Scientific Journal of Intelligent Systems Research
Scientific Journal of Intelligent Systems Research (SJISR) is a monthly online, international, peer-reviewed journal. All articles published are rigorously and fast reviewed meeting the Journal Quality standards. SJISR is dedicated to publish empirical, academic, theoretical and review research articles that contribute to the practice in intelligent systems, and related topics. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Prediction of Suitable Cultivated Land in China amid the Northward Shift of Precipitation
This research develops an evaluation indicator system for cultivated land suitability that integrates climate, soil and topography, including seven indicators: annual precipitation, soil clay content, soil nitrogen content, soil organic matter content, soil pH, slope and slope aspect. The geographically weighted regression (GWR) model is used to analyze spatial heterogeneity of each driving factor. This study also adopts the SSP-RCP multi-scenario framework (SSP1-2.6 to SSP5-8.5) to predict the spatial pattern of suitable cultivated land in China from 2030 to 2100 under different climate pathways. Results show that the existing suitable cultivated land follows a pattern of dense distribution in eastern China and sparse distribution in western China. Highly suitable farmland clusters in three core zones: the Northeast Plain, North China Plain and Middle-Lower Yangtze Plain. Precipitation serves as the dominant driving factor, with the standardized regression coefficient of annual precipitation reaching 0.135. Variations of suitable cultivated land differ greatly across scenarios. Under the SSP5-8.5 scenario, the total area of suitable cultivated land will drop by roughly 84,000 square kilometers by 2100 relative to 2023. Under the SSP1-2.6 scenario, the total area will rise by about 126,000 square kilometers. Newly developed suitable cultivated land mainly locates on the northern margin of the Northeast Plain, eastern Inner Mongolia Plateau and northern Loess Plateau, with features of northward expansion, westward extension and contiguous distribution. The prediction model built in this research offers scientific support for optimizing cultivated land resource allocation and formulating agricultural adaptation policies amid climate change.
Northward Precipitation Shift; Suitable Cultivated Land Prediction; Geographically Weighted Regression; SSP-RCP Scenarios.
Copyright (c) 2026 Scientific Journal of Intelligent Systems Research https://creativecommons.org/licenses/by-nc/4.0
From Source Control to Path Collaboration: A Review of Development Trends in Thermal Management Technologies for High-Power Electronic Devices
The heat flux density of high-power electronic devices continues to rise, and the thermal dissipation bottleneck has become a key factor limiting the performance of wide and ultra-wide bandgap semiconductors.From a full-chain thermal management perspective of "from source to path," this paper systematically reviews the evolution trajectory of source-level thermal resistance control during chip manufacturing and thermal path collaborative management after chip operation.The review indicates that thermal management concepts are undergoing a triple paradigm shift: from post-hoc cooling to source-level control, from single-method optimization to full-chain collaboration, and from fixed-mode operation to intelligent adaptive cooling.Recent breakthroughs have been achieved in areas such as thermal interface resistance control of β-Ga₂O₃/diamond heterojunctions, although shortcomings remain in high-performance thermal interface materials and wafer bonding equipment.This paper can serve as a reference for the selection of thermal management solutions for high-power-density packaging and the planning of technology roadmaps.
High-power Electronic Devices; Thermal Management; Wide Bandgap Semiconductors; Gallium Oxide; Cooling Technologies.
Digitalisation and Intelligentisation of the Inspection and Testing Industry: Current Status, Problems and Strategy Path.
Against the backdrop of a new round of digital construction and intelligent development, the inspection and testing industry has shown steady growth and is progressing well. This paper takes a general look at the current situation of digital and intelligent transformation in the inspection and testing industry, analyses the technology landscape, identifies the key barriers and problems, and puts forward strategic development paths. The study examines the application of digital technologies-such as Laboratory Information Management Systems, Internet of Things technologies, big data analytics, and artificial intelligence-in various parts of the industry. The major challenges found include a lack of digital infrastructure, data security risks, a shortage of talent, the absence of industry-wide standards, and the difficulty of translating technology into practice. A multi-stakeholder framework is proposed to drive market development through strategic planning, institution building, talent development, standard setting and ecosystem construction. Attention is also drawn to how vertically oriented AI agents can help address the lack of specialisation of general-purpose AI tools in specific areas. Policy suggestions are put forward at the end to support related research and development for the purposes of high-quality industry development.
Inspection and Testing; Digital Transformation; Artificial Intelligence; Intelligent Agent; Industry 4.0.
Review on the Properties and Comprehensive Application Development of Novel Semiconductor Materials based on GaN, InP, and GaAs
With the rapid development of 5G/6G communications, data centers, new energy vehicles, autonomous driving, and high-performance optoelectronic systems, traditional silicon-based semiconductors are gradually approaching their performance limits in application scenarios such as high frequency, high power, high temperature, and high-speed photoelectric conversion. Compound semiconductor materials have become key enablers for new-generation devices. Gallium arsenide (GaAs) and indium phosphide (InP) offer high electron mobility and excellent optoelectronic properties for high-frequency communication and photonic applications. Gallium nitride (GaN), distinguished by its wide bandgap and high breakdown field strength, further extends these capabilities to high-power electronics and ultraviolet optoelectronics.This paper focuses on three typical compound semiconductor materials, GaAs, InP, and GaN, and reviews their material properties, current technological development status, and main application directions. It emphatically analyzes the application progress of GaAs and InP in radio frequency (RF) front-ends, microwave and millimeter-wave multifunctional integrated circuits, optical communications, and optoelectronic devices, discusses the development potential of InP/CMOS heterogeneous integration in high-frequency communications above 100 GHz, radar detection, and optoelectronic integration, and outlines the application prospects of GaN in vertical power devices, driver integrated circuits, new energy vehicles, and 6G communications. Finally, it analyzes the challenges faced by compound semiconductors in large-size substrate preparation, process compatibility, device reliability, manufacturing costs, and large-scale applications, and prospects their future development trends toward multi-material collaboration, heterogeneous integration, and system-level integration.
Gallium Arsenide; Indium Phosphide; Gallium Nitride; New Compound Semiconductors; Heterogeneous Integration; Power Devices.
Intelligent Target Recognition Technology Empowers the Improvement of Urban Public Safety Governance Capabilities
The governance of urban public safety faces the dilemma of complex risks and insufficient effectiveness of traditional methods. Intelligent target recognition technology provides core support for solving this problem. This paper analyzes the multi-dimensional mechanism that this technology enables urban public security governance, and points out that it relies on convolutional neural network (CNN), transformer architecture and edge computing to build three mechanisms, namely, real-time accurate perception, active risk early warning, and cross sectoral collaborative decision-making, to promote the transformation of governance mode from passive response to active prevention and control. At the same time, this article reveals the four constraints faced by technology in practical implementation, including technological robustness, data privacy, institutional ethics, and organizational capacity. Based on this, we propose countermeasures and suggestions to promote model optimization and multimodal integration, improve regulatory standards and ethical review, strengthen data sharing and business process reengineering, and look forward to future research directions such as continuous learning, explainable AI, and privacy computing, providing a systematic reference for improving the efficiency of urban security governance.
Intelligent Target Recognition; Urban Public Safety; Public Safety Governance.
An Energy-Efficient Multi-Agent Reinforcement Learning Approach for Spark Job Scheduling in Mobile Edge Computing
Battery life remains a major constraint on the continuous operation of mobile IoT devices represented by drones. Most existing computation offloading strategies rely on static heuristic rules, which fail to deliver stable performance in dynamically changing wireless mobile environments. Targeting this problem, this paper develops a multi-agent deep reinforcement learning approach for Spark job scheduling across local devices, edge servers and cloud resources. The scheduling task is modeled as a Markov decision process: six real-time system and network metrics, including CPU utilization, memory load, handover delay, signal strength, transmission latency and congestion level, form the state space, and the reward function is built directly on actual measured energy consumption data. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, the proposed method adopts a centralized training and decentralized execution paradigm to realize distributed collaborative decision-making. Validation on a public MEC dataset shows that the trained policy finally converges to a hybrid scheduling mode: 57% of tasks are processed locally, 39% are offloaded to edge nodes, and less than 4% are assigned to cloud resources for specific application scenarios. Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Mobile Edge Computing; Energy Optimization; Apache Spark; Multi-agent Reinforcement Learning; Computation Offloading.
Deep Learning for Precipitation Nowcasting
This paper provides a comprehensive review of deep learning approaches for precipitation nowcasting, a challenging spatiotemporal prediction task in meteorology. We systematically categorize existing methods into four major families: recurrent neural network (RNN)-based models, convolutional encoder-decoder architectures, generative adversarial networks (GANs), and transformer-based models. For each category, we analyze its underlying modeling principles, representative architectures, strengths, and limitations. Furthermore, we summarize widely used radar datasets and evaluation metrics to provide a unified benchmark perspective. Finally, we discuss key challenges such as prediction blurriness, class imbalance in extreme precipitation events, and computational inefficiency, and outline promising future directions including multimodal fusion, efficient attention mechanisms, and self-supervised pretraining. This review aims to provide a structured reference for both researchers and practitioners in the field of data-driven weather forecasting.
Precipitation Nowcasting; Deep Learning; Convolutional LSTM; Generative Adversarial Networks.
Research on the Challenges and Optimization Path of Fault Diagnosis Technology for Intelligent New Energy Vehicles
With the continuous increase in the number of new-energy vehicle owners and the rapid development of intelligent connected functions, vehicle failure modes are gradually changing from traditional mechanical wear to complex anomalies caused by the combined effects of batteries, motors, electronic control systems, software and communication modules, etc. Fault diagnosis ability is now required to ensure the safety of the car and the user experience; otherwise, after-sales service will be affected. Currently, intelligent fault diagnosis for new-energy vehicles still faces several problems in its technological application, such as complex fault mechanisms, fragmented data resources, insufficient adaptability of diagnostic models, imperfect service collaboration mechanisms, unclear data security responsibility boundaries, and a shortage of multi-skilled maintenance personnel. Addressing these challenges, this article analyzes the operational logic and application value of intelligent new energy vehicle fault diagnosis, proposing a diagnostic optimization system based on data governance, supported by collaboration between the vehicle, cloud, and service ends, and guaranteed by standards, privacy protection, and talent cultivation. This system should cover status perception, anomaly identification, risk warning, maintenance decision-making, and feedback updates. The study argues that intelligent fault diagnosis should not be limited to single-point detection and fault code identification but should be integrated into the entire lifecycle management process of new energy vehicles, thereby improving vehicle safety management and the resilience of the after-sales service system.
Intelligent new energy vehicles, fault diagnosis, data governance, intelligent operation and maintenance, optimization path.
Hybrid Reasoning with Large Language Models for CFO-Oriented Financial Decision Support
The Chief Financial Officer (CFO) has gone beyond the scope of financial reporting and compliance to strategic decisions in more complex and data-saturated business settings. Although the recent development of large language models (LLMs) demonstrates a potential to succeed in financial text analysis and decision support, the current solutions are more generic and text-focused, which reduces their credibility in quantitative reasoning and financial work that is sensitive to regulations. In the present paper, the vertical large language model framework that we recommend to be used in the CFO-oriented decision support is namely domestic retail and export-oriented manufacturing industries. The suggested structure assumes a modular hybrid reasoning system that incorporates structured financial information processing, explicit number processing, regulatory attitude processing, and language-based decision generation. The separation between deterministic financial calculation and natural language explanation of the framework increases numerical accuracy, consistency of decisions, and interpretability and reduces the hallucination risks often linked to language-based models that are entirely driven by language.
Large language models, hybrid reasoning, decision support systems, financial intelligence.
Evaluating Large Language Models for Psychological Diagnosis and Counseling: A Dual-Task Framework with Cultural Reflection
Large language models (LLMs) have demonstrated high potential in healthcare and mental health related applications, however, their applicability to psychological diagnosis and counseling is not adequately investigated. This paper presents a dual-task assessment model that can be used to systematically evaluate how the mainstream Chinese LLMs can be used in psychology and through two lens psychological diagnosis and psychological counseling. In the diagnosis task, we test four Chinese LLMs, DeepSeek, Doubao, Kimi, and Tongyi Qianwen, on a mental health dataset that consists of five psychological disorders. Accuracy, Precision, Recall, F1-score, AUC, and confusion matrices are the measures of their diagnostic performance. In the case of the counseling task, we develop a prompt-based assessment system and evaluate the generated responses using six dimensions, including Overall, Empathy, Specificity, Medical Advice, Factual Consistency and Toxicity. To lower the expenses of annotating on the large scale with experts, we use an LLM-as-a-Judge approach to rank and evaluate the generated counseling answers. The experimental findings indicate that already existing Chinese LLM has some promising prospects in preliminary psychological diagnosis but fails to differentiate semantically similar disorders resulting in diagnostic confusion. The models themselves tend to produce fluent and empathetic responses in a counseling situation although they display obvious limitations in specificity, safety awareness, and dealing with medical advice. In addition, we also offer a case analysis and cultural reflection, pointing out that good average performance does not always imply the consistent support of culturally diverse adolescent groups. On the whole, our results indicate that the existing LLMs are more appropriate as an assistant, rather than a replacement of a mental health professional, and the research of the future should be aimed at enhancing the safety, cultural sensitivity, and clinical reliability of the system.
Large language models, psychological diagnosis, psychological counseling, adolescent mental health, LLM-as-a-Judge, cultural reflection.
A Data-Driven Financialization Framework for Basketball Star Cards: Multi-Factor Price Prediction and Quantitative Trading Strategy via Performance Analytics
Massive capital have flooded into basketball star card industry over recent years, but the whole market still rely on outdated, unsystematic pricing standards that cannot match its fast financialization speed. Most collector and investor judge card value by personal experience, forum rumors and scattered historical transaction records, those methods lack standardized quantitative risk evaluation and price prediction ability. This paper develop a Dynamic Multi-Factor Asset Pricing (DMFA) model based on classic Arbitrage Pricing Theory (APT), and combine Exponentially Weighted Moving Averages (EWMA) time-series algorithm to capture time-varying player performance momentum effects. Three core factor modules are constructed inside the model: fixed intrinsic card characteristics, time-decay weighted game statistical indicators, and cross-market systemic risk measured by crypto asset beta coefficient. Three representative card samples with different market lifecycle are selected for empirical test: LeBron James veteran blue-chip PSA10 card, Shai Gilgeous-Alexander limited-edition peak superstar RPA card, and Victor Wembanyama high-liquidity rookie silver card. The regression test results show asset volatility, price sensitivity to athletic performance have obvious non-linear relations with athletes’ career development stage, rookie cards always bear far higher overall market risk compared with veteran collectibles. On the basis of DMFA model’s factor output results, this paper also build a complete algorithmic quantitative trading framework. The system can separately calculate idiosyncratic abnormal return alpha and systemic market risk beta, provide clear operation signals for portfolio construction and dynamic position adjustment for this emerging alternative asset category.
Sports memorabilia, alternative assets, multi-factor models, time series prediction, beta coefficient.
Academic Stress and Subjective Well-Being among Chinese and American Adolescents: A Cross-Cultural Analysis Using LLM-Generated Synthetic Data
The well-being of adolescents is tightly connected with the long-term mental health and is vulnerable to the academic demands. This paper will study the correlation between academic stress and subjective well-being among Chinese and US adolescents in terms of cross-cultural comparative design. A culturally sensitive bilingual (Mandarin English) synthetic survey responses were produced using large language models, and then screened by hand using plausibility criteria to help methodologically validate the selection of this methodology before the collection of large amounts of empirical data. The findings show that subjective well-being is very much negatively correlated with academic stress in the two countries. The Chinese adolescents have higher levels of academic stress and lower levels of well-being with an average and stress-well-being relationship is also stronger in China. Mediation analysis also indicates that perceived academic autonomy was a mediator of the negative impact of academic stress on well-being, with the indirect impact being bigger in Chinese sample. Such results indicate culture-specific processes of academic stress and attractive use of the synthetic data produced by LLC to cross-cultural research design.
Adolescent well-being, academic stress, cross-cultural comparison, individualism and collectivism.
An Improved ISCR Model for Rumor Propagation on Social Networks: Dynamics, Reproduction Number, and Intervention Strategies
Rumor diffusion on online social networks (OSNs) is a fast, non-linear and high-impact phenomenon that increasingly threatens public order. We extend the classical SEIR epidemic framework to a four-compartment Improved ISCR (Ignorant–Hesitant–Spreader–Stifler) model that explicitly distinguishes the cognitive hesitation stage and incorporates an official intervention intensity u. Specializing to constant control we derive, via the next-generation matrix method, a closed-form effective reproduction number , which yields an interpretable threshold for rumor extinction. We complement the mean-field analysis with agent-based simulations on Barabási–Albert (BA) scale-free and Watts–Strogatz (WS) small-world networks, run on 5 independently generated graphs 6 random seeds (n=30 per configuration) and extended until to avoid endpoint censoring. We benchmark five governance policies including a novel combined early-intervention plus opinion-leader-immunization rule. Results show that (i) on BA networks, random immunization is essentially inefficient (76.7%→74.7%); opinion-leader immunization alone reduces final reach to 17.5 14.3%; the combined policy further compresses it to under the same 5% immunization budget jointly with early control; (ii) topology-effect claims are robust: under random-seed initialization the BA reach drops to 45.7% but remains higher than WS’s 14.5%, while hub-seeded BA still dominates; (iii) the joint policy is the only one that simultaneously achieves low mean and low variance, which is critical for governance reliability.
Rumor propagation, ISCR model, basic repro-duction number, complex networks, opinion leader, social-media governance.
Optimization of Wavelet Threshold based on Sparrow Search Algorithm Integrating Tent Chaos and Cauchy Mutation with Application in Bearing Fault Diagnosis
Wavelet-threshold denoising is strongly affected by the selected threshold when the signal is non-stationary and the noise level is unknown. An overly large threshold may remove impact components, whereas an overly small threshold leaves residual noise after reconstruction. This paper treats threshold selection as an optimization problem and searches it with an improved sparrow search algorithm (ISSA). Tent chaotic mapping is used in the initialization stage to spread candidate thresholds over the search interval. Cauchy mutation is then applied near the current leading position to keep the search from staying too early in a narrow region. Minimum envelope entropy serves as the fitness value, so the search does not require a clean reference signal. The method is tested first on a composite signal containing sinusoidal components and a step term, and then on an outer-ring bearing fault signal. In the simulated signal test, the ISSA-based method increases the signal-to-noise ratio (SNR) by 2.67 dB and reduces the root mean square error (RMSE) by 26.6% compared with the traditional soft-threshold method. In the bearing fault test, the peak SNR of the envelope spectrum increases by 8.3 dB, and fault-related impact information remains visible after denoising.
Improved sparrow search algorithm, wavelet threshold denoising, tent chaotic mapping, Cauchy mutation, fault diagnosis.
A Data-Driven Optimization of Voting Mechanisms in Dancing with the Stars
The reality show "Dance with the Stars" studied in this article will refer to the scores played by the judges and the votes cast by the fans, and finally decide whether the participants can be promoted, but there has always been a relatively obvious conflict between the evaluation results of professional judges and the fan support based on public popularity. When this study was conducted, it was constructed to build a framework based on actual data, with the aim of reducing the proportion of weekly fan votes, comparing the actual role of different voting methods, and designing fairer promotion decision-making rules. This article first uses sequential secondary programming to solve the entropy regular single-goal optimization model, estimates the previously unknown fan voting relative distribution, the estimation process will ensure that the observed elimination results and the actual broadcast content are consistent. This article also compared the actual performance of the ranking system and the percentage rating across the season, and found that the ranking system will amplify the influence of fan voting, which is easy to cause more controversy. This paper also conducted research through two analytical methods of multivariate regression and random forest, and found that the performance of the participating stars and dance partners has a major impact on the evaluation of professional judges, and the popularity of the players is mainly determined by their popularity in the entertainment industry and their own age. This article finally designed the dual quantitative integration and dynamic intervention, that is, the working mechanism of DWF-DI, which combines the scoring information given by the judges with the voting information of the fans, with the Choquet-integral consensus reward rule, and the restriction double threshold jury intervention rule. Subsequent backtests show that this mechanism designed in this article can reduce the scoring controversy in the program, while not making the audience lose interest in the program, and it is also very practical to use it.
Voting mechanism optimization, fan vote estimation, fairness, entropy regularization, Choquet integral, dynamic intervention.
Resource Allocation Optimization in Power Wireless Private Networks based on Deep Reinforcement Learning
Power wireless private networks are required to support reliable and low-latency communications for diverse power system applications under limited radio resources. Efficient resource allocation in such networks is challenging due to dynamic channel conditions, heterogeneous quality-of-service requirements, and the coupling among multiple resources. To address these challenges, this paper formulates the resource allocation problem as a sequential decision-making process and proposes a deep reinforcement learning-based optimization framework. By modeling network states, allocation actions, and performance-driven rewards, the proposed method enables adaptive and real-time resource allocation without relying on accurate system models. Simulation results demonstrate that the proposed approach significantly outperforms conventional allocation schemes in terms of system throughput and delay performance, while effectively satisfying quality-of-service constraints. These results indicate the effectiveness and robustness of deep reinforcement learning for resource management in power wireless private networks.
Power wireless private networks, resource allocation, deep reinforcement learning, quality-of-service, intelligent communication.
Application of AI Algorithms Integrating High-Frequency Trading Data in Financial Market Liquidity Risk Prediction
With the rapid development of financial technology and the increasing fragmentation of the financial market, high-frequency trading (HFT) has become a core driving force affecting market liquidity, and liquidity risk has gradually become one of the key risks endangering the stability of the financial system. Traditional liquidity risk prediction methods rely on low-frequency financial data, which are difficult to capture the dynamic and nonlinear characteristics of high-frequency trading markets, leading to insufficient prediction accuracy and timeliness. In this study, we integrate high-frequency trading data (including order book data, transaction tick data, and order flow data) and construct a liquidity risk prediction framework based on artificial intelligence (AI) algorithms, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. First, we define and calculate key liquidity indicators based on high-frequency data to characterize market liquidity from multiple dimensions such as tightness, depth, and resiliency. Second, we preprocess the high-frequency data through normalization, outlier detection, and sequence construction to solve the problems of high noise and high dimensionality of HFT data. Then, we establish AI prediction models to predict the liquidity risk level (measured by Liquidity at Risk, LAR) and verify the effectiveness of the models using real high-frequency trading data of the U.S. stock market and cryptocurrency market. The experimental results show that the AI algorithms integrating high-frequency trading data significantly outperform traditional models (such as Logistic Regression, LR, and Support Vector Machine, SVM) in terms of prediction accuracy, recall rate, and F1-score. Among them, the Transformer model, which can capture long-term dependencies in time series, achieves the best prediction effect, with an average accuracy of 89.73%. This study provides a new technical path for financial institutions to conduct real-time liquidity risk management and has important theoretical and practical significance for maintaining the stability of the financial market.
High-frequency trading, liquidity risk, AI algorithms, LSTM, Transformer, Liquidity at Risk.
A Life-Cycle AHP-CVM-VIKOR Framework for Environmental Sustainability Assessment of Super Bowl Host Cities
Large sport events generate short-duration yet high-intensity environmental pressure, through venue operation, spectator mobility, demand for accommodation, water consumption and solid-waste generation. However, current sustainability assessments are often decoupled from host-city selection, reducing their practical decision value. The study develops an integrated life-cycle AHP-CVM-VIKOR framework for sustainability-oriented Super Bowl host-city selection. First, a life-cycle indicator system is developed to characterize the event-related energy use, greenhouse gas emissions, water demand, solid waste, transportation, accommodation and city profile conditions. Then, an AHP-CVM coupling procedure is used to integrate expert-based criterion importance and preference-based environmental valuation, and the resulting weights are embedded in the VIKOR compromise-ranking model to calculate a Comprehensive Environmental Impact Index (CEII). The framework is applied to past Super Bowl host cities and three 2029 candidate cities, Chicago, Denver and Philadelphia. The results indicate that food-related waste, waste-diversion performance, on-site fuel consumption, wastewater generation, and stadium electricity consumption are important contributors to the CEII. Of the 2029 candidates, Chicago has the lowest CEII, with Denver and Philadelphia next. The model is extended to Olympic-scale events and tested by sensitivity analysis to show its transferability to multi-venue sport mega-events. The framework proposed is a transparent decision support tool to compare candidate cities and to identify priorities for environmental mitigation.
Sport mega-events, environmental sustainability, host-city selection, AHP-CVM, VIKOR, Comprehensive Environmental Impact Index.
The Improvement Effect of Interactive Digital Devices on Audience Participation Experience
To apply interactive digital devices to the audience participation experience in exhibition scenarios, firstly, the limitations of traditional exhibition modes were analyzed, and the inherent defects in interaction, information transmission, immersion and personalized services were clarified, which laid a realistic foundation for exploring the improvement path of audience participation experience. Then, the implementation paths of interactive digital devices to improve audience participation experience were elaborated from four aspects: two-way interaction mechanism, multi-modal information presentation, low-threshold operation and personalized scene design. Finally, the improvement effects of interactive digital devices on audience participation experience were summarized from the transformation of participation mode, optimization of information reception, upgrading of immersive experience and optimization of experience texture. The research results can provide theoretical reference and practical paths for immersive exhibitions, new media art creation and pavilion interaction design.
Interactive digital devices, audience participation experience, immersion.
Gendered Patterns of Generative AI Adoption among University Students: A Cross-Cultural Survey of Chinese and International Users
The fast spread of generative artificial intelligence has already become pervasive in university students' media practices, but there is still an inadequate understanding of the gender and culturally embedded factors underlying its adoption process. This paper examines the effect of gender and cultural background in isolation and combination on the frequency, diversity, and types of generative AI adoption by university students in China using a cross-cultural online survey of 412 respondents, including both Chinese students and students of other countries. As shown, statistically significant gender differences occur on all three dimensions – male respondents have more frequent AI use and greater use diversity, with a notable inclination toward AI coding help and entertainment purposes, whereas women are mainly engaged in AI academic writing and language translation. From a two-way analysis of variance (ANOVA), gender and cultural background interact to produce a significant small effect size (=.011), where the gender difference among international students is larger than that of Chinese students (mean difference=0.61 vs. 0.26). Regression analysis also supports the significance of both predictors with a negative interaction coefficient (β=-.15, p=.038), which implies that Chinese cultural orientation reduces gender differences. In all, the results provide a broader understanding of gender digital divide by shifting the discussion from the access stage to the usage stage, indicating that AI technology's institutionalization and the features of the domestic platform ecosystem might partly explain the gender divide in China.
Gender, generative AI, media practice, cultural context, cross-cultural communication, university students.
Deconstructing the Friday the 13th Effect: Evidence from Dummy-Variable Regression, Wavelet Analysis, and XGBoost Prediction
Starting from the question “Is Friday the 13th superstition or fact?”, this paper constructs a comprehensive evaluation framework from statistical falsification, signal deconstruction, to intelligent prediction, and discusses whether “Friday the 13th” is supported by realistic evidence, as well as whether the personal well-being of the public is affected by superstitious date characteristics.For Task 1, we first established a multiple linear regression model with dummy variables (Task 1.1). Through the deconstruction of date attributes, it was found that both the regression coefficient of the “Friday the 13th” variable and the p-value of the one-way analysis of variance were far greater than the significance level. They were statistically insignificant, directly falsifying the special negative effect of “Friday the 13th” at the empirical evidence level. Subsequently, we constructed a dummy-variable regression model with multiple-comparison validation (Task 1.2), and conducted sensitivity analysis by combining the consistency test of residual performance across different regression methods, proving the robustness of the non-significant conclusion. It was also found that, when independence was controlled, the effects of different dates and cycles remained insignificant. To further explore the deeper characteristics of the time series, we used multi-scale wavelet analysis (Task 1.3) to perform time-frequency deconstruction of event frequency based on frequency and significance. The results show that risk energy is mainly concentrated in macro long-wave cycles, such as policy cycles or pandemic cycles, while specific date numbers only appear as random high-frequency noise. This indicates that, from the perspective of the financial market, there is no such thing as a “good day” or a “bad day.”For Task 2, we constructed a multi-dimensional evaluation indicator system to measure the happiness index from five dimensions: the atmosphere of social public opinion, public health safety, social harmony and stability, climate change, and economic conditions, and used the Kendall correlation coefficient for heterogeneity validation. We then used the Entropy Weighting Method to determine the relative importance of each indicator. The results show that pandemic-related and environmental indicators (PM10) account for the highest proportion of influence on individual well-being. We then conducted a reliability analysis of the weight distribution in combination with practical reality. On the whole, the weight setting is consistent with realistic evidence.For Task 3, we used the XGBoost gradient boosting method to establish a risk prediction model. By analyzing various data features, the model can identify signals with real early-warning significance. The empirical research shows that the prediction performance based on physical environmental indicators is far better than the traditional superstitious prediction based on date numbers, providing rational risk early-warning rules for the public. At the same time, the prediction of “good days” and “bad days” largely depends on the updating of real-time information.The conclusion shows that social risks are driven by institutionalized cycles and physical environmental variables, rather than date superstition. This model not only provides the public with a rational quantitative tool, but also provides a robust mathematical paradigm for dealing with heterogeneous social events.
Friday the 13th, dummy-variable regression, multiple cross-validation, wavelet analysis, entropy weighting method, XGBoost, frequency features.
Personal Data Circulation Mechanisms in Embodied AI: Balancing Privacy Protection and Data Value Release
Embodied AI, characterized by multi-sensor fusion and autonomous decision-making capabilities, has transcended traditional personal information protection rules, giving rise to multiple privacy risks including physical privacy and mental privacy concerns, and causing legal principles such as "informed consent," purpose limitation, and data minimization to face application difficulties. However, current academic research still primarily focuses on personal information protection as the main approach to regulating embodied AI, without systematically addressing the circulation of personal information-related embodied AI data. With the goal of balancing personal information protection and data value release, a regulatory philosophy distinguishing personal information from personal data should be established. Personal information should be transformed into circulatable data through gradient anonymization processing using technologies such as Differential Privacy (DP) and Federated Learning (FL), shifting from the user "informed consent" model to enterprise proactive prevention obligations for personal information leakage. Institutional safeguards should be constructed from dimensions including privacy tort liability delineation, introduction of Data Intermediaries, and regulatory mechanism innovation, to resolve compliance dilemmas, mitigate data leakage and re-identification risks, and provide theoretical support and practical guidance for compliant and efficient circulation of embodied AI datasets.
Embodied AI, data circulation, Privacy-Enhancing Technologies (PETs), enterprise compliance.
AIGC Empowering Digital Protection and Gene Genealogy Construction of Traditional Bamboo Weaving in Zhaoqing
With a history of over 1,000 years, Zhaoqing's traditional bamboo weaving is an important component of China's intangible cultural heritage (ICH). Its survival is threatened by rapid urbanization, a shrinking pool of master craftsmen, and fragmented historical documentation. This paper looks into if new developments in AIGC such as LLMs, generative visual models, 3D modeling methods,knowledge graph structures can help conserve digital information of the custom of weaving bamboo in Zhaoqing and reconstruct its genealogy. Fieldwork completed in 6 villages; we had structured dialogue with 34 of the most experienced elderly veteran master knitters & collected more than 2400 patterns in various archives and library books to build systematically. The result shows that 91.3% of them were right, they can be distinguished into the 17 main lineage contexts to make sense of progress in aesthetics. It’s not just an analytic device, a bit more than AI-driven conservation of cultural heritages, it's a scalable digital genealogy to do investigation about the folk art heritage within each province in China.
AIGC, bamboo weaving, intangible cultural heritage, Gene Genealogy, cultural preservation.
Comparative Study of Gender Representation in AI-Generated News Images: Cross-Cultural Evidence from Chinese and Western Media Platforms
The rapid adoption of text-to-image (T2I) tools in newsrooms raises pressing questions about the gendered visual conventions embedded in AI-generated imagery. This study investigates cross-cultural differences in gender representation through a controlled four-model comparative experiment. Two Chinese-origin models (ERNIE-ViLG, Tongyi Wanxiang) and two Western-origin models (DALL-E 3, Midjourney) generated 400 images across ten news-related occupations, coded along five dimensions of gender and visual presentation. Results reveal a cross-cultural gap in female representation, with Chinese-origin models producing female figures in 30.5% of cases versus 42.5% for Western-origin models, a divergence most pronounced in high-status occupations. Both ecosystems nevertheless shared stereotyped patterns in care-oriented roles and in the gendered encoding of visual authority through posture, attire, and age. These findings indicate that cross-cultural divergence operates primarily through the intensity of stereotyping rather than its presence, positioning model selection and prompt design as consequential cultural decisions for news organizations.
Text-to-image generation, gender bias, cross-cultural comparison, AI-generated news images, algorithmic representation.
A Dynamic Modeling Framework for Assessing the Impact of Generative AI on Occupational Evolution and Curriculum Adaptation
The rapid development of generative artificial intelligence Gen-AI can replace practitioners to complete a large number of repetitive cognitive work, can also help improve decision-making efficiency, and can also build a new model of human and AI collaboration, from multiple dimensions to the existing professional system to bring changes. This article wants to clarify the various effects of this technology, and specially built a dynamic model that reflects the correspondence between career change and higher education adjustment. This article from the O*NET database to collect relevant data, combined with the Dutch occupation code, entropy weight calculation method and K-means clustering algorithm, screened out a number of representative occupations, including bioinformatics, film and video editing, as well as telecommunications line installers and maintenance personnel, just to correspond to the science and technology, art and skills practice of the three different career directions. This paper uses the Bass diffusion model to simulate the penetration process of Gen-AI in different industries, and also uses the Evolution-Vector-Dynamics model to disassemble labor processes into four different types: conventional tasks, cognitive tasks, physical tasks, and sudden-like tasks. From the final calculation results, the market demand for science and technology occupations will follow up, art occupations may face relatively large structural substitution pressure, skills practice occupations will basically remain stable, because a large number of physical operations in this type of occupation can not be replaced by technology for the time being. Combined with the conclusions of these studies, this paper proposes that the existing skills training direction can be adjusted to add artificial intelligence verification, algorithm audit, intellectual property-related training, and AI-assisted infrastructure operations to the training system. This research framework built in this paper has a relatively strong practicality, which can be used to evaluate the career changes brought about by the development of artificial intelligence, and can also provide a clear direction reference for subsequent course adjustments.
Generative AI, occupational transformation, labor demand, Bass diffusion model, higher education, curriculum reform.
Research on a Self-enhancing Maintenance Knowledge Base Method based on Fault Causal Association Probabilities
To address the problems of over-reliance on expert experience, knowledge fragmentation, and low diagnostic efficiency in the intelligent maintenance of widely distributed complex equipment, this paper proposes an intelligent maintenance method based on a self-enhancing dynamic knowledge base. First, a structured integration framework for multi-source heterogeneous maintenance knowledge is established to achieve the systematic organization of fault cases and service records. Second, a "high-incidence fault probability model" based on statistical analysis of historical fault data is proposed, forming a priority-based diagnostic strategy of “high-incidence faults first, low-incidence faults later . Furthermore, a large language model is integrated to enable natural language interaction and accurate knowledge retrieval. In addition, a knowledge credibility evolution and closed-loop optimization mechanism based on maintenance log feedback is introduced, driving the knowledge base to evolve from static storage into a dynamic self-enhancing system. On this basis, a progressive knowledge architecture consisting of “Level 1: authoritative knowledge, Level 2: knowledge to be verified, and Level 3: AI-derived knowledge”, together with its self-enhancing interaction workflow is designed. The proposed method is further validated through practical after-sales maintenance scenarios covering three stages: work-order submission, fault analysis, and case accumulation. The proposed method is further validated through practical after-sales maintenance scenarios covering three stages: : work-order submission, fault analysis, and case accumulation . The results show that the proposed method can significantly improves the overall efficiency of fault description, localization, and troubleshooting, thereby providing a systematic solution with continuous self-enhancing capability for the intelligent maintenance of complex electro-mechanical-hydraulic integrated equipment.
Intelligent Maintenance; Fault Diagnosis; Knowledge Base; High-incidence Fault Probability Model; Large Language Model; Closed-loop Learning.
Research and Implementation of YOLOv8+Transformer in Human Fall Detection Image Recognition
Human fall detection is a crucial research direction in the fields of intelligent security and elderly care monitoring. Traditional detection methods suffer from issues such as low accuracy, poor real-time performance, and privacy leakage. To address these problems, this paper proposes a human fall image recognition method that integrates YOLOv8 and Transformer. First, to tackle the limited sample size in public fall datasets, various data augmentation strategies like image rotation and flipping are employed to expand the dataset and enhance the model's generalization capability. Second, the Swin Transformer module is introduced into the YOLOv8 backbone network, leveraging its sliding window self-attention mechanism to improve the model's ability to capture global contextual information. Finally, multi-scale feature fusion is achieved through a feature pyramid network, thereby enhancing the detection accuracy for fall targets of varying scales.
Fall Detection; YOLOv8; Transformer; Attention Mechanism; Image Enhancement.
Front-View Chinese Sign Recognition in a Continuous-Like Setting: A Reproducible Baseline and Evaluation Protocol
This paper introduces a reproducible front-view benchmark setting for Chinese sign recognition in a continuous-like single-sign scenario. Building on the NationalCSL-DP resource, only red, green and blue (RGB) frames from the front camera are used as input, and this paper investigates the performance of a frame-based isolated sign recognition pipeline when exposed to untrimmed sign sequences instead of action-centric samples. To support reuse, the original per-sequence folders are converted into ImageFolder-style training data, a fixed 30-frame sampling policy is used, and preprocessing metadata is saved together with the checkpoints used for inference. The baseline is a two-tier pipeline consisting of a binary activity model and a ResNet-18 gloss classifier. Among the 6,707 front-view sequences, the resulting continuous-like diagnostic evaluation achieved a Top-1 accuracy of 0.2646, a Top-5 accuracy of 0.5184, an average prediction confidence of 0.4777, and zero no-segment failures under the selected segmentation policy. Further ablation studies have been conducted on frame budget, fusion strategy and segmentation thresholds, and the gap between action-centric frame classification and sequence-level continuous-like evaluation has also been analysed. Based on the above, boundary noise, single-view ambiguity and the mismatch between isolated training and sequence-level inference are all still problems. This study will serve as a reusable baseline and evaluation protocol for future research in front-view Chinese sign language resources, but it will not be a continuous state-of-the-art sign language recognition system.
Chinese Sign Language; Language Resources; Reproducibility; Benchmark; Front-view RGB; Isolated-to-continuous Evaluation.
Research Progress in Image De Fogging Technology and Comparison of SOTA Methods (2025-2026)
Through in-depth research on the complex optical characteristics of foggy atmospheres and their effects on images, an image dehazing technology for suppressing and restoring foggy atmospheric scattering in images has been proposed, achieving better restoration results for foggy images. This improves the authenticity of images in fields such as autonomous driving, remote sensing imaging, and surveillance security, providing reliable image data support for these areas. Based on the explosive development of deep learning in recent years, image dehazing technology has gradually evolved, shifting from traditional physics-based model methods to hybrid architectures that combine Transformers, diffusion models, and physical priors, with significantly improved dehazing performance in various complex scenarios. By systematically reviewing the latest research results in the field of image dehazing for 2025-2026 and conducting an in-depth analysis of the current five state-of-the-art core techniques, including PDFormer, DiffDehaze, HazeGen, DehazeXL, HistoFusionNet, and multi-module collaborative U-Net, this study covers both the theoretical aspects and practical comparisons of these representative image dehazing methods. It further reveals the development trends of image dehazing techniques and their profound significance for the future development of image dehazing.
Image Dehazing; Deep Learning; Complex Scenarios; Core Techniques.
Cooperative Flight Control of Multi-UAV Systems under a Data-Driven Paradigm
Aiming at the challenge of highly cooperative control of multi-UAV formation under nonlinear dynamics, communication restriction and malicious attacks, a data-driven Compact Format Dynamic Linearization based Model-Free Adaptive Control (CFDL-MFAC) method is adopted to transform the nonlinear system into a compact format dynamic linear model based on pseudo-partial derivatives, and a distributed control law and pseudo-partial derivative estimation algorithm are designed. Meanwhile, a reset mechanism is introduced to deal with data packet loss. Theoretical analysis verifies the tracking error convergence and the stability of bounded input and bounded output of the system under constant and time-varying trajectories. Simulation experiments indicate that the proposed method can effectively realize multi-UAV highly cooperative tracking with strong anti-jamming and robustness.
Multi-UAV System; Model-free Adaptation; Data Packet Loss; Pseudo-partial Derivative.