World Journal of Information Technology (WJIT) is an international peer-reviewed, open access journal.
A DYNAMIC TRUST EVALUATION METHOD FOR BOUNDARIES OF NEW POWER SYSTEMS BASED ON MULTI-MODEL PARALLEL ANALYSIS
Aiming at the architectural characteristics of numerous nodes and fragmented access in new power systems, as well as the technical pain points of traditional boundary trust evaluation, such as difficulty in capturing short-term anomalies, identifying medium risks, perceiving complex threats, and poor model collaboration, a dynamic trust evaluation method for power system boundaries based on multi-model parallel analysis is proposed. This method constructs a four-level architecture consisting of data collection and preprocessing, multi-model parallel analysis, model collaborative fusion, and trust level output. It collects multi-dimensional boundary data through distributed probes and performs standardization processing, realizes full-complexity risk identification relying on three parallel layers of statistical, machine learning, and deep learning models, completes the fusion of multi-model results combined with a dynamic weight adjustment strategy, and finally maps to four-level grayscale trust levels with targeted grayscale control strategies. Experiments and applications show that this method controls the trust evaluation delay within seconds, realizing real-time, accurate, and grayscale evaluation of the trust status of boundary entities, and provides reliable technical support for boundary security protection of new power systems.
Copyright (c) 2026 Yang Cao; Yang Su; Peng Zhou; XianHan She; Qian Liu; KaiMin Zheng; WeiJie Qiu https://creativecommons.org/licenses/by/4.0
MEASURING DIGITAL GREEN INNOVATION IN ENTERPRISES: A LIGHTWEIGHT TEXT MINING APPROACH BASED ON ANNUAL REPORTS
Digital green innovation has become a critical pathway for Chinese enterprises to achieve sustainable development amid the convergence of the digital economy and green transition strategies. However, existing studies predominantly rely on structured indicators such as patents and financial data, which may fail to capture the strategic and contextual dimensions of firms’ innovation activities reflected in corporate disclosures. To address this gap, this study analyzes the annual reports of 1,285 Chinese listed manufacturing firms using web scraping and text mining techniques. Based on recent literature, a multidimensional indicator system containing 11 digital green innovation keywords is constructed to measure firms’ innovation activities. Empirical results reveal significant heterogeneity in keyword distribution across firms, with “artificial intelligence” and “Internet” appearing most frequently. Further analysis shows that heavily polluting enterprises exhibit significantly higher levels of green innovation, suggesting strong policy and regulatory incentives. By providing a scalable, text-based measurement framework, this study not only enriches the methodological toolkit for assessing digital green innovation, but also offers new empirical evidence on how firms disclose and prioritize innovation under environmental and digital pressures, thereby generating important implications for both policymakers and enterprise managers.
Copyright (c) 2026 QiWen Wei; ZiJie Xu https://creativecommons.org/licenses/by/4.0
A SOFT VOTING MECHANISM-BASED RANDOM FOREST MULTI-MODEL ENSEMBLE APPROACH FOR EMPLOYMENT STATUS PREDICTION
Employment constitutes the cornerstone of people's livelihoods and the foundation of development. Presently, structural employment contradictions remain prominent in China, rendering the achievement of high-quality, full employment the foremost objective of current socio-economic development. Conventional single-model forecasting approaches exhibit limitations when handling high-dimensional, complex employment data, including insufficient generalisation capabilities and restricted capture of feature interactions. To address this, this study constructs a multi-model fusion employment status prediction model based on random forests, thereby enhancing predictive accuracy and stability. The study utilised a dataset comprising 4,980 employment samples, encompassing 57 raw features. Through data cleansing, Pearson correlation analysis, and feature extraction methods based on XGBoost and AUC cross-validation, 12 key feature variables were ultimately selected. Building upon this foundation, a soft voting ensemble strategy was employed to fuse three major prediction models—random forest, SVM, and LSTM—into a novel ensemble learning model. The predictive performance and stability of both the traditional trio of models and the new ensemble model were evaluated. Results indicate: The ensemble model achieved an accuracy of 85.49%, with recall and F1 scores of 97.34% and 93.21% respectively, outperforming each individual model. It effectively synergises the strengths of multiple models, enhancing adaptability to complex employment data and improving prediction robustness. This research provides a scientifically sound and effective ensemble learning approach for employment status prediction, offering practical value in supporting government precision policy-making and optimising employment services.
Copyright (c) 2026 Can Luo; Zhen Liang https://creativecommons.org/licenses/by/4.0
A COLLABORATIVE DECISION-MAKING FRAMEWORK FOR INFLUENCE MAXIMIZATION BASED ON GRAPH CONTRASTIVE LEARNING AND DEEP REINFORCEMENT LEARNING
To address the limitations of traditional influence maximization methods in complex social networks, including insufficient feature representation, strong dependence on predefined diffusion models, and limited capability for collaborative seed selection, this paper proposes a collaborative decision-making framework based on graph contrastive learning and deep reinforcement learning. First, the social network is modeled as a directed weighted graph, and a mathematical formulation of influence propagation and seed selection is established under the Independent Cascade(IC)model. Then, a graph contrastive learning strategy is introduced to conduct self-supervised pre-training over network topology, edge weights, and node attributes. By leveraging multi-view graph augmentation and node-level contrastive objectives, the framework learns high-quality node representations that better characterize diffusion potential. On this basis, the influence maximization task is reformulated as a sequential decision-making process, and a Double DQN-based seed selection mechanism is designed to iteratively select seed nodes. Marginal influence gain is adopted as the immediate reward to enable dynamic collaborative optimization of the seed set. Experiments conducted on six real-world social network datasets, including Petster-hamster, Tv-show, Politician, Advogato, Public, and Epinions, demonstrate that the proposed method consistently outperforms Random, PageRank, gIM, S2V-DQN, and ToupleGDD under different seed budgets. The results verify that combining the representation advantages of graph contrastive learning with the sequential decision-making capability of deep reinforcement learning can effectively improve influence maximization performance in complex networks.
Copyright (c) 2026 JiaWei Kang https://creativecommons.org/licenses/by/4.0
IMPROVED DEEP LEARNING MODEL-BASED SKIN DISEASE IMAGE SEGMENTATION ALGORITHM
Aiming at the problems of complex background interference, low segmentation accuracy of small targets, blurred lesion boundaries and large scale differences in skin disease image segmentation, this paper proposes two improved deep learning models, namely the CAU-Net model fused with the coordinate attention mechanism and the CSwinTransU-Net model integrated with the spatial perception module. Experimental results on the ISIC2018 skin disease dataset show that the mean Intersection over Union (mIoU), mean Dice coefficient (mDice) and average accuracy (aAcc) of the CAU-Net model reach 85.50%, 92.06% and 93.69% respectively, and the corresponding indicators of the CSwinTransU-Net model are 84.84%, 91.66% and 93.49%. Both models outperform the basic models, effectively improving the accuracy and robustness of skin disease image segmentation, and providing technical support for the early diagnosis of skin cancer.
Copyright (c) 2026 Tao Song https://creativecommons.org/licenses/by/4.0
PREDICTION OF 2028 LOS ANGELES OLYMPIC MEDAL TABLE BASED ON MULTI-MODEL INTEGRATION
This study addresses the complex challenge of predicting medal standings for the 2028 Los Angeles Olympics by developing an innovative hybrid modeling framework that integrates time-series analysis, machine learning regression, and Bayesian probabilistic approaches. Building upon previous Olympic prediction research, we propose a multi-layered architecture that combines the temporal processing capabilities of Long Short-Term Memory (LSTM) networks with the feature importance quantification of XGBoost/LightGBM algorithms and the uncertainty modeling of Bayesian hierarchical frameworks. The research incorporates an unprecedented range of predictive features, including historical medal performance (1996-2024), athlete participation metrics, event-scale characteristics, and quantified host-country advantages, to construct a comprehensive predictive system. Our empirical results demonstrate that the United States is projected to maintain its dominance with 40 gold medals (128 total), benefiting significantly from host-country effects (SHAP value +12) and established strengths in swimming and track and field (contributing 43% of gold medal variance). China shows steady growth to 35 gold medals (95 total), while the United Kingdom and Japan exhibit strategic gains in cycling and skateboarding respectively. The model achieves superior predictive accuracy (R²=0. 89 for gold medals) compared to traditional ARIMA approaches, with sensitivity analysis revealing three key insights: (1) track and field and swimming remain the highest-yield events for medal acquisition, (2) host nations experience a quantifiable 15-20% medal boost through venue familiarity and optimized scheduling, and (3) emerging economies demonstrate diminishing marginal returns on GDP investments beyond $5 trillion. The framework provides actionable intelligence for Olympic stakeholders, enabling data-driven resource allocation between sports disciplines and offering probabilistic projections for underdog nations (e.g., Malaysia with 80% probability of first medal in badminton). Methodological innovations include event-level feature engineering, game-theoretic modeling of coaching migrations, and policy-specific recommendations tailored to nations at different development stages.
Copyright (c) 2026 HaoHeng Du; ShengFei Lv; JiaZe Hu https://creativecommons.org/licenses/by/4.0
IDENTIFICATION OF THE PATH OF ENTERPRISE DIGITAL TRANSFORMATION ——EVIDENCE FROM CHINESE A-SHARE LISTED COMPANIES
The foundation of high-quality economic development lies in enterprises' innovation in models, businesses, processes and products, and digital transformation is the only way for enterprise innovation. Then, what drives enterprise digital transformation? And what path does it follow? By using the frequency of digital-related vocabulary in the annual reports of listed companies to measure the degree of digital transformation, this paper adopts the structural equation model to analyze the driving role of R&D in digital transformation and explore the path of digital transformation at the same time. It is found that the fixed asset ratio, digital technology and total asset turnover are the key nodes connecting R&D and the generation of data application scenarios. The transformation path with total asset turnover as the node either has an insignificant effect or shows a negative effect, revealing the problem of the disconnection between digital technology and business in the transformation process.
Copyright (c) 2026 Hao Ma; XiangYu Liu; ShiYu Zhao; FeiYu Qian https://creativecommons.org/licenses/by/4.0
OPTIMIZED EVALUATION OF COMPETITION SCORING MECHANISMS BASED ON UNOBSERVED DATA INVERSION AND COUNTERFACTUAL SIMULATION
Addressing fairness disputes arising from opaque audience voting in competitive reality shows, this study proposes a quantitative evaluation framework integrating latent variable inference, mechanism performance assessment, and feature attribution analysis. First, by combining maximum entropy optimization and Bayesian inference with expert scores and elimination threshold constraints, the study reconstructs the unobservable fan vote distribution, achieving a 99.492% prediction accuracy while quantitatively assessing the confidence level of estimation results. Subsequently, a multidimensional evaluation system incorporating Audience Rescue Rate (ARR) and Technical Fairness Index (TFI) was constructed. Counterfactual simulation experiments compared the efficacy of ranking-based scoring versus proportional scoring in handling disputed cases, revealing the proportional method's pronounced tendency to amplify emotional preferences. Finally, the study introduced a Logistic-Normal share regression model to explore the differential impacts of contestants' demographic characteristics, professional backgrounds, and partner effects on evaluators. Results indicate significant aesthetic divergence between experts and audiences in social media contexts, while the partner effect explains approximately 17.37% of performance variance. This research provides scientific theoretical foundations and computational paradigms for optimizing fairness, interactivity, and professional stability in multi-criteria decision systems.
Copyright (c) 2026 WeiChen Xu; Lin Han; Na Lin https://creativecommons.org/licenses/by/4.0
EXPERIENCE ECONOMY–DRIVEN INNOVATION IN FIVE-STAR HOTELS: SOMATOSENSORY INTERACTION AND PARENT–CHILD EXPERIENCE SPACES
The traditional "accommodation-oriented" marketing model of five-star hotels faces core bottlenecks such as intensified homogeneous competition, insufficient excavation of family customer value, and low proportion of non-accommodation revenue. From the perspective of a five-star hotel marketing supervisor, this study proposes a transformation strategy of "triple space innovation + somatosensory interaction empowerment" based on the experience economy theory and scenario innovation theory. It upgrades the hotel's traditional guest room space into a composite experience space integrating "parent-child interaction space + sports experience space + emotional intervention space", deeply integrating the core technologies of Kuyue Technology's smart screen (millimeter-level capture of 342 skeletal points, ≤20ms low-latency response) with children's somatosensory interaction content, focusing on the core needs of family customers. Through an 8-month empirical study (n=912 family groups, including 648 ordinary families and 264 families with children with ASD precursor defects), the enabling effect of this model on hotel occupancy rate, average customer price, repurchase rate, and market expansion was systematically verified. The results show that after the launch of the experience space, the overall occupancy rate of the sample hotels increased by 38.6%, the average stay duration of family customers extended to 3.2 days, the average customer price grew by 31.2%, and the market radiation radius expanded from 15 kilometers to 40 kilometers, which is significantly higher than the traditional operation model (p<0.001). This study provides a theoretical framework and practical path for five-star hotels to break functional boundaries and realize the transformation of the experience economy, and also provides empirical support for somatosensory interaction technology to empower scenario innovation in the high-end service industry.
Copyright (c) 2026 XiaoQing Wang https://creativecommons.org/licenses/by/4.0
THE CONSTRUCTION, EVALUATION, AND APPLICATION OF AN INTELLIGENT ADMISSIONS Q&A SYSTEM BASED ON RETRIEVAL-AUGMENTED GENERATION (RAG)
This study addresses the pain points in university admissions consultation, such as "human response bottlenecks" and "information overload and fragmentation", by proposing an intelligent admissions consultation system solution based on Retrieval-Augmented Generation (RAG) technology. The system adopts the LangChain framework, integrates Large Language Models (LLMs) with RAG technology, and constructs a complete workflow including knowledge base construction, retrieval enhancement, and answer generation. Experiments show that the system significantly outperforms the control groups in key metrics, including overall accuracy (95.6%), security compliance rate (96%), and dynamic personality matching rate (92.3%). This system not only effectively improves the response efficiency and answer quality of admissions consultation but also provides a replicable and scalable practical reference for the digital and intelligent upgrading of university admissions management services.
Copyright (c) 2026 JiaXin Wang; ZiHan Qi; JingJing Li; XuanEr Chen; Yuan Lin https://creativecommons.org/licenses/by/4.0
PREDICTING OLYMPIC MEDAL DISTRIBUTION FOR LA 2028 BASED ON K-MEANS CLUSTERING AND AN XGBOOST-BOOTSTRAP ENSEMBLE
This study aims to predict the total number of medals for countries at the 2028 Los Angeles Summer Olympics and explore the likelihood of new medal-winning nations. To achieve this, the data was cleaned and normalized to ensure consistency, followed by the use of K-means clustering to classify countries into strong and weak sports nations based on historical average medal counts. Six key features were selected to construct predictive models, including medal numbers, athlete participation, development level, and specialty sports performance. The XGBoost-Bootstrap method was applied for U.S. medal prediction, and the Random Forest-Bootstrap model identified potential first-time medalists. The model demonstrated high accuracy on training data but lower performance on test data, indicating challenges in generalization. Nonetheless, the results offer valuable insights for future Olympic forecasting and sports policy planning. This study contributes innovatively by integrating K-means clustering with ensemble learning to tailor predictions for different country groups, combining XGBoost with Bootstrap resampling to quantify uncertainty in medal forecasts, and simultaneously addressing dual objectives—predicting top performers and identifying emerging nations—offering a more comprehensive and policy-relevant framework for Olympic prediction.
Copyright (c) 2026 YiMing Feng; HaoShuai Yu; LinYu Zhuo https://creativecommons.org/licenses/by/4.0
PREDICTING FOLLOWER GROWTH FOR SOCIAL MEDIA BLOGGERS AND MODELING USER FOLLOWING BEHAVIOR USING XGBOOST AND RANDOM FOREST ENSEMBLE LEARNING
This study addresses user interaction dynamics on social media platforms by constructing separate models for predicting new follower counts for bloggers and classifying users' targeted following behavior. Quantitative analysis of new follower counts reveals a high correlation between interaction behavior and follower growth, with correlation coefficients exceeding 0.89. By constructing an XGBoost model that utilizes second-order Taylor series expansion to optimize the objective function and incorporates 1- to 3-day lagged features to capture time-series patterns, the model demonstrated robust performance on the test set with an average absolute error of 20.71. For the user targeted following behavior classification prediction, a Random Forest model was selected to address the low-dimensionality and strong nonlinearity of the features. The study employed target encoding strategies to smooth user and blogger IDs, preventing overfitting, and defined interaction intensity formulas based on behavior weights. Experimental results show the Random Forest model achieved an AUC of 0.83 and an F1 score of 74.25% in user follow prediction tasks, effectively enhancing behavioral prediction accuracy. This study provides quantitative insights into user growth dynamics and micro-interaction patterns on social platforms through ensemble learning algorithms.
Copyright (c) 2026 ZhiYang Chen; JiaXin Wu https://creativecommons.org/licenses/by/4.0
EVALUATION OF THE IMPLEMENTATION EFFECT OF INTELLECTUAL PROPERTY POLICY BASED ON NAIVE BAYES ALGORITHM
As a core driving force for modern economic development, the scientific evaluation of the implementation effect of intellectual property policies has become an important topic for government decision-making and academic research. Traditional policy evaluation methods often rely on expert experience or simple statistical analysis, which are difficult to effectively handle the massive multidimensional data generated during the implementation of intellectual property policies. The Naive Bayes algorithm, as a machine learning method based on probability theory, can effectively identify key influencing factors in the implementation of intellectual property policies, providing a new technical path. This paper constructs an evaluation model for the implementation effect of intellectual property policies based on the Naive Bayes algorithm, integrating multi-source information such as policy text data, enterprise innovation data, and patent application data, and establishing a probabilistic mapping relationship between policy characteristics and implementation effects. Through analysis and verification of the model, the study shows that the algorithm demonstrates strong practical value in the evaluation of the effects of intellectual property policies, provides a scientific basis for policy optimization, and promotes the transformation of policy evaluation towards data-driven intelligentization. This research aims to provide methodological support for the scientific formulation and precise implementation of intellectual property policies, and to provide theoretical basis and practical guidance for the decision-making optimization of relevant government departments.
Copyright (c) 2026 ShuTong Wei https://creativecommons.org/licenses/by/4.0
MULTI-FEATURE INTEGRATED INTELLIGENT DIAGNOSIS MODEL FOR NON-INVASIVE PRENATAL TESTING BASED ON ENSEMBLE LEARNING
To address the challenges of high false-negative rates and low diagnostic reliability in current Non-Invasive Prenatal Testing (NIPT) for detecting chromosomal abnormalities in female fetuses, this study proposes an innovative multi-feature intelligent diagnosis model based on ensemble learning. The key innovations of this research include: (1) Multi-dimensional feature integration: For the first time, 16 critical features covering chromosome Z-values (13, 18, 21, X), GC content, sequencing metrics, and maternal physiological indicators were systematically integrated to comprehensively characterize fetal chromosomal status. (2) Advanced ensemble framework: We developed a novel hybrid ensemble approach combining Random Forest, XGBoost, and LightGBM algorithms through soft voting, effectively addressing data challenges of high dimensionality, small sample size, and severe class imbalance (only 10.7% abnormal samples). (3) Dual optimization strategy: The model was optimized using both SMOTE oversampling and random undersampling techniques for data balance, combined with grid search and five-fold cross-validation for parameter tuning. Experimental results demonstrate that our ensemble model achieved superior performance with 91.59% accuracy, 0.9583 AUC, 91.49% precision, 89.58% recall, and 90.53% F1-score, significantly outperforming single-algorithm models. Feature importance analysis revealed that BMI, chromosome 18 Z-values, and maternal age were the most influential predictors. This model provides a clinically applicable, highly accurate diagnostic tool that substantially improves the reliability of NIPT-based female fetal abnormality detection.
Copyright (c) 2026 KangYong Wang https://creativecommons.org/licenses/by/4.0
MARKET-DRIVEN ANALYSIS OF JAVA ECOSYSTEM EVOLUTION AND TALENT DEMAND DYNAMICS
Learning Web application design and development requires a deep understanding of the industry’s technological ecosystem. To explore the core position and evolution trend of the Java language in the current computer software field, this paper systematically surveys and analyzes Java’s domestic and international rankings, market share, talent demand structure, and future development direction based on historical data from authoritative international programming language rankings such as TIOBE and PYPL, combined with big data from major domestic recruitment platforms such as Boss Zhipin and Lagou.com . The survey results show that despite challenges from Python in the field of artificial intelligence and Go in the field of cloud-native infrastructure, Java remains firmly in the top tier of global programming languages due to its over 33% market share in enterprise-level server applications and its dominant position in big data processing and Android mobile development. The key innovations of this study lie in: 1) constructing a multi-dimensional analytical framework that integrates technological rankings, market penetration, and talent analytics to assess programming language ecosystems; 2) revealing the structural shift in Java talent demand from basic coding to composite competencies in architecture, cloud-native, and AI engineering. Regarding talent demand, the market exhibits significant structural changes: the demand for entry-level CRUD (Create, Read, Update, Delete) positions is shrinking, while there is a shortage of advanced, multi-skilled talents with microservice architecture, cloud-native technologies, and JVM low-level optimization capabilities. Furthermore, this paper also discusses the profound impact of GraalVM native image technology and the engineering implementation of Spring AI on the future Java ecosystem.
Copyright (c) 2025 ZhengLin Wang https://creativecommons.org/licenses/by/4.0
PARTIAL NONLINEAR FUNCTIONAL REGRESSION MODEL: AN APPROACH VIA REPRODUCING KERNEL AND ENSEMBLE LEARNING
This study proposes a Partial Nonlinear Functional Regression Model (PNFLR) specifically designed to handle complex datasets where the Continuous Scalar Response Variable depends on a mixture of Vector-valued Covariates and Functional Covariates. The structural heterogeneity of these predictors is addressed by assuming a hybrid relationship: the vector components follow a standard Linear Association, whereas the functional inputs exhibit a complex Nonlinear Association with the response. To rigorously model this non-linearity within a Reproducing Kernel Hilbert Space (RKHS), the methodology departs from traditional single-kernel methods often characterized by rigid selection bias. Instead, the framework implements Model Averaging through an Ensemble Learning paradigm to facilitate the Adaptive Selection of kernel functions, thereby enhancing model flexibility. To ensure numerical stability and effective Regularization, a Truncated Approximation strategy is utilized. This process involves projecting the high-dimensional functional data onto a finite subspace via Functional Principal Component Basis Expansion, effectively mitigating overfitting risks while retaining essential structural information. By integrating kernel theory with ensemble mechanics, the PNFLR framework bridges the gap between theoretical function estimation and practical predictive modeling. Empirical evaluations on the Tecator dataset confirm that the architecture articulated herein yields superior Generalization Performance and lower error variance compared to conventional benchmark models across various prediction tasks, demonstrating robustness in real-world analytical scenarios.
Copyright (c) 2026 JiaYi Wang; Yuan Chen; YanRan Liu; HaoDi Lv https://creativecommons.org/licenses/by/4.0
PREDICTING DRUG TOXICITY ON TOX21 WITH A MULTI-TASK GNN-TRANSFORMER MODEL
As research into medicinal chemical synthesis deepens, an increasing number of novel drug molecules with promising clinical therapeutic potential are emerging. During drug development, many compounds are discarded due to excessive toxic side effects, while traditional toxicity testing faces challenges of high cost and lengthy timelines. To enable rapid toxicity assessment by researchers, this paper proposes a drug toxicity prediction model (TGT) based on GNN-Transformer. The model was constructed and validated using the Tox21 dataset, with caffeine selected for practical generalisation testing. The Tox21 dataset encompasses toxicity test results for diverse compounds, providing high-quality data. The model architecture leverages graph neural networks (GNN) to process molecular graph-structured data, effectively capturing complex topological relationships and chemical information within molecules. This transforms molecular graph structures into meaningful node feature representations. The Transformer component, with its exceptional sequence modelling capabilities, further learns from GNN-extracted features, capturing long-range dependencies between molecular structures. Through training and optimisation, the model demonstrated commendable performance in toxicity prediction tasks, achieving an average AUC of 0.7488 on the validation set. Its high accuracy in predicting drug toxicity was further validated through practical application on caffeine molecules, establishing it as an efficient and precise predictive tool for early-stage drug safety assessment.The GNN-Transformer drug toxicity prediction model proposed in this study enhances prediction reliability by integrating multi-task learning with interpretability techniques. It serves as an auxiliary pre-screening tool for drug development, thereby helping to shorten the research and development cycle and reduce costs.
Copyright (c) 2026 YuHui Li https://creativecommons.org/licenses/by/4.0
U-NET MEDICAL IMAGE SEGMENTATION STUDY BASED ON CBAM ATTENTION MECHANISM
In order to solve the problems of blurred lesion boundaries, missed small targets and sensitivity to background interference in 2D medical image segmentation of traditional U-Net, an improved model CB-V-UNET is proposed that fuses channel and spatial attention mechanism. Firstly, the model embeds the Convolutional Block Attention Module (CBAM) in the jump connection path of the standard U-Net to enhance the response of key areas and suppress irrelevant background noise through adaptive weighted channel features and spatial position information. Secondly, relying on the attention enhancement structure of the model, the weak boundary feature capture ability and small target recognition accuracy are optimized to make up for the structural defects of the original U-Net. Finally, model training and validation experiments were carried out on the ISIC 2018 skin lesion segmentation dataset and the DRIVE retinal vascular segmentation dataset, and the results showed that the Dice coefficient of CB-V-UNET on the ISIC 2018 dataset was 85.63%, the average intersection and union ratio (mIoU) was 85.41%, and the Dice coefficient on the DRIVE dataset was 82.15%, which was better than the original U-Net and various mainstream variants. The ablation experiment further confirmed the effectiveness of the CBAM module. This scheme provides an effective solution to improve the segmentation accuracy of small targets and weak boundary structures in medical images, and has important clinical application value.
Copyright (c) 2026 MengYuan Cao; WenXuan Hong; XinRui Li; JiaLu Zhao https://creativecommons.org/licenses/by/4.0
KNOWLEDGE GRAPH-ENHANCED DYNAMIC DIGITAL PROFILING: A TECHNICAL FRAMEWORK FOR INTELLIGENT SUPPLY-DEMAND MATCHING IN TECHNOLOGY TRANSFER
Technology transfer is a critical bridge connecting scientific and technological innovation with industrial application, and its efficiency is largely constrained by the inaccuracy of supply-demand matching and the lack of systematic technical support. With the advancement of computer technologies such as big data, artificial intelligence (AI), and knowledge graphs (KG), digital profiling has emerged as a promising tool to address the aforementioned bottlenecks. However, existing research on the integration of digital profiling and technology transfer lacks in-depth exploration of technical implementation mechanisms, and fails to fully leverage computer technologies to solve core problems such as multi-dimensional feature extraction, dynamic modeling, and intelligent matching. To fill this gap, this paper conducts systematic theoretical and technical research on technology transfer and digital profiling from a computer science perspective. First, we clarify the theoretical connotation of digital profiling in the context of technology transfer, and construct a three-layer technical framework (data layer, model layer, application layer) based on computer system design principles. Second, we propose a two-dimensional digital profiling method system: for the supply side (technological achievements), we design a feature extraction framework integrating BERT-based text mining and KG construction; for the demand side (enterprises), we develop a demand mining model combining LDA topic modeling and multi-source data fusion. Third, we establish an intelligent supply-demand matching mechanism based on hybrid recommendation algorithms and multi-objective optimization. Finally, we verify the feasibility and effectiveness of the proposed framework through theoretical deduction, algorithm simulation, and experimental validation on real datasets. The research enriches the theoretical system of technology transfer from the perspective of computer science, and provides a technical paradigm for the digital transformation of technology transfer. The proposed methods and frameworks can effectively improve the accuracy of supply-demand matching, reduce the transaction cost of technology transfer, and lay a foundation for the development of intelligent technology transfer platforms.
Copyright (c) 2025 HongYu Su https://creativecommons.org/licenses/by/4.0
TIME-SERIES FORECASTING OF STOCK PRICE VIA BIDIRECTIONAL LSTM-ATTENTION NEURAL ARCHITECTURE
Predicting stock prices with high accuracy continues to be a major challenge in financial markets, primarily because of the intricate, non-linear, and highly volatile characteristics of price movements. Traditional statistical methods and standard long short-term memory (LSTM) networks exhibit limitations in capturing temporal dependencies and identifying critical features that significantly influence price movements. To address these challenges, this paper proposes a novel bidirectional LSTM with attention mechanism (BiLSTM-Attention) model for stock price prediction. The proposed model employs bidirectional LSTM layers to process time-series data in both forward and reverse directions concurrently, thereby capturing a more complete picture of past and potential future trends. Additionally, a self-attention mechanism is incorporated to dynamically allocate weights across time steps, enabling the model to focus on salient features that exert substantial influence on price fluctuations. Experimental validation is conducted using real-world stock price data from American International Group (AIG). Results demonstrate that the proposed BiLSTM-Attention model significantly outperforms baseline models across all evaluation metrics, validating the effectiveness of combining bidirectional processing with attention mechanisms for stock price forecasting. The proposed approach offers a stable and effective method for predicting stock prices in the short term.
Copyright (c) 2025 MingXi Ma https://creativecommons.org/licenses/by/4.0
A FINE-TUNED STUDY ON OPTIMAL TIMING FOR NON-INVASIVE PRENATAL TESTING BASED ON GENERALIZED ADDITIVE MIXED MODELS AND SIMULATED ANNEALING OPTIMIZATION
Determining the optimal timing for non-invasive prenatal testing (NIPT) is critical, as it requires balancing the need for early detection with ensuring sufficient fetal DNA concentration for accuracy, particularly in male pregnancies. This study aims to optimize the NIPT window by analyzing the correlation between fetal Y chromosome concentration and maternal factors (gestational age and BMI) and proposing a stratified testing strategy. We first employed a Generalized Additive Mixed Model (GAMM) to capture complex nonlinear relationships, revealing a gradual increase in concentration with gestational age and a nonlinear inflection effect for BMI (with a slowdown after BMI~30). Subsequently, a hierarchical refinement strategy was implemented: pregnant women were clustered into homogeneous groups based on BMI and time-to-target concentration. A risk function quantifying both temporal and accuracy risks was then minimized using a simulated annealing algorithm to identify the optimal gestational week for each cluster. Results indicate that clusters with a BMI around 30 are suitable for early testing at 11 weeks, while high-BMI clusters require postponement to 24-25 weeks. This stratified approach significantly improved expected accuracy, with one group achieving 100%. The key innovations lie in using an interpretable GAMM for nonlinear analysis, data-driven clustering for population stratification, and a simulated annealing framework for balanced timing optimization.
Copyright (c) 2025 XiangMeng Shu https://creativecommons.org/licenses/by/4.0
DESIGNING AN EFFICIENT RESEARCH OUTPUT MANAGEMENT SYSTEM FOR TECHNOLOGY TRANSFER
The rapid growth of research results, such as academic papers, patents, software, datasets, and technological prototypes, poses significant challenges for effective technology transfer from research institutions to practical applications. This study proposes a Research Results Management System (ROMS) aimed at optimizing the specific practices of organizing, tracking, and transforming scientific achievements into social or industrial benefits. Through in-depth analysis of nearly 50 research projects from three institutional partners (a general hospital, a specialized hospital, and a research-oriented hospital), the system combines structured data management with an intuitive user interface to facilitate collaboration between researchers and technology conversion personnel. ROMS adopts advanced features such as metadata indexing for efficient retrieval, status tracking for project supervision, and compatibility evaluation for matching research results with application requirements, all of which are supported by customized algorithms to improve the accuracy of research and industry matching. The research results indicate that the time required for practical application transformation has been reduced by 30%, while the interdisciplinary coordination ability has significantly improved.
Copyright (c) 2026 Jun Wang; Xiang Wei; JiaLei Mo; Fan Wang; MingYuan Liu https://creativecommons.org/licenses/by/4.0
DETECTION OF CHROMOSOMAL ABNORMALITIES IN FEMALE FETUSES BASED ON A FUSED LOGISTIC REGRESSION-RANDOM FOREST MODEL
To address the challenge of detecting chromosomal abnormalities in female fetuses due to the absence of Y chromosome data in non-invasive prenatal testing (NIPT), this paper proposes an innovative dual-layer classification model that integrates logistic regression and random forest. The model comprehensively utilizes 16-dimensional features including Z-scores and GC content of chromosomes 13, 18, and 21, along with key maternal clinical indicators. Through rigorous statistical testing and feature importance analysis, seven key discriminatory features were identified, establishing a progressive "abnormality screening-disease typing" judgment process. The framework employs an ensemble approach where logistic regression provides interpretable initial screening while random forest handles complex non-linear patterns for fine-grained classification. After cross-validation and threshold optimization, the model ultimately achieved an impressive accuracy rate of 99.57%, with precision and recall rates exceeding 98.5% across all abnormality categories. Comparative experiments demonstrated the superiority of this hybrid approach over single-model methods, particularly in handling imbalanced data distributions. The core innovation of this research lies in the integration of feature fusion and model collaboration, enabling high-precision, automated detection of chromosomal abnormalities in female fetuses and providing a new technical pathway for clinical precision diagnosis.
Chromosomal abnormalities in female fetuses
Copyright (c) 2025 DaZhi Wei https://creativecommons.org/licenses/by/4.0
THE OPTIMIZATION PROBLEM FOR TIME-POINT DETECTION IN NIPT BASED ON MULTI-MODEL FUSION
This article focuses on four major issues: correlation analysis of Y chromosome concentration in male fetuses, BMI grouping and determination of optimal detection time points, optimization of time points under multiple factors, and determination of chromosomal abnormalities in female fetuses. Firstly, based on the principle of NIPT technology as the accurate benchmark for results, we analyzed the correlation characteristics between Y chromosome concentration and gestational age, BMI, constructed an adapted relationship model, and verified its significance; Secondly, with the goal of minimizing potential risks, the optimal NIPT timing for male pregnant women with different BMI ranges is determined through reasonable grouping methods, and the impact of detection errors is analyzed; Furthermore, by integrating multiple factors such as height, weight, and age, the time point model is optimized to ensure the reliability of the results; Finally, based on the characteristic of female fetuses without Y chromosome, combined with indicators such as Z value and GC content of X chromosome and chromosomes 21, 18, and 13, an abnormality determination method is established. The research results can provide scientific support for optimizing clinical NIPT testing conditions, improving accuracy, and reducing potential risks for pregnant women, meeting the practical needs of early and accurate detection.This study first preprocessed male fetal data, screened 10-25 week samples, converted gestational weeks into continuous values, and processed duplicate samples and outliers. Use Q-Q chart to test normality and determine the conclusion that Y chromosome concentration, gestational age, and BMI are not normally distributed. Through Spearman rank correlation quantification, it was found that gestational age is strongly positively correlated with Y chromosome concentration, while BMI is moderately negatively correlated with Y chromosome concentration. Construct a quadratic polynomial model for OLS fitting, confirm no severe collinearity through VIF test, and then verify the significance of the model through F-test and t-test. In addition, analysis shows that weight has a significant negative impact on Y chromosome concentration, and this model can reliably reflect the relationship between Y chromosome concentration and gestational age, BMI.Therefore, this study is of great significance. By constructing a multi-model framework that integrates statistical analysis and machine learning, it deepens our understanding of the dynamic patterns of fetal cell-free DNA and promotes cross-disciplinary innovation between bioinformatics and clinical medicine. Meanwhile, this study also has significant practical value by precisely identifying key factors affecting detection accuracy, optimizing the timing of individualized testing, effectively improving the sensitivity and specificity of NIPT, and reducing the risks of missed and incorrect diagnoses, thereby providing data support and a decision-making basis for developing scientific, safe, and efficient prenatal screening strategies in clinical practice. Furthermore, the research findings respond to the urgent national needs of the “Healthy Birth, Healthy China” strategy for a high-precision birth defect prevention and control system, facilitating the shift of prenatal screening from being “experience-driven” to “evidence-driven,” enhancing public health services, and alleviating the medical burden on families and society.
Copyright (c) 2025 JiaJun Tang https://creativecommons.org/licenses/by/4.0
INTELLIGENT RECOGNITION OF STUDENTS’ LEARNING STATES THROUGH MICRO-EXPRESSIONS
Students’ facial micro-expressions can reflect their learning states, and the intelligent recognition of micro-expressions is of great significance for monitoring these states. This paper proposes a micro-expression recognition method based on a Residual Network for detecting the learning and psychological states of college students. Using ResNet18 as the backbone network, the Efficient Channel Attention (ECA) mechanism is embedded to adaptively adjust channel weights, enhancing feature representation capability. Simultaneously, the Mish activation function and Dropout layer are introduced to optimize gradient flow and reduce the risk of overfitting. During training, the Label Smoothing Cross Entropy loss function, the AdamW optimizer combined with the OneCycleLR learning rate scheduler, and an early stopping mechanism are adopted, effectively improving the model’s generalization ability and training efficiency on small datasets. Experiments are based on a self-built dataset (including positive, neutral, and negative expressions, totaling 1800 grayscale images). Through data augmentation and ten-fold cross-validation, the model achieves an accuracy of 97.50%. The experimental results show that this method possesses high accuracy and robustness in micro-expression recognition tasks, providing an effective tool for monitoring the psychological states of college students and optimizing classroom teaching.
Copyright (c) 2025 Juan Yang; Ling Ma; XianBin Zhang; SiYi Tian https://creativecommons.org/licenses/by/4.0
INTELLIGENT OPTIMIZATION TECHNOLOGY FOR SHIELDING GROUNDING OF SAFETY-CLASS CABLES IN NUCLEAR POWER PLANTS BASED ON DEEP REINFORCEMENT LEARNING
The Reactor Protection System (RPS) of nuclear power plants requires stable operation in complex time-varying electromagnetic environments, and the shielding grounding of safety-class cables is critical for anti-interference capability. Traditional static grounding schemes (e.g., single-point, multi-point grounding) lack adaptability to interference spectrum changes, equipment aging, and dynamic operating conditions, leading to issues like false alarms and even accidental reactor trips. This paper proposes an intelligent shielding grounding optimization technology based on deep reinforcement learning (DRL). First, a multi-physics digital twin system was established, integrating key electromagnetic interference sources (e.g., main circulating pump VFD, CRDM pulses) and lifecycle aging factors (e.g., insulation aging, shielding corrosion) to characterize shielding performance attenuation. Then, the optimization problem was modeled as a Markov Decision Process (MDP) with an 18-dimensional state space and a multi-objective reward function (considering shielding effectiveness, leakage current, and cost). The Proximal Policy Optimization (PPO) algorithm was adopted to realize millisecond-level optimal grounding configuration. A four-layer safety architecture was constructed to meet nuclear safety requirements (failure probability <10 -20 per year). This technology breaks the traditional static design paradigm, providing a new path for digital and adaptive cable system optimization in nuclear power plants.
Copyright (c) 2025 YanKun Li; Yuan Zhang; Chao Si https://creativecommons.org/licenses/by/4.0
PREDICTION OF OLYMPIC MEDAL DISTRIBUTION BASED ON LOGISTIC REGRESSION MODELS
The Olympic Games serve as a global platform to showcase athletic performance and national competitiveness. Accurate forecasting of medal outcomes not only provides scientific support for sports policy and resource allocation but also contributes to understanding the dynamics of international competition. This study employs logistic regression to address two key research problems: (1) predicting the distribution of total and gold medals across countries in the 2028 Los Angeles Olympic Games, and (2) estimating the probability of countries historically without medals achieving their first Olympic success. The models integrate variables such as historical performance, number of participants, number of events, and geographical proximity to the host nation. Results indicate that the proposed framework achieves high predictive accuracy, with strong model fit and low error values, while also identifying emerging countries with significant potential for breakthroughs. The findings not only enhance medal prediction methodology but also provide broader insights into the evolving landscape of global sports competitiveness.
Copyright (c) 2025 RunMo Liu; YiWen Gu; Jing Zhang https://creativecommons.org/licenses/by/4.0
SCENARIO CLASSIFICATION DETECTION MODEL FOR SPATIO-TEMPORAL CONTEXTUAL INFORMATION PERCEPTION IN CLASSROOM SETTINGS
This paper proposes a spatio-temporal context-aware scene classification detection model tailored for classroom settings, aiming to address detection accuracy limitations arising from complex classroom environments characterised by fluctuating lighting, frequent occlusions, and the difficulty in capturing small-scale behaviours. By integrating cross-scale attention mechanisms in the spatial domain with long-term dependency modelling in the temporal domain, the model effectively captures subtle behavioural features and spatio-temporal contextual relationships between actions. Experimental results on the SCB-Dataset3 and Classroom-Actions public classroom datasets demonstrate that the proposed model achieves 85.4% scene classification accuracy and 83.2% action detection rate, representing significant improvements over mainstream methods such as YOLOv8m, CSSA-YOLO, and TACNet. Ablation studies further validate the effectiveness of each component: the spatial attention module yields a 2.1% mAP improvement, the temporal context module contributes a 4.5% mAP gain, while the scene context module delivers an additional 2.2% performance enhancement. Maintaining real-time processing speed (68.2 FPS), this model effectively addresses multi-scale detection and temporal dependency modelling challenges in classroom scenarios, providing robust technical support for smart education.
Copyright (c) 2025 Jin Lu; Ji Li https://creativecommons.org/licenses/by/4.0
ANALYSIS OF SHARED BICYCLE TRAFFIC FLOW AND TRAVEL CHARACTERISTICS AT A UNIVERSITY BASED ON THE ARIMA MODEL
To address the uneven spatiotemporal distribution of shared bicycles on university campuses, peak-hour congestion, and insufficient dispatch efficiency, this study targets Guangxi University, aiming to optimize scheduling through demand forecasting. Innovatively integrating univariate and multivariate analyses, it resolves bicycle dispatch challenges while examining student behavioral patterns. Initially, a GAM model revealed that daily rainfall explained 90.08% of ridership variation, with demand exhibiting an exponential decline when precipitation exceeded 8mm. Subsequently, an ARIMA(3,0,0) model confirmed temporal periodicity, and spatial analysis identified academic zones and campus gates as high-demand hotspots. Finally, comparative evaluation of Poisson regression, OLS, and XGBoost multivariate models demonstrated Poisson regression’s superiority for daily predictions, while OLS outperformed in hourly forecasting. Conclusions underscore the strong periodicity and weather sensitivity of campus bicycle demand, affirming that precise forecasting enhances dispatch efficacy. Future work should incorporate variables like class schedules to refine the model, providing a methodological framework for intelligent shared-bicycle management in higher education institutions.
Copyright (c) 2025 BenChao Lan https://creativecommons.org/licenses/by/4.0
DESIGN AND APPLICATION OF A KNOWLEDGE-GRAPH-BASED INTELLIGENT QUESTION ANSWERING SYSTEM FOR REACTOR OPERATION AND MAINTENANCE
Traditional knowledge management in nuclear reactor operation and maintenance (O&M) relies on dispersed documents and individual expertise, limiting knowledge reuse and response efficiency. This paper introduces a knowledge-graph-driven intelligent question answering (QA) system that constructs a unified semantic representation of equipment, faults, and procedures through structured entity extraction, relationship modeling, and rule-based reasoning. Neo4j serves as the graph database, while the Dijkstra shortest path algorithm supports association path computation for fault cause inference and similar-case retrieval. The system integrates with a digital O&M platform for real-time data acquisition and structuring. Simulation-based validation in a reactor refueling system scenario demonstrates that the system shows promising potential in the following aspects: enhancing standardization and traceability of fault information, reducing fault diagnosis and handling time, and providing reliable data support for team capability evaluation and equipment health assessment. This work provides a scalable framework for knowledge management in complex industrial systems, contributing to improved O&M efficiency and safety in nuclear operations.
Nuclear reactor operation and maintenance
Copyright (c) 2025 Yuan Zhang; Chao Si; YanKun Li https://creativecommons.org/licenses/by/4.0