Simon Owen Academic Proceedings Series 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.
Seminar Paper in the Module "Scientific Work"
This research examines the structural dynamics shaping China's biotechnology industry and investigates how Hengrui Medicine---a leading domestic innovator---has navigated evolving macro-environmental conditions and strategic growth imperatives. Drawing on the PEST framework, the study systematically analyzes political, economic, social, and technological forces that have collectively redefined regulatory expectations, funding landscapes, talent mobility, and R&D infrastructure over the past decade. Concurrently, the Ansoff Matrix serves as the core strategic lens to map Hengrui Medicine's deliberate progression across market penetration, product development, market development, and diversification pathways. Empirical analysis reveals a tightly coupled alignment between national policy acceleration---such as the 2015 Drug Review Reform and subsequent innovation incentives---and the firm's internal capability-building in oncology-focused drug discovery, clinical trial execution, and international regulatory engagement. The findings demonstrate that Hengrui's sustained growth is not attributable to isolated tactical decisions but to an iterative, feedback-driven strategy wherein external environmental scanning continuously informs internal resource allocation and portfolio prioritization. Importantly, the study identifies emerging tensions---including intensifying domestic competition, global IP scrutiny, and shifting reimbursement frameworks---that challenge the scalability of prior growth logic. By integrating macro-level institutional analysis with micro-level corporate strategy, this work contributes a contextualized, process-oriented understanding of innovation-led industrial upgrading in a state-influenced emerging economy.
Copyright (c) 2025 Simen Owen Academic Proceedings Series
Internal Architecture and Electrochemical Mechanisms of Redox Flow Batteries
Redox Flow Batteries (RFBs) have emerged as a transformative solution for large-scale, long-duration energy storage, addressing the safety and scalability limitations of conventional lithium-ion systems. This paper provides a comprehensive analysis of the internal architecture and electrochemical mechanisms that define RFB performance. By decoupling power and energy capacity through external electrolyte circulation, RFBs offer unique flexibility and an exceptional cycle life exceeding 15,000 cycles. We detail the critical role of the cell stack components, including bipolar plates, carbon-based electrodes, and optimized flow field designs, in minimizing concentration polarization and enhancing mass transport. A significant portion of the study focuses on the separator as the primary gatekeeper of efficiency. We evaluate the technical pathways of membrane materials, ranging from industry-standard perfluorinated sulfonic acid (PFSA) membranes to emerging porous separators and Polymers of Intrinsic Microporosity (PIMs). These materials are assessed based on their ion exchange capacity, area resistance, and ability to mitigate active species crossover. ly, the paper reviews the industrial landscape, highlighting large-scale demonstration projects and the ongoing transition toward low-cost, non-fluorinated materials. By integrating material innovation with system-level optimization, RFBs are poised to serve as the backbone of resilient, stable, and sustainable energy grids.
Copyright (c) 2026 Simen Owen Academic Proceedings Series
The Reconstruction of Project-Based Curriculum Guided by Core Literacy: Design and Effectiveness in Promoting Deep Learning in Students
Global educational reforms increasingly prioritize core competencies, with Project-Based Learning (PBL) as a key strategy for fostering transferable, deeper learning. However, a gap remains between PBL advocacy and implementation, as projects are often seen as isolated tasks rather than integral to curriculum redesign for systemic competency development. Current research insufficiently examines how PBL can systematically serve as the core structure for curriculum reconstruction to foster core literacy and deep learning. The mechanisms and outcomes of this approach remain underexplored. This study used design-based research to implement the Core Literacy-Embedded Project Cycle (CLPC) model in an 8th-grade biology unit. A mixed-methods approach compared an intervention group (n=60) with a comparison group (n=60), using pre-post tests, scenario-based tasks, project analysis, classroom observations, and reflective journals. The reconstructed curriculum led to equivalent gains in foundational knowledge but significantly better performance in knowledge transfer and application for the intervention group. Qualitative data showed increases in metacognitive engagement, reasoning, and collaboration, alongside challenges in time allocation and teacher facilitation. This study provides the validated CLPC model, demonstrating that integrating core literacy and PBL promotes deep learning without sacrificing content mastery. It offers practical insights for educators and suggests future research into cross-disciplinary applications and long-term impact studies.
Platform–Corridor–Hall and Regional Mechanisms of Taoist Architecture in Central China: A Comparative Study of Wudang Mountain and Taihui Temple
This study investigates the regional mechanisms of Taoist architecture in Central China through a comparative analysis of the Golden Hall complex on Wudang Mountain and Taihui Temple in Jingzhou. Existing scholarship has largely emphasized symbolic meanings, religious iconography, and artistic styles of Taoist temples, but has rarely examined how geography, culture, construction technology, and political context jointly shape regional architectural patterns. Adopting the Platform–Corridor–Hall spatial paradigm as the primary analytical framework, and integrating field surveys, historical documentation, and typological analysis, this research reveals both shared principles and site-specific adaptations. The findings demonstrate that, while both temple complexes embody the Taoist cosmology of the “unity of Heaven and Man” through Bagua-based orientation, axial symmetry, and hierarchical spatial sequences, their construction strategies diverge in response to local conditions. Wudang Mountain employs copper casting, multi-tiered Sumeru platforms, and pronounced imperial symbolism to adapt to humid mountain climates and to materialize royal patronage. By contrast, Taihui Temple raises robust stone foundations, simplifies roof and bracket systems, and incorporates Chu cultural motifs to resist flooding, accommodate urban fabric, and embed local beliefs. Building on these observations, the study develops a four-axis model—nature, culture, technology, and socio-political symbolism—to explain the dynamic interplay driving regional differentiation. This framework offers methodological and practical insights for the conservation, restoration, and adaptive reuse of Taoist architectural heritage in diverse ecological and cultural settings.
Family, School, Neighbourhood and Education
Education inequality has become a global issue, influenced by various factors such as family, school, and neighborhood dynamics. Despite significant research on the role of these factors, there remains a gap in understanding their interrelationship and the mechanisms that drive educational disparities. This paper explores the impact of family and neighborhood on educational outcomes, focusing on family resources, social capital, and school choice, as well as how neighborhood characteristics influence parental decisions and children's education. Using a qualitative approach, this study reviews existing literature and applies theoretical frameworks, such as cultural capital and social capital, to analyze how family background and neighborhood environment shape educational experiences. The results highlight that family economic resources and social capital significantly affect school choice and parental involvement in education. Neighborhood quality also plays a role, particularly in shaping parenting strategies and children's educational outcomes. This research contributes to the understanding of educational inequality by illustrating the complex interactions between family, school, and neighborhood. The findings underscore the need for policy interventions that address these disparities and promote equitable educational opportunities for all children.
An ESG-Oriented Carbon Emission Monitoring System for Industrial Parks: UAV Remote Sensing and IoT Sensor Fusion Method Research
The increasing emphasis on environmental, social, and governance (ESG) frameworks has highlighted the need for effective carbon emission monitoring systems, particularly in industrial parks that contribute significantly to environmental degradation. Although various monitoring methods exist, there remains a notable gap in integrating unmanned aerial vehicle (UAV)-based remote sensing with Internet of Things (IoT) sensor technologies for comprehensive, real-time carbon emission monitoring in complex industrial settings. This study proposes an integrated UAV–IoT system that combines high-resolution spatial data from UAV platforms with continuous in situ measurements from distributed IoT sensors. The system architecture, data fusion workflow, and communication protocols are designed to support real-time acquisition, transmission, and processing of multi-source environmental data. A case study conducted in an industrial park demonstrates that the integrated system substantially outperforms traditional fixed-point monitoring approaches in terms of spatial coverage, detection sensitivity, and timeliness of data delivery. UAV-based mapping effectively identifies emission hotspots and plume dispersion patterns, while IoT sensors capture localized concentration dynamics and relevant meteorological parameters. The fused dataset enables more accurate characterization of emission profiles and supports early warning and rapid response. The findings contribute to ESG-oriented decision-making by providing an operational, scalable solution for carbon emission monitoring, supporting low-carbon governance, regulatory compliance, and continuous improvement of sustainability performance in industrial parks and similar industrial clusters.
Prompt-Based Adaptive English Learning: Leveraging AI for Personalized Practice
The integration of generative artificial intelligence (AI) into language education has enabled scalable and personalized English learning experiences. However, the pedagogical effectiveness of such systems largely depends on the quality of prompt design that guides AI response generation. Most current AI-assisted English learning platforms rely on static or pre-set prompts, limiting adaptability to learners' varying proficiency levels and emotional needs. The absence of a systematic framework connecting prompt construction with adaptive learning theory constrains instructional precision and learner engagement. This study proposes an Adaptive Prompt Learning Model (APLM) that integrates prompt engineering with educational psychology. Using mixed methods, comparative case studies of ChatGPT, Duolingo Max, and iFLYTEK AI Tutor, combined with a six-week classroom experiment involving 120 learners, the research evaluates how structured prompts shape personalized exercises and feedback. Adaptive prompting improved vocabulary retention by 23.6%, grammar accuracy by 17.8%, and reduced learner anxiety through emotionally supportive feedback. The findings demonstrate that pedagogically informed prompt design can align AI behavior with cognitive and affective learning needs, providing a practical framework for scalable, interpretable, and equitable AI-assisted English education.
Carbon Neutrality Applications in Green Technology: Case Studies and System Innovation
As the industrial transition toward carbon neutrality enters a critical deployment phase in 2026, relying solely on incremental efficiency is insufficient to address the high energy intensity of heavy industries. However, current research frequently overlooks the "systemic friction" of standalone Carbon Capture, Utilization, and Storage (CCUS) units, failing to adequately account for parasitic energy loads and infrastructure deficits. To address this integration gap, this study employs the Multi-Level Perspective (MLP) to evaluate the Systemic Innovation Framework through a comparative analysis of the Northern Lights project (Norway) and Baowu Steel's CCU integration (China). Findings indicate that coupling carbon neutrality technologies with industrial symbiosis significantly mitigates operational overheads. Specifically, Baowu's energy-carbon coupling reduced the capture energy penalty by approximately 18%, while Northern Lights' shared infrastructure model effectively lowered the Levelized Cost of Abatement (LCOA) through economies of scale. These results validate the necessity of transitioning from linear mitigation models to "Circular Carbon Hubs," providing a strategic roadmap for policy-makers to prioritize cross-sectoral infrastructure coordination over isolated technical upgrades.
A Review of Efficacy and Neural Mechanisms of Adult ADHD and ASD Treatments
Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are prevalent neurodevelopmental problems for many people, and usually followed them from childhood to adulthood. While many approaches, such as pharmacological, like stimulants; behavioral, like Cognitive Behavioral Therapy; physical interventions, like DCS; are available, their comparative efficacy and underlying neural mechanisms are not clear at all. This review will analyzes current evidence of those treatments and summarizes their neuropsychological basis. By providing a comprehensive overview of existing treatment options, this review aims to help clinical decision-making for medical care workers and guide and search the possibility of related future research for more individualized interventions for adults with ADHD or ASD.
Attention-Deficit/Hyperactivity Disorder
Biomarkers of Depressive Disorder and Dietary Intervention Strategies
Depressive disorder (DD) is a multifactorial mental illness involving dysregulation across neuroendocrine, immune, metabolic, and microbial systems. Despite substantial research progress, diagnostic and therapeutic approaches remain largely symptom-based, lacking biological precision. This study addresses this gap by systematically reviewing key biomarkers, such as cortisol, IL-6, CRP, BDNF, and the kynurenine/tryptophan ratio, and examining their modulation through dietary interventions. Using an integrative literature analysis spanning psychiatry, molecular biology, and nutritional science, the paper explores how the Mediterranean diet, anti-inflammatory diet, and probiotics/prebiotics regulate inflammatory cytokines, neurotrophic factors, and gut microbiota activity. Results indicate that these nutritional models can attenuate systemic inflammation, enhance neurotrophic signaling, normalize HPA axis function, and restore microbial balance, thereby improving depressive symptoms. The findings underscore the potential of a biomarker-guided, diet-based framework for precision management of depression. Academically, the study contributes to the conceptual integration of biomarker and nutritional psychiatry research; practically, it offers an evidence-based foundation for individualized, non-pharmacological adjunct therapies in mental health care.
Analysis of the Success Factors of Shenzhen as a Location
This paper takes Shenzhen, a city that has achieved rapid development from a small fishing village to an international metropolis, as the research object, aiming to explore the core factors driving its economic development and location success. First, the paper sorts out the theoretical basis of location success factors, including their definition, effective operation process, and core objectives. It then adopts the PEST analysis model to systematically elaborate on the influence of economic, political, social, and technological factors on urban development, with a focus on the decisive role of economic factors. On this basis, the paper conducts an in-depth empirical analysis of Shenzhen, exploring its performance as an economic-oriented city in terms of employment, corporate agglomeration, GDP, and technological innovation. It further dissects Shenzhen's key location success factors from six economic dimensions: investment and capital accumulation, industrial structure optimization, employment and labor market construction, consumer demand growth, opening-up to foreign trade, and technological innovation and application. Finally, the paper summarizes the comprehensive driving role of multi-dimensional factors in Shenzhen's development, points out the research limitations caused by time constraints on data and literature, and proposes future research directions related to opportunities and challenges in Shenzhen's sustainable development.
Illness Writing and Subjectivity Construction in Diaries of Republican-Era Women
During the Republican era (1912-1949), Chinese women increasingly turned to diary writing as a medium for articulating experiences of illness amid rapid social and medical transformation. While existing scholarship has examined women's autobiographical texts through literary or social-historical lenses, it often treats the body as metaphorical or marginalizes illness as a passive condition, overlooking its role in shaping epistemic agency. Addressing this gap, this study employs qualitative textual analysis of three anonymized archival diaries from the late 1920s, mid-1930s, and early 1940s, selected for their sustained engagement with physical and mental suffering, medical encounters, and reflective narration. Analysis is guided by four dimensions: symptom lexicon, portrayal of medical authority, narrative agency, and intertextual framing. Findings reveal that diarists consistently reinterpreted illness not as weakness but as a site of critical knowledge, challenging diagnostic dismissal, resisting gendered state ideologies, and asserting bodily specificity against national allegory. Their narratives are marked by fragmentation and chronicity, rejecting restitution models in favor of non-linear, embodied temporality. This research contributes to transnational medical humanities by demonstrating how private writing functioned as a technology of epistemic sovereignty for women whose voices were excluded from public and clinical discourse. It further calls for historically grounded, non-Western approaches to illness narrative that recognize lay interpretation as legitimate knowledge production.
Association of Substance Use and Mental Health among U.S. High School Students from 2023 YRBS
This study investigated the association between substance use and mental health among U. S. high school students. The 12679 adolescents (50.68% female) sample data was pooled from the 2023 Youth Risk Behavior Survey (YRBS). The multivariate logistic regression models were used for explaining its potential relationship. The dependent variables were the mental health issues, and the independent variables were the use of substance, including cigarette, electronic vapor product, alcohol, marijuana and prescription pain medication. The results suggested that the female or male students who reported the use of different substances had increased risks of having mental health issues compared to the counterparts who had never used substances. The research findings will be useful for professionals and governments when they are considering an intervention strategy to reduce the mental health issues among teenagers.
Discrete Images in Archaeology and the Future: Spatiotemporal Folds and Memory Construction in Asian Immigrant Cinema
Asian immigrant cinema has emerged as a vital site for exploring the interplay between migration, cultural memory, and identity. While existing scholarship often addresses themes of belonging and heritage, it largely overlooks how specific narrative techniques, particularly spatiotemporal folds that disrupt linear time and reconfigure space, contribute to memory construction. This study addresses that gap by examining how temporal dislocation and spatial juxtaposition function as formal strategies for negotiating past and present in diasporic contexts. Through qualitative textual analysis and comparative case studies of two contemporary films, the research identifies recurring patterns in the use of non-linear timelines, cyclical structures, and liminal spaces. Findings reveal that spatiotemporal folds enable a dynamic integration of individual and collective memory, challenging binary distinctions between homeland and host society. Theoretically, this work advances film and memory studies by proposing a framework that links cinematic form to diasporic meaning-making. Practically, it offers filmmakers and cultural practitioners a nuanced understanding of how narrative structure can authentically convey the complexities of immigrant experience without resorting to linear historiography or cultural essentialism.
A Comparative Study of Settlement Patterns and Socio-political Organization in the Yellow River Basin and the Maya Lowlands
The Yellow River Basin and the Maya Lowlands represent two distinct yet influential ancient civilizations. While existing research has explored each region individually, there is a significant gap in comparative studies addressing how environmental factors, political organization, and religious structures shaped settlement patterns and governance. This study aims to fill this gap by providing a comparative analysis of these regions, focusing on key case studies, such as Anyang and Luoyang from the Yellow River Basin, and Tikal and Copán from the Maya Lowlands. Using a comparative historical analysis, qualitative research, and case study methodology, the study explores how environmental challenge, such as flooding in the Yellow River Basin and tropical conditions in the Maya Lowlands, shaped urban development and governance models. The findings reveal that the Yellow River Basin's centralized political structure contrasted with the decentralized city-states of the Maya Lowlands, and that both regions used religious practices to reinforce political authority. This study contributes to the broader understanding of ancient urbanism and governance, offering insights that can inform contemporary urban planning and resource management practices. It also proposes directions for future research, particularly in exploring the role of ecological factors in shaping social structures.
Heterogeneous Isomorphism and Perceptual Experience: Why Music Evokes Emotional Feelings
Music has long been recognized for its ability to evoke emotional responses, yet the precise mechanisms underlying this phenomenon remain unclear. Existing research often isolates individual musical elements such as rhythm, melody, and harmony, neglecting the interactions between these components. This study introduces the concept of heterogeneous isomorphism, which proposes that the emotional impact of music arises from the synergy between various musical features, rhythm, melody, harmony, and timbre. The research combines case studies, comparative analysis, and phenomenological methods to explore how these elements interact to produce complex emotional responses. The findings demonstrate that emotional experiences are intensified when musical elements work together, with cultural context playing a crucial role in shaping emotional perceptions. For example, Western listeners associate major harmonies with happiness, while Eastern listeners interpret the same music in a more nuanced, emotionally ambiguous manner. This research contributes a novel framework for understanding how music induces emotions and offers practical insights for applications in music therapy, education, and cross-cultural studies. By integrating diverse musical features and cultural perspectives, this study provides a comprehensive approach to the complex relationship between music and emotion.
Exploring the Changing Elements of I Ching in Artistic Creation
In the post-World War II era, amid widespread questioning of artistic intentionality and historical truth, the I Ching (Book of Changes) offered Western artists a structured philosophy of change. This paper examines how Merce Cunningham and John Cage engaged with the I Ching, not as a divinatory or holistic system, but as a methodological framework for introducing chance into creative process. Cunningham operationalized its hexagrams to determine choreography through coin tosses and spatial mappings, while Cage adapted its symbolic systems into generative rules for music and visual art. Their practices reflect a selective translation, prioritizing procedural logic over cosmic harmony, shaped by 20th-century consumer culture and Zen philosophy, ultimately codifying the I Ching into an artistic tool for navigating chaos and contingency. Inspired by this approach, the author's interactive digital work Trace further explores themes of journey, perception, and chance through real-time video and sound, examining the tension between systematic generation and lived experience, while reflecting on the material and technical constraints of exhibition practice.
Optimization of Power Systems for Low-Carbon Manufacturing in New Energy Vehicles
The transition toward carbon neutrality has intensified the need for low-carbon manufacturing in the new energy vehicle (NEV) industry. While existing research has primarily focused on the use phase of vehicles, limited attention has been given to the carbon emissions and energy consumption arising during the development and manufacturing of power systems. To address this gap, this study establishes an integrated Energy-Carbon-Cost (ECC) framework that combines lifecycle assessment (LCA), empirical industrial data, and multi-objective optimization modeling. The framework quantifies how design parameters, such as material substitution, motor efficiency, and cooling configurations, affect lifecycle carbon intensity, cost, and energy efficiency. Results based on case studies from SAIC, BYD, and Volkswagen demonstrate that optimized configurations can reduce total lifecycle carbon emissions by approximately 24% while maintaining economic feasibility. The proposed model transforms traditional LCA from a static evaluation tool into a dynamic, prescriptive decision system, enabling both theoretical innovation and practical applicability. This research provides manufacturers and policymakers with an actionable pathway for achieving synergistic carbon reduction and cost optimization, contributing to the sustainable advancement of NEV manufacturing.
Electronic Coupling Effects of 2D Material Heterostructures and Their Applications in Flexible Optoelectronic Devices
In the post-Moore era, flexible optoelectronic devices are rapidly advancing towards wearable and portable applications, placing higher demands on material optoelectronic performance and mechanical flexibility. Single 2D materials have inherent limitations, graphene lacks a bandgap, limiting photoelectric conversion efficiency; transition metal dichalcogenides (TMDs) like MoS₂ have low carrier mobility; and black phosphorus is prone to oxidation, affecting long-term stability. These challenges are difficult to address through single material modification. 2D material heterostructures, formed through interlayer composite structures, leverage electronic coupling effects to enhance performance synergistically, providing an effective solution to overcome these limitations. This paper reviews common construction methods for typical 2D heterostructures, such as graphene-based, TMDs-based, and black phosphorus-based structures, including van der Waals stacking, covalent bonding, and solution assembly. It focuses on the mechanisms of interlayer charge transfer, exciton transfer, and orbital hybridization, and discusses how layer number control, strain application, and interface modification influence these coupling effects. Additionally, this work surveys the application of these heterostructures in flexible photodetectors, flexible light-emitting diodes (LEDs), and flexible solar cells, comparing their photoelectric response speed, energy conversion efficiency, and bending stability. Finally, challenges such as interlayer delamination, environmental instability, and difficulties in large-scale fabrication are discussed, with future directions in interface engineering and multifunctional heterostructure integration proposed for enhancing practical applications.
Research on Innovative Strategies of Physics Experiment Teaching in Senior High School Based on the Cultivation of Physics Core Literacy
This paper focuses on high school physics experiment teaching based on the cultivation of physics core literacy. Firstly, the connotation of physics core literacy, the characteristics of physics experiment teaching in senior high school and their relationship are expounded, and the problems existing in teaching objectives, contents, methods, evaluation and teachers' literacy are pointed out. Then put forward innovative strategies, including optimizing the design of teaching objectives and running the core literacy elements through the whole process; Innovating teaching content, combining reality with cutting edge; Improve teaching methods and means and make use of modern technology; Improve the evaluation system and consider the process and results; Strengthen the improvement of teachers' professional quality. Through the comprehensive application of these strategies, it is expected to improve the teaching quality and cultivate students' core literacy in physics.
AliExpress's Internationalization: Strategies and Practices on Cross-Border E-Commerce Platforms
This study conducts an in-depth examination of AliExpress's globalization strategies on cross-border e-commerce platforms, highlighting how the integration of "multi-tier ecological empowerment" and "glocalization" strategies fosters systemic innovation across five critical dimensions: market selection, platform operations, product assortment and pricing, logistics and payment, and marketing promotion. The findings indicate that AliExpress adopts a "mature-emerging" dual-track model for global market expansion: leveraging overseas warehouses and localized services to enhance timeliness in European and American markets, while deploying strategies such as "cross-border express delivery" to penetrate emerging markets including Southeast Asia. In addition, the company has developed a four-dimensional operational matrix, "POP self-operation + full hosting + semi-hosting + overseas hosting", to empower diverse types of sellers. Supported by data scraping and flexible testing mechanisms, the full-hosting model has increased the blockbuster success rate to 67%. By constructing six million square meters of overseas warehouses and implementing blockchain-based settlement technologies, AliExpress has optimized logistics and payment experiences. Moreover, the platform harnesses social media channels such as TikTok to achieve viral marketing at scale. These strategies have positioned AliExpress among the global top three in personalized shopping experience in PYMNTS rankings, while its share of organic traffic has risen to 63%. The paper also underscores the challenges AliExpress faces, including compliance risks, insufficient localization depth, and ESG-related pressures, and proposes targeted optimization measures. Overall, the research not only offers a replicable pathway for the globalization of Chinese manufacturing but also provides important insights for the future evolution of the cross-border e-commerce industry.
Graph Neural Networks for Business Relationship Mining: Applications and Performance Analysis
Business ecosystems, such as supply-chain networks, financial transaction systems, and e-commerce platforms, exhibit complex relational structures that challenge traditional machine-learning models. Although graph neural networks (GNNs) have shown promise in capturing such dependencies, existing studies often focus on single domains, rely on static graphs, or lack systematic comparison across heterogeneous commercial settings. To address these gaps, this study proposes a unified analytical framework that integrates relational embeddedness theory, graph representation learning, and dynamic capability perspectives. Using three representative real-world scenarios, a retail procurement graph, an AML transaction network, and an e-commerce product affinity graph, we evaluate four GNN architectures (GCN, GraphSAGE, GAT, and Temporal-GNN) through link prediction, fraud detection, and recommendation tasks. The results show that attention-based models outperform others in heterogeneous supplier and transaction environments, temporal GNNs better capture evolving fraud patterns, and inductive architectures excel in high-turnover product graphs. These findings deepen theoretical understanding of relational learning in commercial systems and offer practical guidance for deploying GNN-based analytics in procurement risk assessment, financial compliance, and personalized recommendation services.
AI-Driven Optimization of Healthcare Resources: An Intelligent Management Model for Hospital Operational Efficiency in the Post-Pandemic Era
The COVID-19 pandemic has underscored the critical need for flexible and adaptive hospital management systems to handle fluctuating patient volumes and constrained medical resources. Traditional resource allocation methods, relying on fixed schedules and static capacity planning, are increasingly inadequate in addressing the dynamic challenges faced by hospitals. This study addresses this gap by proposing an AI-driven hospital resource optimization model, integrating demand forecasting, dynamic scheduling, and system simulation. The model was evaluated using real-world hospital data, demonstrating significant improvements in operational efficiency, including a 20% reduction in patient waiting times and a 33% increase in bed turnover rate. The AI model also enabled proactive staff scheduling and bed management during peak demand periods, such as flu seasons. This research contributes to the understanding of hospitals as complex adaptive systems and provides a replicable framework for optimizing resource allocation. The findings have significant implications for improving hospital resilience, operational efficiency, and patient care quality, particularly in post-pandemic healthcare settings. Future work will focus on refining the model for multi-hospital applications and addressing the challenges of data integration and ethical concerns in AI decision-making.
Single-Atom Catalysts Based on Two-Dimensional Transition Metal Carbides for Efficient Carbon Dioxide Reduction
The electrochemical reduction of carbon dioxide (CO₂RR) into value-added products is a promising route toward carbon neutrality, yet its efficiency is often limited by the weak activity and instability of conventional catalysts. Single-atom catalysts (SACs) anchored on two-dimensional transition metal carbides (MXenes) offer a tunable platform with high atomic utilization and strong metal-support interactions. However, the fundamental understanding of how coordination geometry and electronic coupling jointly regulate CO₂ activation remains insufficient. This study integrates density functional theory (DFT) calculations with experimental electrochemical characterization to investigate MXene-supported SACs. The Fe₁/Ti₃C₂O₂ system exhibits a pseudo-square-planar coordination environment with strong Fe-O bonding, leading to an optimized d-band alignment and enhanced σ-back-donation to CO₂π* orbitals. The calculated free-energy barrier for COOH formation is only 0.41 eV, resulting in high Faradaic efficiency (91.2%) and a partial current density of 34.8 mA·cm⁻². These findings reveal that ligand field stabilization and interfacial charge transfer are key to achieving efficient CO₂ reduction. The proposed coordination-electronic coupling framework provides actionable guidelines for the rational design of high-performance catalytic materials in sustainable energy conversion.
Energy-Adaptive Micro-Mechanical Systems Driven by Intelligent Materials: Experimental and Simulation Study Based on the Piezoelectric Effect
The miniaturization of mechanical and electronic systems has driven demand for self-powered micro-electro-mechanical systems (MEMS) in robotics, biomedical implants, and distributed IoT networks. Piezoelectric materials offer direct mechanical-to-electrical energy conversion, making them promising candidates for energy-autonomous microsystems. Existing studies largely optimize either material performance or system-level design independently, neglecting the integration of microstructural electromechanical behavior with adaptive energy regulation. Moreover, nonlinear piezoelectric responses under micro-scale cyclic loading remain insufficiently quantified, leading to 15-30% discrepancies between simulations and experiments. This work develops a hybrid PZT-PVDF composite thin film integrated into a cantilever MEMS with a closed-loop adaptive feedback controller. A coupled multi-physics finite element model links stress-strain fields to voltage output, while real-time stiffness adjustment maximizes energy harvesting efficiency under variable vibration conditions. Experimental validation spans 150 samples with cyclic loading across 50-500 Hz. The proposed adaptive MEMS achieves a peak voltage of 5.8 ± 0.2 V, output power density of 2.45 ± 0.05 mW·cm⁻³, and conversion efficiency of 77.1 ± 1.1%, representing a 28% improvement over non-adaptive hybrid systems. Voltage degradation after 10⁵ cycles is limited to 3.2 ± 0.4%, and the system converges to steady-state power within 12 ± 2 control iterations. By integrating material-level physics, multi-physics modeling, and adaptive control, this framework enhances energy conversion reliability and provides a reproducible approach for designing energy-autonomous MEMS suitable for micro-robots, implantable sensors, and distributed IoT devices.
Evaluating the Effectiveness of Legal Regulation of AI-Generated Content and Optimizing Regulatory Pathways
The rapid expansion of generative artificial intelligence (AI) has transformed fields such as media, education, and politics, while simultaneously raising urgent legal and ethical challenges. Issues including disinformation, copyright disputes, and threats to democratic processes have intensified debates on how best to regulate AI-generated content. Yet existing research often focuses on single jurisdictions, lacks systematic evaluation criteria, and rarely explores pathways for globally adaptive governance, leaving both scholars and policymakers without a coherent framework for assessing effectiveness. This study addresses these gaps by developing a multidimensional evaluative framework grounded in technology-law co-evolution, risk-benefit balancing, and human rights-based governance. Employing comparative legal analysis, doctrinal review, and case studies from the European Union, the United States, and China, the research assesses regulatory effectiveness across three dimensions: enforceability, adaptability, and rights protection. Findings indicate that the EU ensures comprehensive safeguards but struggles with consistent enforcement, the U.S. emphasizes expressive freedom but remains fragmented, and China achieves strong compliance at the cost of transparency and rights. The paper contributes theoretically by integrating diverse perspectives into a coherent model and practically by proposing a hybrid regulatory pathway that combines hard law, soft law, and oversight mechanisms. This approach provides a roadmap for more balanced and adaptive governance of AI-generated content.
Applications and Developments in Semiconductor Memory Technology
With the rapid evolution of information technology, semiconductor memory has become a cornerstone of modern computing, supporting big data, artificial intelligence, and the Internet of Things. Traditional magnetic storage technologies are increasingly unable to meet demands for speed, capacity, and energy efficiency, highlighting the urgency of advancing semiconductor memory research. Despite progress in DRAM, SRAM, and flash memory, existing studies reveal gaps in addressing production sustainability, high costs, and environmental challenges. This paper systematically reviews the development and industrial landscape of semiconductor memory, analyzes its manufacturing processes and associated pollutants, and evaluates emerging sustainable solutions such as advanced abatement systems and wastewater treatment strategies. It further explores cutting-edge alternatives, particularly Resistive Random Access Memory (ReRAM), which integrates high-speed and non-volatile features but still suffers from device variability, circuit complexity, and limited market readiness. Results show that innovative process optimization and architecture-aware training can partially mitigate these constraints, while sustainable practices significantly reduce environmental impacts. The study underscores that advancing semiconductor memory requires not only technological breakthroughs but also robust sustainability and intellectual property strategies, offering critical implications for global competitiveness, industrial upgrading, and the transition toward low-carbon intelligent infrastructures.
Optimization Analysis of Stability and Deformation Control Methods for Deep Excavation Support Structures Based on Field Measurements
Deep excavation support systems in urban environments present demanding engineering challenges due to their inherent stability risks and potential for inducing detrimental deformations in adjacent infrastructure. Conventional design methodologies often inadequately address the complex soil-structure interaction dynamics and temporal construction effects, leading to either excessive conservatism or unforeseen performance issues. This study develops an integrated optimization framework that synergistically combines high-frequency field instrumentation data with advanced computational modeling to enhance the stability and deformation control of deep excavation support structures. The proposed methodology employs a physics-informed Bayesian calibration approach to continuously update finite element models using real-time measurements from inclinometers, strain gauges, and piezometers. A multi-objective optimization algorithm subsequently identifies optimal support configurations that simultaneously maximize stability margins, minimize deformation, and reduce material costs. Validation through a major metropolitan excavation case study demonstrates that this field measurement-driven approach achieves significant improvements in deformation control while maintaining structural integrity. The framework's ability to adaptively refine support designs during construction phases offers substantial advancements over static design paradigms. By transforming conventional excavation support into a responsive, data-informed process, this research provides a foundation for intelligent infrastructure development in spatially constrained urban environments.
Dynamic Scheduling Strategy for Complex Industrial Processes Based on Multi-Agent Collaboration
The increasing complexity of modern industrial processes, characterized by frequent disturbances such as equipment failures and urgent order changes, demands more adaptive scheduling solutions. Traditional centralized scheduling methods often fail to address real time dynamics, while existing multi agent systems face challenges in balancing local autonomy with global optimization. This study proposes a novel dynamic scheduling strategy integrating multi agent collaboration with a credit based coordination mechanism to enhance responsiveness and efficiency in complex industrial environments. The research develops a three layer agent architecture comprising resource, task, and coordinator agents, linked through an event driven communication protocol. A hybrid negotiation framework enables both rapid response to emergencies and deliberative optimization for long term scheduling. The core innovation lies in a dynamic credit allocation model that evaluates agents' historical performance and collaborative contributions to guide task assignment. These findings advance distributed industrial control theory by formalizing the relationship between agent incentives and system wide performance. The proposed approach provides actionable insights for implementing Industry 4.0 adaptive scheduling in discrete manufacturing sectors.
Research and Optimization of a Real-Time Quality Monitoring System for Smart Production Lines Based on IoT Sensors
The rise of Industry 4.0 has accelerated the adoption of IoT-enabled sensing for real-time quality assurance in smart manufacturing. However, most existing systems depend on cloud-centric analytics or supervised learning models that require extensive labeled defect data, leading to latency, poor adaptability, and limited applicability in dynamic production environments. To address this gap, this study proposes an IoT sensor-driven quality monitoring framework based on multi-modal signal acquisition, edge computing, and adaptive thresholding informed by short-term process variability. The system was deployed and evaluated on two industrial production lines, Bosch automotive components and CATL lithium-ion module assembly, using longitudinal tracking of defect rate, first-pass yield, and overall equipment effectiveness. Results indicate a 26-31% reduction in defects, a 26.9% increase in first-pass yield, and a 7% improvement in OEE, alongside a 47.8% decrease in false alarms compared with static control methods. These findings demonstrate that real-time adaptive monitoring can enhance quality performance without dependency on large labeled datasets. The study provides a replicable implementation methodology and insights into sensor contribution, offering practical guidance for scalable deployment and future advancements in intelligent quality control.