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.
Impact of Energy Transition on Regional Carbon Emissions: Evidence from China
Against the backdrop of global climate change and China’s commitment to carbon reduction, energy transition has become an important pathway toward low-carbon development. Using panel data from 26 Chinese provinces between 2013 and 2022, this study investigates how regional energy transition affects carbon emissions. An evaluation framework covering three dimensions—cleanliness, security, and efficiency—is constructed, with indicator weights determined through the entropy method. The development levels of the three subsystems are measured and further integrated through a coupling coordination model to assess the overall progress of energy transition and its evolution over time. Regression analysis is then employed to examine the relationship between energy transition and carbon dioxide emissions. The results suggest that China’s energy transition has generally advanced during the study period, although the development of different subsystems remains uneven. The relationship between energy transition and carbon emissions follows an inverted U-shaped pattern. At relatively low levels of transition, carbon emissions continue to increase, whereas the emission-reduction effect becomes more evident as the transition deepens, eventually contributing to a decline in emissions. The impacts are not uniform across regions and differ according to urbanization level, economic development, and coastal–inland location. By examining both the evolution of energy transition and its environmental consequences, this study offers additional evidence on the role of energy transition in reducing carbon emissions and provides implications for region-specific low-carbon development under China’s dual-carbon strategy.
energy transition, carbon emissions, coupling coordination
Copyright (c) 2026 Yanqin Jiang (Author) https://creativecommons.org/licenses/by/4.0
Human-AI Collaborative Learning: A Hybrid LLM Orchestration Framework Driven by Cognitive Dissonance
The rapid evolution of generative AI, particularly large language models (LLMs), is reshaping education from individualized instruction toward collective intelligence-driven collaborative learning. However, current intelligent educational systems face critical challenges, including fragmented collaboration ecosystems, isolated AI functionalities, and superficial learning analytics that hinder deep interactions among students, peers, and instructors. To address these gaps, this review proposes an integrated theoretical–technological–analytical framework grounded in constructivist learning theory and cognitive dissonance theory, conceptualizing knowledge as socially co-constructed. The framework comprises three core innovations: (1) a multi-agent collaborative architecture for real-time interaction; (2) a hybrid LLM orchestration mechanism for dynamic model deployment; and (3) a data-driven analytical engine leveraging semantic NLP to identify cognitive conflicts and trace collective intelligence trajectories. The framework advances next-generation intelligent collaborative learning systems by bridging learning sciences, artificial intelligence, and educational data mining. It also identifies key implementation challenges, including technical stability, ethical data governance, and pedagogical adoption, and proposes future directions such as affective computing integration, cross-institutional learning ecosystems, and multidimensional assessment reform. This work contributes a theoretically grounded and operationalizable design paradigm for AI-supported collaborative learning systems.
hybrid large language models, crowd-intelligence collaborative learning, constructivism, intelligent educational system, learning analytics
Copyright (c) 2026 Yiqing Wu (Author) https://creativecommons.org/licenses/by/4.0
ESG Constraints and Portfolio Efficiency: A Review of Risk–Return Trade-offs in Modern Asset Allocation
ESG (Environmental, Social, and Governance) investing has become an important trend in global financial markets, as more investors consider sustainability factors alongside financial performance. However, research findings on how ESG constraints affect portfolio efficiency remain inconsistent. Some studies suggest that ESG integration does not harm risk-adjusted returns, while others argue that adding sustainability requirements may limit diversification and shift the efficient frontier. This paper reviews academic studies and industry reports published between 2010 and 2025 to examine how ESG constraints influence portfolio optimization under modern asset allocation models. The selected studies were organized into three thematic sections. The review finds that ESG integration generally does not significantly reduce long-term risk-adjusted returns, although its effect on the efficient frontier remains debated. Differences in ESG rating systems and regional regulatory environments contribute to these mixed results. Future research should focus on improving ESG measurement consistency and expanding empirical analysis in emerging markets.
ESG constraints, portfolio efficiency, efficient frontier, risk–return trade-off, asset allocation
Copyright (c) 2026 Hongli Jiao (Author) https://creativecommons.org/licenses/by/4.0
AI-Assisted 3D Modeling and Engineering Optimization of Artistic Mechanical Parts for TWS Earbud Charging Cases
Under the fast-growing demand for consumer electronic products, traditional computer-aided design contains several issues, like a long design cycle, insufficient cross-link collaboration, and limited creative expression. To explore the application effect of AI-driven technology for the design of consumer electronic products, this study takes True Wireless Stereo (TWS) earbud charging cases as a research object, analyzes the improvement influences of generative artificial intelligence and digital twin for the design process. By integrating multimodal interaction, prototyping process, data interoperability, and other technologies to achieve full-lifestyle design. The results show that the method effectively increasing the design efficiency, engineering feasibility, and decreasing the total costs of business. This study provides a practical reference for balancing artistic expression and engineering performance in consumer electronic products field.
AI-assisted 3D modeling, engineering optimization, TWS earbud charging cases, artistic mechanical parts
Copyright (c) 2026 Jiayi Qi (Author) https://creativecommons.org/licenses/by/4.0
Research on Cross-modal and Semantic Collaborative Unsupervised Domain Adaptation for All-weather Autonomous Driving Perception Enhancement
Autonomous driving perception systems often encounter severe “domain shift” during real-world deployment. Although deep learning models perform well under ideal weather, their accuracy drops significantly in extreme conditions like rain, snow, fog, or low-light nighttime, as well as during sensor modality transitions. Current Unsupervised Domain Adaptation (UDA) methods mostly focus on class-agnostic global alignment, often neglecting fine-grained semantic correlations and complex visual gaps. This paper systematically explores how to use UDA to enhance the robustness of autonomous driving in complex environments. We propose a multi-dimensional framework that synergizes visual style, feature extraction, and semantic correlation. This study combines theoretical analysis with an algorithmic review to organize several cutting-edge technical paths. First, we introduce Style Prompt Tuning guided by pre-trained Vision-Language Models (VLM). This achieves style transfer while preserving image geometry. Second, we utilize the Transformer-based DTN-DETR model, which cooperates with frequency and spatial domain optimization to improve feature extraction in low-light conditions. Finally, we analyze the Graph Embedding Interclass Relation-Aware Adaptive Network (GelraA-Net) based on Graph Convolutional Networks (GCN) and the Dual Semantic Correlation Alignment (DSCA) mechanism. These methods strengthen cross-scene semantic consistency through topological structures and contextual logic.
autonomous driving, Unsupervised Domain Adaptation, all-weather perception, cross-modal knowledge transfer, Semantic Correlation Alignment
Copyright (c) 2026 Zhuochao Du (Author) https://creativecommons.org/licenses/by/4.0
Digital Visualization and Siting Optimization of Power Plants Using High-Resolution Meteorological Data and GIS
The global transition to high-quality renewable energy systems requires precise siting of power plants to maximize generation efficiency and economic returns. Despite similar installed capacities, suboptimal site selection can lead to significant power generation disparities, such as the 14% efficiency gap observed between solar facilities in Australia and Spain. This study presents a data-driven spatial optimization framework that integrates Geographic Information Systems (GIS) with high-resolution meteorological data to address the limitations of traditional, proximity-based siting methodologies. Utilizing a Multi-Criteria Decision Analysis (MCDA) approach, a Python-based TOPSIS algorithm was developed to evaluate candidate sites across four digital layers: meteorological potential, terrain slope, grid infrastructure proximity, and economic/land-use constraints. The model successfully quantified the geographic “Resource Dividend,” demonstrating that the 14% real-world generation gap closely aligned with an 11% disparity in the calculated digital suitability scores (0.82 for Australia versus 0.71 for Spain). By translating complex, heterogeneous spatial data into visual suitability heat maps, this digital optimization framework allows stakeholders to bypass local optima, minimize the Levelized Cost of Energy (LCOE), and enhance the internal rate of return. Ultimately, algorithmic site selection mitigates human bias and ensures a technically efficient and economically sustainable deployment of renewable energy.
Geographic Information Systems (GIS), site selection optimization, Multi-Criteria Decision Analysis (MCDA), TOPSIS algorithm, Levelized Cost of Energy (LCOE)
Copyright (c) 2026 Pengyu Wang (Author) https://creativecommons.org/licenses/by/4.0
Integrating Speech into Large Language Models: Architectures, Training Strategies, and Emerging Challenges
This paper provides a thorough examination of methods for incorporating speech into large language models (LLMs), with particular emphasis on architectural frameworks, training methodologies, and evaluation protocols. The analysis assesses three critical dimensions: the performance of speech encoders on tasks such as speech recognition, translation, dialogue, and affective computing, cross-modal alignment training techniques, and approaches for integrating speech encoders with LLMS. A total of 18 studies published between 2023 and 2024 were included in the survey. The unified decoder framework, the encoder-adapter LLM pipeline, and the multi-stream hierarchical model are the three architectural approaches identified. Each methodology demonstrates unique trade-offs between modularity and integration depth. Our findings indicate that dual-encoder architectures and hierarchical token representations significantly improve model robustness. Additionally, catastrophic forgetting is effectively mitigated in cross-modal training through curriculum learning and activation tuning. The computational efficiency, uniformity of evaluation, and scaling performance of spoken language models are persistently challenged, in contrast to text-based models. To further investigate real-time full-duplex communication, systematic scaling techniques for speech foundation models, and low-resource language documentation, additional research is needed.
speech language models, multimodal large language models, cross-modal alignment, speech-text integration, foundation models
Copyright (c) 2026 Jiajun Li (Author) https://creativecommons.org/licenses/by/4.0
A Review of Mixed Batch Scheduling with Fuzzy Processing Times on Parallel Machines
Mixed batch scheduling has emerged as a pivotal scheduling paradigm for industrial processes such as vacuum heat treatment, where batch processing time depends on both the maximum and total physical attributes of workpieces. However, the inherent fuzzy uncertainty in processing time, driven by equipment fluctuations, operator variability, and raw material inconsistencies, poses significant challenges for scheduling optimization. Existing algorithms often fail to reconcile the unique weighted-sum characteristic of mixed batches with fuzzy uncertainty, resulting in compromised solution quality or inefficiency in practical applications. This paper focuses on the core pain points in algorithm design for mixed-batch scheduling with fuzzy processing times on parallel machines. It conducts an in-depth review of approximation and metaheuristic algorithms. Specifically, it elaborates on the derivation logic of key models (e.g., fuzzy processing time calculation and defuzzification methods). It emphasizes performance discrepancies between algorithms through quantitative analyses of solution quality, computational efficiency, and stability. By narrowing the research scope to algorithmic adaptability and optimization mechanisms in fuzzy-mixed batch coupling, this review identifies the limitations of current methods. It provides insights for future algorithmic improvements, aiming to bridge the gap between theoretical scheduling and industrial practice.
mixed batch scheduling, fuzzy processing time, parallel machines, triangular fuzzy number, scheduling algorithm, production scheduling
Copyright (c) 2026 Dongyu Yang (Author) https://creativecommons.org/licenses/by/4.0
Application Risks of Artificial Intelligence Chatbots in Psychotherapy and Feasible Solutions for Legal and Policy Issues
This paper reviews existing literature on therapeutic misconception in AI mental health chatbots, a fast-growing field that integrates digital technology with mental health services. As AI-driven psychological intervention tools become increasingly widespread in clinical and daily settings, the applicability of traditional ethical frameworks to human-AI psychological interaction has sparked intense academic debate. This review synthesizes relevant empirical and theoretical studies, compares competing ethical perspectives, most notably the traditional Therapeutic Misconception (TM) theory and the alternative framework, and then systematically examines the consistencies, contradictions, emerging trends, and inherent limitations in current research. It aims to clarify the theoretical disputes surrounding therapeutic misconception in AI mental health contexts, identify key gaps in the existing body of knowledge, and lay a solid foundation for formulating rational norms that balance technological innovation and user rights protection. Furthermore, the paper puts forward targeted legal and policy outlooks, drawing on typical global regulatory practices, and proposes feasible solutions to address current regulatory ambiguities, responsibility vacuums, and inadequate protection for vulnerable groups, thereby providing practical reference for the healthy development of the AI mental health industry and the effective safeguarding of user interests.
artificial intelligence chatbots, psychotherapy, application risks, solutions, human-machine collaboration
Copyright (c) 2026 Chuyu Wang (Author) https://creativecommons.org/licenses/by/4.0
Research on Energy-Efficient Multi-Objective Satellite Orbit Control Based on Deep Reinforcement Learning
To address the challenges of multi-objective trade-offs, uncertainty, and nonlinearity in satellite orbit control, this paper proposes a data-driven control framework based on deep reinforcement learning. Methodologically, a novel composite reward function is designed that integrates fuel consumption, trajectory-tracking accuracy, control smoothness, and mission constraints, such as collision avoidance. An adaptive weighting mechanism is introduced to balance these competing objectives. The algorithm employs an enhanced Soft Actor-Critic architecture, in which both the actor and critic networks are constructed with deep residual networks and augmented with an attention mechanism to capture long-term dependencies in orbital dynamics. Experimental results demonstrate that, for low-Earth orbit transfer and proximity operations, the proposed approach reduces average fuel consumption by approximately 18.7% compared to conventional optimal control and baseline deep deterministic policy gradient methods, while meeting the same mission accuracy requirements. Additionally, control stability is improved by 22.4%. Under conditions of model parameter perturbations and measurement noise, the method achieves a success rate of 99.2%, confirming its strong robustness and adaptability. In conclusion, the developed deep reinforcement learning framework enables effective multi-objective coordination and long-horizon fuel-efficient planning, providing a viable pathway toward autonomous and intelligent orbital control.
deep reinforcement learning, satellite orbit control, multi-objective optimization, fuel efficiency, continuous control, sim-to-real transfer, interpretability, trajectory optimization
Copyright (c) 2026 Ruihan Zhang (Author) https://creativecommons.org/licenses/by/4.0
Low-Rank Adaptation (LoRA) for Visual Art Style Learning and Feature Fusion: A Narrative Review
Recent developments in diffusion-based generative models, particularly Denoising Diffusion Probabilistic Models (DDPMs), have significantly advanced the field of Artificial Intelligence Generated Content (AIGC). Despite their strong generalization ability, base models such as Stable Diffusion often lack the precision required for capturing specific artistic styles or niche visual aesthetics. In this context, Low-Rank Adaptation (LoRA) has emerged as an efficient and practical solution for style-specific fine-tuning within Latent Diffusion Models (LDMs). This paper reviews the underlying mechanism of LoRA and examines how low-rank updates interact with attention layers to encode stylistic features. Compared with alternative approaches such as DreamBooth and Textual Inversion, LoRA offers a more balanced trade-off between computational efficiency and stylistic fidelity. In addition, the paper discusses the challenges of multi-style synthesis, with particular attention to the limitations of linear fusion methods and the potential of Dynamic Layer-wise Fusion (DLF). Finally, several future research directions are outlined, including interpretability and automated optimization, which are essential for improving controllability in generative systems.
generative AI, Latent Diffusion Models, Parameter-Efficient Fine-Tuning (PEFT), Low-Rank Adaptation (LoRA), artistic style fusion
Copyright (c) 2026 Jialang Liu (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence in Judicial Decision-Making: Risks, Due Process, and Governance Frameworks
Artificial intelligence (AI) is increasingly being integrated into judicial decisions worldwide, influencing outcomes in bail, sentencing, and parole determinations. This review examines the application of AI in judicial practices and the potential risks associated with its use in the courts of the United States, China, and Europe. An incident involving AI-generated cases at the Beijing Tongzhou District People's Court in 2025 highlights the threat that algorithmic errors and AI hallucinations could pose to judicial proceedings. The primary risks identified include algorithmic bias, lack of transparency (the black box problem), erosion of due process, and the potential weakening of judicial discretion. Existing legal frameworks, from experiential oversight in the United States to protections under Europe's General Data Protection Regulation (GDPR), remain insufficient in addressing these challenges. Synthesizing literature on AI applications in judicial practice, algorithmic fairness, and due process, this article proposes a multi-level governance framework involving individual judges, court institutions, professional associations, and legislators. This framework aims to provide ethical and procedural guidance for the use of AI in the judiciary, ensuring that technological advancements enhance efficiency without undermining fairness, accountability, and the rule of law. Future research should focus on the practical effects of AI in judges' decision-making, governance strategies across different jurisdictions, and the development of normative guidelines to safeguard justice in an increasingly automated judicial environment.
artificial intelligence, judicial decision-making, algorithmic bias, due process, legal technology
Copyright (c) 2026 Yi Liu (Author) https://creativecommons.org/licenses/by/4.0
A Review on the Application of Artificial Intelligence and Multi-source Data in Refined Prediction of Atmospheric Pollution
Refined prediction of atmospheric pollution captures the dynamic evolution of pollutant concentrations at high spatiotemporal resolution, serving as the core technical support for precise governance of the atmospheric environment and early warning of air quality risks. Artificial intelligence technologies have enabled new methods for exploring the complex spatiotemporal correlations and nonlinear response relationships hidden in data. This paper systematically sorts out the research achievements of the integration of artificial intelligence and multi-source data in the field of refined atmospheric pollution prediction from 2019 to 2024, defines the technical connotation and evaluation system of refined prediction, and analyzes the principles and characteristics of typical technical routes from two dimensions: data fusion level and model design paradigm. Combined with case studies in typical regions of China, such as the Beijing-Tianjin-Hebei region and the Yangtze River Delta, the actual efficiency and engineering bottlenecks of the technical implementation are dissected. The study finds that the field is still grappling with core problems, including heterogeneous data quality across multiple sources, limited interpretability of models, and insufficient generalization in complex scenarios. Based on this, future research directions are proposed, including deep fusion of multimodal data integrating atmospheric physical mechanisms, the construction of interpretable artificial intelligence models for pollution prevention and control, and the design of lightweight models driven by edge computing. This paper aims to provide a systematic reference for technological innovation and engineering applications in this field and to promote the transformation of atmospheric pollution prediction from “data-driven” to “data-mechanism dual-driven”.
atmospheric pollution, refined prediction, multi-source data, artificial intelligence, graph neural network, data-mechanism dual-driven
Copyright (c) 2026 Chengbo Zheng (Author) https://creativecommons.org/licenses/by/4.0
A BERT-Based Interpretable Deep Learning Model for Medical Diagnosis Assisted by Natural Language Processing
The combination of deep learning and natural language processing holds great potential in the medical field. This study aims to explore and develop a deep learning-based natural language processing model to assist in medical diagnosis. We used a large, de-identified dataset of electronic health records, which includes patient complaints, medical histories, and final diagnoses. First, we preprocessed these unstructured text data, for example, by tokenization and removing stop words. Then, we built a deep learning model based on a pre-trained language model like BERT. This model can automatically extract key features from clinical texts and learn the complex relationships between these features and specific diseases. Experimental results show that our proposed model achieves significantly higher accuracy and recall than traditional baseline models on several disease prediction tasks. More importantly, we also introduced an attention mechanism, which gives the model a certain level of interpretability: it can highlight the keywords or phrases that most influence the diagnostic decision. We conclude that deep learning and natural language processing can not only improve the accuracy of disease prediction but also provide valuable references for clinicians, thereby enhancing the overall quality and efficiency of medical services.
deep learning, natural language processing, medical diagnosis, electronic health records, interpretability
Copyright (c) 2026 Yutong Chen (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence and Data Science in the Transformation of Internet Business Models
The internet industry has experienced a profound evolution of business models since its inception, with advertising, e-commerce, and content platforms emerging as the core paradigms. In the digital era, the integration of artificial intelligence (AI) and data science has become a transformative driving force, redefining the operational logic and profit-making mechanisms of internet enterprises. This paper reviews the historical evolution of internet business models, systematically analyzes the multi-dimensional roles of AI and data science in optimizing user experience, enabling personalized services, and innovating profit models across advertising, e-commerce, and content sectors. The study identifies key trends of AI-driven internet business model transformation, including algorithmic monetization, data-driven precision operation, and cross-domain ecological integration. Meanwhile, it also explores potential risks such as data privacy breaches, algorithmic bias, and market monopoly caused by the over-reliance on AI and data technologies. The findings of this paper reveal that AI and data science are not only technical tools but also core strategic elements for the sustainable development of internet businesses, and provide insights for enterprises and regulators to balance technological innovation and risk control.
artificial intelligence, data science, internet business models, personalized service, profit model innovation, platform ecosystem, digital transformation, small and medium-sized enterprises
Copyright (c) 2026 Xi Han (Author) https://creativecommons.org/licenses/by/4.0
Utilizing Cryptography to Decrease Privacy and Security Risks in Machine Unlearning
Machine Unlearning (MU) aims to remove specific data from trained models without requiring complete retraining, thereby complying with regulations such as the “right to be forgotten.” However, unlearning algorithms alone cannot fully guarantee secure data deletion-even if model performance is restored, information about the deleted objects may still be leaked through avenues such as model differentiation, update artifacts, gradients, or residual traces in the representation space, creating new privacy and security risks. To address this, this paper proposes a cryptographically enhanced machine unlearning framework (Crypto-MU): after the conventional approximate unlearning or hybrid unlearning process, AES-GCM authenticated encryption is employed to encrypt and securely store the artifacts generated during the unlearning process. Additionally, key destruction is implemented to achieve a “computationally irreversible” deletion effect. Experimental results demonstrate that Crypto-MU reduces the leakage risk caused by unlearning artifacts from 0.568 ± 0.031 to 0.183 ± 0.032 (a relative reduction of approximately 67.8%) while maintaining nearly unchanged model accuracy. The encryption process incurs minimal overhead (<0.001 seconds). This study shows that incorporating lightweight cryptographic mechanisms into machine unlearning practices can significantly mitigate the additional privacy risks introduced by the unlearning process, with almost no increase in computational burden.
machine unlearning, cryptography, AES-GCM, key destruction, privacy protection
Copyright (c) 2026 Xun Zhang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Multimodal Affective Computing in the Field of Mental Health Monitoring
Mental health is of great significance for an individual's development. As the worldwide mental health crisis grows more severe, depression and anxiety have turned into the primary causes of disability. Traditional approaches to evaluating mental health mainly depend on patients' self-reports and clinical interviews, which are frequently limited by strong subjectivity, high latency and social stigma, resulting in missed diagnoses. To overcome these limitations, multimodal affective computing integrates multi-source data such as text, speech, vision, and physiological signals to provide a revolutionary technical means for the objective quantification and monitoring of psychological states in real-time. This review systematically summarizes the technological advancements in this field, covering multimodal data processing, feature extraction, fusion architectures, and emotion recognition models. It also explores various application scenarios such as depression monitoring, stress management, and crisis intervention. Nevertheless, its application also encounters some difficulties, including technical problems, worries about data privacy and ethical dilemmas. Corresponding strategies involving technological invention, privacy-protecting computing, and ethical frameworks are discussed. Finally, this review concludes that multimodal affective computing has potential to transform mental health care. Its achievement depends on continued technological refinement and responsible, human-centered implementation within interdisciplinary collaboration.
multimodal affective computing, mental health, depression monitoring, multimodal data processing
Copyright (c) 2026 Shunyao Zhang (Author) https://creativecommons.org/licenses/by/4.0
A Systematic Review on Architecture, Applications, and Challenges of Large Model-Driven Intelligent Business Information Systems
Against the backdrop of rapid IT development, large, model-driven intelligent business information systems have become a research focus. Integrating large-scale deep learning models, they have significantly boosted business intelligence. However, with explosive data growth, traditional systems face severe challenges in processing efficiency and decision-making quality. Large models exhibit powerful NLP and image recognition capabilities, achieving technological leaps and expanding commercial possibilities. By efficiently adjusting algorithms in resource-constrained environments, they meet diverse business needs, enhancing both data processing efficiency and decision intelligence. Yet, challenges persist. Key issues including data privacy, security, model interpretability, and system compatibility remain urgent to solve. In today's era of increasing focus on commercial secrets and user privacy, ensuring safe, efficient, and rights-respecting data processing and intelligent decision-making with large models has become an important topic in academic research and industrial practice. To address such problems, the academic community has explored various solutions. By building a dynamic data governance system, designing a highly transparent model architecture, and promoting cross-subject collaboration, it has laid a solid theoretical foundation and provided practical guidance for the application of large models in business intelligence. From the perspective of existing research, the intelligent business information system empowered by large models is not merely a technical upgrade, but an in-depth reconstruction of the business operation model. Its transformative impact far exceeds the traditional management paradigm, promoting a fundamental shift in business decision-making logic from manual experience judgment to data-driven scientization.
large model, intelligent business information system, business intelligence, data-driven decision-making, risk governance
Copyright (c) 2026 Kejia Wang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Surface Defect Detection Technologies Based on Machine Vision
Machine vision-based surface defect detection offers the advantages of being non-contact, non-destructive, and highly automated; consequently, it is widely applied across various industrial production processes. This article provides a brief overview of commonly used methods for surface defect detection, evaluation indicators for detection results, and key challenges currently faced. Defect detection methods are categorized into three types: traditional image processing methods, traditional machine learning methods, and deep learning methods. The core principles and representative studies of each method are reviewed, and their respective advantages and limitations are analyzed. We briefly describe the method for evaluating detection results, examine the few-shot learning problem encountered in practical applications, and provide an outlook on feasible pathways for addressing this issue in the future.
surface defect detection, machine vision, machine learning, deep learning, few-shot learning
Copyright (c) 2026 Ruihong Zhang (Author) https://creativecommons.org/licenses/by/4.0
A Multi-Model Predictive Analysis of China's Pet Industry Development: Market Trends, Global Demand, and Strategic Responses to Foreign Economic Policies
This study aims to analyze the development trajectories of China’s pet industry and its pet food sector, forecast future market demands, and quantitatively assess the impact of foreign economic policies to formulate sustainable development strategies. By integrating multivariate linear regression, nonlinear regression, and polynomial linear regression models within a weighted framework optimized via a particle swarm optimization algorithm, we first evaluate China’s pet industry development based on pet populations and market sizes, predicting that by 2026, the number of pet cats and dogs in China will reach approximately 93.95 million and 42.66 million, respectively. To forecast global pet food demand, we apply cubic spline interpolation to supplement missing international data and construct a similar weighted model incorporating per capita GDP, projecting pet population trends across seven major markets. Focusing on China’s pet food sector, we develop a multiple linear regression model to predict production value, alongside a hybrid weighted model combining support vector regression (SVR) and random forest regression to forecast export values, yielding expected production and export values of 5,993.98 and 46.79 (in 100 million USD) by 2026, respectively. All models are validated using mean absolute percentage error (MAPE), root mean square error (RMSE), and comparisons with auto regressive integrated moving average (ARIMA) models. To quantify the influence of foreign tariff policies, we conduct correlation analysis revealing a coefficient of up to 0.87 between exchange rates and imports, then employ an SVR model to predict that China’s pet food exports would reach 44 (100 million USD) by 2028 under policy shifts. An independent samples t-test indicates a significant difference in China’s pet food export values before and after 2021. Consequently, we propose feasible strategies for the sustainable development of China’s pet food industry, including market diversification and supply chain optimization to mitigate external policy risks.
multivariate linear regression model, nonlinear regression model, polynomial regression model, support vector regression (SVR), random forest regression model, t-test
Copyright (c) 2026 Gaojiayue Shen (Author) https://creativecommons.org/licenses/by/4.0
A Review of Automatic Text Summarization Quality Evaluation Based on Attribution Explainability
As automatic text summarization systems become more widely used, there is growing interest in how to evaluate their outputs in an interpretable and explainable way. The paper reviews existing work on automatic text summarization, explainable evaluation, and attribution-based interpretability methods. Particular attention is given to the differences between ROUGE and BERTScore in evaluating summary quality. Based on these metrics, the study further explores how SHAP can be used to interpret evaluation results generated by ROUGE and BERTScore. Attribution analysis based on ROUGE mainly reflects lexical overlap between generated and reference summaries, and can help identify missing or overemphasized information. In comparison, attribution analysis based on BERTScore is more sensitive to semantic similarity and may provide additional insights into paraphrasing behavior, hallucinations, and missing key information. Future research may focus on improving the efficiency and reliability of attribution-based evaluation methods, as well as exploring how attribution results can be incorporated into summarization model training and refinement.
natural language processing, automatic text summarization, explainability, attribution analysis, SHAP
Copyright (c) 2026 Yingying Qi (Author) https://creativecommons.org/licenses/by/4.0
Anti-jamming Technology for Integrated Communication and Sensing of Unmanned Aerial Vehicles under Obstacle Factors
The obstacle factors will deteriorate communication links and sensing performance when the unmanned aerial vehicles (UAVs) are working in an environment like a urban canyon, which limit the applications of them. However, Integrated Communication and Sensing (ISAC), one of the key enabling technologies for 6G, provides new approaches for reducing occlusion interference. This paper reviews the research on Anti-jamming Technology for ISAC of UAVs under Obstacle Factors in recent years by analyzing the characteristics of occlusion and sorting out the existing technologies from three aspects: communication, sensing and cross-domain collaboration. In the future, constructing a high-fidelity dataset for urban canyon, developing lightweight intelligent anti-jamming architectures and exploring Integrated Communication, Sensing and Computing Collaboration (ISCC) will be an important research direction, which can provide technical support for the large-scale application of scenarios like urban air traffic, emergency response and Wide-Area Internet.
UAV, ISAC, occlusion effect, anti-jamming technology
Copyright (c) 2026 Huiyu Liang (Author) https://creativecommons.org/licenses/by/4.0
Design of Electric Vehicle Wireless Charging System Based on Series Resonant Circuit
With the rapid development of new energy vehicles, traditional wired charging methods are facing challenges in terms of safety and convenience, while wireless charging technology has become a research hotspot because of its convenience and security. In this paper, the wireless charging system of electric vehicle is taken as the research object, and the wireless charging system with RLC series resonant circuit as the core is designed. In this paper, the basic principle and characteristics of the resonant circuit and the working principle of the coil are analyzed. The corresponding circuit structure is designed, and the performance of the system 's power transmission is verified by simulation. The simulation results show that the system has high power factor and transmission efficiency in the resonant state, which verifies the feasibility of the design and provides a reference for the design and optimization of the wireless charging system for electric vehicles.
wireless charging, electric vehicles, series resonance
Copyright (c) 2026 Jiaqi Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research Progress and Prospects of Fragile Watermarking for Model Integrity Protection
Traditional deep learning models represented by convolutional neural networks face severe security threats in open environments, including model tampering and backdoor injection. As an active defense technique, fragile model watermarking aims to produce a sensitive response to any unauthorized modification of a model, thereby providing a “digital seal” for model integrity protection. This paper systematically reviews fragile watermarking techniques for model integrity protection and categorizes three mainstream paradigms: black-box verification based on sensitive samples, white-box/gray-box authentication based on parameter hashing and reversible embedding, and self-embedding and recovery mechanisms. Through comparative analysis, these three paradigms exhibit distinct advantages. Black-box watermarking offers convenient deployment and is well suited for model-as-a-service scenarios centered on classification tasks; parameter-level watermarking provides fine-grained authentication with cryptographic strength; self-embedding mechanisms extend the protection boundary from models to input content, offering a proactive solution for countering deepfakes. Finally, this paper discusses technical challenges and future development trends, providing references for building a trustworthy AI ecosystem.
fragile watermarking, model integrity, artificial intelligence security, black-box verification
Copyright (c) 2026 Zixin Zhou (Author) https://creativecommons.org/licenses/by/4.0
Revisiting Challenger: A Probabilistic Decision-Support Framework Using Bayesian Inference
This study presents a retrospective Bayesian statistical analysis of the Space Shuttle Challenger disaster, examining whether O-ring failure could have been predicted from available Space Shuttle launch data. Using Bayesian logistic regression with Markov Chain Monte Carlo sampling, the relationship between ambient temperature and the probability of O-ring damage across 24 historical shuttle launches is modeled. The analysis reveals a strong monotonic relationship between decreasing temperature and increased damage risk, with the posterior distribution of the midpoint temperature t_0 (the temperature with 50% damage probability) yielding a mean of 63.45°F and 95% highest density interval (HDI) of [58.12, 68.94°F], marking the transition from low to elevated risk in the low-to-mid 60°F range. When extrapolated to the Challenger launch temperature of 36°F, the posterior predictive distribution yields an extremely high mean O-ring damage probability of 99.6% (median 99.8%, 95% HDI [98.7%, 99.95%]), a level of modeled risk substantially higher than at any previously observed temperature. This Bayesian approach provides full posterior distributions over parameters and predictions, enabling explicit quantification of uncertainty, which is crucial for extrapolation beyond observed conditions. The findings demonstrate how rigorous probabilistic modeling could have informed the launch decision and underscore the importance of appropriate statistical risk assessment in safety-critical engineering systems.
Bayesian logistic regression, O-ring failure prediction, space shuttle challenger, temperature-dependent reliability, posterior predictive distribution
Copyright (c) 2026 Zewei Zhang (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Bottlenecks and Solutions for Enhancing the Capabilities of Large Language Models
Large language models, with their ability to understand human language, have become intelligent assistants and efficiency-enhancing tools for people in learning, medical care, entertainment, and more, driving the intelligent development of society. This paper conducts relevant research on the current problems in the improvement of capabilities of large language models from three aspects: data, algorithms, and the models themselves, and reviews them successively from the perspectives of problems and solutions. Through the study of the problems and solutions from the above three perspectives, this paper finds that there are common problems in the process of capability improvement of large language models, such as insufficient data diversity, redundant and low-quality generated text, inability to stably identify their own errors, inability to achieve efficient iterative evolution, model fragility and reduced generalization ability. In the future, the focus of development of large language models can be shifted to improving their accuracy, security, and controllability, and researchers can mainly focus on breaking through their self-thinking, self-correction, and efficient reasoning capabilities. This paper aims to provide researchers in related fields with ideas and theoretical references for model optimization to help large language models break through development bottlenecks.
large language models, data, algorithms, bottlenecks and solutions
Copyright (c) 2026 Yihan Wang (Author) https://creativecommons.org/licenses/by/4.0
Application of Artificial Intelligence Optimization Algorithms Coupled with Machine Learning in Water Treatment Process Optimization and Water Quality Prediction
The increasing scarcity of water resources and the continuous aggravation of water pollution have made the efficient optimization of water treatment processes and the accurate prediction of water quality parameters critical technical supports for ensuring urban and rural water supply safety. Traditional water treatment operation modes generally exhibit limitations such as unstable treatment performance, high energy and chemical consumption, and limited precision in automated control when addressing complex water quality variations and multi-objective synergistic optimization requirements. In recent years, the deep integration of artificial intelligence optimization algorithms with machine learning methods has provided a data-driven new pathway for the intelligent upgrading of water treatment systems. This paper systematically reviews the fundamental principles and application characteristics of typical intelligent optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Harris Hawks Optimization (HHO). It focuses on analyzing the fusion strategies and operational mechanisms of these algorithms with predictive models such as deep neural networks (DNN), Support Vector Machines (SVM), and Random Forests (RF). Furthermore, the paper elaborates on the practical application effectiveness of this technical framework in process parameter optimization, water quality variation forecasting, and multi-objective system scheduling. Finally, future development directions are proposed to address existing challenges, including data reliability, model generalizability, and computational costs, providing theoretical references for related research and engineering implementation.
artificial intelligence optimization algorithms, machine learning, water treatment processes, water quality prediction, multi-objective optimization, coupled models
Copyright (c) 2026 Xinwei Liu (Author) https://creativecommons.org/licenses/by/4.0
Application of Artificial Intelligence and Large Language Models in Clinical Decision-Making for Diabetes
The global prevalence of Type 2 Diabetes Mellitus (T2DM) and the complexity of its management pose significant challenges to traditional healthcare models. In recent years, the rapid advancement of Artificial Intelligence (AI) technologies—particularly Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs)—has provided revolutionary tools for developing intelligent, precise, and personalized Clinical Decision Support Systems (CDSS). This review systematically maps the application landscape of AI and LLMs across the full-cycle management of T2DM, covering the complete chain from risk prediction and precise diagnosis to individualized treatment and long-term prognosis management. In risk prediction, AI models significantly improve early identification of progression from prediabetes to T2DM, as well as diabetic nephropathy, cardiovascular events, and other complications by integrating multidimensional data. At the diagnostic level, in addition to differential typing based on electronic health records, emerging studies have digitized traditional Chinese medicine tongue images and combined them with biomarkers such as oral-gut microbiota through multimodal machine learning, opening new pathways for non-invasive and objective auxiliary diagnosis. In treatment decision support, AI not only recommends glucose-lowering medications and optimizes insulin dosages but also predicts individualized postprandial glucose responses using continuous glucose monitoring data, offering possibilities for dynamic behavioral interventions. LLMs, exemplified by ChatGPT, demonstrate strong potential in interpreting clinical text, simulating doctor-patient communication, and generating preliminary management plans through their powerful natural language processing capabilities, although the reliability and safety of their independent decision-making still require cautious evaluation. Nevertheless, a substantial gap remains between technological advancement and clinical translation. This review further analyzes the core challenges currently faced, including data quality and algorithmic bias, the “black-box” nature of models and insufficient explainability, barriers to integration into clinical workflows, and profound medical ethical issues such as equity, privacy protection, and the risk of marginalizing human decision-making agency. Finally, the paper outlines future directions for the next generation of intelligent CDSS that integrate multimodal data, federated learning, causal inference, and human-centered design principles, emphasizing the need to strike a balance between technological innovation and ethical governance in order to build a safe, effective, equitable, and trustworthy AI-empowered paradigm for diabetes management.
type 2 diabetes mellitus (T2DM), artificial intelligence, large language models, clinical decision support system, machine learning, tongue image diagnosis
Copyright (c) 2026 Hanyu Yang (Author) https://creativecommons.org/licenses/by/4.0
An Interpretability Study of the Air Quality Index in Lanzhou City Based on Random Forest
This study is based on monthly air quality data of Lanzhou City from 2014 to 2022. A Random Forest model is constructed to predict the Air Quality Index (AQI), and interpretability is achieved through feature importance analysis. The results show that PM10, NO2, and PM2.5 are the core factors driving AQI variation, with a cumulative weight of 84.7%. Among them, PM10 (46.1%) plays a dominant role, followed by NO2 (21.9%). The annual average concentration of PM2.5 decreased by 42.4% compared with 2014, indicating significant governance effects, yet it remains a key influencing factor. The concentration of NO2 is higher in winter than in summer, reflecting the impacts of heating and motor vehicles. The model achieves an R2 of 0.900 on the training set, which decreases to 0.405 on the test set, indicating the presence of overfitting. This study quantitatively identifies the key pollution factors affecting AQI in Lanzhou City, providing a basis for air pollution prevention and control. However, future research should incorporate meteorological factors and improve the model’s generalization ability.
Lanzhou city, air quality index, random forest, feature importance
Copyright (c) 2026 Guoxuan Zu (Author) https://creativecommons.org/licenses/by/4.0
Digital Vitality, Finite Energy: A Continuous-Time Framework for Battery Discharge Behavior
With the widespread adoption of smartphones, battery endurance has become a critical factor affecting user experience. To systematically characterize battery discharge behavior under real-world usage conditions, this study proposes a Continuous-Time Battery Discharge (CTBD) model. The model is grounded in fundamental physical principles and is progressively extended to incorporate multiple influencing factors, including screen display, processor workload, network connectivity, GPS operation, and background activities. Furthermore, a State of Health (SOH) correction factor is introduced to capture the effects of environmental temperature and battery aging on the effective capacity. Distinct modeling strategies are employed for high- and low-temperature operating conditions. To improve parameter estimation accuracy, a Bayesian inference framework is adopted, combined with the Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints (L-BFGS-B) and Laplace approximation. Experimental results on the test dataset show that the proposed model achieves a mean absolute percentage error (MAPE) of 5.54%, demonstrating strong predictive performance. To simulate stochastic signals in practical electronic systems, a random process simulation method based on time discretization, Gaussian sequence generation, and rectangular approximation is developed. Based on this approach, three representative usage scenarios—daily use, office applications, and gaming—are constructed. Under fully charged conditions, the predicted battery lifetimes are approximately 7.8 hours, 5.3 hours, and 4.5 hours, respectively, showing good consistency. Finally, sensitivity analysis is conducted to identify the key factors contributing to battery consumption. The results indicate that screen power consumption dominates (approximately 40%–60%), followed by CPU usage. Based on these findings, the model is further extended to other portable electronic devices, and practical recommendations are proposed from both user behavior and operating system optimization perspectives.
CTBD model, bayesian inference, L-BFGS-B, laplace approximation, random process simulation
Copyright (c) 2026 Heguang Luan, Guangning Wang, Yuxuan Lu, Tianrui Chen (Author) https://creativecommons.org/licenses/by/4.0