World Journal of Information Technology (WJIT) is an international peer-reviewed, open access journal.
SENTIMENT ANALYSIS MODEL BASED ON ATTENTION MECHANISM MULTIMODAL FUSION AND SPATIO-TEMPORAL GRAPH NEURAL NETWORK
As a key task in the field of artificial intelligence, the application of sentiment analysis has expanded from a single text modality to cover multi-modal information such as vision and hearing. However, the existing multi-modal sentiment analysis methods often ignore the time sequence and structure of the dynamic interaction between modalities, and fail to fully model the evolution of emotional States in the time dimension. Therefore, this study proposes a sentiment analysis model based on multi-modal fusion of attention mechanism and spatio-temporal neural network. The model first extracts the high-dimensional features of text, visual and auditory modalities through a dedicated encoder, and then designs a hierarchical attention fusion mechanism to adaptively weight the contributions of different modalities at the feature level and decision level. In order to capture the dynamic evolution of emotional expression, the spatio-temporal graph neural network module is introduced into the model, and the fusion features of each time step are regarded as graph nodes, and the dynamic edges are constructed based on the correlation and temporal continuity between modalities, so as to model the spatio-temporal dependence of cross-modal interaction. Experiments are conducted on three open multimodal emotion datasets, including CMU-MOSEI, IEMOCAP and MOSI. The results show that the proposed model significantly outperforms the baseline method in both sentiment classification and sentiment regression tasks. On the CMU-MOSEI data set, the accuracy of the model reaches 88. 7% in the binary classification and 53. 2% in the seven-class classification, both of which reach the current advanced level. The ablation experiment further verifies the effectiveness of the hierarchical attention mechanism and the spatiotemporal neural network module. This study provides a new framework for multimodal sentiment analysis that can simultaneously model the complex interaction and temporal dynamics between modalities, which has theoretical significance for understanding the complex mechanism of human emotional expression, and provides technical support for the development of more accurate emotional intelligence applications.
Copyright (c) 2026 AiQun Zhu; Jin Lu https://creativecommons.org/licenses/by/4.0
CONTAINER DAMAGE DETECTION BASED ON DEEP RESIDUAL NETWORKS AND REAL-TIME OBJECT DETECTION ALGORITHMS
Container damage detection is a critical component for enhancing automation and transport safety in modern smart ports. To address practical challenges such as complex background interference in port environments, a wide range of damage scales, and uneven data distribution, this study constructs a damage detection framework based on the deep residual network ResNet50 and the lightweight detection algorithm YOLOv8. The study first employs a Super-Resolution Generative Adversarial Network (SRGAN) to enhance image resolution, thereby improving the distinguishability of fine cracks and dents. By introducing a cross-entropy loss function and a cosine-annealed learning rate strategy, the model achieves robust learning for three major damage types: dents, perforations, and corrosion. Experimental results demonstrate that the ResNet50 model excels at feature extraction in complex backgrounds, achieving a classification accuracy of 76.99% on the validation set, with particularly outstanding performance in identifying dent-type damage with distinct features. Meanwhile, the YOLOv8 model, which incorporates an attention mechanism, exhibits significant advantages in inference speed and multi-object localization, with an average accuracy of approximately 0.85, effectively meeting real-time monitoring requirements. This study confirms the efficiency and practical value of deep learning technology in handling container image recognition tasks characterized by high dynamics and diverse operating conditions.
Copyright (c) 2026 XingYu Zhao https://creativecommons.org/licenses/by/4.0
PROBING THE COGNITIVE APPRAISAL STRUCTURE OF EMOTION REPRESENTATIONS IN LARGE LANGUAGE MODELS (LLMS): A FULLY AUTOMATED GEOMETRIC ANALYSIS BASED ON CHINESE–ENGLISH BILINGUAL CORPORA
The emotional capabilities of large language models (LLMs) have been extensively validated, yet the organizational principles and cognitive regularities underlying their internal emotion representations remain poorly understood. This study introduces Smith and Ellsworth’s (1985) cognitive appraisal theory and designs an automated experimental pipeline in which five mainstream LLMs rate Chinese and English emotional texts along six appraisal dimensions. Combining representational similarity analysis, principal component analysis, and unsupervised clustering, we systematically probed the geometric structure of the LLMs’ emotion space. The results show that the appraisal structures of all LLMs are significantly aligned with human templates; however, under the English condition, the alignment did not surpass a purely semantic baseline, whereas under the Chinese condition the LLMs’ appraisals significantly outperform the semantic baseline. The LLMs’ appraisal space exhibits a systematic “responsibility shift,” in which the responsibility dimension is disproportionately amplified on higher-order principal axes. Chinese alignment is significantly higher than English alignment for all models, and clustering structures were driven more by linguistic properties than by model architecture differences. These findings reveal that LLMs form emotion-cognitive structures that exhibit both commonalities and language-specific particularities, providing a psychology-theory-driven quantitative framework for explainable artificial intelligence (AI).
Copyright (c) 2026 HuaYing Liu; LiJie Luo https://creativecommons.org/licenses/by/4.0
HIGH-PRECISION SEISMIC SOURCE LOCALIZATION ALGORITHMS BASED ON NONLINEAR OPTIMIZATION AND 3D DEPTH LAYOUT
Addressing the core challenge in seismic monitoring—where the accuracy of seismic source localization is affected by geological medium heterogeneity and geometric constraints of the monitoring network—this study constructs a progressive modeling framework that integrates geometric analysis, nonlinear optimization, and statistical sampling. The study first establishes an algebraic analytical model based on the principle of spherical intersection and, through iterative least-squares methods, reduces the sum of squared positioning residuals in an ideal scenario to 0.02 units. To address the ±0.2% ranging errors encountered in practical engineering applications, the Levenberg–Marquardt algorithm and Monte Carlo sampling are introduced to quantify the anisotropy of the error distribution and reveal the sensitivity of vertical positioning accuracy to station geometric constraints. To further enhance system robustness, the study utilized the Shapiro-Wilk test to remove outliers and employed multi-source data fusion via high-density station network redundancy constraints, significantly reducing vertical positioning errors. Finally, numerical simulations validated the scientific validity of the three-dimensional depth layout of monitoring stations, demonstrating that multi-depth configurations can reduce the average positioning error by 42.84%. The “theoretical modeling—error analysis—engineering optimization” technical chain established in this study provides critical technical support for the development of earthquake early warning systems and three-dimensional monitoring networks.
Three-dimensional station network layout
Copyright (c) 2026 PengShuo Zhao https://creativecommons.org/licenses/by/4.0
THE SPATIO-TEMPORAL EVOLUTION OF OLYMPIC MEDALS BASED ON MULTILEVEL REGRESSION ANALYSIS AND MULTIDIMENSIONAL FEATURE COUPLING
Olympic medal performance is a measure of a country’s competitive sport achievements but also largely shaped by various socioeconomic aspects. This study attempts to comprehensively investigate the spatiotemporal development of Olympic performance and to build a highly accurate hierarchical forecasting model. Firstly, using data from the 2008-2024 Olympic Games (five editions) after data cleaning and feature engineering it can be observed an extremely positive correlation between size of the games and total number of medals with correlation coefficient. The exploratory analysis suggests that GDP is the main socio-economic factor determining a nation’s medal competitiveness and its relationship with the number of medals has significant structural differences. It can be seen that for high-income countries, the conversion efficiency of economic resources into competitive advantages is best. This research devised separate forecast systems for nations on varying levels of competition; it used a share-smoothing prediction method on top-performing countries so grasp long-term trend; added up weight least-squares shares regression plus reliability shrinkage system for mid-ranking countries; and innovatively developed a two-step prediction framework integrating logistic regression and random forests models for those nations without any previous medal record. Forecast results suggest at the 2028 Los Angeles Olympics USA will get 150 medals because they are the host nation while China UK and France which are all powerhouses will stay in the lead positions. With the coupling of multiple features, this paper gives scientific evidence for people to know about the change in Olympic performance.
Copyright (c) 2026 YuHang Xiao https://creativecommons.org/licenses/by/4.0
PREDICTING OLYMPIC MEDALS: UNVEILING THE INFLUENCES
This study focuses on the prediction of Olympic medal data and the quantitative analysis of its influencing factors, aiming to address the complexity and nonlinearity in performance data. The research primarily employs the Autoregressive Integrated Moving Average (ARIMA) model to forecast time-series trends in medal counts, capturing the inherent periodicity of medal acquisition. Furthermore, by integrating linear regression with the Gradient Boosting Tree (GBT) model, the study quantifies correlations among event structures, medal attainment, and the host effect. The primary innovation lies in establishing a multi-level comprehensive model that overcomes the limitations of traditional methods in handling time-series dependence. The research findings provide quantitative support for predicting future Olympic trends, offer a research reference for quantifying the host country's advantages, and provide a new perspective for sports policy-making and related academic research.
Copyright (c) 2026 ChaoYin Liu; JinLing Chen; Jin Lu https://creativecommons.org/licenses/by/4.0
THE ASSOCIATION ANALYSIS AND TIMING OPTIMIZATION OF NIPT DETECTION INDICATORS BASED ON GENERALIZED ADDITIVE MODELS AND CLUSTERING DECISION-MAKING
Addressing the challenge of insufficient accuracy in non-invasive prenatal testing (NIPT) caused by traditional empirical grouping and a uniform testing timeline, this study focuses on the dynamic evolution mechanism of Y-chromosome concentration in male fetuses and the scientific optimization of the clinical intervention window. The study first utilized the Generalized Additive Model (GAM) to overcome the limitations of linear assumptions, providing an in-depth analysis of the nonlinear driving effects of gestational age, BMI, and their interaction terms on Y-chromosome concentration. Empirical results indicate that concentration exhibits significant three-stage fluctuations with gestational age, and BMI demonstrates a strong negative inhibitory effect beyond the critical threshold of 28. Subsequently, the study applied the K-means clustering algorithm combined with the elbow rule to achieve scientific stratification of the pregnant population, dividing it into two core subgroups: normal-to-high BMI and high BMI. Building on this, a logistic regression model was constructed and coupled with a comprehensive risk function incorporating weights for testing failure and diagnostic delay, thereby establishing a testing paradigm aimed at minimizing risk. The study confirmed that the optimal testing time point for both groups is 12 weeks of gestation, effectively balancing detection accuracy and clinical timeliness. Residual diagnosis and risk validation demonstrated that this modeling system possesses high statistical robustness, providing mathematical support for improving the quality of decision-making in precision prenatal screening.
Copyright (c) 2026 YiHan Ma https://creativecommons.org/licenses/by/4.0
GLOBAL MAJOR STOCK INDEX VOLATILITY PREDICTION AND RISK ASSESSMENT BASED ON LSTM-ATTENTION
Volatility is a core indicator for risk assessment and investment decisions in financial markets, and accurate prediction of stock index volatility is crucial for risk management. Traditional time series methods struggle to capture the nonlinear characteristics and long-term dependencies of financial data, while single deep learning models have limited ability to capture key time-series information, making it difficult to meet the needs of global stock index volatility prediction. This paper takes major global stock indices such as the S&P 500 and the Shanghai Composite Index as research objects, and constructs a stock index volatility prediction model based on LSTM-Attention. By collecting stock index trading data from the past ten years, the model undergoes cleaning, feature engineering, and standardization preprocessing. A three-layer LSTM stacked structure is designed and an Attention mechanism is integrated to achieve dynamic weighting of key time-series information. The model is validated using indicators such as MSE and MAE combined with a rolling window. Comparative experiments are conducted with pure LSTM and CNN-LSTM models, and a multi-dimensional risk assessment system is constructed based on the prediction results. Experimental results show that the MAE and MSE of the LSTM-Attention model are reduced by more than 12% and 20% respectively compared with the pure LSTM model. It exhibits better predictive stability and adaptability under extreme market conditions. In actual investment cases, the Sharpe ratio of portfolios guided by this model is significantly improved, and the maximum drawdown is effectively reduced. The LSTM-Attention model constructed in this paper provides a more accurate technical method for predicting global stock index volatility. Its accompanying risk assessment system and investment strategy suggestions can provide investors with scientific quantitative references for optimizing asset allocation and regulatory agencies for conducting market risk warnings, promoting the refined development of financial market risk management.
Copyright (c) 2026 GuanXiao Zhang https://creativecommons.org/licenses/by/4.0
AN AUTOMATED SOFTWARE DEFECT PREDICTION MODEL BASED ON OPEN-SOURCE CODE AND META-LEARNING
Software defect prediction is essential for improving software quality, yet traditional models often struggle with cross-project generalization and require extensive manual tuning. To address these challenges, this paper proposes an automated defect prediction approach based on open-source data and meta-learning. The method constructs project-level meta-features from multiple open-source datasets and builds a meta-dataset by associating these features with the best-performing models. A meta-learner is then trained to automatically recommend suitable prediction models for new projects. Experiments on PROMISE datasets demonstrate that the proposed approach consistently outperforms traditional machine learning and cross-project methods in terms of AUC and F1-score, while significantly enhancing cross-project generalization. Statistical tests confirm the significance of these improvements, and analysis of model selection shows high consistency with optimal choices. Overall, the proposed method effectively reduces manual intervention and provides a robust, automated solution for software defect prediction.
Copyright (c) 2026 JingXuan Guo; ShiYu Jiao https://creativecommons.org/licenses/by/4.0
OPTIMIZING THE TIMING OF NIPT BASED ON MULTI-FACTOR REGRESSION AND DECISION ANALYTICS
Noninvasive prenatal testing (NIPT) is widely used for screening common fetal aneuploidies, but its reliability is strongly affected by fetal fraction and is often reduced in pregnant women with elevated body mass index (BMI). This study develops an integrated statistical and optimization framework to generate practical recommendations on testing timing under heterogeneous maternal characteristics and sequencing quality conditions. Using real-world NIPT records from male fetal samples, Y chromosome concentration was adopted as a proxy for fetal signal adequacy, and qualification was defined by the empirical threshold of Y ≥ 4%. First, multivariate regression and weighted least squares were used to quantify the joint effects of gestational age and BMI on Y chromosome concentration and to derive a closed-form qualification probability. Second, BMI-stratified logistic models were constructed to estimate the earliest gestational week required to achieve a target qualification probability, such as 90%, and to reveal systematic timing delays associated with increasing BMI. Third, a risk-based optimization strategy was applied to generate a smooth BMI-dependent timing trajectory by explicitly balancing non-qualification risk against delay cost, and robustness was assessed through perturbation-based resampling. Finally, a multi-factor logistic generalized linear model incorporating maternal age, weight, unique aligned reads, and GC content was established, and joint optimization of BMI boundaries and testing times was used to eliminate infeasible extreme-BMI subgroups and improve group-level fairness. The proposed framework translates observational associations into deployable timing recommendations and provides quantitative support for individualized NIPT timing decisions.
Copyright (c) 2026 Shuo Bai; Lei Cheng https://creativecommons.org/licenses/by/4.0
INTELLIGENT FALL DETECTION SYSTEM FOR DRONES IN AGRICULTURAL FIELD SCENARIOS
Aiming at the high fall risk of elderly farmers during field operations and the lack of suitable intelligent monitoring technology for complex agricultural scenarios, this paper proposes an intelligent UAV-based fall detection and alarm system dedicated to farmland environments. We constructed a dedicated fall posture dataset covering diverse field vegetation, lighting conditions, and clothing styles, and expanded the sample size to 3,360 images through rich data augmentation. A fall detection model based on the YOLOv8 algorithm is designed to realize real-time and accurate positioning of fall events. Once a fall is detected, the system automatically triggers an on-board buzzer alarm and sends a timely notification email to emergency contacts via the SMTP protocol. Experimental results demonstrate that the proposed model achieves 92.6% mAP@50 and 92.3% precision, with an average system response time of 16 seconds and email notification completed within 40 seconds. The system maintains favorable robustness and generalization ability under complex conditions such as low illumination, and can effectively adapt to fall detection tasks in actual farmland scenes. It reduces the delay of manual monitoring and expands the coverage of safety protection, providing a practical and reliable technical approach for safety monitoring and emergency assistance in smart agriculture.
Copyright (c) 2026 SiQi Wang; JiaLei Guo; KeYi Lv; JiaXin Liang https://creativecommons.org/licenses/by/4.0
RESEARCH PROGRESS ON DECENTRALIZED FINANCE
Decentralized Finance (DeFi) has emerged as one of the most transformative applications of blockchain technology, constructing a financial ecosystem that operates without traditional intermediaries through smart contracts and distributed protocols. This paper systematically reviews the research progress of DeFi in terms of technical architecture, core protocols, governance models, and application scenarios, with particular focus on analyzing key domains including lending platforms, decentralized exchanges, stablecoin mechanisms, and asset tokenization. Research indicates that although DeFi demonstrates revolutionary potential in enhancing financial inclusion and transaction efficiency, it still faces significant challenges in smart contract security, regulatory compliance, and systemic risk prevention. This paper further explores future research directions including the integration of DeFi with traditional finance, technological innovation, and real-world asset integration, providing reference for scholars, policymakers, and industry participants to understand this rapidly evolving field.
Copyright (c) 2026 ChaoFu Qin; YanLing Liu https://creativecommons.org/licenses/by/4.0
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
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
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
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
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
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
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
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