Journal of Computing and Electronic Information Management
Journal of Computing and Electronic Information Management (JCEIM) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computing, electronics, information management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
From "Digital Collection" to "Educational Scene": The Practical Logic of Red Culture Resource Databases Empowering Research-Based Learning
In the digital age, the protection and inheritance of red culture resources are undergoing a profound paradigm shift. Currently, a vast amount of red cultural resources has been digitized, forming large-scale "digital collections." However, this collection model, centered on data preservation, easily leads to the dilemmas of "data silos" and "disconnection between teaching and application," failing to meet the advanced demands of new-era research-based learning for experiential, interactive, and contextualized engagement. This article aims to explore the internal mechanism of the transformation of red culture resource databases from "digital collections" to "educational scenes." The study posits that this transformation is not a simple technological iteration but a profound transmutation of practical logic. Based on qualitative analysis and theoretical construction, the article proposes that this transformation relies on three interconnected practical logics: First, the reconstruction logic of data, shifting from fragmented data storage to semantic association based on knowledge graphs, laying the data foundation for scene construction. Second, the construction logic of scenes, moving from static digital presentation to immersive interaction based on Extended Reality (XR) and Artificial Intelligence (AI), achieving a sense of "presence" and "personalization." Third, the empowerment logic of research-based learning, transitioning from unidirectional knowledge infusion to deep participation and value creation covering the entire cycle (pre-, mid-, post-learning), ultimately realizing value identification and internalization. The conclusions of this study attempt to provide a theoretical reference for the digitized "activation" and utilization of red cultural resources and offer a feasible path for model innovation in research-based learning under the context of the "Big Ideological and Political Course."
Copyright (c) 2025 Journal of Computing and Electronic Information Management https://creativecommons.org/licenses/by-nc/4.0
Responsible Generative AI: Governance Challenges and Solutions in Enterprise Data Clouds
The emergence of a faster generative artificial intelligence (AI) has changed the way businesses use data, automate choices and innovate within cloud-based ecosystems. But as companies start deploy large language models and other generative systems to the data clouds of their enterprises, governance, accountability, and ethical oversight issues have also become more complicated. This paper discusses the governance issues related to generative AI implementation in data infrastructure of the enterprise and suggests a model of responsible governance that balances innovation and compliance, security, and ethics. Based on the recent scholarship and policy changes, the research outlines the key pillars of governance - transparency, accountability, data stewardship, fairness, and regulatory compliance and evaluates their adoption in the multi-cloud settings. The article, by synthesizing the concepts and providing illustrative examples in the industry, emphasizes the necessity to have combined mechanisms of data and AI governance that could help resolve such problems like data provenance, mitigation of bias, and monitoring of the lifecycle. Finally, the paper concludes that responsible governance does not hinder enterprise innovation, but is a strategic facilitator of a credible and sustainable application of AI.
From 'Cultural Origin' to 'Spatial Landing': The Construction and Model Innovation of the Integrated Culture-Commerce-Tourism Value Chain for Foshan's 'Kung Fu' and 'Lion Dance' IP
In the contemporary context, the value realization of traditional cultural intellectual property (IP) is undergoing a profound transformation from singular resource exploitation to systematic value chain construction. As world-class cultural symbols, the potential for the integrated culture-commerce-tourism development of Foshan's "Kung Fu" and "Lion Dance" is far from being fully tapped. The fundamental reason lies in a significant structural rupture within its value chain—the profound "cultural origin" has not been effectively translated into a systematic "industrial extension," nor has it ultimately achieved "spatial landing" as a consumer scene for deep experience. This paper aims to construct an integrated theoretical framework to bridge this rupture. The research first diagnoses the current bottlenecks in the value chain across three dimensions: the translation of the cultural core, the synergy of the industrial chain, and the experiential capacity of the spatial carrier, identifying issues of "hollowing-out," "fragmentation," and "isolation." On this basis, this paper proposes a three-dimensional linkage value chain model that extends from "cultural origin" to "spatial landing." This model emphasizes that an efficient IP value chain must achieve dynamic synergy and value resonance across three dimensions: the "deep activation of the cultural origin" (vertical), the "broad extension of the industrial chain" (horizontal), and the "experiential landing of the spatial scene" (in-depth). To promote the practical application of this theoretical model, this paper further explores two innovative models: first, the "IP + Community Co-creation" model, which aims to ensure the authenticity of cultural inheritance and the endogenous motivation for development by establishing mechanisms for benefit-sharing and collaborative creation; second, the "Digital-Physical Symbiosis" model, which aims to utilize cutting-edge digital technologies to construct a new experiential ecosystem where the online virtual world and the offline physical space are deeply integrated and mutually empowering. The theoretical contribution of this study lies in providing a systematic, integrated framework for the study of the cultural IP value chain. Its practical significance lies in offering a model reference that is both sustainable and innovative for the industrial development of Foshan and similar cultural heritage sites across the nation.
A study on how LLMs (e.g. GPT-4, chatbots) are being integrated to support tutoring, essay feedback and content generation
The adoption of Large Language Models (LLMs) and, specifically, GPT-,4 has rapidly altered the educational landscape and changed the concept of pedagogy and interaction with learners. This paper explores the role of LLMs in learning institutions to supplement tutoring, essay feedback, and content creation. Based on recent empirical studies and theories, it assesses the potential of LLMs in pedagogy and its potential consequences in higher and secondary education. Through a combination of multiple dimensions, such as adaptive tutoring, personalized feedback, and generative lesson design, the paper clarifies the opportunities and challenges of implementing the use of LLMs in teaching practice. It has been proved by empirical results that GPT-4-based systems can significantly increase student engagement, writing, and conceptual understanding when used in a responsible manner. However, such issues as data privacy, overreliance, and the risk of academic dishonesty are discussed in the course of the study. It concludes in the end with the conclusion that to successfully use LLMs, a balanced approach, which integrates human judgment and algorithmic intelligence, is necessary to develop equitable and future-oriented learning settings.
Robust aggregation algorithms for federated learning in unreliable network environments
Federated learning (FL) allows joint model training on distributed devices without losing data locality, but its results are significantly worse in unreliable network systems where packets are dropped, clients fail, resources are heterogeneous, and adversarial (Byzantine) agents exist. The viability of FL to withstand these unfavorable conditions is keyed on the robust aggregation algorithms. The paper meticulously examines powerful methods of aggregation, which include: geometric-median methods (RFA), Krum/Multi-Krum, trimmed-mean/coordinate-wise defenses, g-divergence estimators, trust-based aggregators (FLTrust), and layer-wise aggregation methods (FedRoLA) and compares their performance on simulated unreliable networks, which model packet loss, communication delay, and malicious client actions (McMahan et al., 2017; Blanchard et al., 2017; P We examine accuracy, convergence speed, communication cost and resilience in the face of model-poisoning attacks with the help of benchmark image tasks and a set of network unreliability scenarios. We find that robust aggregators combining statistical outlier resistance and structural (layer-wise) aggregation or trust calibration (especially RFA and FedRoLA) are more accurate and converge more quickly than naive FedAvg in high packet-loss and moderate Byzantine contamination and we observe up to 12 percent improvement in test accuracy with 30 percent simulated packet loss. We also talk about the trade offs between robustness, communication overhead and privacy (secure aggregation) and present a hybrid design pattern that incorporates robust aggregation and adaptive client selection with secure aggregation so as to address both unreliable links as well as adversarial updates. The results provide prescriptive advice on the use of FL in mobile, IoT, and vehicular networks that have limited reliability and security requirements and provide future directions such as privacy-conscious robust aggregation, fairness-conscious weighting, and testbed implementation.
Exploration of Experimental Course Reform in Control Majors under the Background of New Engineering
The Ministry of Education is actively promoting the development of new engineering disciplines, placing new demands on talent cultivation. Considering issues such as aging hardware resources, limited experimental content, and a disconnect between industry and education in control courses, this article discusses experimental curriculum reform initiatives from the perspectives of teaching philosophy, experimental course design, teaching models, evaluation systems, and safety education. This article provides a reference for other universities in cultivating innovative talent in new engineering disciplines.
Curriculum ideological and political education
Vision Transformers (ViTs): A New Era in Computer Vision – A Review
Vision Transformers (ViTs) have become a strong substitute to Convolutional Neural Networks (CNNs) in computer vision, providing a new method to learn global dependencies using self-attention operations. This survey paper provides an in-depth analysis of the development, application, optimization, and deployment difficulties of ViT models. We begin by reviewing the evolution of ViTs from their base architecture, and its subsequent adaptations to newly developed versions, including hybrids with CNNs and multi-scale attention. We then investigate the applications of ViTs such as image classification, object detection, segmentation, depth estimation, medical image analysis, and industry vision inspection. Methods to enhance ViT efficiency—such as model pruning/quantization, hybridization with CNNs, and dynamic adaptation—are extensively discussed. However, ViTs also have significant limitations including computational complexity, scaling and data challenges. Spatial Usage of Scratch Programming Blocks Some potential solutions and future directions are addressed, such as deploying the work on edge device and inclusion in multimodal learning systems. Synthesizing knowledge from recent literature, this paper provides a comprehensive overview of the trends that have developed and six paradigms that currently exist for ViTs in computer vision.
Design of Intelligent Bicycle Anti-theft System Based on Internet of Things and Computer Technology
With the acceleration of urbanization, bicycles are widely favored as a green travel mode, but their anti-theft problems are becoming more and more prominent. This study aims to design an intelligent bicycle anti-theft system based on Internet of Things and computer technology to enhance the efficiency and safety of bicycle theft prevention. The system realizes remote monitoring, real-time positioning and intelligent alarm by integrating sensor, wireless communication and cloud computing technologies. The hardware design covers the main control and sensor module, communication module, etc., while the software design includes embedded software and mobile application. The system is equipped with key functions such as real-time positioning display, alarm information receiving and pushing, remote control, etc., and adopts a multi-layer security protection mechanism to ensure the safety of user data. This study not only promotes the wide application of IoT technology in the field of anti-theft, but also provides a useful reference for the research and development of intelligent bicycle anti-theft system.
Dynamic Measurement of Urban Science and Technology Innovation Capability Based on Analysis of Scientific Research Data
Based on the scientific research data, this paper makes a dynamic measurement and evaluation of the scientific and technological innovation ability of Zhanjiang's modern industrial system. By constructing 24 index systems covering innovation environment, input, output and diffusion, this paper makes an empirical analysis of Zhanjiang's scientific and technological innovation ability from 2018 to 2023 by using entropy weight GC-TOPSIS model. It is found that the ability of scientific and technological innovation in Zhanjiang city is on the rise, and the quality of innovation has improved significantly. However, compared with the core cities in the Pearl River Delta, Zhanjiang still has obvious shortcomings in terms of high-end talent reserve, R&D sustainability, policy synergy and the cultivation of emerging industries. The paper further analyzes the influencing factors from the perspectives of policy, talents, capital and market, and puts forward targeted promotion countermeasures to provide decision-making reference for Zhanjiang to integrate into Greater Bay Area innovation network and realize high-quality development.
Scientific and technological innovation ability
AI Empowerment: Digital Transformation of Graduate Education Governance
With the advent of the digital era, Artificial Intelligence (AI) has emerged as a pivotal force reshaping the governance framework of graduate education. Deeply integrated into every aspect of postgraduate training, AI plays a vital role in optimizing educational models. However, this AI-driven transformation faces multiple challenges including data governance, technical ethics, and stakeholder adaptation. To address these issues, innovative approaches are required—such as fostering scenario innovation through technology integration, restructuring collaborative governance models, and establishing data-driven closed-loop systems. These measures will facilitate the digital transformation of graduate education governance structures, enhance governance efficiency, and propel the field toward high-quality development.
Timing of NIPT and Fetal Abnormality Determination
Non-invasive prenatal testing (NIPT) screens for chromosomal abnormalities by analyzing fetal cell-free DNA (cfDNA) in maternal blood, with its accuracy highly dependent on fetal DNA concentration. This study systematically analyzed the quantitative relationship between fetal Y chromosome concentration and gestational age/BMI in high-BMI pregnant women. We established an individualized optimal NIPT timing strategy based on BMI grouping and developed a high-precision machine learning model to enhance the detection efficacy of female fetal chromosomal abnormalities. Results indicate that Y chromosome concentration increases logarithmically with gestational age but decreases significantly with rising BMI, exhibiting a negative interaction effect. For male fetuses, controlling the testing window between 11 and 19 weeks based on BMI grouping ensures over 90% of pregnant women meet the threshold during the low-risk mid-pregnancy period, with Monte Carlo simulations showing a false negative rate below 2%. For female fetuses, a multi-feature Stacking ensemble model achieved an AUC of 0.94 on the test set, significantly outperforming the traditional Z-score method. This study provides a scientific, robust, and scalable personalized decision-making framework for NIPT screening in high-BMI pregnant women, effectively reducing clinical risks associated with delayed testing.
Research on Facial Emotion Recognition Based on Deep Learning
With the continuous advancement of technology, the demand for intelligent devices is rapidly increasing. Emotion serves as a crucial channel for human information transmission, and facial expressions convey rich emotional cues. These cues not only possess significant research value but also have wide-ranging applications in intelligent systems. Consequently, facial emotion recognition has emerged as a prominent research focus in the field of computer vision. In this study, a facial expression classification model is developed based on the Mini-Xception neural network. The Mini-Xception architecture eliminates the large fully connected layers and adopts depthwise separable convolutions in place of standard convolutions. By reducing the network depth and structural complexity, it effectively lowers the overall computational cost while maintaining performance.
Prediction method of coal seam porosity based on BP neural network
Coal seam porosity is a key parameter for evaluating the physical properties of coalbed methane (CBM) reservoirs, and its accurate prediction is crucial for CBM exploration and development. Traditional log interpretation methods exhibit limited accuracy when dealing with multifactor nonlinear relationships. This paper proposes a coal seam porosity prediction model based on an error back propagation (BP) neural network, which establishes the nonlinear relationship between optimized conventional logging curves (AC, CAL, CNL, DEN, GR, RT) and porosity (POR). Input variables were screened using Spearman correlation analysis, and a three-layer BP neural network structure was constructed. The network weights and thresholds were optimized via the back-propagation algorithm. Experimental results demonstrate that the model effectively learns the complex mapping relationship between logging parameters and porosity. The predicted results show strong agreement with measured values, with an average relative error of less than 8%, outperforming traditional regression methods. This approach provides a reliable technical means for predicting coal seam porosity.
Automatic Modulation Recognition Based on Deep Learning
Automatic modulation recognition is an important aspect of wireless communication. In recent years, the rapid development of deep learning technology has provided new solutions for modulation recognition. Deep learning-based modulation recognition has strong feature extraction and classification capabilities, resulting in higher recognition accuracy compared to traditional detection methods. However, the commonly used neural networks currently all face the problem of low recognition accuracy under low signal-to-noise ratio (SNR). To address this issue, this paper proposes a hybrid neural network model based on multi-channel input, which utilizes three channels of input: I/Q signals, time-frequency (T-F) distribution matrix, and signal-to-noise ratio. By incorporating SNR to introduce an environmental perception mechanism and a gated fusion module, the model's ability to understand the features of complex sequence information is enhanced. In addition, a phased block based on the Transformer architecture is employed to learn local and global features by combining tokens of different scales. Experimental results on the open-source dataset RML2016.10A indicate that the proposed method outperforms the current state-of-the-art modulation recognition methods, with an average accuracy of 47.60% at signal-to-noise ratios from -20dB to 0dB, and an overall average recognition accuracy of 68.52%.
The impact of short video factors on the purchase intention of users on the Rednote platform: Through the social capital perspective
This study examines the impact of the short video model on the Rednote platform on purchase intention through the perspective of social capital. The objectives of this study were (1) Exploring the key factors influencing purchase intention in short videos, (2) Analyze the impact of short video factors on purchase intention, and (3) Exploring the mediating role of social capital between short video factors and purchase intention. This quantitative study conducted an online questionnaire survey on users who have the habit of watching short videos on the Rednote platform. Through simple random sampling, 400 valid results were finally obtained. Data analysis was performed using SPSS, including descriptive statistics, reliability analysis, validity analysis, correlation analysis and mediation effect analysis. The final results: (1) The key factors of short videos that influence purchase intention include personalization, entertainment, and presence; (2) The three short video factors (personalization, entertainment, and immersion all have a significant positive impact on purchase intention; (3) Social capital plays a mediating role between short video factors and purchase intention, among which the indirect effect of the "personalization → social interaction → purchase intention" path is the strongest, indicating that the most important dimension is social interaction; and under the mediating role of social capital, personalization is the most important factor in promoting purchase intention through short videos.
Temporal and Causal Modeling of Learner Behavior in Online Platforms via Hybrid Bayesian-Transformer Networks
Online learning platforms generate vast amounts of learner interaction data that contain rich temporal patterns and causal relationships essential for understanding learning processes and optimizing educational outcomes. Traditional behavioral modeling approaches struggle to simultaneously capture the complex temporal dependencies in learner trajectories while identifying causal factors that drive learning success or failure. The challenge lies in developing frameworks that can model both the sequential nature of learning interactions and the underlying causal mechanisms that influence learner behavior across diverse online educational environments. This study proposes a novel Hybrid Bayesian-Transformer Network (HBTN) framework that integrates probabilistic causal modeling with transformer-based temporal sequence analysis to comprehensively model learner behavior in online platforms. The framework employs transformer architectures to capture long-range temporal dependencies in learner interaction sequences while utilizing Bayesian networks to model causal relationships between behavioral factors, learning outcomes, and contextual variables. The hybrid approach enables simultaneous discovery of temporal learning patterns and causal behavioral mechanisms through joint optimization of sequence modeling and causal structure learning objectives. Experimental evaluation using large-scale online learning platform datasets demonstrates that the proposed framework achieves 45% improvement in learner behavior prediction accuracy compared to traditional approaches. The HBTN method results in 38% better identification of at-risk learners and 42% improvement in personalized intervention recommendation effectiveness. The framework successfully combines temporal sequence modeling with causal reasoning to provide 34% better interpretability of learner behavioral patterns while maintaining computational efficiency suitable for real-time online platform applications.
Transformer-Based Discovery of Dynamic Relationships in High-Frequency Financial Time Series
High-frequency financial data presents unique challenges for relationship discovery due to its complex temporal dependencies, microstructure effects, and rapidly evolving market dynamics. This paper presents a novel transformer-based framework specifically designed to discover and analyze dynamic relationships in high-frequency financial time series. Our approach leverages multi-head self-attention mechanisms of transformers to capture both short-term microstructure patterns and long-term dependencies while maintaining interpretability for financial practitioners. We introduce adaptive temporal position encoding that accounts for irregular trading intervals and market microstructure effects, specialized multi-head attention architectures designed to capture different types of market relationships at various scales, and temporal pattern recognition techniques that can identify time-varying correlations, lead-lag effects, and volatility clustering patterns. The framework incorporates advanced attention mechanisms with temporal pattern extraction capabilities and hyperparameter optimization strategies specifically designed for high-frequency data characteristics. Experimental evaluation demonstrates superior performance in discovering meaningful relationships compared to traditional econometric methods and standard deep learning approaches, with validation loss improvements of up to 40% across different model configurations. Our results reveal previously unknown intraday relationship patterns, provide insights into microstructure-driven correlations, and demonstrate the framework's ability to adapt to changing market regimes through attention weight analysis.
Physics-Informed Neural Networks for High-Fidelity Electromagnetic Field Approximation in VLSI and RF EDA Applications
The increasing complexity of Very Large Scale Integration (VLSI) circuits and Radio Frequency (RF) systems demands sophisticated electromagnetic field analysis capabilities that traditional Electronic Design Automation (EDA) tools struggle to provide efficiently. This research presents a novel Physics-Informed Neural Network (PINN) framework specifically tailored for high-fidelity electromagnetic field approximation in VLSI and RF Electronic Design Automation applications. The proposed methodology integrates Maxwell's equations with advanced neural network architectures to enable accurate field prediction across diverse frequency ranges from DC to millimeter-wave operations while maintaining computational efficiency suitable for interactive design workflows. Through comprehensive validation across representative VLSI interconnect structures, RF passive components, and millimeter-wave integrated circuits, our PINN framework demonstrates superior accuracy compared to conventional moment-based methods while achieving computational speedup factors of 45-150× for typical design scenarios. The framework successfully captures complex electromagnetic phenomena including substrate coupling, crosstalk mechanisms, and frequency-dependent losses with mean absolute errors below 3.8% across frequency ranges spanning DC to 300 GHz. The adaptive mesh-free formulation eliminates geometric discretization constraints that limit traditional EDA tools, enabling seamless analysis of irregular conductor geometries and multi-layer dielectric stackups common in advanced semiconductor processes. Real-time field visualization capabilities facilitate intuitive understanding of electromagnetic coupling mechanisms, supporting design optimization workflows that were previously computationally prohibitive. The framework incorporates specialized handling of conductor loss mechanisms, dielectric dispersion effects, and substrate characteristics specific to semiconductor manufacturing processes, ensuring practical relevance for modern VLSI and RF design challenges. Validation against commercial EDA software demonstrates comparable accuracy for standard benchmarks while providing substantial performance advantages for parametric analysis and optimization applications critical to contemporary circuit design methodologies.
Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions
The optimization of energy consumption in smart buildings through adaptive control systems represents a critical challenge in achieving sustainable building operations while maintaining occupant comfort and satisfaction. This research presents a comprehensive Reinforcement Learning (RL) framework designed to intelligently control smart building energy systems by dynamically adapting to real-time occupant behavior patterns and changing weather conditions. The proposed system employs Deep Q-Networks enhanced with attention mechanisms to learn optimal control strategies for Heating, Ventilation, and Air Conditioning (HVAC) systems, lighting, shading, ventilation, and load management while continuously monitoring occupancy levels, behavioral preferences, and environmental parameters. Through extensive empirical evaluation conducted across 89 commercial and residential buildings over an 18-month period, our findings demonstrate substantial improvements in energy efficiency with average reductions of 27.3% in total energy consumption and 31.8% in peak demand loads compared to conventional Building Management Systems (BMS). The adaptive framework achieved remarkable performance in maintaining thermal comfort within acceptable ranges for 96.7% of occupied hours while reducing energy costs by 24.1% on average. Furthermore, the system demonstrated exceptional responsiveness to weather variations with prediction accuracy of 94.2% for load forecasting and adaptation times averaging 8.3 minutes for significant environmental changes. These results establish RL-based approaches as highly effective solutions for next-generation smart building energy management, contributing significantly to sustainable building operations and occupant well-being.
Smart Building Control, Energy Management
A Sequential Recommendation Model Based on Convolutional Neural Networks and State Space Models
Sequential recommendation aims to predict users' future preferences by analyzing their historical behavior sequences. In recent years, deep learning techniques have been widely applied to sequential recommendation tasks, achieving remarkable improvements in recommendation accuracy. However, existing methods often suffer from structural imbalance in modeling the rapidly changing short-term interests and the relatively stable long-term preferences of users. Moreover, these methods face limitations such as high computational costs and low inference efficiency when dealing with long behavior sequences. To address these challenges, this paper proposes a novel sequential recommendation model named CNN-Mamba, which integrates Convolutional Neural Networks (CNN) with State Space Models (SSM). Specifically, the CNN component leverages local receptive fields to efficiently extract short-term interest features from recent user interactions, thereby enhancing the model’s ability to capture local behavior patterns. Meanwhile, the SSM component is introduced as a long-term interest modeling module to capture global dependencies within long sequences. Furthermore, an adaptive fusion layer is designed to dynamically integrate the short- and long-term modeling outputs, thereby improving the model's generalization ability. In addition, the implicit recurrence mechanism in the state space model effectively reduces computational complexity and enhances the efficiency of long-sequence modeling. Experimental results on three real-world datasets demonstrate that the proposed CNN-Mamba model outperforms state-of-the-art baselines in both recommendation accuracy and inference efficiency, validating its effectiveness and practicality.
Entropy-Constrained and Sparse Relation-Constrained Subspace Clustering
This paper proposes a novel Entropy-Constrained and Sparse Relation-Constrained Subspace Clustering (ECSSC). By integrating information entropy weighting with sparse representation, the proposed method enhances both the accuracy and robustness of high-dimensional data clustering. Key improvements include: the introduction of an information entropy weight matrix to quantify feature discriminability and improve adaptability to noise and redundant features; the use of Frobenius norm constraints on the coefficient matrix to balance computational efficiency and model performance; and the incorporation of block-diagonal constraints to refine the sparsity structure of subspaces. Experiments conducted on three image datasets—MNIST, ORL, and COIL20—demonstrate that EWSSC outperforms traditional methods, classical sparse subspace clustering algorithms, and related state-of-the-art variants in terms of clustering accuracy (ACC), normalized mutual information (NMI), and adjusted Rand index (ARI). Notably, on the ORL dataset, ECSSC achieved an NMI of 0.9031, representing a 6.18% improvement over Sparse Representation-based Clustering (SRR). Ablation studies further confirm the effectiveness of the entropy-weighting module, which contributes to an average performance gain of 10%–18% across different datasets.
Design of an Interactive Visualization System for the Process Workflow of an Intelligent Sub-Assembly Workshop
With the rapid development of intelligent manufacturing in the inland shipbuilding industry, traditional sub-assembly workflows urgently require transformation toward visualization, interactivity, and intelligence. This paper develops an interactive process workflow visualization system based on Unity. The system reconstructs the workshop environment and major equipment through 3D modeling, covering key operations such as steel plate cutting, laser cutting, intelligent sorting, AGV transportation, and welding. Users can select individual operations or the complete workflow via interface controls, enabling autonomous process control and dynamic demonstration. Furthermore, the system integrates a real-time equipment monitoring module, which leverages IoT technology to acquire operational parameters, working states, and performance indicators of critical equipment including laser cutters, welding robots, and AGVs, and presents them in a 3D visualized form. Compared with traditional flowcharts or video demonstrations, the proposed system provides greater intuitiveness and interactivity, supporting applications such as process training, production planning, bottleneck analysis, and equipment monitoring. This approach demonstrates significant application value and potential in process visualization, workforce training, equipment monitoring, and digital twin construction.
Overview of Personalized 3d Human Body Modeling Technology for Garment CAD
3D human models serve as the foundation for 3D garment design, while personalized human modeling has emerged as a significant research focus in computer graphics and computer vision. Over time, numerous implementation approaches have been developed. This paper reviews and summarizes recent advancements in personalized human modeling, categorizing them into scanning-based methods, standard model deformation techniques, template matching approaches, image-based model reconstruction, and wireframe-assisted deep learning methods. The study analyzes the strengths and limitations of existing methodologies while outlining future development trends.
Developing an Integrated Talent Cultivation Model of Post-Course-Competition-Certificate-Innovation-Application under the Context of New Quality Productive Forces: A Case Study of the Business Data Analysis and Application Program in Higher Vocational Col
In the context of continuous advancements in digitalization and intelligent technologies, New Quality Productive Forces have become a core driver of high-quality economic and social development. This concept emphasizes not only technological innovation and industrial upgrading but also the cultivation of highly skilled talents capable of adapting to emerging industrial structures and the demands of digital transformation within the education sector. Taking the Business Data Analysis and Application program in higher vocational colleges as a case study, this paper explores the construction of an integrated Post-Course-Competition-Certificate-Innovation-Application (GKCZY) talent cultivation model. Through analyzing the current situation, diagnosing existing problems, and proposing targeted strategies, the study develops a comprehensive implementation pathway aligned with the requirements of New Quality Productive Forces. The findings reveal that this model effectively enhances students’ job competence and overall professional quality, providing valuable insights and practical experience for the reform and innovation of talent cultivation models in higher vocational education.
Post-Course-Competition-Certificate-Innovation-Application (GKCZY)
Retraction: Self-attention fusion for audiovisual emotion recognition with incomplete data
This article has been retracted by Darcy & Roy Press following an allegation that this article is an almost direct copy of another published work. Darcy & Roy Press has investigated in line with the COPE guidelines, and agree the evidence shows this work was first published elsewhere by different authors [1]. The authors have not been able to provide a satisfactory explanation for such substantial overlap. As a result, in line with the COPE retraction guidelines, Darcy & Roy Press retracts this article for plagiarism. Despite responding in earlier stages of the investigation, the authors have not commented on the wording of the retraction notice. Retraction published: September 02, 2025. Reference [1] K. Chumachenko, A. Iosifidis and M. Gabbouj, "Self-attention fusion for audiovisual emotion recognition with incomplete data," 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 2822-2828, doi: 10.1109/ICPR56361.2022.9956592.
System Integration and Cold Energy Utilization: Advancing LNG Technology for a Low-Carbon Future
As a clean and efficient energy carrier, Liquefied Natural Gas (LNG) plays an increasingly critical role in the global energy transition. This paper conducts a comprehensive review of the LNG technology system, focusing on the optimization of LNG regasification processes, cold energy recovery and utilization, integration innovation with renewable energy systems, and analysis of the economic and environmental benefits of technological applications. By summarizing key research achievements in the LNG field in recent years, particularly the coupling technologies with solar energy systems, Organic Rankine Cycle (ORC), and hydrogen production, this paper systematically elaborates on the core value of LNG technology in improving energy efficiency, reducing carbon emissions, and promoting the development of multi-energy complementary systems. Meanwhile, combined with relevant case studies, it analyzes the impact of different parameters on LNG system performance, and prospects the future development trends and challenges of LNG technology, providing references for related research and engineering applications.
Constructing a New "Teacher-AI" Collaborative Teaching Paradigm in International Chinese Language Education Enabled by Generative AI
The advent of Generative Artificial Intelligence represents a watershed moment for educational paradigms across disciplines. Within the field of International Chinese Language Education, Generative Artificial Intelligence presents both profound opportunities and significant challenges, compelling a reconceptualization of traditional pedagogical models. This paper moves beyond the discourse of AI as a mere tool or replacement for educators, and instead, proposes a comprehensive "Teacher-AI" collaborative teaching paradigm. This paradigm is conceptualized as a synergistic ecosystem wherein the human teacher and AI systems engage in a dynamic partnership to foster a more personalized, efficient, and profound learning experience for students of Chinese as a second language. This inquiry is qualitative and theoretical, employing a conceptual analysis methodology. It begins by establishing a theoretical foundation grounded in Vygotsky's Sociocultural Theory, particularly the Zone of Proximal Development, and Siemens' Connectivism, arguing that these frameworks provide the necessary scaffolding for understanding a collaborative intelligence model. The core of the paper delineates the proposed paradigm, systematiComputer-Assisted Language Learningy redefining the roles of both the human teacher and the AI. The teacher's role evolves from a primary knowledge dispenser to that of a pedagogical architect, a humanistic mentor, a facilitator of complex interactions, and an assessor of deep competency. Concurrently, the AI is positioned as an indefatigable personalized tutor, a dynamic content co-creator, an immersive practice partner, and an intelligent assessment assistant. A three-stage implementation framework—encompassing pre-class preparation, in-class facilitation, and post-class consolidation—is detailed to provide a practical roadmap for integrating this model into International Chinese Language Education curricula. The paper further explores specific application scenarios, including personalized linguistic practice, simulated communicative contexts, enhancement of intercultural competence, and scaffolded academic writing. Finally, it discusses the pedagogical implications, potential challenges such as ethical considerations and the need for new teacher literacies, and charts directions for future research. This paper contributes a structured, theoretiComputer-Assisted Language Learningy-grounded framework for harnessing Generative Artificial Intelligence not as a disruptive force, but as a collaborative partner in advancing the mission of international Chinese language education.
International Chinese Language Education
Towards Accurate and Interpretive Disease Diagnosis for Aging Adults Through Deep Learning Framework
Population aging has become a serious problem all around the world. The world's population is aging faster than ever before. What’s worse is that as the population ages, a serious problem arises: as the number of elderly people increases, medical resources begin to become scarce. For example, during the COVID-19 pandemic, many elderly people died due to lack of treatment and other medical resources such as masks. Similar to the COVID-19, some diseases are believed to be closely related to age and lead to higher mortality rates among the elderly. It is becoming more important to establish an efficient and accurate diagnostic system for such diseases. In this way, the impact of population aging on society can also be further alleviated. The rapid development of artificial intelligence technology can bring a turning point to this problem. Deep learning has become one of the most current topics in the field of artificial intelligence. With the swelling data of the Internet, deep learning showed emerging power to solve problems from various subjects that traditional machine learning methods could not handle well. Disease diagnosis is exactly one of them. In this research, We are aimed to build an accurate and interpretive disease diagnosis model leveraging the deep learning framework, specifically the Transformer, to diagnose the physical conditions of the elderly and reveal the relationship between specific microbiomes and disease. The key to the problem lies in what the classification model uses as features. Human gut microbiome has been proved to involve in a variety of chemical and biological processes in the human body and have complex connection with our health including specific diseases. In fact, sampling gut microbiome from patients to do disease diagnosis has been expected to be a more efficient and non-invasive method comparing to existed method such as blood test. Furthermore, the rapid development of gene sequencing technology also provides strong hardware support for this idea such as High-throughput sequencing. The core idea of this study is to use the relative abundance of human intestinal microorganisms as features to train a high-precision classification model for specific disease diagnosis. We evaluated five algorithms on six different and independent datasets with three age-related diseases. The results showed that Transformer could show the best performance in most of the cases, although it demands more computing resources to train. Furthermore, we managed to improve the generalization ability with the MK-MMD method. Finally, we did biomarker discovery to all the models with decent performance by SHAP and student t-test.
A Review and Prospect of the Application of New Building Materials
Ultra-high-performance concrete (UHPC), as a typical representative of new building materials, demonstrates unique advantages in the field of civil engineering with its ultra-high strength, high toughness, and excellent durability. This paper systematically combs the material properties and preparation processes of UHPC, deeply analyzes its application status in prefabricated buildings, bridge engineering, tunnel reinforcement, and other fields. Combined with practical cases such as the south extension project of Ningbo Airport Road and the reinforcement of expressway bridges, this paper discusses the technical paths for UHPC performance optimization, including the regulation of steel fiber content, the improvement of thermal curing systems, and composite interface treatment. The study shows that UHPC can significantly improve the bearing capacity and durability of components through material composition optimization and structural design innovation, but it still faces challenges such as cost control, specification adaptation, and construction process standardization. Future research should focus on green preparation technologies, intelligent applications, and multi-functional integration to promote the wide application of UHPC in complex engineering environments.
Fine-grained control strategy for high-concurrency transactions in the consortium chain Hyperledger Fabric
Hyperledger Fabric is a consortium chain framework that is widely used at home and abroad. When conducting business with Fabric technology, high-concurrency transaction conflicts are often encountered. To optimize the high concurrent transaction processing performance of Hyperledger Fabric, this paper proposes a targeted fine-grained control strategy, relying on the Dynamic Priority Reordering - Chain Code Cache mechanism (DPRC-Cache) framework and attribute-based Access Control (ABAC). Experiments show that fine-grained resource access control can effectively reduce the transaction conflict rate. Meanwhile, the optimized transaction sorting mechanism plays a positive role in improving the overall throughput of the system.