Exploring Science Academic Conference Series
The Conference Proceedings Series publishes peer-reviewed papers presented at the Exploring Science conferences. These proceedings are a valuable resource for the scientific community, providing access to the latest research findings, innovative methodologies, and interdisciplinary studies. Key Features of the Proceedings: Peer-Reviewed Content – All submitted papers undergo a rigorous peer-review process to ensure academic quality and validity. Multidisciplinary Scope – Covers a wide range of fields, including but not limited to: Physical Sciences (Physics, Chemistry, Materials Science) Life Sciences (Biology, Biochemistry, Medicine) Engineering & Technology (AI, Robotics, Renewable Energy) Environmental Sciences (Climate Change, Sustainability) Social Sciences & Humanities (Psychology, Economics, Education) Open Access & Indexing – Many proceedings are published open access, ensuring wide visibility. Some are indexed in major databases like Scopus, Web of Science, Google Scholar, and others. Digital & Print Formats – Available in both digital (PDF/eBook) and print versions for accessibility. Benefits for Authors & Participants: Publication Opportunity – Presenters can publish their work in a recognized proceedings series. Networking – Connect with global experts and potential collaborators. Career Advancement – Enhances academic profiles for researchers and students. Impactful Research Dissemination – Contributes to the global scientific discourse. Submission Guidelines: Abstracts and full papers must follow the conference’s template. Submissions should present original, unpublished research. Deadlines and review timelines are announced on the official conference website. For more information, visit the official Exploring Science Conference website or contact the organizing committee.
The Impact of Digital Transformation on Corporate Performance: Case Study of Nike, Inc.
Given the impacts of the digitalization models of similar sports brands and the digitalization of consumer behavior, Nike Inc. has actively pursued digital transformation. This paper employs a combination of a specific case study, literature review, and comparative analysis to synthesize relevant domestic and international information on digital transformation and corporate performance. It focuses on the current state, motivations, and corporate performance outcomes of Nike’s digital transformation, specifically examining profitability, cost control capability, and operational capability. The study revealed that while digital transformation has positively impacted Nike’s long-term performance, risks remain in terms of cost control and operational efficiency.
digital transformation, corporate performance, Nike, Inc.
Copyright (c) 2026 Hao Xu (Author) https://creativecommons.org/licenses/by/4.0
Research on the Company Competitive Strategy: A SWOT Analysis and Five Forces Analysis in DJI Drones
In recent years, with the continuous development of technological innovation, the frequency of the combined application of Unmanned Aerial Vehicles (UAVs) with various industries has been on the rise, and the development of the UAVs industry has become an inevitable trend. This report will provide an industry analysis for the UAV industry. We will first give an overview for UAV industry, then discuss the attractiveness and potential of the industry, and using the Five forces analysis and the SWOT analysis to demonstrate the strength and weakness of the DJI Technology Co., Ltd as a leading company within the industry. By analyzing the case of DJI and introducing its current development status and product innovation points, this paper explores how DJI maintain its competitiveness by innovations and strategies. Based on those analysis, this paper aims to study what corporate strategies DJI adopts to enter overseas markets and become an outstanding enterprise in a highly competitive environment. This paper hope to provide experience and inspiration for the development of new enterprises that want to enter or have already entered the drone market in the future, namely core technological innovation, differentiation strategy, and achieving cost advantages. Furthermore, we also provide suggestions for DJI's future development direction.
five forces analysis, SWOT analysis, technological innovation, differentiation strategy
Copyright (c) 2026 Kangzhu Yixi (Author) https://creativecommons.org/licenses/by/4.0
The Global Computing Power Game: NVIDIA’s Profit Structure and China’s Domestic Substitution Under the ‘Chip Tax’
The intensifying geopolitical frictions between the United States and China, exemplified by the recent implementation of the ‘Chip Tax’ and advanced semiconductor export controls, are fundamentally transforming the global artificial intelligence computing power landscape. This paper examines the dual impacts of these measures through an analysis of their influence on NVIDIA’s profit structure and the simultaneous acceleration of China’s domestic substitution strategy. By adopting a mixed-methods approach that integrates quantitative financial analysis of NVIDIA’s filings with the U.S. Securities and Exchange Commission (SEC) and qualitative policy evaluation, this research reveals that U.S. restrictions have imposed substantial yet temporary financial burdens on NVIDIA. These include an inventory charge of $4.5 billion and a compression of the gross margin by 1,250 basis points in the first quarter of the fiscal year 2026. Concurrently, these policies have served as catalysts, expediting China’s development of a parallel semiconductor ecosystem. This is manifested by Cambricon’s 4,300% surge in revenue and Huawei’s Ascend 910B achieving performance close to that of H2O at 70% of the cost. The study concludes that while the ‘Chip Tax’ effectively exerts short-term pressure on established players, it inadvertently contributes to the formation of a bifurcated global technology landscape, resulting in a fragmented ‘one world, two systems’ scenario that may ultimately enhance China’s determination for technological self-sufficiency.
geopolitics, export controls, NVIDIA, semiconductor industry, chip tax
Copyright (c) 2026 Ruixi Zhao (Author) https://creativecommons.org/licenses/by/4.0
Vol. 10 (2025): Proceedings of the 2nd International Conference on Business, Management and Sustainability (ICBMS 2025)
The Proceedings of ICBMS 2025 compile cutting-edge research and innovative insights presented at the 2025 International Conference on Business, Management, and Sustainability. This peer-reviewed collection features interdisciplinary contributions from scholars, practitioners, and policymakers worldwide, addressing key challenges and opportunities in sustainable business practices, strategic management, and socio-economic development. Covering themes such as green innovation, corporate social responsibility (CSR), circular economy, digital transformation, and sustainable governance, the proceedings serve as a vital academic resource for advancing knowledge at the intersection of business excellence and environmental stewardship. Published with rigorous editorial standards, this volume fosters dialogue on achieving the United Nations Sustainable Development Goals (SDGs) while promoting resilient and ethical business models.
Copyright (c) 2026 ICBMS Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Relationship by Which Patent-Based Enterprise Clusters Influence the Development of Midstream Enterprises in a Low-Altitude Economy
This paper examines how patent-driven enterprise clusters influence the development of midstream industries within the low-altitude economy. Using provincial-level panel data from China covering 2015–2025, we construct an econometric framework with the number of midstream enterprises as the dependent variable and the concentration of patent-based enterprises as the core explanatory variable. Controlling for factors such as industry scale, innovation environment, upstream/downstream support, infrastructure, and policies, we employ OLS, two-way fixed effects, and Poisson/negative binomial count models, incorporating lagged terms and multiple robustness tests. Results indicate a stable positive correlation between patent-intensive enterprise agglomeration and midstream development. Count models yield significant findings, while fixed effects show consistent directionality but weaker short-term statistical significance, suggesting cumulative and lagged effects. Mechanism analysis supports two primary transmission pathways: “knowledge spillovers” and “supply chain collaboration.” Policy recommendations include anchoring midstream development to establish standard/testing and certification platforms, enhancing patent pools and intellectual property finance, and promoting upstream-downstream collaborative innovation and high-quality clustering. Study limitations lie in identification and data granularity; future research should integrate firm-level microdata and patent citations to further validate causality and characterize heterogeneous effects.
low-altitude economy, industrial clusters, patent-intensive firms, knowledge spillovers, midstream manufacturing
Copyright (c) 2026 Lai Wei (Author) https://creativecommons.org/licenses/by/4.0
Vol. 9 (2025): Proceedings of the 2025 International Conference on Artificial Intelligence, Modern Engineering and Environmental Sustainability(IC-AIMEES 2025)
The 2025 International Conference on Artificial Intelligence, Modern Engineering and Environmental Sustainability (IC-AIMEES 2025)is a premier interdisciplinary forum uniting global researchers, engineers, policymakers, and industry leaders to address the critical nexus of technology innovation and planetary stewardship. Hosted in Italy , Milan , on November 13-14, 2025, this conference will catalyze knowledge exchange on leveraging AI-driven engineering solutions for a sustainable future. Core Mission IC-AIMEES 2025 seeks to bridge theoretical advancements with real-world applications, fostering collaborations that accelerate the transition toward resilient, low-carbon economies. By integrating cutting-edge AI, transformative engineering practices, and environmental ethics, the conference aims to: Propel breakthroughs in sustainable technology deployment. Establish actionable frameworks for the UN Sustainable Development Goals (SDGs). Cultivate partnerships across academia, industry, and governance.
IC-AIMEES Conference Organizing Committee
Copyright (c) 2025 IC-AIMEES Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Survey of Human-computer Interaction Based on Multimodal Fusion
Multimodal fusion technology achieves information communication and exchange between human and computer by integrating different modal information such as vision, speech, and touch, which has become an important research direction in the field of human-computer interaction. This paper focuses on the four mainstream multimodal fusion methods of graph-based feature fusion, cross-modal attention technology, cross-correlation attention architecture and multimodal emotion recognition technology, compares and analyzes their technical principles, advantages, disadvantages and application scenarios, and systematically sorts out the differences in technical characteristics. By integrating multiple input methods, these methods significantly improve the user interface interaction experience, optimize the efficiency of multi-source information processing, and provide new ideas for interaction design in complex scenes. Research shows that multimodal fusion human-computer interaction technology can effectively reduce user cognitive load and improve operation efficiency, which has important application value in education, medical care, smart home and other fields. In the future, it is necessary to solve the challenges of insufficient cross-modal data alignment accuracy and high real-time requirements, and explore the deep combination of affective computing and multimodal fusion.
multi-modal fusion, human-computer interaction, feature fusion, cross-modal attention, emotion recognition
Copyright (c) 2025 Zihui Zhao (Author) https://creativecommons.org/licenses/by/4.0
A Review of Vehicle Longitudinal Speed Control in Autonomous Driving
With the continuous development of the automotive industry and intelligent transportation technologies, vehicles are gradually moving toward unmanned operation. Within this context, longitudinal control, which mainly focuses on regulating speed, acceleration, and braking, has become a core component of intelligent driving systems and has attracted significant attention. In recent years, various approaches have been proposed in both academia and industry; however, existing studies still lack systematic reviews and comprehensive classifications, limiting the overall understanding of their characteristics and applications. To address this gap, this paper provides a systematic review of longitudinal control methods for autonomous vehicles. These methods are categorized into three groups: rule-based control, optimization-based control, and learning-based control. This review summarizes the state of the art, highlights the strengths and limitations of existing methods, and provides a reference framework and potential directions for future research and applications.
autonomous driving vehicles, longitudinal control, control method, model predictive control
Copyright (c) 2025 Mingqi Jiang (Author) https://creativecommons.org/licenses/by/4.0
Spatiotemporal Characteristics Analysis of the Berlin Extreme Heat Event Based on ERA5 and MODIS
This study takes the Berlin urban agglomeration in Germany as an example and uses ERA5 reanalysis data and Moderate-resolution Imaging Spectroradiometer (MODIS) remote sensing surface temperature data to systematically identify and assess the trends of extreme high-temperature events between 2010 and 2023.Using 2010–2019 as the base period, the 90th percentile temperature threshold was determined. Combining the Mann–Kendall trend test and Sen’s slope estimation, the variation characteristics of heat wave frequency and intensity were analyzed, and the consistency between the two was compared. The results show that the number of heatwave days in the Berlin area has increased significantly over the past decade, with temperatures in the central urban area being significantly higher than those in the surrounding areas, showing a steady upward trend. ERA5 and MODIS show a high degree of consistency in their temporal variation, but ERA5 is slightly underestimated in densely built-up areas. The two types of data complement each other in terms of spatiotemporal resolution, and their combined application can effectively improve the accuracy of urban heat wave monitoring, providing a reference for climate risk assessment and adaptive planning in mid-to-high latitude cities.
climate change, ERA5 reanalysis data, MODIS surface temperature, urban heatwave trend, spatiotemporal feature analysis
Copyright (c) 2025 Zhuoning Li (Author) https://creativecommons.org/licenses/by/4.0
During the Water Crisis, Coal-Fired Power Plants Use Membrane Distillation Technology to Treat Wastewater
The contradiction between the current situation of water shortage and the high-water consumption, high sewage discharge of coal-fired power plants is an urgent problem that needs to be improved in the traditional wastewater treatment process. As a new separation technology with high efficiency and energy saving, membrane distillation technology shows unique advantages in treating high-salt desulfurization wastewater. Based on this, this paper systematically sorts out China’s current situation in terms of water resources and the typical wastewater treatment process of coal-fired power plants today. Four membrane distillation technologies are analyzed: Direct Contact Membrane Distillation (DCMD), Air Gap Membrane Distillation (GMD), Vacuum Membrane Distillation (VMD), Sweeping Gas Membrane Distillation (SGMD), with their rationale and potential in desulfurization wastewater treatment. The results show that membrane distillation technology can achieve near-zero discharge and high quality of the produced water, the recovery rate can reach more than 90%. However, there are still uncertainties and performance differences in actual power plant applications. Membrane distillation technology also faces membrane pollution, high energy consumption, poor long-term operation stability, etc. Future work should focus on development and development of high-performance materials, optimize not only the integrated system but operation strategy. What’s more, vigorously promotes the application of membrane distillation technology in wastewater treatment of coal-fired power plants to help power plants achieve efficient water resource recycling and green and low-carbon transformation.
water resources, coal-fired power plants, wastewater treatment, membrane distillation technology
Copyright (c) 2025 Chenxuan Sun (Author) https://creativecommons.org/licenses/by/4.0
A Trust Construction Model in Human-AI Social Interactions: A Dual-Path Analysis Based on Algorithmic Transparency and Emojis
In the context of the rapid development of social AI applications today, user trust has become a crucial prerequisite for their long-term adoption, yet the mechanisms underlying its formation still lack systematic research from an emotional perspective. To address this, this study proposes a dual-path model based on “algorithmic transparency and emojis,” introducing social presence as a mediating variable to explore its influence mechanism on user trust. Employing empirical methods for research and analysis, the results demonstrate that both algorithmic transparency and emoji usage can significantly enhance users’ trust in social AI, with social presence playing a mediating role between algorithmic transparency, emoji usage, and user trust. The findings of this study not only expand the theoretical perspectives on trust construction in social AI but also offer practical suggestions for algorithmic transparency design and interaction optimization.
social AI, user trust, AI trust, social presence, algorithmic transparency, emojis
Copyright (c) 2025 Yunjun Han (Author) https://creativecommons.org/licenses/by/4.0
Research on Hallucination Mitigation in Large Language Models
The proliferation of large language models has brought the issue of hallucinations, which are outputs that deviate from inputs or fabricate information, to the forefront of concerns regarding reliability and deployment safety. This paper provides a systematic review of existing mitigation strategies and evaluation frameworks for hallucinations. Among mitigation approaches, supervised fine-tuning and reinforcement learning from human feedback have demonstrated moderate success; however, their heavy reliance on extensive, high-quality human annotations renders them costly and difficult to scale. On the evaluation side, this study examines several multimodal benchmarks, including POPE, MMHal-Bench, MM-Vet, and MMBench, which enable comprehensive assessment of model outputs through a blend of automated metrics and human judgment across multiple dimensions. Overall, substantial progress has been achieved in hallucination evaluation frameworks, yet scalable and cost-effective mitigation techniques remain elusive, particularly for hallucinations in complex multimodal settings, where further breakthroughs are urgently needed.
large language models, hallucination mitigation, multimodality
Copyright (c) 2025 Yihan Wang (Author) https://creativecommons.org/licenses/by/4.0
Existing Results and Recent Advancements of Fine-grained Image Classification
Fine-grained image classification emphasizes the resolution of recognizing the subtle differences between subclass objects from the same category. Its classic examples of application include the classification of bird species, car brands and types of plants. During recent years, with the rapid popularization of machine learning and computer vision, there have been growing real circumstances concerning the issue of Fine-grained image classification, which greatly boosts the relevant research. However, there are few reviews of the existing results and recent advancements of Fine-grained image classification. This paper will systematically conclude the major research focuses of fine-grained image classification, highlighting two major aspects. The first aspect is fine-grained image classification based on feature fusion theory. The second aspect is fine-grained image classification based on an attention mechanism. Then, this paper introduces the collaborative application of feature fusion and attention mechanisms in fine-grained image classification. Next, the paper lists the mainstream data collection of fine-grained image classification. Lastly, this paper summarizes the main problems in current research and proposes future directions of this issue.
fine-grained image classification, feature fusion, attention mechanism, deep learning
Copyright (c) 2025 Weisheng Kong (Author) https://creativecommons.org/licenses/by/4.0
Investigation of the Microstructure and Performance of Composite Cathodes in Sulfide-Based Solid-State Batteries
Sulfide-based solid-state batteries have emerged as a leading candidate for next-generation energy storage solutions, owing to their superior safety characteristics and high energy density potential. The composite cathode, as a critical component of sulfide-based solid-state batteries, requires precise microstructure optimization to significantly enhance overall battery performance. This paper compiles and synthesizes current research advancements, providing a comprehensive review of how sulfide electrolyte particle size distribution, active material loading ratios, and interface engineering critically influence composite cathode performance. The study also examines methods and mechanisms for developing high-loading cathodes to enhance cycling stability and rate capability, while summarizing associated challenges and potential strategies. Currently, this field faces notable challenges, including discrepancies between experimental and practical performance, reliance on static models, and difficulties in achieving low impedance with high-loading electrodes. To overcome these limitations and enable widespread application in electric vehicles and large-scale energy storage, we must establish multi-scale in-situ characterization platforms, develop multi-physics dynamic models, investigate novel interfacial materials, and optimize electrode manufacturing processes.
sulfide-based solid-state batteries, composite cathode, microstructure regulation
Copyright (c) 2025 Jingyan Yu (Author) https://creativecommons.org/licenses/by/4.0
Customized 3D Printing of Plant-based Artificial Meat
In recent years, with the continuous increase in consumers’ demands for personalized nutrition and sensory experiences, the homogenization issue of traditional plant-based meat has become increasingly prominent. This paper systematically reviews the advancements in the application of 3D printing technology in the customization of plant-based meat, focusing on the analysis of three key technical aspects: precise control of material rheology, multi-material co-printing and structural bionic processes, as well as targeted nutrition enhancement. Research indicates that by establishing a “material-process-structure” collaborative optimization framework, it is possible to effectively achieve biomimicry of muscle texture, targeted nutrition distribution, and customized texture, meeting the specific needs of diverse groups such as the elderly and athletes. However, this technology still faces bottlenecks in the industrialization process, such as multi-material interfacial delamination, low printing efficiency, and insufficient sustainability assessment. In the future, efforts should be made to develop intelligent responsive materials, high-speed multi-nozzle printing equipment, and conduct in-depth life cycle assessments to promote the technology from the laboratory to the market, realizing a new model of personalized and sustainable food manufacturing.
3D printing, customized plant-based meat, protein ink, structural biomimicry, material-process synergy
Copyright (c) 2025 Jining Guo (Author) https://creativecommons.org/licenses/by/4.0
Research on Optical Communication Technologies and Applications in Satellite Systems
A systematic review of the architectural framework, key technological breakthroughs, and future development trends in satellite optical communication is presented in this paper. An analysis of the limitations of conventional radio frequency (RF) communication in fulfilling the increasing demands for high data rates, large capacity, low latency, and high reliability in space-based applications is needed. This study emphasizes that laser communication, which has exceptional advantages such as ultrawide bandwidth, strong anti-interference capability, low power consumption, and high security, has become a strategic focal point in international space communication development. The composition and working principles of satellite optical communication systems are expounded upon. Case studies regarding low-Earth orbit (LEO) satellite networks and deep-space exploration communication links are utilized to examine key design considerations and performance constraints across diverse application scenarios. At the core technological level, an analysis of the principles, recent advancements, and challenges of several critical technologies is carried out, including high-power lasers with advanced modulation, high-sensitivity reception, precision beam pointing–acquisition–tracking (PAT), atmospheric channel compensation, and Doppler shift compensation. Emerging research frontiers, such as AI-driven dynamic link optimization, integrated space–air–ground optical networks, intelligent adaptive modulation, and the integration of quantum communication, are emerging. This work offers a theoretical basis and technical reference for constructing global, efficient, and reliable space information networks.
Copyright (c) 2025 Daohan Yu (Author) https://creativecommons.org/licenses/by/4.0
Model Construction of Consumer Credit Ratings Based on Nonparametric Statistical Testing and Machine Learning Fusion
With the rapid growth of consumer credit, there is a need to assess it. Given the limitations of traditional credit scoring methods, which rely on linear assumptions or lack interpretability, and artificial intelligence (AI) methods, more robust models are needed. Against this backdrop, this study proposes a novel consumer credit rating model that integrates nonparametric statistical testing with machine learning to increase accuracy and interpretability. The research focuses on screening statistically significant features via Kolmogorov‒Smirnov tests (e.g., D=0.28, p=0.002 for annual income) and Mann‒Whitney U tests (U=210, p=0.008) while addressing multicollinearity via Spearman rank correlation. A fusion framework combining the Cox proportional hazards model and conformal prediction is employed to generate confidence intervals for default probability. The results demonstrate the model’s superior performance, achieving an area under the curve (AUC) of 0.775, higher recall (0.72), and lower Brier score (0.158) than logistic regression and survival forest. The study bridges the gap between predictive accuracy and interpretability, offering a reliable tool for financial institutions to assess credit risk.
consumer credit rating, nonparametric testing, cox proportional hazards model, machine learning, risk assessment
Copyright (c) 2025 Yawen Wang (Author) https://creativecommons.org/licenses/by/4.0
Research Progress on Artificial Intelligence and Brain–Computer Interfaces in the Emotion Recognition of Patients with Depression
Depression represents a significant public health concern on a global scale. Conventional diagnostic approaches rely on subjective scales, which have limitations, including low consistency and difficulty in early identification. In recent years, the integration of artificial intelligence (AI) and brain‒computer interface (BCI) technologies has led to the development of new solutions for mood recognition in patients with depression. This review examines emotion recognition in depression via multimodal data, including EEG signals, eye tracking, and facial expression analysis. This highlights the application and performance of deep learning methods, such as the EEGNet and LSTM-CNN parallel models. The study also discusses multimodal fusion techniques, including graph neural networks and dynamic weighted fusion. Research indicates that AI and BCI technologies enable objective quantification, real-time monitoring, and personalised intervention. These capabilities significantly increase the accuracy and robustness of depression recognition. Nevertheless, this field continues to face challenges, including ethical controversies and the limited generalizability of models. Future work should prioritise algorithmic improvements, facilitate clinical translation, and strengthen ethical frameworks to support broader implementation.
artificial intelligence, brain-computer interface, depression, emotion recognition, deep learning
Copyright (c) 2025 Zhenlin Yang (Author) https://creativecommons.org/licenses/by/4.0
Study on the Performance Degradation Mechanisms and Structural Optimization Strategy of Lithium-ion Batteries in High-altitude and Low-pressure Environments
High-altitude and low-pressure environments pose severe challenges to the electrochemical performance, structural stability and thermal safety of lithium-ion batteries. This paper systematically reviews the degradation mechanisms of battery performance in this environment, including increases in electrochemical impedance and mechanical stress-induced structural damage, with a focus on the core causes of the increased risk of thermal runaway. On this basis, this paper reviews the research progress on coping strategies, focusing on two directions: First, optimize electrode materials and structures through the construction of nanostructures and functional material coatings. Second, optimize thermal management systems through composite phase change materials and intelligent algorithms. Future research should emphasize an in-depth understanding of the multiphysics coupling mechanism and the development of a collaborative optimization design that integrates material–structure–thermal management to improve the comprehensive performance and safety of batteries in high-altitude environments.
lithium-ion battery, high-altitude environment, low pressure, electrochemical properties, structural optimization
Copyright (c) 2025 Shang Liu (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning in Medical Diagnosis: From Image Analysis to EHRs
This article researched the widespread application of machine learning in medical diagnosis in depth, hoping to solve many of the current challenges in the medical field by improving the accuracy and efficiency of diagnosis. This paper expounds the important role of machine learning in disease prediction and patient risk stratification from three aspects: medical image analysis, electronic health record (EHR) processing and time series data analysis. Through algorithms such as supervised learning, unsupervised learning, and reinforcement learning, machine learning can extract valuable information from massive medical data to help doctors make more accurate diagnostic decisions. In the paper, the application of convolutional neural network (CNN), bidirectional encoder representation (BERT) and long short-term memory network (LSTM) in medical diagnosis is studied. Specifically, CNN performed well in medical image analysis and was able to quickly identify diseased areas; BERT extracts key information from unstructured text through natural language processing to support disease prediction and drug recommendations. LSTM specializes in processing time series data and can predict a patient’s disease risk. In addition, the successful cases of these models in data analysis such as pneumonia detection and heart disease prediction are also introduced. This shows their great potential in the field of medicine. Finally, the paper discusses the challenges of data privacy, model interpretability and algorithm optimization, and looks forward to future research directions such as federated learning, multimodal data fusion and real-time diagnosis, providing theoretical support and practical guidance for the intelligent transformation of healthcare industry.
machine learning, medical diagnosis, electronic health records (EHR), deep learning models
Copyright (c) 2025 Xin Wang (Author) https://creativecommons.org/licenses/by/4.0
AI-Enabled Design and Additive Manufacturing of Mechanical Materials: Methods and Future Directions
This review provides a comprehensive overview of recent advancements in the development of mechanical metamaterials through the synergistic integration of artificial intelligence (AI) and additive manufacturing (AM). By combining computational design strategies with high-precision fabrication techniques, researchers can rapidly identify optimized microstructures and translate them into functional prototypes. This approach accelerates the exploration of multifunctional and adaptive metamaterials, enabling properties that are difficult to achieve with conventional materials. This paper highlights key methodologies for AI-assisted design and AM-based fabrication, examines their capabilities and limitations, and outlines workflows that bridge theoretical modeling and practical implementation. Emerging trends, including self-adaptive metamaterials and application-specific architectures, are discussed to provide guidance for future research. Overall, this review emphasizes how AI and AM collectively transform the landscape of mechanical metamaterial design, offering pathways toward faster innovation, enhanced performance, and real-world applicability.
mechanical metamaterials, artificial intelligence, additive manufacturing, topology optimization
Copyright (c) 2025 Peng Bian (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning-based Milk Quality Prediction
With improvements in people's living standards, food safety has gradually received increasing attention. Milk quality is an important aspect of food safety. By classifying quality, we can obtain milk of different grades for consumers to choose from. This report applies the MLP, logistic regression, SVM and XGBoost algorithms to determine a suitable model through comparison and decision-making.
milk, food quality, multilayer perceptron, XGBoost, SVM, logistic regression
Copyright (c) 2025 Bojun Zhao (Author) https://creativecommons.org/licenses/by/4.0
Proceedings of the 2025 International Conference on Business, Management and Sustainability (ICBMS 2025)
The 2025 International Conference on Business, Management and Sustainability (ICBMS 2025), held from 2–4 August 2025 in Blagoveshchensk, Amur Region, Russia, brought together researchers, industry leaders, and policymakers to explore transformative strategies for sustainable development in business and management. This volume presents a curated selection of peer-reviewed papers that reflect the conference’s multidisciplinary dialogue on pressing global challenges—from green innovation and digital transformation to ethical governance and circular economies. By bridging theory and practice, these contributions advance our understanding of how businesses can align profitability with planetary stewardship, particularly in the dynamic contexts of emerging markets and cross-border collaboration. We extend our gratitude to the authors, reviewers, and organizing committee for their dedication to academic rigor and actionable insights. Special thanks to our host institution and partners for fostering this vital exchange in the unique setting of the Amur Region, a gateway to Asia-Pacific sustainability dialogues. May these proceedings inspire further research and partnerships toward a resilient, equitable future.
Copyright (c) 2025 ICBMS Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Proceedings of the 2025 International Conference on Social Sciences and Cultural Studies (ICSSCS 2025)
It is with great pleasure that we present the Proceedings of the 2025 International Conference on Social Sciences and Cultural Studies (ICSSCS 2025). This volume collects a selection of pioneering research papers presented at the conference, held from October 17 to 19, 2025, in the historic city of Athens, Greece—a cradle of Western civilization and a fitting backdrop for dialogues that seek to understand the complexities of human society and culture. The diverse scholarship contained within these pages reflects the interdisciplinary and dynamic nature of contemporary social sciences and cultural studies. The contributions in this proceeding traverse a wide spectrum of critical topics, demonstrating a shared commitment to addressing pressing issues in education, psychology, family dynamics, and societal transformation. The collection opens with innovative explorations in pedagogy, examining the application of drama in secondary school English classes and the predictive factors of English reading anxiety among university students. It then moves to the vital realm of mental health, presenting empirical research on the practical effects of art therapy for student anxiety. Further deepening the psychological inquiry, one study proposes the novel concept of "Meta-Integration" to deconstruct the psychological mechanisms of game addiction. Shifting focus to the fundamental unit of society, this volume includes research grounded in Family Ecosystem Theory, analyzing the factors that shape sibling relationships in two-child families. Finally, the scope expands to a macro-cultural and societal level with a literary analysis that draws profound educational implications from a narrative of social transformation in Bulgaria. We extend our deepest gratitude to the authors whose rigorous research and insightful perspectives form the core of this publication. Our sincere thanks also go to the dedicated program committee members and reviewers for their invaluable efforts in ensuring the quality and integrity of the scholarship presented here. It is our firm belief that the research contained in this proceeding will not only contribute valuable knowledge to the academic community but will also inspire further inquiry, collaboration, and positive impact across the multifaceted fields of social sciences and cultural studies.
Copyright (c) 2025 ICSSCS Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Proceedings of the 2025 International Conference on Linguistics, Psychology andCreative Education (ICLPCE 2025)
This volume presents the Proceedings of the 2025 International Conference on Linguistics, Psychology, and Creative Education (ICLPCE 2025), a compilation of the innovative research and insights shared during the conference held from September 26 to 28, 2025, in the vibrant and dynamic city of Hong Kong, China. As an interdisciplinary platform, ICLPCE 2025 successfully bridged the crucial fields of linguistics, psychology, and education, fostering a unique collaborative environment. The papers contained within these proceedings reflect this synthesis, offering a rich tapestry of inquiry that spans from the micro-level analysis of individual cognitive and emotional processes to the macro-level application of creative pedagogical strategies. The diversity and depth of the research are evident in the contents of this collection. The volume opens with explorations into creative educational methodologies, such as the application of drama pedagogy in language classrooms. It then delves into the psychological dimensions of learning, featuring studies on intervention for student anxiety and the psychological mechanisms underlying behaviors like game addiction. The intersection of language and individual cognition is further examined through research on the predictors of reading anxiety. Grounding this academic exploration in real-world contexts, other contributions analyze family ecosystems and their impact on child development, as well as the profound educational implications drawn from cultural and social narratives. We extend our sincere gratitude to all the authors for their valuable contributions and for sharing their pioneering work. Our special thanks go to the members of the international program committee and the numerous reviewers for their diligent efforts and expert judgment in selecting and refining the papers for this publication. We are also grateful to all the participants whose vibrant discussions and shared perspectives made the conference a truly enlightening event. It is our firm belief that the research presented in this proceeding will serve as a valuable resource for scholars, educators, and practitioners, inspiring further interdisciplinary collaboration and contributing to the advancement of knowledge in linguistics, psychology, and creative education.
Copyright (c) 2025 ICLPCE Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
A Study on the Attitudes of Chinese and Foreign Media toward DeepSeek
In recent years, large - scale language models (LLMs), such as ChatGPT, Claude, and DeepSeek, have rapidly developed, sparking global discussions on technology and ethics. DeepSeek, as an open - source large - scale model developed by the Chinese team DeepSeek, has shown outstanding technical performance and application potential, attracting widespread attention from domestic and foreign media. However, there are significant differences in the reporting framework of DeepSeek among media from different cultural backgrounds, political stances, and technological ecosystems. The article adopts a sample selection method, and the media scope includes the following: Chinese media: Science and Technology Daily; Asian media: The Straits Times (Singapore); and European and American media: The New York Times. Time range: June 2023 to March 2025. Focusing on China’s local AI representative ‘DeepSeek’, filling the gap in current LLM media research on ‘Chinese model’ cases. By combining framing theory with critical discourse analysis (CDA), media coverage in the Chinese and American regions can be compared. Mixed content analysis (quantitative) and discourse analysis (qualitative) reveal explicit and implicit attitude structures.
Copyright (c) 2025 Zihan Liu (Author) https://creativecommons.org/licenses/by/4.0
The Influence of Interactive Clues Between Human and Products on Social Media Sharing
Users Generated Content (UGC) is one of the most important marketing method of social media. In recent years, people tend to project their emotions into products, engaging in accompanying social interactions with these products, and posting their interactions on social media. The topic about social media has already have lots of attentions, but researches about interactive clues in social media content still have some weaknesses. This research focuses on the influence of interactions between UGC publishers and companion products on target audiences. This paper analyses that interactive clues between humans and products can promote social media sharing behaviors. Emotional resonance is the mediation between interactive clues and sharing behaviors. Based on these conclusions, the following suggestions are raised in this paper: Publishers of UGC could provide more emotional clues on their posts so as to promote users’ sharing and spread. Platforms and enterprises could introduce new features about interactive clues, eliciting emotional resonance and optimizing content-propagation mechanism.
interactive clues, sharing willingness, emotional resonance
Copyright (c) 2025 Yaxuan Qiu (Author) https://creativecommons.org/licenses/by/4.0
A Study on Australia’s Cooperation with the World Health Organization during the Cold War Period
During the Cold War era, Australia’s need for collaboration with the World Health Organization (WHO) arose from domestic pressures to control infectious diseases and the risks posed by transnational disease transmission. At the same time, Australia’s robust national public health system and advanced medical technologies equipped it with the capacity to engage in global health partnerships. The establishment of the WHO provided an ideal international platform for such cooperation. Australia not only actively aligned with the WHO’s global health agenda but also took proactive steps in regional health governance and international aid efforts, evolving from a mere participant in collaborations to a key regional coordinator. This progression ultimately helped transform the WHO from a Euro-American-centric health institution into a more inclusive and dynamic platform for global health governance.
Cold War, Australia, World Health Organization, health governance, infectious diseases
Copyright (c) 2025 Sicheng Liu (Author) https://creativecommons.org/licenses/by/4.0
Exploring the Impact of Various Factors on the Salaries of Players in the National Basketball Association
This study focuses on the factors affecting NBA player salaries. On the basis of multidimensional data from 288 players in the 2022--2023 season, a random forest model and multiple linear regression model are used to explore the impacts of individual competitive level, implicit traits, commercial endorsement value, and institutional restrictions on salaries. Research has shown that a player’s performance in the field, especially their offensive contribution, is the core determinant of salary. Multiple linear regression validation reveals that scores and team win rates have a significant positive effect on salary, whereas the number of games has a negative effect. Defensive indicators and injury absenteeism do not significantly affect player salaries. In addition, the team’s luxury tax and off-field commercial value have weak explanatory power for salaries, revealing that the current salary system is still centered around competitive performance. This research provides a quantitative basis for player salary negotiations, team salary structure optimization, and alliance policy adjustments. In the future, real-time game data and social media indicators can be further integrated to improve the prediction accuracy.
NBA player salary, random forest model, multiple linear regression model, performance on the field, team winning percentage
Copyright (c) 2025 Yunlong Guo (Author) https://creativecommons.org/licenses/by/4.0
The Decay Cycle of Public Opinion: A Case Study of the Nongfu Spring Boycott Movement within Douyin’s News Framing
With the widespread popularity of short video platforms, short video news has become a key force in shaping public perception and guiding public opinion. Moreover, news frames serve as core variables in shaping the trajectory of the public opinion life cycle. However, research on the cycle of public opinion decay under specific news frames is scarce. Taking the 2024 Nongfu Spring boycott movement as a case study, this research focuses on three dominant news frames on the Douyin platform-namely, the moral judgment frame, the nationalism frame, and the crisis response frame. On the basis of frame theory and the case study method, this research explores the differences in their decay cycles and their dynamic mechanisms. It aims to identify the laws governing public opinion decay on short video platforms, facilitate positive interaction between theory and practice, and contribute to the sustained and healthy development of the short video news industry.
news frame, public opinion decay, Douyin platform, Nongfu Spring boycott movement
Copyright (c) 2025 Haoyuxuan Yang (Author) https://creativecommons.org/licenses/by/4.0