Contemporary Professional Practice
Performance evaluation methods have become fundamental mechanisms for organizational success in contemporary professional practice, enabling businesses to assess operational efficiency, strategic alignment, and competitive positioning in rapidly evolving markets. This paper examines the multifaceted landscape of performance measurement and management systems, exploring their implementation processes, design frameworks, and practical applications across diverse professional contexts. The study investigates how organizations adapt traditional performance evaluation approaches to address challenges posed by digital transformation, post-pandemic operational shifts, and the increasing complexity of business environments. Through systematic analysis of established frameworks and emerging methodologies, this research demonstrates how performance measurement systems serve as critical tools for change initiatives, continuous improvement programs, and strategic decision-making processes. The paper further explores the role of organizational culture in shaping measurement practices and examines obstacles that hinder effectiveness in implementation. By synthesizing contemporary research and practical applications, this work provides comprehensive insights into how performance evaluation methods support professional practice across sectors including small and medium enterprises, industrial collaborations, and project-based organizations, while highlighting the theoretical foundations that underpin successful measurement and management control systems.
SecureCodeBERT: An Ai-Powered Model for Identifying and Categorizing High-Risk Security Vulnerabilities in Php-Based Critical Infrastructure Applications
Critical infrastructure systems extensively utilize PHP applications which face significant security challenges that traditional detection methods inadequately address. This paper presents SecureCodeBERT, a specialized transformer-based model for detecting and classifying high-risk security vulnerabilities in PHP applications deployed within critical infrastructure environments. The architecture incorporates PHP-specific adaptations through specialized tokenization strategies and contextual code understanding mechanisms. A comprehensive multi-stage detection framework combines syntactic parsing, semantic analysis, and contextual vulnerability pattern recognition to identify complex exploitation vectors. The multi-level classification system categorizes vulnerabilities based on both technical severity and operational impact, enabling prioritized remediation. Experimental evaluation on a dataset comprising 140 applications across five critical infrastructure sectors demonstrates SecureCodeBERT's superior performance with precision rates of 0.892 and recall rates of 0.867, representing significant improvements over traditional static analysis tools (+21.0%) and generic code analysis models (+7.6%). Sector-specific vulnerability pattern analysis reveals distinct security challenges across energy management, healthcare, financial services, transportation, and water management applications. Case studies validate the model's effectiveness in production environments, demonstrating particular strengths in detecting sophisticated authentication bypass, SQL injection, and command injection vulnerabilities that conventional tools frequently miss.
RLHF-Powered Multilingual Audio Understanding: A Cross-Cultural Emotion Analysis Framework for International Communication
The proliferation of multilingual audio content across global communication platforms presents significant challenges in understanding cross-cultural sentiment expressions. This paper introduces a novel framework that integrates Reinforcement Learning from Human Feedback (RLHF) with advanced multilingual audio processing techniques to enhance cross-cultural sentiment analysis capabilities. Our approach addresses the complexities of language-specific emotional expressions and cultural nuances through an adaptive learning mechanism that continuously refines understanding based on human feedback. The proposed framework demonstrates superior performance in identifying sentiment patterns across diverse linguistic and cultural contexts, achieving accuracy improvements of 18.3% over traditional approaches. The system incorporates multi-dimensional feedback fusion mechanisms and dynamic reward estimation to optimize sentiment classification across 12 major languages. Experimental results reveal enhanced cross-cultural communication effectiveness through improved sentiment detection accuracy and cultural context preservation. The framework's applications extend to global diplomatic communications, international business negotiations, and cross-border social media monitoring, contributing to more effective intercultural understanding and communication facilitation in increasingly connected world environments.
Federated Learning-Based Framework for Privacy-Protected Cross-Border Financial Risk Evaluation: Analyzing US-Asia Investment Flows
This paper presents a novel federated learning framework for privacy-preserving cross-border financial risk assessment, specifically focused on US-Asia investment flows. Cross-border financial transactions face significant challenges in risk assessment due to disparate regulatory environments, data sovereignty requirements, and privacy constraints across jurisdictions. Our proposed architecture addresses these challenges through a multi-layered approach that incorporates differential privacy, homomorphic encryption, and secure aggregation techniques while enabling collaborative model training without raw data exchange. Experimental results demonstrate that the proposed framework achieves 94.5% detection accuracy with 217ms latency in real-world case studies, outperforming conventional federated learning approaches by 4.3-7.2% across key performance metrics while maintaining regulatory compliance. The architecture reduces false positives by 73% compared to baseline methods while preserving data locality requirements. Privacy protection analysis confirms resilience against multiple attack vectors with only 0.4% model inversion success rate compared to 7.2% for state-of-the-art alternatives. This research establishes a foundation for enhanced cross-jurisdictional financial risk assessment that balances analytical capabilities with strict privacy preservation, enabling financial institutions to develop more sophisticated risk models across US and Asian markets without compromising regulatory compliance or data sovereignty.
DeepAd-OCR: An AI-Powered Framework for Automated Recognition and Enhancement of Conversion Elements in Digital Advertisements
Digital advertisements represent a critical business asset requiring continuous optimization to maximize conversion rates. While traditional approaches rely on manual analysis and heuristic adjustments, this paper introduces DeepAd-OCR, an AI-driven framework for real-time recognition and optimization of conversion elements in digital advertisements. The framework integrates enhanced optical character recognition with deep learning techniques to automatically identify, analyze, and optimize critical conversion elements including call-to-action buttons, pricing information, and value propositions. Experimental evaluation conducted on a dataset of 15,000 advertisements across multiple platforms demonstrates that DeepAd-OCR achieves 96.8% text recognition accuracy and 94.3% visual element detection accuracy, significantly outperforming traditional methods. Implementation across various industry sectors resulted in average conversion rate improvements of 22.8%, with e-commerce platforms experiencing the highest gains at 27.4%. The system's real-time optimization capabilities prove particularly valuable during time-sensitive promotions, dynamically adjusting emphasis elements based on performance metrics. Case studies validate the framework's effectiveness in practical applications, while acknowledging limitations in handling animated content and computational requirements. The DeepAd-OCR framework advances the state-of-the-art in advertisement optimization by combining sophisticated element recognition with adaptive optimization algorithms, enabling advertisers to maximize conversion potential through automated, data-driven adjustments.
Bayesian Optimization-Based AI Framework for Nanobody Screening: Minimizing Experimental Failures in ELISA Detection Systems
This paper presents a novel AI-driven Bayesian optimization framework for nanobody screening that significantly reduces experimental failures in ELISA-based detection systems. Nanobody screening protocols traditionally suffer from high failure rates, resource inefficiency, and poor reproducibility due to complex parameter interdependencies. The proposed framework integrates Gaussian process surrogate models with dynamically adjusted acquisition functions to navigate high-dimensional parameter spaces efficiently. A comprehensive parameter space definition encompasses eight critical ELISA variables, including incubation conditions, reagent concentrations, and protocol timing. The framework employs a Matérn 5/2 kernel function with empirically determined hyperparameters to model the relationship between experimental parameters and detection performance. Validation across multiple target proteins demonstrates a 3.42× improvement in experimental efficiency compared to traditional grid search methods, with success rates increasing from a baseline of 27.3% to 78.3% for SARS-CoV-2 RBD detection. Statistical validation confirms these improvements with high effect sizes (d = 1.82) and statistical power (0.997). The framework achieved a 67.8% reduction in experimental costs while improving reproducibility scores from 0.85 to 0.91. Cross-laboratory validation confirms protocol transferability, addressing a critical challenge in biomedical research standardization. This approach establishes a foundation for more efficient and reliable nanobody development pipelines with broad implications for biomedical research optimization.
A Reinforcement Learning Approach for Adaptive Budget Allocation in Pharmaceutical Digital Marketing: Maximizing ROI Across Patient Journey Touchpoints
This paper introduces a novel reinforcement learning framework for dynamic budget allocation in pharmaceutical digital advertising, addressing critical challenges in optimizing marketing resources across patient journey touchpoints. Traditional budget allocation methods in pharmaceutical marketing fail to adapt to complex, multi-channel environments and regulatory constraints, resulting in suboptimal ROI. We propose a comprehensive reinforcement learning approach that models budget allocation as a sequential decision-making problem, with a state space encompassing channel performance metrics, audience characteristics, and regulatory parameters. The framework incorporates a multi-objective reward function balancing immediate conversion metrics with long-term value generation while maintaining compliance requirements. Experimental validation using 24 months of real-world pharmaceutical marketing data across five therapeutic areas demonstrates significant performance improvements over conventional methodologies. The reinforcement learning framework achieved an average ROI increase of 42.3% compared to baseline methods, with particularly strong performance in rare disease categories (69.9% improvement). The system demonstrates effective learning convergence across diverse therapeutic contexts while maintaining regulatory compliance. This research provides both theoretical contributions to AI applications in healthcare marketing and practical implementation strategies for pharmaceutical companies seeking to optimize digital advertising investments across increasingly complex patient journey touchpoints.
Structural Modulation and Bioactivity Assessment of Mixed-Ligand Copper Complexes Targeting Urease
Mixed-ligand copper complexes have emerged as promising candidates for enzyme inhibition due to their versatile coordination chemistry and tunable biological activity. In this study, a series of mixed-ligand copper complexes were designed and synthesized with structural modifications aimed at enhancing urease inhibitory activity. The complexes were comprehensively characterized using NMR, IR, UV-Vis spectroscopy, mass spectrometry, and X-ray crystallography where feasible, confirming successful coordination and revealing variations in coordination geometry. Urease inhibition assays demonstrated that ligand structure significantly influenced biological activity, with electron-donating and sterically accessible ligands exhibiting higher potency. Kinetic studies indicated competitive or mixed-type inhibition, while molecular docking simulations supported these findings by showing favorable interactions with key residues in the urease active site. Structure-activity relationship analysis highlighted the critical roles of electronic properties, steric effects, and coordination geometry in modulating enzyme inhibition. The results provide valuable insights into rational ligand design for copper-based metalloenzyme inhibitors and establish mixed-ligand copper complexes as potential candidates for therapeutic applications targeting urease-related pathologies.
Two-Dimensional Copper Coordination Polymers Regulated by Tridentate Auxiliary Ligands as Efficient Urease Inhibitors
Two-dimensional (2D) copper coordination polymers, characterized by their unique layered structures, multiple coordination sites, and tunable pore channels, have attracted considerable attention due to their potential applications in urease inhibition and agricultural environmental management. The combination of structural flexibility, high surface area, and adjustable metal-ligand coordination environments allows these polymers to interact effectively with enzymatic active sites, offering a promising platform for the development of highly efficient and controllable enzyme inhibitors. In this study, tridentate auxiliary ligands were employed as structural regulators to design and construct novel 2D copper coordination polymers. The influence of these ligands on the polymeric architecture and urease inhibitory performance was systematically investigated. Single-crystal X-ray diffraction analysis revealed that the tridentate ligands not only effectively modulate the coordination environment of copper centers but also direct the topology of the 2D layered networks, promoting the exposure of active sites that are critical for enzyme binding. The resulting polymers exhibit a well-defined layered structure with interconnected channels, which can facilitate substrate accessibility and enhance inhibitory efficiency. Thermogravimetric analysis (TGA) and powder X-ray diffraction (PXRD) measurements confirmed that the synthesized polymers possess excellent thermal stability and retain their crystallinity under experimental conditions, indicating robust structural integrity suitable for practical applications. In vitro urease inhibition assays demonstrated that the novel polymers exhibit high inhibitory activity, with IC₅₀ values significantly lower than those of analogous systems constructed using bidentate auxiliary ligands. These results suggest that the incorporation of tridentate ligands markedly enhances enzyme inhibition, likely due to the combined effects of increased active site exposure, optimized pore channels, and the rigidity of the polymeric network. Mechanistic analysis indicates that copper ions may interact directly with the urease active center through coordination or electrostatic interactions, thereby blocking substrate binding and suppressing enzymatic activity. Furthermore, the network rigidity and pore structure controlled by the tridentate ligands appear to reinforce these interactions, contributing to enhanced inhibitory efficiency. Overall, this study provides a novel strategy for the rational design of 2D copper coordination polymers as effective urease inhibitors. The findings offer valuable theoretical guidance for the development of high-performance, controllable enzyme inhibitors with potential applications in agriculture and environmental management. By combining structural tunability, high stability, and functional performance, tridentate-ligand-directed 2D coordination polymers represent a versatile and promising platform for sustainable nitrogen management and other bifunctional applications.
two-dimensional copper coordination polymers
Post-Pandemic Digital Adaptation Across Varied Industry Sectors
The COVID-19 pandemic catalyzed unprecedented digital transformation across diverse industry sectors, fundamentally altering operational paradigms and accelerating technology adoption timelines by years. This paper examines post-pandemic digital adaptation patterns across multiple domains including architectural design, business model innovation, software development, manufacturing, construction management, and professional training. The investigation reveals that digital adaptation extended beyond emergency responses to encompass strategic repositioning, operational restructuring, and fundamental reconsideration of value delivery mechanisms. Through systematic analysis of transformation patterns across sectors, this study identifies convergent themes including remote work integration, digital platform adoption, automation acceleration, and data-driven decision-making enhancement. The findings demonstrate that successful digital adaptation required balancing technological implementation with organizational culture transformation, workforce capability development, and stakeholder engagement. Organizations demonstrating resilience during pandemic disruptions exhibited characteristics including leadership commitment to digital initiatives, investment in technological infrastructure, and cultivation of adaptive organizational cultures. These insights contribute to understanding how crisis-driven digital acceleration creates lasting changes in professional practice, competitive dynamics, and operational methodologies across varied industry contexts, providing guidance for organizations navigating ongoing digital transformation challenges.
Transition Metal Catalysts for Sustainable Chemical Transformations
The development of sustainable chemical transformations has become increasingly critical in addressing global environmental and resource challenges. Transition metal catalysts, particularly those based on earth-abundant metals, have emerged as pivotal enablers of green chemistry and sustainable industrial processes. This comprehensive review examines the role of transition metal catalysts in facilitating sustainable chemical transformations, with emphasis on biomass conversion, carbon dioxide reduction, and coordination polymer applications. The review explores various catalyst systems including transition metal carbides, dichalcogenides, and copper-based coordination polymers, highlighting their mechanisms, advantages, and applications in renewable fuel production and value-added chemical synthesis. Special attention is given to the design principles that enhance catalytic efficiency and selectivity while maintaining environmental compatibility. The integration of earth-abundant metals such as iron, copper, cobalt, and nickel offers economically viable alternatives to precious metal catalysts, thereby advancing the commercial feasibility of sustainable chemical processes. This review provides insights into current challenges and future directions for developing next-generation transition metal catalysts that can meet the demands of sustainable chemistry.
Contemporary Challenges and Solutions Across Multiple Disciplines
Contemporary professional environments face unprecedented challenges requiring integrated approaches that transcend traditional disciplinary boundaries. This paper examines critical issues across diverse sectors including digital technology implementation, performance optimization strategies, risk management frameworks, and adaptive professional practices. The investigation reveals that modern challenges share common characteristics including rapid technological evolution, data-driven decision requirements, and the necessity for continuous adaptation to changing operational contexts. Through systematic analysis of current practices in professional development, digital transformation initiatives, strategic planning methodologies, and performance evaluation systems, this study identifies convergent solutions applicable across multiple domains. The findings demonstrate that successful navigation of contemporary challenges requires holistic frameworks that integrate technological innovation with human-centered design principles. The paper presents comparative analyses of implementation strategies, efficiency metrics, and optimization approaches that provide actionable insights for practitioners across various professional fields. These insights contribute to understanding how interdisciplinary collaboration and knowledge integration can address complex problems facing modern organizations and professional communities.
Quality Enhancement through Systematic Analysis across Industries
Quality enhancement has emerged as a fundamental imperative for organizations operating across diverse industrial contexts where competitive pressures and stakeholder expectations continuously escalate. This paper examines systematic approaches to quality improvement through cross-sectoral analysis encompassing professional practice development, technological innovation, digital transformation, and operational excellence frameworks. By investigating quality management principles within contexts ranging from traditional pedagogical methods to contemporary digital manufacturing systems, this research identifies universal mechanisms that transcend industry boundaries. The study explores theoretical foundations of quality management including critical success factors, implementation practices, and performance relationships while examining how organizations leverage systematic analysis to achieve sustainable quality improvements. Through comprehensive examination of measurement frameworks, continuous improvement methodologies, and adaptive quality strategies, this paper demonstrates that successful quality enhancement requires integrated approaches combining rigorous assessment, evidence-based decision making, and sustained organizational commitment. The findings reveal that organizations achieving superior quality outcomes implement systematic analysis processes that identify improvement opportunities, validate intervention effectiveness, and foster cultures of continuous excellence. This interdisciplinary investigation provides practitioners with actionable frameworks for quality enhancement applicable across varied industrial environments while advancing theoretical understanding of quality management effectiveness.
Copper-Based Coordination Polymers for Catalysis and Inhibition
Copper-based coordination polymers have emerged as versatile materials exhibiting remarkable performance in catalytic transformations and biological inhibition applications. These crystalline materials feature copper ions coordinated with organic ligands to form extended network structures that combine the advantages of homogeneous and heterogeneous catalysis. The structural diversity, tunable porosity, and accessible metal centers make copper coordination polymers attractive candidates for various catalytic reactions including carbon dioxide conversion, organic transformations, and photocatalytic processes. Additionally, these materials demonstrate significant potential as enzyme inhibitors, particularly for urease inhibition relevant to agricultural and medical applications. This paper comprehensively examines the synthesis, structural characteristics, catalytic properties, and inhibition activities of copper-based coordination polymers. The discussion encompasses design principles, structure-activity relationships, and mechanistic insights into their catalytic and biological functions. Special emphasis is placed on how structural modifications through ligand selection and secondary building units influence catalytic efficiency and selectivity. The findings highlight the multifunctional nature of copper coordination polymers and their promising prospects for advancing sustainable chemistry and therapeutic applications.
Dual-Metal Catalysts in Electrochemical Carbon Dioxide Conversion
The escalating atmospheric carbon dioxide concentration has intensified the urgency for developing efficient carbon dioxide conversion technologies. Electrochemical carbon dioxide reduction represents a promising approach to convert carbon dioxide into valuable chemicals and fuels while utilizing renewable electricity. Dual-metal catalysts have emerged as a breakthrough strategy, offering synergistic effects that surpass the limitations of single-metal catalysts. These bimetallic systems demonstrate enhanced catalytic activity, improved product selectivity, and superior stability through electronic modification, geometric arrangement, and cooperative catalytic mechanisms. This paper comprehensively examines recent developments in dual-metal catalysts for electrochemical carbon dioxide conversion, focusing on copper-based bimetallic combinations including copper-palladium, copper-bismuth, and copper-zinc systems. The discussion encompasses mechanistic insights, synthetic strategies, structure-activity relationships, and performance optimization approaches. By analyzing various bimetallic configurations and their catalytic behaviors, this work highlights critical factors governing product distribution and conversion efficiency. The findings underscore the potential of dual-metal catalysts in advancing sustainable carbon dioxide utilization technologies toward industrial implementation.
User Engagement and Retention Strategies in Practice
User engagement and retention have become critical determinants of organizational success across diverse professional domains. This paper examines contemporary strategies for fostering sustained user interaction and commitment through an interdisciplinary lens that encompasses digital platforms, professional practice evolution, and technological innovation. By analyzing engagement frameworks within contexts ranging from online brand communities to efficiency-driven operational systems, this research identifies core principles that transcend sector boundaries. The study explores multidimensional engagement constructs, including cognitive, emotional, and behavioral components, while examining how modern organizations leverage continuous innovation and adaptive methodologies to maintain user investment. Through systematic analysis of engagement metrics, measurement frameworks, and retention mechanisms, this paper demonstrates that successful engagement strategies share common characteristics of value creation, personalized interaction, and sustained quality delivery. The findings reveal that organizations achieving high retention rates implement integrated approaches combining technological advancement, responsive adaptation to changing contexts, and meaningful stakeholder relationships. This comprehensive examination provides actionable insights for practitioners seeking to optimize user engagement across various professional environments.