Huaxia XinZhi is an academic journal that publishes research in the humanities and social sciences. It seeks to showcase cutting-edge ideas that explore the evolving cultural, philosophical, educational, and societal developments in China. It provides a platform for original scholarship, theoretical reflections, and critical essays addressing issues such as identity, culture, education reform, philosophical thought, and contemporary social dynamics.
Data-Driven Digital Transformation in the Financial Industry: Strategic Logic, Platform Support, and Market Microstructure Effects
This review paper explores the multifaceted impact of data-driven digital transformation within the financial industry, focusing on strategic logic, platform support, and market microstructure effects. Digital transformation, fueled by advancements in data analytics, artificial intelligence, and blockchain, is reshaping financial services. We analyze how financial institutions are leveraging data to enhance strategic decision-making, improve operational efficiency, and create innovative products and services. The paper examines the role of platform-based business models in facilitating this transformation, highlighting the challenges and opportunities associated with integrating diverse data sources and legacy systems. Furthermore, we investigate the effects of data-driven technologies on market microstructure, including algorithmic trading, high-frequency trading, and market manipulation. A comprehensive review of existing literature and empirical studies is conducted to provide a nuanced understanding of the complex interplay between data, technology, and financial markets. Finally, we identify key research gaps and suggest directions for future research in this rapidly evolving field, with particular attention to regulatory implications and ethical considerations. This literature review aims to provide a strategic guideline for banking institution leaders, policymakers, and academics to understand various data strategies.
Enhancing User Engagement in Digital Platforms: The Role of Data Analytics and Algorithmic Recommendation
This review paper explores the multifaceted landscape of user engagement within digital platforms, focusing on the pivotal role of data analytics and algorithmic recommendations. Digital platforms thrive on sustained user activity, making engagement a crucial metric for success. This paper synthesizes existing literature to examine how data analytics techniques, including user behavior analysis, sentiment analysis, and predictive modeling, are leveraged to understand and enhance user engagement. Furthermore, it investigates the application of algorithmic recommendation systems in tailoring content, products, and experiences to individual user preferences, ultimately driving engagement. The review encompasses various types of digital platforms, such as social media, e-commerce, online learning, and entertainment. It analyzes the effectiveness of different data-driven strategies in fostering user interaction, retention, and overall satisfaction. Challenges related to data privacy, algorithmic bias, and the potential for over-personalization are critically discussed. Finally, the paper identifies emerging trends and future research directions in the field, highlighting the potential of AI-powered personalization, ethical considerations in data usage, and the development of more sophisticated engagement metrics. This review provides a comprehensive overview for researchers and practitioners seeking to deepen their understanding of how data analytics and algorithmic recommendation can be strategically employed to create more engaging and user-centric digital platforms. Furthermore, the review explores the ethical complexities of using personal data to drive engagement, advocating for a user-centric approach that prioritizes transparency and control.
Balancing Organizational Restructuring and Technological Innovation in Fintech Enterprises under Regulatory Compliance Pressure
This review paper examines the intricate relationship between organizational restructuring and technological innovation within Fintech enterprises, particularly under the increasing pressure of regulatory compliance. Fintech companies operate in a dynamic environment where they must simultaneously adapt their organizational structures to leverage emerging technologies and adhere to evolving regulatory frameworks. This paper synthesizes extant literature to explore how different organizational structures (e.g., functional, divisional, matrix) affect the adoption and implementation of technological innovations. Furthermore, it analyzes the impact of regulatory compliance, such as KYC/AML regulations and data privacy laws (e.g., GDPR, CCPA), on both organizational design and innovation strategies. The review identifies the key challenges and opportunities Fintech firms face in balancing these competing demands. Specifically, it investigates how regulatory compliance can be integrated into the innovation process to foster sustainable growth and maintain a competitive edge. The paper also explores the role of agile methodologies, open innovation, and ecosystem partnerships in navigating this complex landscape. It argues that a proactive and adaptive approach to regulatory compliance is crucial for unlocking the full potential of technological innovation in the Fintech sector. Finally, the review proposes future research directions, focusing on the development of novel organizational models and regulatory technologies that can facilitate seamless innovation and compliance in the rapidly evolving Fintech industry.
Sequential Behavior Modeling and Unsupervised Data Augmentation for Personalized Healthcare Recommendations Using AI
Personalized healthcare recommendations powered by artificial intelligence (AI) hold immense promise for improving patient outcomes and healthcare efficiency. This review paper examines the current landscape of sequential behavior modeling and unsupervised data augmentation techniques in the context of personalized healthcare recommendations. We delve into various methods for capturing the temporal dependencies in patient data, such as recurrent neural networks (RNNs), transformers, and Markov models, and assess their effectiveness in predicting future health events or treatment responses. Furthermore, we explore unsupervised data augmentation strategies, including generative adversarial networks (GANs), variational autoencoders (VAEs), and rule-based methods, which aim to enhance the quality and diversity of patient data, especially in scenarios with limited labeled information. The paper synthesizes existing research, compares different approaches, and identifies key challenges and opportunities in this rapidly evolving field. Finally, we discuss potential future directions for integrating sequential modeling and data augmentation techniques to advance personalized healthcare recommendations using AI. This review provides a comprehensive overview for researchers and practitioners interested in leveraging AI to improve healthcare delivery and patient well-being.
Research on the Synergistic Optimization of Smart City Service Architectures Based on Cloud-Enhanced Distributed Systems
This review paper investigates the synergistic optimization of smart city service architectures, focusing on the integration of cloud computing and distributed systems. Smart cities aim to improve the quality of life for their citizens through the innovative use of technology. A critical aspect of achieving this goal is the design and implementation of robust and scalable service architectures. Cloud-enhanced distributed systems offer the potential to create such architectures by leveraging the benefits of both cloud computing (e.g., scalability, cost-effectiveness) and distributed systems (e.g., resilience, low latency). This paper provides a comprehensive overview of existing research in this area. It examines various approaches to optimizing smart city service architectures, including the use of microservices, edge computing, and serverless computing. The paper also identifies key challenges and opportunities for future research, such as the need for improved security, privacy, and interoperability. The primary goal is to provide a valuable resource for researchers and practitioners interested in developing and deploying effective smart city solutions. This review synthesizes current knowledge, identifies research gaps, and proposes directions for future innovation promoting the improvement of smart city infrastructures.
Research on Machine Learning-Based Threat Detection and Security Defense Systems for Telecom Operator Networks
Telecom operator networks face increasing and evolving threats, necessitating advanced security defense systems. Traditional security mechanisms often struggle to keep pace with sophisticated cyberattacks. Machine learning (ML) offers promising solutions for proactive threat detection and automated security responses. This review paper surveys the research on machine learning-based threat detection and security defense systems specifically designed for telecom operator networks. We begin by providing a historical overview of security challenges and the evolution of defense mechanisms in telecom networks. Subsequently, we delve into two core themes: (A) ML-based intrusion detection systems (IDS) focusing on anomaly detection and signature-based techniques, and (B) ML-driven security defense, including automated threat mitigation and adaptive security policies. We compare different ML algorithms применяются in these themes, analyze their performance metrics, and identify the challenges associated with their deployment in real-world telecom environments, with a focus on data privacy and computational complexity. Finally, we explore future research directions, highlighting the potential of federated learning, explainable AI (XAI), and reinforcement learning to enhance the resilience and security of telecom operator networks against emerging cyber threats. This review aims to provide a comprehensive understanding of the current state-of-the-art and future trends in this critical area.
A Review of Land Cover Change Modeling for Precision Conservation and Green Infrastructure Planning
As global urbanization and climate change intensify, the need for targeted, data-driven spatial planning has become paramount. This paper reviews the evolution of Land Use and Land Cover Change (LUCC) modeling and its pivotal role in advancing Precision Conservation and Green Infrastructure (GI) planning. We trace the methodological trajectory from traditional statistical models to modern hybrid frameworks and machine learning approaches, illustrating how these tools enable a shift from reactive to proactive environmental management. By integrating LUCC simulations with connectivity theories and ecosystem service assessments, planners can identify critical habitats and optimize the placement of GI to maximize ecological resilience. However, challenges such as data granularity, model interpretability ("black box" issues), and the gap between raster outputs and parcel-based policy persist. We conclude that the future of resilient landscapes depends on transdisciplinary collaboration and the development of "Digital Twins" to provide real-time decision support for sustainable urban and rural development.
Bridging Technology and Social Sciences: Applications of AI, Data Analysis, and Policy Evaluation
This review examines the interdisciplinary integration of artificial intelligence, data analytics, and policy evaluation across social sciences, engineering, energy, and urban planning. We highlight applications ranging from personalized recommendation systems and market-oriented strategies to urban mobility optimization, gig economy interventions, digital platform engagement, biomedical prediction, autonomous systems, renewable energy assessment, and socioeconomic policy analysis. By synthesizing evidence from diverse domains, we demonstrate how AI and data-driven methodologies enhance decision-making, optimize operational efficiency, and support sustainable and socially responsible outcomes. The review also identifies future research directions, emphasizing multi-modal data integration, cross-domain methodology transfer, intelligent system deployment, and the collaboration between technological development and social science to address complex socio-technical challenges.
Sustainable Development in Industry and Society: Insights from AI, Energy, and Market Dynamics
This review examines sustainable development in industry and society through the lens of artificial intelligence, market dynamics, organizational behavior, digital platforms, and urban mobility. Sequential behavior modeling and context-aware personalization enable more efficient digital ecosystems while reducing resource waste and enhancing user engagement. Market-oriented development, capacity sharing, and behavioral incentives promote resource-efficient industrial growth and resilient labor systems. Data-driven analytics improve platform sustainability by optimizing user retention and minimizing operational inefficiencies. Mobility modeling and integrated urban planning further support environmental and social sustainability in cities. By synthesizing these interdisciplinary insights, the review highlights strategies for aligning technological, economic, and behavioral systems with long-term sustainability goals, and outlines future directions involving multi-modal AI, cross-domain integration, and responsible data governance.
Integrated Risk Management and Optimization Strategies across Diverse Industrial Sectors
Risk management and optimization have become fundamental pillars for organizational sustainability and competitive advantage across diverse industries. This paper examines the integration of risk management frameworks with optimization strategies in multiple sectors, including enterprise operations, technology development, construction project management, and architectural design. The study explores how organizations assess, manage, and mitigate risks while simultaneously pursuing operational efficiency and strategic opportunities. Through comprehensive analysis of contemporary risk management practices, this research demonstrates how different sectors adapt fundamental risk principles to their unique operational contexts. The paper highlights the critical role of enterprise risk management systems in enhancing decision-making processes, improving financial reporting accuracy, and strengthening organizational resilience. Furthermore, it examines how digital transformation and technological innovations have reshaped risk assessment methodologies and optimization approaches. By analyzing sector-specific applications and cross-industry patterns, this study provides insights into effective risk management strategies that balance exposure mitigation with opportunity exploitation, ultimately contributing to enhanced organizational performance and sustainable growth across varied operational environments.
Structural Control and Functional Diversification in Metal Coordination Polymers through Ligand Engineering
Copper-based coordination polymers (Cu-CPs) have emerged as a versatile class of materials with tunable structural and functional properties. The introduction of secondary auxiliary ligands plays a critical role in regulating coordination geometry, framework topology, and supramolecular interactions, which in turn influences biological activities such as urease inhibition. This study analyzes the effects of ligand geometry and steric constraints on the structural features, crystal stability, and electronic characteristics of Cu-CPs. In particular, “V”-shaped auxiliary ligands promote two-dimensional layer formation, enhance interlayer interactions, and optimize active-site accessibility, leading to improved urease inhibitory efficiency. The relationship between structure and activity is further supported by electronic considerations, including orbital distribution and potential coordination interactions. The findings highlight the importance of rational ligand design as a strategy for optimizing both structural and functional performance, and provide insight into potential applications in medicine, catalysis, and environmental remediation.
Design and Functional Evaluation of Cu(II) Coordination Polymers with Tunable Co-Ligands for Enhanced Urease Inhibition
A series of copper(II) coordination polymers (Cu(II) CPs) with tunable co-ligands were designed, synthesized, and systematically evaluated for their urease inhibitory activity. The structural characterization, including FT-IR, UV–Vis spectroscopy, X-ray diffraction, and electron microscopy, confirmed the formation of stable polymeric frameworks with diverse topologies and coordination geometries. The co-ligand composition significantly influenced both the structural and functional properties of the polymers, with nitrogen-rich and electron-donating ligands exhibiting enhanced inhibition. Urease inhibition assays revealed dose-dependent activity and varying kinetic modes, ranging from competitive to mixed-type inhibition, depending on the ligand structure and polymer topology. The study establishes clear structure–activity relationships, demonstrating that electronic, steric, and dimensional factors synergistically govern enzyme inhibition. Compared to conventional small-molecule inhibitors, the Cu(II) coordination polymers displayed superior stability, multidentate binding capacity, and tunable activity, highlighting their potential applications in agriculture and biomedicine. This work provides a robust framework for the rational design of metal-based functional materials with enhanced enzymatic inhibition.
Tuning the Architectures of Copper Coordination Frameworks through Nitrogen-Containing Auxiliary Ligands and Evaluating Their Urease Inhibition Behavior
This study systematically investigates the role of nitrogen-containing auxiliary ligands in modulating the structures of copper coordination frameworks and their effects on urease inhibitory activity. A series of copper coordination frameworks were successfully synthesized using different types of nitrogen-containing ligands. The frameworks were comprehensively characterized by X-ray diffraction, infrared spectroscopy, UV–visible spectroscopy, thermogravimetric analysis, and elemental analysis to determine their crystal structures, metal center coordination environments, pore sizes, and thermal stabilities. Experimental results indicate that the electronic effects and steric hindrance of the auxiliary ligands significantly influence the formation and stability of the frameworks, thereby affecting their overall topology and pore characteristics. In urease inhibition assays, frameworks with different structures exhibited distinct inhibitory effects, which were closely related to framework stability, metal center geometry, and pore features. Structure–activity relationship analysis further revealed that stable frameworks with favorable electronic environments enhance interactions with urease, resulting in higher inhibitory efficiency. This study not only elucidates the regulatory mechanisms of auxiliary ligands on the structure and bioactivity of copper coordination frameworks but also provides theoretical guidance for the design of novel metal-based urease inhibitors and lays a foundation for the further development of coordination frameworks in biological applications.
Design and Evaluation of Copper-Based Coordination Polymers for Enhanced Urease Inhibition in Agricultural Systems
Efficient nitrogen utilization remains a critical challenge in modern agriculture due to rapid urea hydrolysis catalyzed by urease, which results in nitrogen loss and environmental pollution. The development of stable and environmentally friendly urease inhibitors is therefore essential. In this study, a series of copper-based coordination polymers (Cu-CPs) were designed and synthesized to evaluate their potential as novel urease inhibitors. The Cu-CPs were characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and UV–Vis spectroscopy, confirming the formation of stable three-dimensional coordination networks. In vitro assays demonstrated that the Cu-CPs effectively inhibited urease activity in a dose-dependent manner, with some compounds exhibiting non-competitive inhibition and lower IC50 values compared to conventional small-molecule inhibitors. Soil incubation experiments further revealed that Cu-CPs significantly delayed urea hydrolysis, resulting in more gradual nitrogen release and improved nitrogen use efficiency. Importantly, their application showed minimal disturbance to soil microbial communities, suggesting potential safety for agricultural use. Mechanistic analysis indicated that the inhibitory effect arises from the coordination of copper ions with the urease active site, while the polymeric network allows controlled release, maintaining sustained enzyme inhibition. Compared to traditional urease inhibitors, Cu-CPs provide enhanced stability, prolonged activity, and reduced environmental risk, making them promising candidates for practical agricultural applications. This study highlights the potential of metal-based coordination polymers as efficient, stable, and environmentally compatible urease inhibitors, offering a new strategy to optimize nitrogen fertilizer utilization and mitigate environmental nitrogen loss in agricultural systems.
Digital Platform Development Across Diverse Industry Applications
Digital platforms have emerged as transformative frameworks that facilitate interactions, transactions, and value creation across multiple industries. This paper examines the development and implementation of digital platforms in software development, construction, and architectural design sectors. The research explores how continuous integration practices enhance software delivery efficiency, how construction project management benefits from digital technologies, and how architectural design adapts to post-pandemic requirements. Through comprehensive analysis of platform architectures, governance structures, and ecosystem dynamics, this study demonstrates the universal applicability of platform-based approaches while acknowledging sector-specific implementation challenges. The findings reveal that successful platform development requires balancing control mechanisms with external contributions, establishing appropriate boundary resources, and fostering complementary innovation. This research contributes to understanding how digital platforms serve as catalysts for industry transformation, enabling enhanced efficiency, improved collaboration, and sustainable practices across diverse professional domains.
The Evolution of Professional Practice in the Digital Age: Transformations, Challenges, and Emerging Paradigms
The transformation of professional practices in the contemporary digital landscape represents a fundamental shift in how expertise is constructed, delivered, and validated across diverse sectors. This paper examines the evolution of professional practice through multiple disciplinary lenses, analyzing how digital technologies reshape traditional boundaries of expertise while creating new paradigms for knowledge application and service delivery. The investigation explores professionalization frameworks, technological integration patterns, and organizational adaptations that characterize modern professional environments. Through comprehensive analysis of various professional domains including performance optimization, software efficiency, project management, and post-pandemic adaptation strategies, this study reveals convergent trends in digitalization, efficiency optimization, and structural reconfiguration. The findings demonstrate that professional evolution transcends simple technological adoption, encompassing deeper transformations in organizational logic, knowledge management, and stakeholder relationships. This research contributes to understanding how professions navigate tensions between traditional expertise models and emerging digital imperatives, offering insights into sustainable pathways for professional development in an increasingly interconnected world.
Integration of Traditional Expertise with Digital Tools
The integration of traditional expertise with digital tools represents a fundamental transformation reshaping professional practices across diverse industries. This paper examines how organizations successfully combine established domain knowledge with contemporary technological capabilities to enhance performance, efficiency, and innovation outcomes. Through systematic analysis of integration approaches spanning professional practice development, software engineering, architectural design, construction management, and manufacturing operations, this research identifies critical success factors and implementation strategies that enable effective synthesis of traditional and digital elements. The study explores theoretical foundations including digital infrastructure development, materiality of technological artifacts, and organizational change mechanisms while examining how practitioners navigate tensions between preserving valuable heritage knowledge and embracing technological advancement. The findings reveal that successful integration requires careful orchestration of technical implementation, human capability development, and organizational culture transformation that respects traditional expertise while leveraging digital capabilities. Organizations achieving effective integration demonstrate adaptive approaches that contextualize digital tools within established practice frameworks rather than pursuing wholesale replacement of traditional methods. This interdisciplinary investigation provides practitioners with comprehensive frameworks for managing digital integration while advancing theoretical understanding of technology-enabled professional practice evolution.
From Isolation to Integration: Post-Pandemic Paradigms in Public Building Design
The COVID-19 pandemic has profoundly reshaped public building design, prompting a shift from isolation-oriented strategies focused on containment and physical distancing toward integration-oriented paradigms that prioritize adaptability, health, and social connectivity. This paper theorizes the post-pandemic evolution of architectural design, emphasizing the convergence of resilience, human-centered approaches, and technological innovation. It examines spatial and functional transformations, including adaptive layouts, ventilation optimization, biophilic elements, and smart, contactless systems, highlighting their roles in balancing safety and sociability. Furthermore, the study explores socio-environmental integration, addressing psychological recovery, community inclusivity, and the alignment of sustainable technologies with occupant well-being. Key challenges, such as cost, regulatory constraints, technological inequality, and privacy concerns, are analyzed, alongside future research directions involving digital twins, AI modeling, and behavioral data integration. The findings underscore the importance of an interdisciplinary framework that unites public health, environmental sustainability, and architectural design, advancing a vision of human-centered resilience as the guiding principle for post-pandemic public architecture. This paradigm offers a comprehensive approach for designing spaces that are simultaneously safe, adaptable, inclusive, and psychologically restorative.