Pinnacle Academic Press Proceedings Series
Pinnacle Academic Press Proceedings Series (PAPPS) is an international, refereed, double-blind peer-reviewed, open-access journal that compiles the proceedings of academic conferences, workshops, and symposia across various disciplines. The series highlights groundbreaking research, emerging trends, and innovations presented by scholars, researchers, and practitioners in their respective fields. With a focus on high-quality contributions, the series serves as a valuable resource for the academic community, facilitating the dissemination of knowledge, fostering collaboration, and offering insights into the latest developments in science, technology, and academia.
Business Analysis: User Attitude Evaluation and Prediction Based on Hotel User Reviews and Text Mining
In the post-pandemic era, the global hotel industry plays a crucial role in broader economic recovery, with consumer sentiment increasingly influencing market trends and operational strategies. This study utilizes advanced natural language processing (NLP) techniques and the Bidirectional Encoder Representations from Transformers (BERT) model to systematically analyze hotel user reviews, extracting profound insights into customer satisfaction and guiding targeted service improvements. By transforming unstructured textual reviews into high-dimensional feature vectors, the BERT model accurately classifies complex consumer emotions, uncovering nuanced patterns of satisfaction and dissatisfaction that traditional analytical methods often overlook. This sophisticated approach provides highly valuable, data-driven evidence for hotel management, helping them refine service offerings, optimize resource allocation, and significantly improve overall customer experiences. Furthermore, from a financial perspective, understanding consumer sentiment is vital for predicting market performance, as shifts in customer attitudes frequently correlate with stock price fluctuations and overall industry profitability. Additionally, the study rigorously addresses the pervasive issue of data imbalance in sentiment analysis by employing advanced techniques such as oversampling and undersampling to enhance model robustness and predictive accuracy. The empirical results offer actionable insights not only for hospitality practitioners but also for financial analysts, aiding in precise market forecasts and strategic investment decisions. Ultimately, this research highlights the transformative potential of deep learning-based sentiment analysis to drive sustainable business growth, improve financial outcomes, and enhance competitive advantage in the dynamic tourism and hospitality sectors, thereby contributing significantly to the broader economic landscape.
Copyright (c) 2026 Ruochun Zhao, Yue Hao, Xuechen Li (Author) https://creativecommons.org/licenses/by/4.0
Research on Teaching Strategies for Practical Business Chinese Expression Based on Needs Analysis
In the context of globalization, practical Business Chinese expression competence plays a crucial role in enhancing the effectiveness of international communication and cooperation. Learners engaged in business-related Chinese often face specific communicative tasks, such as negotiations, presentations, and written correspondence, which require accurate, appropriate, and context-sensitive language use. A needs analysis–based approach enables teachers and curriculum designers to systematically identify learners’ communicative purposes, linguistic gaps, and pragmatic difficulties in real business settings. On this basis, teaching content can be more closely aligned with authentic business scenarios, emphasizing situational simulations, case analysis, and task-based activities that foster practical application ability. This study conducts detailed needs research on a target learner group to clarify common problems and expression barriers in business communication, including limited vocabulary range, insufficient awareness of register, and challenges in intercultural pragmatics. The findings indicate that teaching strategies grounded in needs analysis can effectively enhance students’ language application ability, increase their confidence in professional interactions, and strengthen their competitiveness in business environments. Furthermore, the study highlights the importance of flexible adjustment of teaching content and methods according to evolving learner needs, thereby supporting personalized instruction and promoting autonomous learning. By integrating practice and theory, the research explores core elements of Business Chinese expression and contributes to the construction of a more comprehensive and systematic Business Chinese education framework.
Copyright (c) 2026 Siyu Hong (Author) https://creativecommons.org/licenses/by/4.0
Research on Target Domain Adaptation and Explainability Enhancement of AI Models Based on Functional Specialized Region Reinforcement
This study investigates comprehensive strategies for enhancing the target domain adaptation and explainability of artificial intelligence (AI) models through the novel approach of functional specialized region reinforcement. As machine learning systems are increasingly deployed in complex, real-world environments, the dual challenges of domain shift and the opaque nature of deep neural networks have become significant bottlenecks. To address these critical issues, this research proposes a robust framework that systematically integrates domain-specific feature extraction, advanced attention mechanisms, and localized model training protocols. By isolating and reinforcing functionally specialized regions within the neural architecture, AI models can effectively adapt to target domains with diverse and shifting characteristics while simultaneously providing highly interpretable insights into their underlying decision-making processes. Extensive experimental results conducted on multiple standard cross-domain datasets rigorously demonstrate that the proposed method substantially improves overall model generalization. Furthermore, the empirical evaluation confirms that this approach significantly reduces the adverse effects of domain shift and dramatically enhances the clarity and traceability of specific feature contributions in the final predictive outputs. The comprehensive findings highlight the immense potential of functional region-focused reinforcement in simultaneously optimizing AI performance, structural interpretability, and practical applicability across various demanding domains, paving the way for more trustworthy and adaptable intelligent systems in safety-critical applications.
Copyright (c) 2026 Hongyi Tan (Author) https://creativecommons.org/licenses/by/4.0
Research on Emotional Interaction AI Algorithm Optimization and Human Robot Collaboration Models for Elderly Care Robots
This paper conducts an in-depth study on emotional interaction artificial intelligence algorithm optimization and human-robot collaboration models specifically designed for elderly care robots. In the context of a rapidly aging global society, the demand for comprehensive elderly care services continues to increase exponentially. Consequently, advanced emotional recognition and empathetic interaction capabilities have become key factors in improving the overall service quality and acceptance of care robots. This study proposes a novel, deep learning-based emotion recognition algorithm that achieves highly accurate identification of complex emotional states in older adults. This is accomplished through the integrated, multimodal analysis of speech dialogue, facial expressions, and continuous physiological signals. With regard to human-robot collaboration models, the study systematically examines collaborative working mechanisms between intelligent robotic assistants and human care staff. Furthermore, it proposes a dynamic adjustment strategy based on real-time emotional feedback to substantially improve collaboration efficiency and user satisfaction. Experimental results demonstrate that the optimized emotional interaction algorithm significantly improves emotion recognition accuracy and computational response speed. Additionally, the newly proposed collaboration model effectively reduces the physical and psychological workload pressure on human care staff while elevating the overall quality of elderly care services. The core innovations of this study lie in the development of a context-aware human-robot interaction mechanism and an adaptive task allocation model, which collectively provide novel technical solutions to enhance the intelligence, empathy, and responsiveness of modern elderly care services.
Copyright (c) 2026 Jieyao Liu, Duhuang Lin (Author) https://creativecommons.org/licenses/by/4.0
Research on the Application Strategy of AI-Enabled Intelligent Management and Effectiveness Improvement in the Full Process of Sports Rehabilitation
This study investigates the application strategies of artificial intelligence (AI) in the full process of sports rehabilitation, with the goal of realizing intelligent management and measurable effectiveness improvement. Building on deep learning algorithms and data-driven decision-making, AI techniques are integrated into multiple rehabilitation stages, including initial assessment, program design, real-time monitoring, feedback adjustment, and outcome evaluation. By combining sensor-based motion capture, wearable devices, and clinical assessment data, the study constructs intelligent evaluation and treatment models capable of tracking patients’ functional recovery trajectories and automatically updating individualized rehabilitation plans. Comparative experiments between AI-assisted and conventional rehabilitation approaches indicate that AI support can shorten functional recovery time, improve the precision of exercise prescription, and enhance patient adherence and satisfaction. Furthermore, AI-based systems facilitate more efficient allocation of medical and rehabilitation resources, support standardized yet personalized interventions, and provide clinicians with objective, continuous data for decision support. The findings demonstrate that AI enables comprehensive and refined management of sports rehabilitation processes, strengthens the scientific basis and targeting of interventions, and offers a scalable technical framework for future clinical practice. This work provides methodological references for integrating AI into rehabilitation management platforms and highlights key challenges such as data quality, model interpretability, and interdisciplinary collaboration that must be addressed to promote wider application.
Copyright (c) 2026 Weifeng Gao, Xiaohan liu (Author) https://creativecommons.org/licenses/by/4.0
Contextual Learning Support for Low-Resource Language Large Language Models: Efficient Training Strategies for Zero-Shot and Few-Shot Learning
In natural language processing tasks for low-resource languages, building high-quality large language models faces numerous challenges, including data scarcity, domain mismatch, and limited annotation resources. This paper proposes an efficient training strategy based on contextual learning, aimed at addressing the difficulties encountered by low-resource languages in zero-shot and few-shot learning scenarios. The approach leverages cross-lingual knowledge transfer from high-resource languages and systematically enhances contextual information in prompts and representations to maximize the model’s generalization ability. We explore the combination of multi-task learning and self-supervised learning to exploit heterogeneous corpora, using existing multilingual and monolingual resources for pretraining. A lightweight fine-tuning stage with a small amount of labeled data is then employed for targeted adaptation to specific downstream tasks and languages. The proposed framework is designed to be computationally efficient, reducing training cost while maintaining or improving performance. Experimental results on a range of language understanding and generation benchmarks demonstrate significant improvements in task performance across various low-resource languages under both zero-shot and few-shot conditions. Ablation studies further highlight the contribution of contextual learning components and cross-lingual transfer mechanisms. These findings provide practical guidance for developing scalable large language models for underrepresented languages and offer new ideas and methods for future research on inclusive and resource-efficient language technologies.
Copyright (c) 2026 Tanya Xiang (Author) https://creativecommons.org/licenses/by/4.0
Research on Reconstructing the Rural Music Industry Value Chain and Coordinating Regional Economic Development
This paper discusses the impacts of value chain reconstruction in the rural music industry on regional economic development in the current economic context. With the rapid development of digital technology, the rise of online platforms, and changes in consumer demand, the rural music industry is facing new opportunities and challenges in production, distribution, and consumption. By systematically analyzing the structure and functions of the rural music industry value chain, this study proposes market-oriented innovation strategies, including the diversification of content creation, communication channels, and business models, in order to improve resource allocation efficiency and maximize economic benefits. Particular attention is given to the integration of digital technologies, such as streaming media and social networks, into traditional rural cultural resources. Furthermore, the paper examines how the rural music industry influences local employment, income distribution, cultural inheritance, and social cohesion, thereby contributing to more balanced regional development. It emphasizes the importance of supportive regional policies, infrastructure investment, and coordinated development among related industries such as tourism, cultural services, and creative industries. The objective of this research is to establish a theoretical framework and provide practical guidance for constructing a more integrated regional economic development model. In addition, it aims to offer a reference for enhancing the core competitiveness, innovation capacity, and sustainable development of the rural music industry.
Copyright (c) 2026 William Zi-You Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on the Dilemmas and Breakthrough Paths of Elite Education and Education Equity in Policy from the Perspective of Role-Based Policy Debate
With the rapid development of society and the economy, elite education has increasingly become a focal point in discussions of educational equity and social justice. Elite education, while aiming to cultivate high-level talent, often relies on highly selective admission mechanisms and concentrated high-quality resources, which may exacerbate existing inequalities in access and outcomes. From the perspective of role-based policy debate, this study examines how different stakeholders—such as policymakers, educators, parents, and students—frame and contest the relationship between elite education and education equity in policy processes. The analysis identifies key dilemmas, including uneven allocation of educational resources, class stratification reinforced by schooling pathways, and the strong influence of family economic background on educational opportunities and trajectories. These factors hinder the realization of substantive educational equity and contribute to the concentration of advantages among specific social groups. On this basis, the paper explores potential breakthrough paths through more inclusive enrollment policies, diversified support systems, and mechanisms for sharing social and educational resources. It further discusses how digital technologies can help reduce geographical and economic barriers, thereby expanding equal learning opportunities. By employing role-based policy debate and role play, participants can articulate interests, reveal policy trade-offs, and co-construct more sustainable policy designs that balance the development of elite education with the pursuit of education equity.
Copyright (c) 2026 Ke Zhu (Author) https://creativecommons.org/licenses/by/4.0
Research on Identity Ethics and the Reconstruction of Social Relationships in Metaverse Virtual Spaces
In the era of the Metaverse, identity construction and the reconstruction of social relationships in virtual spaces have become an increasingly important and complex field of academic research. This study aims to comprehensively explore the multifaceted ethical issues related to user identity in Metaverse virtual spaces and their profound impact on contemporary social relationships. With rapid technological development, users' identities in virtual environments are no longer strictly limited to their real-life physical selves; instead, they are diversely constructed and continuously modified through advanced digital means. This unprecedented trend has inevitably given rise to numerous critical ethical issues, such as identity theft, the deliberate dissemination of false identities, and their detrimental impact on fundamental trust within digital social networks. Furthermore, novel interaction patterns in virtual spaces have fundamentally changed the formation, evolution, and maintenance of traditional social relationships. Consequently, users' behavioral expressions and intricate social network relationships within these emerging virtual communities need to be rigorously reexamined. Through a robust combination of qualitative and quantitative research methods, this study conducts an in-depth, systematic analysis of the ethical challenges surrounding identity construction in Metaverse environments. It further evaluates their direct impact on the redefinition of interpersonal relationships, ultimately aiming to provide solid theoretical support and actionable practical guidance for future virtual space design, ethical framework development, and effective user governance.
Copyright (c) 2026 Zixuan Cai (Author) https://creativecommons.org/licenses/by/4.0
Research on the Construction of a Cognitive-Psychological Adaptation System for Digital Teaching Empowered by Healing Design
This study constructs a cognitive-psychological adaptation system for digital teaching centered on healing design to address students' mental health and learning efficiency in the contemporary educational environment. By combining theoretical analysis with empirical research, it explores how to integrate healing design concepts into the digital teaching process to improve students' learning motivation and emotional engagement. The research framework clarifies the mechanisms through which healing-oriented visual, spatial, and interactive elements in digital platforms can regulate learners’ cognition, emotion, and behavior. Quantitative and qualitative data are used to examine changes in students’ anxiety, stress, and perceived support, as well as their attention, persistence, and satisfaction with learning tasks. The results show that healing design can effectively enhance students' learning experience, reduce anxiety and stress, promote positive emotions, and further improve learning outcomes. In addition, the study identifies key design principles, such as emotional safety, cognitive load balance, and supportive feedback, which contribute to a more adaptive digital learning environment. These findings provide theoretical support and practical guidance for optimizing digital teaching and introduce the concept of psychological adaptation into teaching design, moving toward a more humanized, inclusive, and effective educational model that better responds to the psychological needs of diverse learners in digital contexts.
Copyright (c) 2026 Meishan Cheng (Author) https://creativecommons.org/licenses/by/4.0
Improvement Effect of Repeated Sprint and Cognitive Combined Training on Tennis Players' Performance and High Pressure Decision Making
This study investigates the effects of a combined repeated sprint and cognitive training program on tennis players’ match performance and high-pressure decision-making. Competitive tennis requires rapid changes of direction, repeated high-intensity efforts, and the ability to make accurate tactical decisions under time pressure and psychological stress. In this intervention, athletes completed an eight-week training program integrating repeated sprint drills with structured cognitive tasks, such as decision-making simulations, attention and reaction exercises, and scenario-based problem solving. Technical indicators, physical fitness parameters, and psychological adaptability were assessed before and after the intervention using match video analysis, standardized performance tests, and decision-making evaluation tools. The results indicate that players who underwent the combined training demonstrated marked improvements in speed, agility, and on-court responsiveness, as well as enhanced quality of tactical choices in critical points. Under high-pressure situations, athletes showed higher decision-making efficiency, greater accuracy, and reduced hesitation compared with their pre-training performance. These findings suggest that coupling physical conditioning with cognitive training produces synergistic benefits that better reflect the complex demands of competitive tennis. The study highlights the necessity of integrating cognitive components into traditional conditioning programs and provides practical guidance for coaches seeking to optimize training design to improve both physical performance and decision-making under pressure.
Copyright (c) 2026 Zhenyu Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Construction of Moral Boundaries and Ethical Norms of AI Nursing in an Aging Society
With the acceleration of global population aging, artificial intelligence (AI) nursing has become an important approach to addressing the rapidly growing demand for care services. However, the ethical risks and moral uncertainties associated with AI-assisted care require systematic clarification and normative guidance. This study investigates the construction of moral boundaries and ethical norms for AI nursing in an aging society. Drawing on the ethics of care, technology ethics, and responsibility ethics, it analyzes the mechanisms through which ethical risks emerge in AI nursing, including algorithmic bias, opaque decision-making, data privacy vulnerabilities, and the potential erosion of genuine human care under the dominance of technical rationality. The paper argues that traditional nursing ethics, centered on human caregivers, faces challenges of subject generalization, role ambiguity, and unclear standards when AI systems participate in care practices. On this basis, the research demonstrates the necessity of integrating AI ethics into nursing ethics to promote a paradigm shift toward human–machine collaboration. It proposes the construction of a multidimensional nursing framework that embeds ethical review throughout the life cycle of AI nursing technologies, emphasizes algorithm interpretability and accountability, and safeguards the dignity, autonomy, and rights of older adults. The study aims to provide theoretical support and practical guidance for the healthy development of AI nursing, promote the deep integration of technological empowerment and humanistic care, and achieve both quality improvement and ethical balance in nursing services in an aging society.
Copyright (c) 2026 Kevin Meng (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Construction Technology Integration for Green Transformation of Existing Buildings in Urban Renewal
As China's urbanization process enters a new stage, urban renewal has become a key pathway for promoting sustainable urban development and improving the quality of the built environment. The green transformation of existing buildings, as a core component of urban renewal, faces prominent challenges such as low construction efficiency, high disturbance to occupants, and difficulties in coordinating multiple stakeholders and complex building systems. These issues urgently require the support of intelligent construction technologies and new construction models. This paper focuses on the integration of intelligent construction technology with the green transformation of existing buildings, with the aim of constructing a systematic and operable technical framework. First, core concepts such as intelligent construction, urban renewal, and green transformation are clarified, and the research foundation is established based on sustainable development theory, systems engineering, and life-cycle thinking. Second, by analyzing the current application status of technologies such as building information modeling, the Internet of Things, and big data in urban renewal projects, the paper systematically elaborates a full-chain technical system covering digital surveying, intelligent design, construction management, and energy-saving operation and monitoring. The study highlights how intelligent technologies enhance reconstruction accuracy, reduce resource and energy consumption, shorten construction periods, and optimize building performance. Finally, the proposed technical system provides theoretical support and practical guidance for the green renovation of existing buildings driven by intelligent construction, offering important reference value for promoting green, low-carbon, and high-quality urban development.
Copyright (c) 2026 Binyuan Yang (Author) https://creativecommons.org/licenses/by/4.0
A Real-time Object Tracking Strategy for a Mobile Terminal Based on a Lightweight Visual Model
Due to the increasing popularity of mobile intelligent terminals and the rapid advancement of computer vision technology, real-time object tracking technology on mobile devices has become a demand in fields such as augmented reality, intelligent security, and unmanned systems. Target tracking algorithms often have difficulties in achieving efficient and real-time performance on mobile devices due to limitations such as limited computing resources and high power consumption. Therefore, it is urgent to improve its comprehensive performance through a lightweight visual model and an optimization strategy. This paper focuses on the application of a lightweight visual model in mobile real-time object tracking. Firstly, it analyzes the basic process of object tracking and its practical application scenarios, and introduces the structural characteristics and technical advantages of various lightweight models. To address the challenges of limited computing resources, the need for real-time performance, and the ability to adapt to varying scene complexities when deployed on mobile devices, a feature extraction approach is introduced that combines multi-scale, lightweight feature fusion with a dynamic adjustment system. It will improve the model's efficiency and performance when running on mobile devices. Additionally, to address the obstacles caused by illumination changes and target occlusion in complex scenes, an adaptive feature calibration method and a tracking state correction mechanism are proposed, which significantly enhance the system's robustness and tracking accuracy. The experimental results demonstrate that the proposed strategy enables efficient and stable real-time object tracking on mobile devices, providing a feasible technical path for applications.
Copyright (c) 2026 Bohan Qiu, Ao Liu (Author) https://creativecommons.org/licenses/by/4.0
Research on Cross-Border Capital Flows and Financial Risk Prevention under Global Central Bank Monetary Policy Adjustments (Interest Rate Cut Cycle)
Against the background that major global economies have entered a cycle of interest rate cuts, the international financial environment presents a complex situation of liquidity easing, leverage expansion and asset price fluctuations. Cross-border capital flows show a trend of acceleration, reversal and structural differentiation, which has a double impact on the financial stability, exchange rate mechanism and macroeconomic management of emerging markets. Based on international financial transmission theory and the macroprudential management framework, this article systematically analyses the impact mechanism, transmission paths and risk spillover effects of the global central bank interest rate cut cycle on cross-border capital flows. It distinguishes between portfolio, banking and direct investment flows, and examines how interest rate differentials, risk appetite and expectations interact with domestic financial structures. In addition, the paper discusses policy countermeasures for financial risk prevention and control with reference to typical country experiences, highlighting the role of capital flow management tools, exchange rate flexibility and reserve buffers. The study finds that capital flows under an interest rate cut cycle are highly volatile and procyclical, and are prone to induce asset price bubbles, currency mismatches, maturity mismatches and systemic financial risks, especially in economies with shallow financial markets and weak regulatory capacity. Based on this, the article proposes a comprehensive governance strategy combining macroprudential regulation, cross-border capital flow management, foreign exchange reserve optimisation and international policy coordination, providing a reference for reconciling financial security with global monetary policy cycles in emerging markets.
Copyright (c) 2026 Ngoc Anh Thy Nguyen (Author) https://creativecommons.org/licenses/by/4.0
The Era of Short Dramas: Aesthetic Reconstruction, Cognitive Influence, and Reception Transformation in Art Communication
With the rapid development of digital technology and mobile media, short dramas characterized by brief episodes and highly condensed plots have become extremely easy to disseminate and consume. In 2023, the short drama market in China exceeded 30 billion yuan, with more than 600 million users, indicating that this format has moved beyond pure entertainment to deeply penetrate the field of art communication. This article adopts a mixed approach combining case studies, literature review, and basic data analysis to examine the impact of short dramas on art communication from three interrelated dimensions: the aesthetic communication of art, the audience’s cognitive logic, and the transformation of reception patterns. The study argues that short dramas diversify and scenarize the dissemination of artistic aesthetics, and can in some cases exercise audiences’ abilities in logical reasoning, narrative tracking, and information integration. At the same time, they reshape reception habits by adapting to fragmented, mobile, and personalized media environments. However, the research also reveals negative tendencies, including the superficialization and homogenization of artistic aesthetics, the weakening of deep thinking and critical discrimination, and the emergence of impatient, depth-deficient modes of reception. The article concludes that creators should balance commercial and artistic value, platforms should assume responsibility for content guidance and aesthetic cultivation, and audiences should develop more rational and reflective reception concepts. Future development of short dramas should emphasize higher quality and greater depth to promote a healthier and more sustainable art communication ecosystem.
Copyright (c) 2026 Xiaoran Zhou (Author) https://creativecommons.org/licenses/by/4.0
Research on the Commercial Law Guarantee Mechanism for Green Credit in New Energy Enterprises
In the context of climate change and the promotion of energy transformation, the development of new energy enterprises is crucial for achieving sustainable development goals and advancing low-carbon economic restructuring. Green credit has become a key financial instrument for guiding capital toward environmentally friendly projects, yet its effectiveness depends on a sound commercial law framework that can allocate risks, protect the interests of stakeholders, and promote market confidence. This paper explores the commercial law guarantee mechanism for green credit in new energy enterprises. First, it clarifies the core concepts of green credit, commercial law guarantees, and new energy enterprises, and outlines the relevant theoretical foundations in financial law, environmental law, and contract law. Second, it analyzes the current situation and dilemmas of green credit protection in China, identifying deficiencies in legislation, contract design, guarantee structures, information disclosure, risk-sharing arrangements, and dispute resolution mechanisms. On this basis, the paper proposes targeted suggestions, including strengthening top-level legislative design, refining the legal status and obligations of green credit participants, innovating green credit contract models, and establishing a diversified guarantee system that integrates collateral, credit enhancement, and policy support. Furthermore, it advocates improving risk-sharing mechanisms, enhancing regulatory coordination, and optimizing dispute resolution procedures to foster a stable and transparent market ecology for green credit in new energy enterprises.
Copyright (c) 2026 Jingjie Li (Author) https://creativecommons.org/licenses/by/4.0
Research on Trust Mechanism Construction and Risk Governance in Public Policy Communication under the Perspective of Human-Machine Collaboration
With the deep integration of artificial intelligence into the field of public policy communication, the paradigm of human–machine collaboration is profoundly reshaping traditional trust building and risk governance mechanisms. This study aims to explore how collaborative interaction between human actors and intelligent systems affects the credibility, transparency, and public acceptance of policy information. Drawing on theories of communication, governance, and science and technology studies, the paper constructs a theoretical framework that integrates trust mechanism design with risk governance strategies in digitally mediated policy processes. Within this framework, it analyzes optimization paths for enhancing information authenticity, improving algorithmic transparency, and broadening meaningful public participation. Particular attention is paid to how algorithmic decision support, automated content generation, and data-driven targeting influence perceptions of fairness, accountability, and reliability in policy communication. The study further discusses potential risks, including information distortion, opacity of algorithmic logic, and the amplification of bias, and proposes corresponding governance tools such as disclosure norms, oversight mechanisms, and participatory feedback channels. The research findings are expected to provide theoretical support and practical implications for enhancing the efficiency, responsiveness, and legitimacy of policy communication, thereby contributing to the modernization of governance capacity in the intelligent era.
Copyright (c) 2026 Yantong Lin, Yulin Zhao (Author) https://creativecommons.org/licenses/by/4.0
Research on Intrinsic Motivation Strategies and Chinese Learning Outcomes of Second Language Learners Based on Self-Determination Theory
This study focuses on the application of self-determination theory (SDT) in stimulating intrinsic motivation among second language learners and enhancing their Chinese learning outcomes. As global interest in the Chinese language continues to grow rapidly, educators face persistent difficulties in maintaining long-term student interest and active participation. Through comprehensive theoretical analysis and rigorous practical investigation, the research identifies the underlying mechanisms by which learner autonomy, competence, and relatedness needs directly influence language acquisition processes. Furthermore, it proposes actionable pedagogical strategies to promote and sustain intrinsic motivation across diverse educational contexts. Current challenges in Chinese language learning, such as low learner engagement, insufficient autonomous learning capabilities, and significant motivational decay over time, are systematically addressed through targeted motivational strategies. Optimization paths developed in this study include the implementation of autonomy-supportive teaching methodologies, targeted competence enhancement exercises, active social interaction facilitation, and sustainable motivational curriculum design. By integrating these critical elements into daily instructional practices, educators can successfully create a more supportive, inclusive, and highly engaging learning environment. Ultimately, the study provides robust practical guidance for improving overall Chinese learning effectiveness, fostering resilient learner motivation, and significantly advancing contemporary academic research on motivation-driven second language acquisition frameworks.
Copyright (c) 2026 Chenyang Feng (Author) https://creativecommons.org/licenses/by/4.0
Research on International Layout and Regional Brand IP Construction of China's Ceramic Industry under the RCEP Framework
The Regional Comprehensive Economic Partnership (RCEP) creates unprecedented opportunities for China's ceramic industry to expand its global presence and strengthen regional brand influence within an integrated Asia-Pacific market. However, intensified market competition, heterogeneous consumer preferences, cultural differences, and persistent challenges in intellectual property protection continue to constrain strategic internationalisation. This paper examines how the RCEP framework can support China's ceramic industry in designing an effective international layout and constructing distinctive regional brand intellectual property (IP). By integrating trade policy analysis, market positioning strategies, and brand IP management, the study develops a comprehensive analytical framework to guide enterprises and industrial clusters in leveraging tariff reductions, rules of origin, and trade facilitation measures. Case studies of representative ceramic clusters, including Jingdezhen and Foshan, are used to illustrate how institutional advantages, cultural branding, design innovation, and IP protection mechanisms jointly contribute to competitive positioning and value upgrading. The research further discusses the coordination between industrial policy, regional cooperation, and enterprise-level branding strategies under RCEP. The findings highlight the synergy between international trade liberalisation and cultural brand building in enhancing the global competitiveness, value chain status, and sustainable development capacity of China's ceramic industry, and provide policy and managerial implications for other traditional manufacturing sectors seeking regional brand IP construction.
Copyright (c) 2026 Mengting Jin (Author) https://creativecommons.org/licenses/by/4.0
The Ecological Construction and Sustainable Development of University-Enterprise Cooperation in English Education in the Era of Artificial Intelligence
In the era of artificial intelligence (AI), English education is undergoing a profound transformation from traditional knowledge transmission to intelligent, interactive, and practice-oriented models. University–enterprise cooperation has become a key mechanism for aligning academic training with evolving industry demands, cultivating application-oriented talents, and promoting educational innovation. This study investigates the ecological construction and sustainable development of university–enterprise cooperation in AI-driven English education from a systems perspective. It first clarifies the theoretical correlations among AI, educational ecology, and sustainable development, emphasizing the dynamic balance between technological progress, institutional arrangements, and humanistic values. The paper then explores four core mechanisms of AI-driven cooperation: innovation in teaching models through intelligent platforms and data-driven feedback; integration of talent training with employment pathways; coordination of industrial structure with curriculum design; and co-creation of organizational culture and value systems between universities and enterprises. Based on an analysis of current practices, the study identifies major challenges, including uneven cooperation quality, insufficient depth of innovation, weak integration of resources, and an imbalance between technological empowerment and humanistic education. In response, four optimization paths are proposed: strengthening policy and institutional guarantees, deepening the integration of industry, education, and technology, enhancing talent cultivation and brand building, and promoting sustainable development with explicit humanistic protection. The findings provide a reference for constructing a resilient, open, and future-oriented cooperative ecosystem that supports educational modernization and industrial upgrading.
Copyright (c) 2026 Ying Yang (Author) https://creativecommons.org/licenses/by/4.0
Influencing Factors and Intervention Strategies of Early Mathematical Cognition
Early mathematical cognition constitutes a critical foundation for children’s subsequent academic achievement and broader cognitive development. Its growth is shaped by multiple interacting factors, including the family environment, parental beliefs and behaviors, school experiences, and the increasing presence of digital resources in children’s daily lives. With the rapid popularization of information technology and the transformation of family education models, clarifying the mechanisms through which these factors operate has important theoretical and practical significance. This review synthesizes major theoretical perspectives on early mathematical cognition, such as number sense, spatial reasoning, and logical thinking, and summarizes empirical findings on how family learning atmosphere, parent–child interaction, and guided use of digital tools support these core abilities. Particular attention is given to the complementary roles of family-based activities and digital learning platforms, which together can enrich children’s opportunities for mathematical exploration and practice. The review further discusses intervention strategies at the levels of parents, schools, and communities, including parental education programs, guidance on the selection and use of digital resources, curriculum optimization, and multi-agency collaboration. Current research is limited by a predominance of cross-sectional designs, insufficient longitudinal and causal evidence, and a lack of attention to cultural and socioeconomic diversity. Future studies should adopt diversified, long-term, and cross-cultural approaches to refine evidence-based interventions and promote the sustainable development and wider dissemination of high-quality early mathematics education.
Copyright (c) 2026 Xiaoying Cui (Author) https://creativecommons.org/licenses/by/4.0
Research on Data-Sharing Mechanisms and Value Chain Reconfiguration in the Macao--Hengqin Collaborative "Big Health" Industry
This paper examines how cross-border data-sharing between Macao and Hengqin ("Macao--Hengqin collaboration") can catalyze value chain reconfiguration in the Big Health industry spanning biomedicine, medical devices, digital health, wellness tourism, and elderly care. A governance-ready framework is proposed that aligns interoperable data infrastructure with clinical, industrial, and tourism-service workflows, integrating privacy-preserving technologies, trustable exchange rules, and outcome-oriented incentives. The study develops a five-layer architecture—data, features, analytics and forecasting, coordination and optimization, and risk-and-compliance—that links micro-level data flows to meso-level cluster synergies and macro-level productivity and competitiveness. Within this architecture, federated learning, tokenized data assets, and standardized interfaces are used to support secure cross-border collaboration in research and development, clinical trials, and service delivery. A semi-synthetic evaluation calibrated to Greater Bay Area statistics suggests that federated and tokenized data-sharing can shorten R&D cycles, increase cross-border patient recruitment efficiency, and elevate service export value while controlling privacy risk and regulatory exposure. The analysis further highlights institutional frictions, interoperability gaps, and governance challenges that may constrain implementation. In response, policy and operational strategies are proposed for platform design, standard harmonization, regulatory sandboxes, and public–private partnerships to realize sustained industrial upgrading, promote regional integration, and enhance the global competitiveness of the Macao–Hengqin Big Health cluster.
Copyright (c) 2026 Shuang Diao, Yuting Zhang (Author) https://creativecommons.org/licenses/by/4.0
A Comparison of the Impact of Differences in New Media Content on the Self-Efficacy of Female Adolescents
This study investigates the mechanisms through which differences in new media content influence the self-efficacy of female adolescents. Drawing on contemporary theories of media effects and adolescent psychological development, the paper distinguishes between positive-oriented and negative-oriented new media content and examines their respective impacts across multiple domains of self-efficacy. Positive-oriented content, including knowledge popularization, inspirational narratives, and materials that foster social and communication skills, is found to enhance academic confidence, interpersonal competence, self-cognition, emotional regulation, life skills, and problem-solving abilities. In contrast, negative-oriented content, such as excessive entertainment consumption, appearance-focused and comparison-driven materials, and misleading or falsely curated social media displays, is associated with diminished self-efficacy in these same domains. The analysis highlights a notable symmetry and pertinence between the type of content consumed and the specific dimension of self-efficacy affected, suggesting a patterned and domain-sensitive relationship rather than a uniform global effect. These findings underscore the pivotal role of new media environments in shaping the psychological development and adaptive functioning of female adolescents. The study further provides an academic perspective for understanding the correlation mechanisms linking media content characteristics with self-beliefs, and it offers implications for educators, parents, and policymakers seeking to guide healthier media use and design targeted interventions to support adolescent well-being.
Copyright (c) 2026 Zitong Hao (Author) https://creativecommons.org/licenses/by/4.0
The Social Influence and Critical Thinking of the Neo-Chinese Painting Movement in the 20th Century
The Neo-Chinese painting movement in 20th-century China represents not only an innovative approach to artistic techniques but also a significant exploration of Chinese painting as it responds to the challenges of modernity within a specific historical context. This paper examines the social impact of the movement, with particular attention to its role in shaping patriotic emotional expression and transforming the relationship between artworks and their audiences. By situating key artists and works within broader political and cultural developments, the study analyzes how visual strategies, subject matter, and exhibition practices contributed to new forms of national consciousness and public engagement. Furthermore, the paper summarizes the core disputes surrounding the inheritance of traditional painting conventions and the integration of Chinese and Western artistic concepts, comparing the positions of different art schools and critical discourses. Through this comparative lens, it clarifies how debates on form, medium, and ideology reflected deeper tensions between continuity and rupture in cultural identity. Finally, in conjunction with contemporary art practices, the paper reflects on the ongoing dilemma of Chinese painting between inheritance and innovation, and offers critical insights into possible future directions for the development of Chinese painting in a globalized art world.
Copyright (c) 2026 Shengjie Zhao (Author) https://creativecommons.org/licenses/by/4.0
Research on Technical Characteristics and Training Optimization of Starting Technique in Speed Slalom of Freestyle Roller Skating Based on Kinematics and Dynamics
This study investigates the starting technique in the speed slalom event of freestyle roller skating from both kinematic and dynamic perspectives, with the aim of providing a scientific basis for performance enhancement. Using a combination of literature review, controlled experiments, and mathematical statistics, the research decomposes the start into distinct phases and systematically examines key spatiotemporal and joint-angle parameters, ground reaction forces, and impulse characteristics. The technical characteristics of elite skaters are summarized in terms of body posture, center-of-mass trajectory, push-off sequence, and force application patterns. On this basis, movement norms, typical parameter ranges, and internal relationships between kinematic variables and dynamic outputs are clarified, highlighting the determinants of rapid acceleration and stability in the initial strides. Guided by these findings, targeted training content and optimization strategies are designed, including specific strength exercises, coordination drills, and start-phase simulation training that emphasize force direction, rate of force development, and technical consistency. The proposed optimization scheme is implemented in practice and its effectiveness is preliminarily verified through comparative performance tests and technical evaluations. The results indicate that structured, biomechanically informed training can significantly improve starting efficiency and technical quality in speed slalom, offering a useful reference for coaches and athletes and contributing to the scientific development and standardization of freestyle roller skating training.
Copyright (c) 2026 Jingxiao Wei (Author) https://creativecommons.org/licenses/by/4.0
A Strategy-Oriented Study on the Mechanism by Which Corporate Social Responsibility Influences Sustainable Performance from a Supply Chain Collaboration Perspective
In a highly interconnected and increasingly regulated environment, corporate social responsibility (CSR) is evolving from a compliance burden into a strategic lever for supply chain collaboration and sustainable performance. This study develops a strategy-oriented mechanism linking CSR intensity, collaborative capability, and sustainable performance, and examines how this mechanism operates in complex supply networks. We conceptualize collaborative capability through information sharing, joint decision-making, and coordinated resource allocation, and identify data governance, process intelligence, organizational coordination, and ecosystem openness as key amplifiers of this capability. Security and compliance are treated as gating conditions that shape the effectiveness and scalability of CSR initiatives. The paper explains how CSR practices enhance environmental, social, and governance outcomes, while simultaneously improving operational resilience through information symmetry, risk sharing, and co-innovation among supply chain partners. Building on this framework, we propose phased investment paths, portfolio-based governance of CSR projects, and platform-oriented scaling strategies that align CSR with core business objectives. The study further offers context-specific managerial guidance for firms operating under varying regulatory pressures, digital maturity levels, and ecosystem structures. Overall, the findings clarify the strategic role of CSR in strengthening collaborative advantage and provide a structured basis for designing, evaluating, and continuously improving CSR-driven supply chain strategies.
Copyright (c) 2026 Minnan Zhang, Shuang Han (Author) https://creativecommons.org/licenses/by/4.0
Biotechnology Evaluation of Biocompatibility and Nutritional Activity of 3D Printed Edible Materials
Biocompatibility and nutritional activity are fundamental considerations in the development and application of 3D printed edible materials. As 3D food printing technologies become more accessible and customizable, there is a growing need for systematic evaluation frameworks to ensure safety, functionality, and health benefits. This review summarizes current regulations and standards relevant to 3D printed foods, and categorizes commonly used materials, including natural polymers, synthetic biodegradable matrices, and incorporated bioactive compounds. It outlines conceptual criteria for biocompatibility, in vitro and in vivo assessment methods, and key factors influencing biological responses, such as material composition, degradation behavior, and printing parameters. Nutritional activity is discussed in terms of compositional analysis, stability of nutrients during processing, bioavailability evaluation, and the impact of structure and formulation on digestion and absorption. Particular attention is given to challenges such as heterogeneous material behavior, process-induced quality loss, and technological limitations in resolution and temperature control. The review further highlights opportunities for designing novel composite formulations, establishing multidimensional evaluation standards that integrate safety, functionality, and nutrition, and developing low-temperature or gentle printing strategies to preserve sensitive components. Overall, this work provides a structured perspective to guide the rational design, assessment, and regulation of 3D printed edible materials, supporting their safe and effective integration into future food systems.
Copyright (c) 2026 Junming Feng (Author) https://creativecommons.org/licenses/by/4.0
Optimization and Application of Large Language Model in Higher Education Question Answering System
With the rapid development of large language model technology, its application in diverse domains has expanded significantly, and higher education is no exception. This study investigates the optimization and application of large language models in higher education knowledge question-answering systems. First, it reviews the theoretical foundations of large language models and domain-oriented question-answering systems, clarifying their core architectures, training paradigms, and typical deployment modes in educational contexts. Second, it examines strategies to enhance system performance from three key dimensions: data and model optimization, integration of structured and unstructured knowledge resources, and the design of effective human–computer interaction mechanisms. On this basis, the study elaborates on innovative applications across disciplines, including intelligent tutoring, personalized learning support, formative assessment, academic research assistance, and institutional knowledge management. Furthermore, it analyzes major challenges such as data quality, model bias, interpretability, privacy protection, and alignment with pedagogical goals, and summarizes corresponding coping strategies and governance frameworks. Finally, the paper discusses future development trends, including multimodal integration, adaptive learning analytics, and closer collaboration between educators and AI systems. The study aims to provide a systematic reference for the effective deployment of large language models in higher education knowledge question-answering systems and to promote the transformation and sustainable development of higher education teaching models.
Copyright (c) 2026 Hui Jia, Kaimei Luo (Author) https://creativecommons.org/licenses/by/4.0
Digital Twin Technology-Driven Smart Construction and Application Research of Abandoned Mine Pit Tourism Scenes
Abandoned mine pits, as distinctive post-industrial landscapes, hold considerable potential for tourism development, ecological restoration, and regional regeneration. However, their sustainable utilization is constrained by complex safety risks, environmental degradation, fragmented data, and difficulties in real-time visitor management. This study investigates the application of digital twin technology to construct intelligent, immersive, and interactive tourism scenarios for abandoned mine pits. By integrating multi-source data, including IoT sensor networks, GIS mapping, high-precision 3D modeling, and real-time visitor behavior analysis, a comprehensive digital twin platform is developed to mirror physical conditions and operational states of the site. The platform supports continuous environmental monitoring, structural safety assessment, and dynamic scenario simulation under different visitor flows and climatic conditions. Case studies demonstrate the system’s capacity for early risk prediction, emergency response support, and personalized service delivery through adaptive guidance, information visualization, and experience optimization. The research further discusses system architecture, data integration workflows, and key technical challenges related to interoperability and model updating. The findings highlight the transformative potential of digital twins in revitalizing abandoned industrial sites, promoting sustainable tourism, enhancing environmental governance, and supporting smart destination management and planning decisions.
Copyright (c) 2026 Lening Li (Author) https://creativecommons.org/licenses/by/4.0