Journal of Science, Innovation & Social Impact
Journal of Science, Innovation & Social Impact (JSISI) is an international, refereed, double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research at the interface of scientific discovery, technological innovation, and societal impact. The journal welcomes interdisciplinary studies that advance both theoretical knowledge and practical applications, with a focus on topics such as emerging scientific breakthroughs, innovation-driven solutions, societal challenges, policy implications, and the broader social consequences of scientific and technological advancements.
Research on Deep Learning Models for Forecasting Cross-Border Trade Demand Driven by Multi-Source Time-Series Data
Against the backdrop of rapid changes in the global foreign trade environment, cross-border commodity demand exhibits complexity and uncertainty influenced by multiple factors including seasonality, price indices, promotional rhythms, and exchange rate fluctuations. To enhance the accuracy of inventory and transportation capacity planning for foreign trade enterprises, this study constructs a deep learning forecasting model integrating multi-source data. This model is based on approximately 84,000 time-series data points spanning 3.5 years and covering 11 countries from an export enterprise. The model integrates features including historical orders, price indices, promotional schedules, international holidays, search trends, exchange rates, and shipping cycles, employing TCN combined with attention mechanisms for sequence modeling. Experimental results demonstrate that compared to benchmark models such as ARIMA, PROPHET, and LSTM, MAPE, SMAPE, and RMSE improvements range from 14% to 21%, with more robust performance in forecasting demand peaks. The study demonstrates that multi-source time series fusion effectively captures the dynamic characteristics of cross-border demand, providing reliable predictive support for digitalized foreign trade operations. Wine export operations serve as the validation scenario.
Copyright (c) 2025 Journal of Science, Innovation & Social Impact
An Integrative Review of Dynamic Pricing Strategies under Inventory and Market-Oriented Constraints
Dynamic pricing has been extensively studied as an optimization problem under inventory constraints; however, its effectiveness in real-world markets is increasingly challenged by demand uncertainty, product return mechanisms, and complex consumer behavior. Existing research often treats pricing decisions in isolation, overlooking their embeddedness within broader market-oriented strategies and adaptive market systems. This integrative review synthesizes insights from operations management, marketing strategy, data-driven demand analysis, and cross-disciplinary complex system research to advance a holistic understanding of dynamic pricing under inventory and market-oriented constraints. By reviewing optimization-based pricing models, market-oriented development perspectives, and data-driven approaches to demand intelligence, this study highlights the limitations of static demand assumptions and purely analytical pricing solutions. Drawing conceptual inspiration from mechanism-oriented system regulation in other disciplines, the paper reframes dynamic pricing as an indirect intervention that operates through intermediate structures such as demand perception, consumer expectations, and market feedback. An integrative conceptual framework is proposed to bridge optimization rigor with market realism. The review contributes to pricing research by emphasizing system-level stability, strategic integration, and adaptability, and it offers managerial implications for designing resilient pricing strategies in complex and uncertain market environments.
Intelligent Detection and Protection of Personally Identifiable Information in Clinical Text: An Advanced NLP Approach with Optimized Attention Mechanisms
The protection of Personally Identifiable Information (PII) in clinical text is a critical challenge in healthcare data management, particularly as medical institutions increasingly adopt digital health records and data-sharing initiatives. This paper presents a novel natural language processing framework that leverages optimized attention mechanisms and context-aware tokenization strategies to achieve high accuracy in detecting and protecting sensitive information within clinical documents. Our approach integrates transformer-based architectures with domain-specific enhancements, achieving a 95.3% F1-score on standard benchmarks while satisfying HIPAA Safe Harbor requirements through a combination of deep learning and rule-based processing. The proposed method introduces a hierarchical detection system that processes clinical text at multiple granularity levels, employing specialized attention heads for different PII categories. Experimental results on three large-scale clinical datasets demonstrate that our framework outperforms existing state-of-the-art methods by 8.7% in detection accuracy and reduces false positives by 59% compared to ClinicalBERT (from 12.8% to 5.2%). Furthermore, our intelligent redaction strategy preserves the semantic integrity of clinical content, enabling secure data sharing while maintaining the utility of medical information.
personally identifiable information detection
GAN-Based Intelligent Keyframe Interpolation Method for Character Animation: An Automated In-betweening Approach
Character animation production faces significant efficiency challenges due to labor-intensive keyframe interpolation processes that require extensive manual intervention. This paper presents a novel intelligent keyframe in-betweening technology based on generative adversarial networks (GANs) to automate intermediate frame generation while preserving character consistency and motion quality. The proposed framework incorporates a multi-scale temporal feature extraction mechanism that captures complex motion patterns through residual connections and attention-based aggregation. An improved GAN architecture employs dual-path processing streams combining spatial and temporal information, enhanced with spectral normalization and adaptive instance normalization for stable training dynamics. The character consistency preservation algorithm integrates deep feature matching with geometric constraint enforcement to maintain visual coherence across generated sequences. Experimental validation on a comprehensive dataset of 55,000 animation sequences demonstrates superior performance with SSIM scores reaching 0.923 and temporal consistency measures achieving 0.856, representing substantial improvements over existing methodologies. User studies involving 165 participants confirm practical applicability, with professional animators rating the generated sequences at 4.19/5.00 for overall quality. The technology enables significant productivity gains in animation production workflows, achieving 30-45% cost reductions while maintaining professional quality standards, making high-quality animation more accessible across diverse commercial applications.
Extreme Stock Fluctuation Early Warning Model Based on Causal Inference and Machine Learning
Large price swings increase portfolio risk and make trading decisions difficult. This study builds a short-term warning model that uses Granger-based feature selection and a LightGBM classifier tuned with Bayesian search. The model is tested on three years of CSI 300 data. Daily log-returns are used to mark extreme events, and all evaluation follows time order to avoid future information. The model signaled about 70% of large price swings two trading days in advance and showed better results than a model without causal features. These findings indicate that adding predictors with leading effects helps identify warning signs before sharp movements. The framework can support routine risk control and position planning.
Research on the Time-Varying Mechanism of Market Sentiment of Artificial Intelligence Industry Stocks Based on Multi-Factor PCA-HMM Framework
The artificial intelligence (AI) sector has become one of the most active areas in financial markets, where investor mood and trading activity strongly affect price changes. This study uses a multi-factor Principal Component Analysis-Hidden Markov Model (PCA-HMM) to examine how sentiment, trading volume, and momentum influence stock returns in the U.S. AI market from 2017 to 2025. Weekly data from 40 listed companies across cloud computing, semiconductor, and autonomous driving industries are analyzed. Principal Component Analysis is used to extract common factors from financial and macroeconomic variables, and the Hidden Markov Model is used to identify market states and their transitions. The results show that sentiment explains about 48% of the variation in returns and plays a stronger role than trading volume or momentum. Momentum has little effect and turns negative during volatile periods. The PCA-HMM model divides the market into stable and turbulent phases lasting between 6 and 12 weeks. The findings show that market sentiment is a main source of fluctuation in AI-related stocks and that the state-based model can be used to track market cycles and assess risk. Future studies should include cross-country data and higher-frequency observations to test the model's wider use.
Long-Term Efficacy Evaluation of Controlled Release Systems Based on Fullerene Nanoparticles in Chronic Lung Diseases
This study developed a polymer-coated fullerene nanoparticle system (C₆₀@PCL) for long-term gene delivery in chronic lung inflammation. The carrier was prepared by solvent evaporation, forming uniform particles with a mean diameter of 145 nm and a narrow size distribution (PDI = 0.17). The system successfully encapsulated IL-10 plasmid DNA and showed a controlled release profile lasting over 21 days. In mouse experiments, nasal delivery of C₆₀@PCL-IL-10 maintained IL-10 expression for at least 21 days and reduced lung inflammation scores by 68% compared with the control group. Blood biochemical tests showed no abnormal values, confirming good safety. These findings indicate that the fullerene-polymer hybrid structure can provide sustained gene expression and reduce inflammation in chronic lung disease. The approach offers a simple, stable, and biocompatible platform for future pulmonary gene therapy applications.
Federated Learning Framework for Privacy-Preserving Depth-Based AR Surgical Registration
The increasing use of AR in surgical navigation raises concerns over patient data privacy when training AI-enhanced registration models. We introduce a federated learning framework that enables distributed training of depth-based markerless registration networks across multiple hospitals without sharing raw patient data. Each local node trains a CNN-Transformer hybrid for point cloud alignment, and only encrypted weight updates are aggregated on a central server. Experiments conducted across three institutions with 2,000 intraoperative scans demonstrated a 32% reduction in generalization error compared with single-site models. Registration accuracy improved from 2. 1 mm to 1.3 mm, while training convergence time decreased by 27% due to adaptive aggregation. This work confirms the feasibility of collaborative yet privacy-preserving AR surgical registration pipelines.
Improving Latency and Stability in Edge-Based Voice Assistants Through Memory and Scheduling Optimization
Intelligent voice assistants are now widely used on smartphones and embedded boards, where short response time and stable operation are essential. Heavy computation and limited hardware, however, constrain efficiency. This study tested a dual-path method that applied fine-grained memory control together with asynchronous scheduling. A total of 110 trials were run in both laboratory and office conditions. Results showed that median latency fell by 37.3% and 95th percentile latency by 39.8%. Jitter was reduced by 24.6%, and timeout events dropped by 74% compared with baseline runs. Accuracy remained stable, with word error rate changes not exceeding 0.2 and F1 score changes not exceeding 0.3. The results indicate that combining algorithm-level and system-level methods gives stronger benefits than using them alone. The study also reports jitter and timeout metrics, which are often not considered in related work. These findings suggest that dual-path optimization can support efficient and reliable deployment of voice assistants on edge devices. The main limits are the small number of device types, short test periods, and the use of only English speech. Future work should extend to multilingual datasets, longer trials, and secure execution tests.
Enhanced Feature Engineering and Algorithm Optimization for Real-Time Detection of Synthetic Identity Fraud and Money Laundering in Financial Transactions
The proliferation of digital financial transactions has created unprecedented opportunities for sophisticated fraud schemes, particularly synthetic identity fraud and money laundering activities that evade traditional rule-based detection mechanisms. This research introduces an enhanced feature engineering framework coupled with optimized machine learning algorithms to address the dual challenges of improving detection accuracy while minimizing false positive rates. The proposed methodology integrates temporal, behavioral, and network-based features specifically designed to capture the subtle patterns characteristic of synthetic identity fraud and money laundering transactions. Seven (including stacking ensemble) machine learning algorithms were systematically evaluated using real-world financial transaction datasets, with comprehensive performance analysis conducted through stratified cross-validation. Experimental results demonstrate that XGBoost achieved an F1-score of 0.938 and a Precision of 0.947, delivering the best balance between accuracy and real-time performance.
Machine Learning-Based Credit Risk Early Warning System for Small and Medium-Sized Financial Institutions: An Ensemble Learning Approach with Interpretable Risk Indicators
Small and medium-sized financial institutions encounter distinct challenges in implementing effective credit risk management systems due to limited resources and technological infrastructure. This study develops an ensemble learning framework tailored for early warning detection in credit portfolios, addressing the urgent need for cost-efficient risk assessment solutions. The proposed methodology combines multiple machine learning algorithms through a hierarchical voting mechanism, effectively handling imbalanced financial datasets with specialized resampling techniques. Experimental validation using real-world credit data from regional banks demonstrates strong performance, achieving 87.3% accuracy in default prediction over a 12-month forecast horizon. The framework also incorporates interpretable feature importance analysis, enabling risk managers to identify key indicators of portfolio deterioration, including debt-to-equity ratios, cash flow volatility patterns, and industry-specific economic signals. Implementation analysis indicates potential cost reductions of 34% compared to traditional risk assessment methods while maintaining compliance with regulatory standards. Furthermore, the system's modular architecture supports incremental deployment, allowing institutions to adopt machine learning capabilities without extensive infrastructure overhaul.
Research on Mobile Advertising Click-Through Rate Prediction Algorithm Based on Differential Privacy
Mobile advertising represents a key revenue stream in digital marketing, driven primarily by personalized content. Its effectiveness relies heavily on click-through rate (CTR) prediction models based on user behavior. With growing privacy concerns and increasingly stringent data protection regulations, ensuring user privacy in CTR prediction has become an essential requirement. In response, differential-privacy-based approaches have garnered significant attention. In this study, we propose differentially private mechanisms specifically designed for advanced machine learning models. The approach employs adaptive noise-injection strategies to balance prediction accuracy and privacy effectively. It optimizes the allocation of privacy budgets in CTR estimation while maintaining user anonymity. Experimental results demonstrate that the proposed algorithm achieves prediction accuracy comparable to conventional methods while providing strong privacy guarantees. This framework offers practical solutions that enable mobile advertising platforms to comply with privacy regulations without sacrificing advertising performance.
Research on Intelligent Firmware Vulnerability Detection and Priority Assessment Method Based on Hybrid Analysis
Binary firmware underpins critical infrastructure but often contains vulnerabilities that conventional detection mechanisms fail to identify. In this work, we develop a hybrid analytical framework that integrates static pattern extraction with runtime behavioral monitoring, achieving detection rates of 93.7% across a corpus of 40 million procedures collected from production firmware. Static pattern recognition leverages control flow graph embeddings, while probabilistic scoring quantifies contextual risk. Cross-architecture evaluation across ARM, MIPS, x86, and PowerPC demonstrates robustness against variations in compilation. Our methodology also uncovers zero-day vulnerabilities, and the computational overhead remains manageable for deployment on resource-constrained platforms, reducing false positive rates by 56.7% compared to existing approaches.
Multi-Source Data Fusion for Short-Term Demand Forecasting of Seasonal Retail Products: An Empirical Study Using Weather and Social Media Signals
Recent advances in machine learning have enabled retailers to leverage diverse data streams for demand prediction, yet most systems continue to rely primarily on historical sales. In this study, we address the challenge of seasonal demand forecasting by integrating meteorological observations, social media activity, and economic indicators with point-of-sale transactions. We propose a hierarchical ensemble that combines gradient boosting machines with bidirectional LSTMs, where component weights adapt according to product category and forecast horizon. Through mutual information screening, we reduce feature dimensionality from 123 to category-specific subsets averaging 38 features, lowering computational costs by 60% while maintaining predictive accuracy. Our approach requires only standard training of the base models along with a lightweight meta-learner, without necessitating end-to-end joint training. Evaluation on 10.3 million transactions from three U.S. retail chains demonstrates that our method achieves a mean absolute percentage error of 19.6%, compared to 37.5% for seasonal naive baselines. Multi-source fusion thus provides a practical pathway to more accurate retail forecasting, without the complexity associated with end-to-end deep learning systems. Crucially, this approach relies on lightweight feature learning and selection techniques combined with meta-learner weighting, rather than on end-to-end joint representation training.
FTAFO: A Federated Transparent Adaptive Financial Optimizer for Reducing Third-Party Dependencies in Workflow Management
Modern financial institutions face increasing challenges due to opaque third-party AI systems governing critical workflow decisions. After firsthand experience with the limitations of existing solutions during a complex 18-month implementation project, we developed FTAFO (Federated Transparent Adaptive Financial Optimizer), a novel framework that fundamentally rethinks financial workflow optimization by introducing three core innovations: a federated consensus mechanism enabling distributed decision-making without reliance on centralized black-box systems, an adaptive transparency engine providing real-time explanations while maintaining computational efficiency, and a multi-objective optimization algorithm that simultaneously balances performance, interpretability, and regulatory compliance. Extensive testing across five financial institutions demonstrated promising results, with workflow processing times improving by approximately 18-28%, third-party licensing costs reduced by roughly 60-70%, and regulatory audit preparation time decreasing from several weeks to around 2-3 days. Implementation revealed challenges including initial setup complexity and the need for substantial staff retraining. Nevertheless, the framework's open architecture offers a practical alternative to vendor lock-in while meeting stringent regulatory requirements. This work contributes to the growing field of explainable AI in finance, though limitations remain in ultra-high-frequency scenarios and environments with restricted technical infrastructure.
Enhanced Multi-Modal Feature Fusion Algorithm for Early-Stage Cancer Detection: A Comparative Study of Optimization Strategies
This study presents an adaptive multi-modal fusion algorithm for early-stage cancer detection that dynamically integrates imaging, genomic, and clinical data using learned attention mechanisms. Unlike traditional approaches that treat fusion weights as fixed parameters, our method models them as probabilistic distributions, allowing adaptation to variations in data quality and modality availability in clinical environments. The key innovation is a meta-learning framework that predicts optimal fusion strategies based on the characteristics of incoming data. Experimental validation across 12,847 patients from eight medical centers demonstrates an AUROC of 0.947, with 89.3% sensitivity at 95% specificity. The algorithm exhibits particular robustness in managing minority cancer classes through hierarchical attention mechanisms that capture both local and global patterns. Comparative analysis against current state-of-the-art methods shows consistent performance improvements while maintaining computational efficiency suitable for clinical deployment.
Deep Reinforcement Learning-Driven Efficacy-Toxicity Balance Optimization Strategy for Personalized Drug Combination in Cancer Patients
Precision medicine demands the careful optimization of dose regimens in cancer therapy, particularly when adjusting doses to balance therapeutic effectiveness against adverse toxicities. In this study, we introduce a deep reinforcement learning (DRL) framework that leverages multimodal patient data to optimize personalized drug combination strategies, aiming to maximize efficacy while minimizing toxicity. The DRL agent employs multi-objective reward functions to identify optimal treatment strategies by integrating genomic, clinical, and pharmacokinetic data through an advanced feature engineering pipeline. This approach was evaluated in a cohort of 2,847 cancer patients encompassing a diverse range of tumor types. Experimental results demonstrate that the algorithm improved predicted treatment response by 23.4% compared to conventional methods, while reducing serious adverse events by 18.7%. These findings highlight a significant advancement in computational approaches for personalized therapy optimization, providing clinically interpretable outputs to guide patient-specific treatment decisions.
Graph Attention-Based Feature Selection for Multi-Omics Drug Target Prediction in Cardiovascular Diseases
Drug target identification in cardiovascular diseases faces computational challenges due to the high dimensionality of multi-omics datasets. Here, we present a graph attention framework that integrates genomic variants, proteomic expression profiles, and metabolomic signatures using hierarchical attention mechanisms applied to molecular interaction networks. In these networks, nodes represent molecular entities and edges capture experimentally validated functional associations, thereby encoding key biological relationships. The attention mechanism assigns adaptive importance weights αᵢⱼ to neighboring nodes, facilitating selective feature propagation while maintaining the integrity of biological signals. Validation across three cardiovascular cohorts-encompassing 12,226 patients with whole-genome sequencing, proteomics, and metabolomics data-achieves 87.3% target identification accuracy alongside a 72.0% reduction in feature dimensionality. Analysis of attention weights highlights differential pathway contributions, with MAPK signaling (0.342), calcium homeostasis (0.298), and PI3K-AKT cascades (0.276) identified as principal therapeutic nodes. The framework successfully recovers 23 FDA-approved cardiovascular drugs and predicts 17 investigational compounds currently in clinical trials. Computational complexity decreases from O(n²d) to O(nkd), where k denotes the selected features (k << n), resulting in a 4.2-fold speedup in execution. Gradient-based attribution methods further provide mechanistic interpretability, linking molecular features to pathway-level biological processes. This approach bridges the computational and biological gap in precision cardiovascular medicine by offering mathematically grounded feature selection with preserved mechanistic transparency.
Adaptive Privacy-Preserving Techniques for Multimedia Content Processing in Cloud Environments: A Differential Privacy Approach
We propose a novel adaptive differential privacy framework for multimedia content processing in cloud environments, designed to achieve optimal privacy-utility trade-offs through content-aware noise calibration and dynamic budget allocation. The framework introduces three core technical innovations: (1) a sensitivity-guided privacy budget allocation mechanism that reduces utility loss by 38.7% compared to uniform allocation, (2) a frequency-domain noise injection strategy that preserves perceptual quality while ensuring epsilon-differential privacy, and (3) an optimization algorithm that solves the budget allocation problem in O (n log n) time. Extensive experiments on the COCO, AudioSet, and UCF101 datasets demonstrate that the proposed framework maintains 91.3% task accuracy at epsilon = 1.0 while reducing membership inference attack success rates to 52.8%. Moreover, the system processes up to 312 images per second on commodity hardware, underscoring its practicality for deployment in large-scale production cloud environments.
Efficiency Comparison of Automated Tools versus Traditional Methods in Anti-Money Laundering Compliance Auditing for Banking Institutions
Banking institutions process 8.8 million daily transactions requiring anti-money laundering (AML) compliance verification, creating computational and operational challenges that exceed manual processing capabilities. This study quantifies performance differentials between automated compliance systems and traditional manual methods through empirical analysis of 15 banking institutions over 36 months. We develop a multi-dimensional efficiency framework measuring processing speed, detection accuracy, cost structures, and false positive rates across institutional tiers. Automated systems demonstrate 73% increased processing throughput (12,500 transactions/hour versus 7,200), reduce false positive rates from 27.6% to 15.2%, and achieve 89.3% detection accuracy compared to 67.1% for manual methods. Cost-benefit analysis reveals 52% reduction in per-transaction processing costs after five-year amortization periods, with break-even points occurring at 22 months post-implementation. Machine learning algorithms employing pattern recognition reduce Type I errors by 45% while increasing genuine threat detection by 62%. The framework incorporates real-time transaction monitoring, customer due diligence protocols, and suspicious activity reporting mechanisms. Implementation analysis across three institutional tiers (assets > $200B, $50-200B, < $50B) demonstrates scalability constraints and resource allocation patterns. Hybrid approaches combining automated screening with selective manual review optimize performance across eight evaluation dimensions (speed, precision, recall, F1, false-positive rate, cost / tx, scalability@200% load, p99 latency). These findings establish quantitative benchmarks for compliance technology adoption while identifying implementation barriers and regulatory acceptance factors.
Data-Driven Analysis of Transportation Route Efficiency and Carbon Emission Correlation in Retail Distribution Networks
Transportation networks coordinate intricate patterns of efficiency and sustainability within retail distribution systems. This study quantitatively examines the relationships between route optimization parameters and carbon emission trajectories through an extensive empirical analysis encompassing 2,847 distinct delivery routes. Statistical evaluations-using Pearson correlation (r = -0.742, p < 0.001) and Spearman rank analysis-reveal strong interdependencies between operational efficiency and environmental impact indicators. Multivariate regression models accounting for 73.8% of emission variance demonstrate that strategic route consolidation enhances operational efficiency by 23.7% and reduces emissions by 18.4% (quasi-experimental estimate: 19.3%; see Section 4.2). Among all variables, load factor optimization exhibits the highest correlation with emission reduction (r = -0.836). The derived performance metrics integrate both operational and environmental dimensions, highlighting that urban networks possess an optimization potential of 21.4%, significantly surpassing rural networks at 12.8%. Notably, operational optimization alone achieves 78.4% of the theoretical emission reduction potential without the necessity of additional technological interventions.
AI-Enhanced Cybersecurity for Financial Networks: A Federated Learning Implementation
During a 14-month deployment across four financial institutions, including a tier-1 bank in the Northeast US, we developed a hybrid threat detection system that integrates Transformer models with Graph Neural Networks. The system was implemented using Python 3.8.10 and PyTorch 1.12.1 on NVIDIA RTX 3090 GPUs (24GB VRAM). Our team, despite frequent methodological disagreements, achieved a detection accuracy of 86.7%, which fell short of the anticipated 95% or higher. The federated learning component, initially planned for six months, was extended due to regulatory compliance requirements. This component enables collaborative threat intelligence while preserving data privacy. Under normal operating conditions, the system processes approximately 1.1 million events per second, with throughput decreasing to around 400,000 events per second during periods of market volatility, such as Q4 2023. The architecture reduces false positives to 2.1%. Implementation costs exceeded the original $127,000 NSF grant by roughly 40%, necessitating additional university cost-sharing. Three preliminary approaches were abandoned before the current architecture was finalized. Real-world deployment highlighted hardware bottlenecks that were not evident in simulations, requiring compromises in system design. The system is now operational in production, although stability issues persist during high-frequency trading periods.
Medical Terminology Definition-Enhanced Retrieval-Augmented Generation for Hallucination Mitigation in Medical Question Answering
The rapid emergence of large language models (LLMs) in healthcare applications presents critical challenges related to factual accuracy and hallucination control. This paper proposes an alternative approach that integrates enhanced medical terminology definitions with retrieval-augmented generation (RAG) techniques to mitigate hallucinations in medical question-answering systems. The primary technical contributions include: (1) a Medical-Adaptive Confidence Calibration (MACC) algorithm that departs from traditional RAG methods by dynamically adjusting thresholds based on clinical risk; (2) a multi-source medical knowledge fusion framework that incorporates hierarchical relationships from SNOMED-CT, UMLS, and ICD-10; and (3) a comprehensive robustness validation procedure featuring real-time monitoring. The proposed approach achieves substantial accuracy improvements, reducing hallucinations by 23.7% (p < 0.001, 95% CI: 19.4%, 28.0%) compared with baseline systems. Experimental evaluations on medical consultation datasets demonstrate superior precision and reliability in clinical information delivery, yielding an 18.4% increase in precision and a 15.2% enhancement in recall. The framework effectively addresses major limitations of existing automated medical consultation systems while maintaining computational efficiency and scalability for practical deployment.
Cultural-Intelligent Dynamic Medical Animation Generation for Cross-Lingual Telemedicine Communication Enhancement
Cross-lingual communication in telemedicine presents significant challenges that impede effective healthcare delivery across diverse cultural contexts. Traditional medical visualization approaches fail to address cultural nuances and language barriers that affect patient comprehension of complex medical information. This research introduces a novel cultural-intelligent dynamic medical animation generation framework designed to enhance cross-lingual telemedicine communication through adaptive visualization technologies. The proposed system integrates multi-modal medical data processing with cultural context recognition algorithms to generate culturally sensitive medical animations in real-time. Cultural adaptation mechanisms analyze patient demographics, linguistic preferences, and medical terminology complexity to dynamically adjust visual representation strategies. The framework employs semantic medical concept translation engines coupled with patient comprehension assessment modules to optimize communication effectiveness. Experimental validation demonstrates significant improvements in patient understanding rates across different cultural backgrounds, with cross-cultural user studies showing 73.2% enhancement in medical concept comprehension compared to conventional static visualization methods. Performance analysis reveals computational efficiency suitable for real-time telemedicine applications, with average animation generation latency of 2.8 seconds and scalability supporting concurrent multi-user sessions. The system addresses critical gaps in culturally aware healthcare technology, particularly benefiting underserved populations with limited medical literacy and non-native language speakers seeking remote medical consultations.
AI-driven Sustainable Urban Intelligence: Integrating Smart Technologies for Efficient, Resilient, and Inclusive City Management
Artificial intelligence (AI) is increasingly shaping the evolution of urban ecosystems, yet current implementations often optimize isolated objectives—such as efficiency or automation—without integrating environmental and social dimensions. This study proposes a three-layer framework for sustainable urban intelligence, encompassing cognitive, behavioral, and environmental layers to harmonize personalization, mobility optimization, and carbon management. The cognitive layer leverages large language models and knowledge graphs for sustainability-oriented recommendation; the behavioral layer employs reinforcement learning and graph neural networks for adaptive mobility optimization; and the environmental layer integrates AI-enabled carbon forecasting and energy management. Through cross-layer data flow and dynamic feedback loops, the framework establishes an adaptive AI ecosystem that connects human decision-making, technological performance, and ecological feedback. The proposed model advances the conceptual transition from “smart cities” to sustainably intelligent cities, providing a blueprint for future urban AI systems that optimize not only for humans, but with humans—aligning personal actions with collective sustainability goals.
Machine Learning and Cloud-Enhanced Real-Time Distributed Systems for Intelligent Urban Services
Real-time distributed systems have become fundamental to modern digital infrastructure, yet current centralized or semi-distributed frameworks still face major limitations in predictive accuracy, response latency, and resource utilization. In critical domains such as public safety and telecommunication network management, these shortcomings result in delayed decision-making, inefficient resource allocation, and vulnerability to network disruptions. To address these challenges, this paper proposes an intelligent real-time distributed architecture integrating machine learning (ML) and cloud computing (CC). By combining ML-driven predictive analytics with cloud-based elastic resource orchestration, the proposed framework enhances adaptive scheduling, dynamic fault tolerance, and real-time decision-making across heterogeneous nodes. This hybrid approach enables systems to anticipate network anomalies, optimize load distribution, and allocate resources in a risk-informed, latency-aware manner. Case studies in public safety emergency communication systems and telecommunication network optimization demonstrate how multi-source data integration, AI-assisted analytics, and cloud-edge-end collaboration can improve operational resilience, accelerate response times, and strengthen system reliability. Results indicate that the integration of ML and CC not only overcomes traditional bottlenecks but also establishes a scalable foundation for intelligent, self-adaptive distributed infrastructures. This study contributes to the advancement of resilient, data-driven urban systems and aligns with national strategic goals for digital infrastructure security, real-time disaster response, and intelligent network governance.
Fabrication of Copper(II) Coordination Complexes Modulated by Flexible Dicarboxylate Ligands with Notable Urease Inhibitory Performance
Copper(II) complexes have attracted increasing attention as potential urease inhibitors owing to their unique coordination versatility, structural diversity, and intrinsic biocompatibility. Urease, a nickel-containing metalloenzyme responsible for the hydrolysis of urea, plays a key role in various pathological processes, making its inhibition a promising therapeutic strategy. In this work, a series of copper(II) complexes were rationally constructed using flexible dicarboxylate ligands as structural modulators to fine-tune molecular geometry and supramolecular organization. The synthesized complexes were comprehensively characterized by infrared and UV–visible spectroscopy, thermogravimetric analysis, and single-crystal X-ray diffraction, which revealed the diverse coordination environments of the copper centers, the variable chelation behaviors of the ligands, and the intricate network of hydrogen bonding and π–π stacking interactions within the crystal lattices. Biological assays demonstrated that the obtained complexes exhibit pronounced and tunable urease inhibitory activities, with several compounds achieving inhibition efficiencies comparable to or exceeding those of known reference inhibitors. Correlation of structural parameters with bioactivity revealed that the degree of ligand flexibility, denticity, and spatial arrangement around the copper center collectively govern enzyme binding affinity and inhibition strength. These findings underscore the importance of molecular design in modulating metal–enzyme interactions and provide new insights into the structure–activity relationships governing copper-based inhibitors. Overall, this study establishes a strategic framework for the development of efficient, adaptable copper(II)-based urease inhibitors, offering both theoretical foundations and experimental references for future exploration in metalloenzyme inhibition and related biomedical applications.
Efficiency and Productivity Enhancement in Modern Professional Practice: Strategies, Challenges, and Best Practices
Efficiency and productivity enhancement represent critical imperatives for organizations across diverse professional sectors seeking competitive advantage and sustainable performance in increasingly complex operational environments. This paper examines contemporary approaches to productivity improvement through comprehensive analysis of determinants, management practices, technological integration, and human resource strategies that collectively influence organizational effectiveness. The research explores how modern practices in software development, construction management, architectural design, and business operations leverage digital transformation, systematic management methodologies, and performance-oriented human resource systems to achieve superior productivity outcomes. Through examination of empirical evidence and theoretical frameworks, this study identifies key factors driving productivity enhancement including management quality, technological adoption, workforce development, and organizational systems that align individual efforts with strategic objectives. Findings reveal that sustainable productivity improvement requires integrated approaches combining technological capabilities with human resource excellence, systematic management practices, and organizational cultures supporting continuous improvement. The analysis demonstrates that productivity enhancement transcends simple efficiency gains to encompass comprehensive organizational transformation that builds competitive capabilities while maintaining workforce engagement and operational sustainability. This synthesis provides actionable insights for practitioners seeking evidence-based approaches to productivity improvement applicable across diverse professional contexts.
Design and Synthesis of Novel Copper-Based Coordination Polymers with Enhanced Urease Inhibition Activity
Urease is a key metalloenzyme involved in biological, agricultural, and environmental processes, and its excessive activity can lead to pathogenic infections, nitrogen loss, and environmental pollution. Developing efficient, stable, and controllable urease inhibitors is therefore of considerable significance. In this study, we report the rational design and theoretical framework for novel two-dimensional copper-based coordination polymers (Cu-CPs) regulated by tridentate auxiliary ligands. The integration of multidentate ligands, optimized copper coordination geometries, and tailored polymer architectures enables spatial and electronic complementarity with urease active sites, enhancing inhibitory efficiency. Computational modeling and crystal structure prediction reveal that multinuclear copper centers and functionalized ligands synergistically improve binding and prolong inhibition. The synthetic strategies, including solution-based synthesis, self-assembly, and stepwise assembly, yield highly crystalline and thermally stable polymers, while adhering to principles of green chemistry. The proposed inhibition mechanisms involve direct coordination with the enzyme, steric hindrance, and multivalent effects. These Cu-CPs demonstrate potential applications in medical treatment of urease-producing pathogens, improved nitrogen fertilizer utilization, and environmental nitrogen management. Future work may explore multifetal synergistic designs, high-throughput computational screening, and biocompatibility optimization to develop efficient, selective, and sustainable urease inhibitors.
Coordination Polymer Design for Catalytic Efficiency Enhancement
Coordination polymers and metal-organic frameworks represent a revolutionary class of porous crystalline materials that have transformed heterogeneous catalysis through their unprecedented structural tunability, high surface areas, and designable active sites. This review examines strategic design approaches for enhancing catalytic efficiency in coordination polymer systems through rational manipulation of framework topology, metal center selection, ligand engineering, and pore environment optimization. The discussion encompasses reticular chemistry principles that guide framework construction, structure-property relationships governing catalytic performance, and emerging strategies including dual-metal site incorporation and programmable logic systems. Recent advances in copper-based coordination polymers demonstrate how auxiliary ligand selection and geometric control enable optimization of catalytic activities ranging from organic transformations to electrocatalytic carbon dioxide reduction and enzyme inhibition. Through systematic analysis of synthesis methodologies, structural characterization techniques, and catalytic evaluation protocols, this work illustrates fundamental design principles that connect framework architecture to catalytic efficiency. The integration of computational modeling with experimental validation enables predictive catalyst design and accelerates discovery of high-performance systems for industrial applications including wastewater treatment, fine chemical synthesis, and sustainable energy conversion. Understanding coordination polymer design principles provides essential guidance for developing next-generation catalytic materials with enhanced activities, selectivities, and stabilities that address critical challenges in chemical manufacturing and environmental remediation.