World Journal of Information Technology (WJIT) is an international peer-reviewed, open access journal.
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
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
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
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
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
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
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
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
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
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
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
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
MULTI-GRANULARITY TIME SERIES FORECASTING METHODS BASED ON DUAL-CHANNEL FUSION
In high-frequency data environments, traditional time-series forecasting methods generally face two major challenges. First, the structures of these models are too simple to capture both the long-term trends and short-term disturbances. Second, the forecasting granularity is too coarse to meet the refined requirements for real-time dynamic decision-making. To address these issues, this study proposes a dual-channel fusion forecasting framework, the Dual-Resolution Adaptive Forecasting Topology (DRAFT) architecture. The architecture comprises two modules: a trend modeling module and a disturbance modeling module. The modules are responsible for processing the linear trend components and nonlinear fluctuation signals in the time series data. They achieved adaptive integration of the prediction results using a lightweight fusion mechanism. Experiments on real-world datasets demonstrated that the DRAFT architecture significantly outperformed traditional single-model approaches in terms of metrics such as mean squared error (MSE) and mean absolute error (MAE), with error reductions exceeding 54.05% in certain scenarios. Furthermore, DRAFT possesses the capacity to refine the prediction output granularity to the 10-minute level, thereby providing more actionable prediction information for high-timeliness scenarios. This study establishes a new paradigm for the precise prediction of complex time-series data and provides theoretical and practical references for the construction of modular prediction systems.
Copyright (c) 2025 XueYuan Zhu; JiaXin Peng https://creativecommons.org/licenses/by/4.0
SMART CONTRACTS: A COMPREHENSIVE SURVEY OF TECHNOLOGY, APPLICATIONS, AND CHALLENGES
Smart contracts, as self-executing code on blockchain platforms, are transforming digital agreements across multiple industries. This paper reviews the technical foundations, applications, security challenges, and emerging directions of smart contract technology through an analysis of recent academic literature and real-world implementations. While smart contracts demonstrate significant potential in decentralized finance, supply chain management, and healthcare, they face critical challenges, including security vulnerabilities, ecosystem centralization risks, and legal uncertainties. Layer-2 scaling solutions, cross-chain interoperability protocols, and AI-assisted security auditing represent promising directions for addressing these challenges. Our analysis reveals that despite technological advances, fundamental issues in security verification and regulatory frameworks require continued research attention.
Copyright (c) 2025 RongHua Li; Yun Li; XinMan Luo 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
BLOCKCHAIN AND LARGE LANGUAGE MODELS: A SURVEY OF INTEGRATION APPROACHES AND OPEN CHALLENGES
This paper surveys recent research on the integration of blockchain technology and Large Language Models (LLMs), examining how decentralization, immutability, and cryptographic verification can address challenges in AI governance, data sovereignty, and model accountability. We review key integration strategies including blockchain-based data repositories, smart contract-driven governance mechanisms, and decentralized AI marketplaces. Our analysis identifies three primary application domains—cybersecurity, healthcare data sharing, and financial services—where this convergence demonstrates practical value. However, significant barriers remain: current blockchain throughput falls short of high-frequency AI requirements, privacy-preserving techniques introduce substantial computational overhead, and regulatory frameworks have yet to reconcile blockchain immutability with data protection mandates. We conclude by proposing prioritized research directions to bridge these gaps.
Copyright (c) 2025 Yun Li; ShuRui Xiao 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
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
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
A NOVEL SIMILARITY MEASURE BASED ON ENTROPY OF INTERVAL-VALUED PICTURE FUZZY SET AND ITS APPLICATION IN PATTERN RECOGNITION
In this paper, challenges in existing similarity measures for interval-valued picture fuzzy sets are addressed, including high computational complexity, parameter dependency, and failure to satisfy similarity axioms. First, the fundamental concepts of interval-valued picture fuzzy sets are introduced, along with definitions of distance measure, entropy measure, and similarity measure. Then, by representing interval-valued picture fuzzy sets as tetrahedrons in a 3D coordinate system and selecting 11 representative boundary points, novel entropy and similarity measures are developed based on the distances among these points. Finally, numerical examples are provided to demonstrate the effectiveness and practicality of the proposed measures, which are successfully applied to pattern recognition. Compared with existing measures, the proposed entropy and similarity measures exhibit three advantages: simplified computational procedures, reduced parameter dependency, and strict adherence to similarity axioms.
Copyright (c) 2025 XiaoXuan Hao; ChenCheng Liu; XiaoDong Pan 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
AN INTELLIGENT PREDICTION METHOD FOR STUDENT DEPRESSION RISK INTEGRATING ENSEMBLE LEARNING AND FEATURE ENGINEERING
Depression has become a global public health issue, with depression risk among students showing a persistent upward trend. Traditional mental health screening primarily relies on manual interviews and questionnaire assessments, exhibiting limitations such as high subjectivity, high cost, and narrow coverage. To address this, this paper proposes an intelligent prediction method for student depression risk based on a fusion mechanism of ensemble learning and feature engineering. Using the Kaggle Open Mental Health Dataset as the experimental foundation, the study first constructs high-quality data samples by repairing missing values through multi-strategy data cleaning, KNN, and regression interpolation. Subsequently, it extracts key psychological and behavioral features using Random Forest feature importance evaluation and Linear Discriminant Analysis (LDA) supervised dimensionality reduction techniques, enhancing model interpretability and training efficiency. During model construction, a multi-model framework incorporating heterogeneous classifiers—including Deep Neural Networks (DNN), Support Vector Machines (SVM), LightGBM, CatBoost, and Random Forest (RF)—was designed. Model fusion was achieved through blending strategies such as Blending, weighted averaging, and soft voting. Experimental results demonstrate that the proposed Blending ensemble model outperforms individual models in metrics including AUC, accuracy, and recall, achieving a maximum AUC of 0.9189 and exhibiting robust performance and generalization capabilities. These findings validate the effectiveness of synergistic optimization through feature engineering and ensemble learning, providing a feasible algorithmic framework and practical pathway for constructing intelligent mental health screening systems for university students.
Copyright (c) 2025 YuHao Yan; LinLu Chen; JingNing Huang 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
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
OLYMPIC MEDAL PREDICTION AND COACHING EFFECTS BASED ON XGBOOST REGRESSION AND BIDIRECTIONAL FIXED EFFECTS DID MODELING
Olympic medal counts reflect both athletic strength and national soft power. Existing research often gives point estimates without confidence intervals, uses single models, and neglects factors like host‐country influence and coaching effects. To address these gaps, this study develops two complementary approaches: (1) an XGBoost regression model with Tree‐structured Parzen Estimator (TPE) optimization to predict gold, silver, and bronze medal counts (1988–2024 data) and construct confidence intervals from residuals; (2) a two‐way fixed effects Difference‐in‐Differences (DID) model to quantify the “great coach effect” by comparing China’s table tennis team before and after 2003 against control groups. The XGBoost model achieves R2 scores of 0. 842 for gold and 0. 850 for silver, providing credible intervals for 2028 predictions. The DID analysis shows elite coaches (e. g. , Liu Guoliang) increased China’s annual medal count by about three, with results robust under various specifications. These findings offer data‐driven guidance for National Olympic Committees in target setting, resource allocation, and coach investment, while presenting a generalized framework for evaluating talent effects in sports policy.
Copyright (c) 2025 YunShan Cai; MeiNa Li; HengYuan Fan https://creativecommons.org/licenses/by/4.0
DIGITAL CLOCK DESIGN BASED ON PROTEUS SIMULATION SOFTWARE
In the context of the limitations of traditional timekeeping tools, the continuous progress of electronic and digital technologies, the increasingly diversified needs of different application scenarios, and the ever-increasing requirements of user experience, the research on digital clock circuit design has emerged and continues to develop. This digital clock circuit employs a cascaded configuration of 74160 adder counters to achieve sexagesimal counting for seconds and minutes, and twenty-four-ary counting for hours, thereby enabling the counting of hours, minutes, and seconds, which are displayed via seven-segment displays. Simulation verification was conducted using Proteus simulation software. The circuit operates stably within the timing range of 00:00:00 to 23:59:59. The second counter switches to the minute counter every 60 seconds, the minute counter switches to the hour counter every 60 minutes, and the hour counter automatically resets every 24 hours. This paper provides low-cost and easy to reproduce the simulation circuit, on the understanding and mastery of the principle of digital clock has a certain role in assisting, for the subsequent design of complex digital systems to lay the foundation.
Copyright (c) 2025 YuanQing Dou https://creativecommons.org/licenses/by/4.0
ADAPTIVE GAIT PLANNING FOR QUADRUPED ROBOTS IN COMPLEX TERRAINS VIA REINFORCEMENT LEARNING
This study addresses the challenge of adaptive gait planning for quadrupedal robots in complex terrains by proposing a reinforcement learning-based solution. First, the kinematic model of the quadruped robot and the complex terrain model are established, providing a theoretical foundation for subsequent algorithm design. Second, a hierarchical reinforcement learning framework is introduced, comprising a high-level gait policy and a low-level joint control policy, to accommodate varying locomotion demands across different terrains. Additionally, an adaptive exploration mechanism and a safety layer based on control barrier functions are incorporated to ensure efficient exploration and operational safety.The proposed algorithm demonstrates robust gait performance across diverse terrains, exhibiting notable advantages in motion performance, adaptability, and computational efficiency. Specifically, simulation results highlight improvements in terrain adaptability and gait stability, while hardware experiments further validate the feasibility and effectiveness of the method in real-world applications.Compared to existing approaches, the main innovations of this study lie in the incorporation of a curriculum learning-based strategy for progressively increasing terrain difficulty and an uncertainty-driven exploration reward mechanism. These designs significantly enhance the adaptive capability of the robot in complex environments. However, the algorithm still faces limitations in computational complexity and real-time performance. Future research may focus on optimizing the algorithmic structure to achieve more efficient real-time control.In summary, this work offers an effective solution for adaptive gait planning of quadruped robots in complex terrains, with both theoretical significance and practical value.
Copyright (c) 2025 ShuoPei Yang; ChangCheng Zhao https://creativecommons.org/licenses/by/4.0