Transactions on Computer Science and Intelligent Systems Research
Transactions on Computer Science and Intelligent Systems Research (TCSISR) aims to publish peer-reviewed proceedings from conferences on research and applications in the field of computer science and intelligent systems.
Study on Variation Characteristics and Annual and Temporal Patterns of Water and Sediment in the Yellow River
The Yellow River, known as the mother river of the Chinese nation, the study on the variation patterns of its water and sediment flux holds significant theoretical guiding significance for the environment, climate, people's livelihood, and optimal allocation of water resources. This paper first focuses on the annual characteristic evaluation of sediment concentration and water-sediment flux in the Yellow River. Through the analysis of actual monitoring data from the Yellow River hydrological station over the past six years, it is found that sediment concentration has significant correlations with time, water level, and water discharge. On this basis, a multiple linear regression model between sediment concentration and water discharge is established using the stepwise regression method. Based on this model and the calculation relationship between total water discharge and total sediment discharge, the annual total water discharge of the hydrological station in the past six years (2016-2021) is estimated to be approximately 197.952 billion m³, and the annual total sediment discharge is approximately 1211.364 billion kg.Subsequently, this paper delves into exploring the temporal variation patterns of water and sediment flux in the Yellow River. The M-K test method is used to analyze the mutation of monthly data of water discharge and sediment discharge over the past six years. The results show that the overall water discharge and sediment discharge exhibit a trend of slow fluctuation first and then significant increase, with the substantial growth mainly occurring after 2018. Through the seasonal decomposition model, the seasonal characteristics of water and sediment flux are revealed, which significantly increase in spring and summer and gradually decrease in autumn and winter. To further identify its periodicity, a wavelet analysis model is established, which clarifies that the variation period of water and sediment flux is one year. These research results provide a scientific basis for the management of water and sediment in the Yellow River and future trend prediction.
Linear regression; M-K mutation test; Seasonal decomposition.
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Optimization of multi-stage production inspection based on a dynamic decision tree
This study proposes an optimized production monitoring solution to address these challenges. By employing hypothesis testing, binomial distribution, and Poisson distribution, a sampling inspection method is designed to determine the minimum sample size and defect threshold for detecting defective products. Subsequently, a multi-stage dynamic decision tree model is developed to decompose the complex production process into multiple decision nodes, enabling the identification of the optimal decision sequence and improving process efficiency. Furthermore, considering more complex real-world production scenarios involving various processes and components, the concept of semi-finished products is introduced. A mixed-integer programming model is constructed, integrating defect rates and process performance, to evaluate the overall performance under different strategies. The results demonstrate that comprehensive inspection of components, avoidance of redundant inspections for semi-finished and finished products, and non-disassembly of non-conforming products can significantly reduce the inspection workload while ensuring quality and efficiency.
decision optimization; hypothesis testing; dynamic decision tree models; mixed integer programming.
A Multi-Criteria Evaluation of Sustainable Olympic Events: Integrating AHP, EWM, and Natural Breaks
Facing the sustainable development challenges of Olympics, this study aims to identify the Sports, Disciplines, or Events (SDEs) that best align with the Olympic spirit and the International Olympic Committee’s (IOC) long-term strategy. All 47 SDEs held between 1896 and 2028 were evaluated using the IOC’s six Sports Inclusion Criteria (C1–C6) as primary indicators, each subdivided into 3-4 secondary factors (21 total). Subjective weights for P1–P21 were derived via the Analytic Hierarchy Process (AHP), objective weights via the Entropy Weight Method (EWM), and comprehensive weights using the Linear Weighted Average Method (LWAM, α = 0.5). Normalized indicator data were used to compute overall SDE scores, which were then classified into 5 strategic groups using the Natural Breaks (NB) method. Breakpoints were identified at 0.7823, 0.7689, 0.6503, 0.5983, and 0.3128. Swimming ranked highest (0.7823) as an elite event, followed by gymnastics (0.7689) and athletics (0.7641), while Roque (0.3128) and Basque Pelota (0.5326) were considered outdated or alternative. This study introduces the AHP-EWM approach to sports event evaluation for the first time, creating the AHP-EWM-NB comprehensive model for sustainable assessment and strategic grouping of Olympic SDEs. The model fills a research gap and provides a data-driven decision-making tool, expanding the application of multi-criteria decision-making (MCDM) in sports management.
Analytic Hierarchy Process; Entropy Weight Method; Natural Breaks Classification; Sport; Discipline; or Event.
Multi-Stage Defect Rate Optimization Using a Hybrid Model of Simulated Annealing and Monte Carlo Simulation
Amid global value chain restructuring and Industry 4.0 transformation, manufacturers face significant challenges in controlling defect rates across multi-stage production processes. This study develops a hybrid optimization model combining Simulated Annealing and Monte Carlo Simulation to balance quality control and cost efficiency. The framework incorporates a comprehensive cost structure covering components, semi-finished/finished products, and defect-related losses, while introducing a dynamic defect rate adjustment coefficient to quantify inspection-skipping risks at different stages.The model employs Simulated Annealing to optimize inspection strategies, with Monte Carlo methods verifying solution robustness. Sampling inspection techniques are integrated to enhance defect detection efficiency. Experimental results demonstrate that the optimized strategy reduces total production costs substantially, with the best-performing solution achieving a unit cost of 145 CNY while maintaining defect rates below 2.25%. Compared to conventional approaches, the new model cuts unnecessary inspection costs by approximately 14% without compromising quality standards.This research provides manufacturing enterprises, particularly in complex sectors like electronics, with a practical decision-making tool for dynamic production environments. The framework's adaptability supports supply chain quality management in the era of smart manufacturing, offering both methodological innovation and operational value for industry applications.
Multi-stage inspection strategy; Defect rate adjustment; Simulated annealing; Monte Carlo simulation; Quality control optimization.
Branch Road Traffic Flow Prediction Using Big Data, Neural Networks, and Genetic Algorithm Optimization Based on Main Road Data
The paper focuses on predicting electric load using a neural network prediction model for big data analysis. Accurate traffic flow estimation on branch roads, a crucial element for urban traffic management, is explored in the study. Traditional methods face challenges due to the high costs of equipment installation. The paper presents models that utilize main road data to indirectly estimate traffic flow, including linear and piecewise models. Additionally, the study integrates advanced techniques like genetic algorithms and Q-learning to optimize traffic signal scheduling and traffic flow forecasting. For complex intersections with multiple branch roads, the paper proposes a hybrid model combining constant, piecewise, and periodic traffic flow functions, validated through MATLAB and Python simulations. The results demonstrate that the developed models offer high accuracy in predicting traffic behavior, with a low RMSE of 2.51 and R² value of 0.9598. The models provide significant advantages in dynamic traffic environments and can be applied to optimize traffic signal control and congestion management.
Neural Network; Traffic Flow; Genetic Algorithm; Prediction Model; Urban Traffic Management.
Observational and Theoretical Constraints on First-Order Phase Transitions in Neutron Stars
Understanding the equation of state of neutron stars (NSs) is a fundamental challenge in astrophysics and nuclear physics. A first-order phase transition at high densities could lead to the formation of a quark core, significantly affecting NS properties. This review explores observational and theoretical constraints on such transitions from observational effects. X-ray observations, including mass-radius measurements from NICER and spectral features like Quasi-Periodic Oscillations, Cyclotron Resonance Scattering Features and Fast Radio Bursts, provide indirect evidence of EOS modifications. Gravitational wave detections, particularly from binary NS mergers GW170817, constrain tidal deformability and post-merger oscillations, which may carry signatures of phase transitions. Measurements of mass, spin evolution, and glitches, with millisecond pulsars exceeding 2M posing challenges to purely hadronic EOSs. Theoretical models and numerical simulations predict that an FOPT could impact gravitational wave signals, twin-star configurations, and NS cooling. Future advancements, including next-generation gravitational wave detectors, high-precision X-ray telescopes, and improved theoreti-cal modeling, will enhance our ability to probe phase transitions in NSs. A combination of these approaches will provide crucial insights into the existence and properties of deconfined quark matter in NS interiors.
First-Order Phase Transition; Tidal Deformability; X-ray Observations; Hybrid Stars.
Research on the Formation Mechanism of Enterprises' AI Technology Collaborative Innovation Network
Based on the invention patent data in the AI field jointly applied by enterprises in Chinese mainland from 2015 to 2024, this paper constructs a dynamic collaborative innovation network and employs the Temporal Exponential Random Graph Model (TERGM) to explore the formation mechanism of enterprises' AI innovation collaborative relationships. Considering both endogenous and exogenous factors affecting network construction, it incorporates structural dependence, temporal dependence, node characteristics, and exogenous network covariates into the model framework. The results show that the formation of enterprises' AI collaborative innovation relationships is influenced by the peer effect, and network evolution presents characteristics of structural self-organization and path dependence. This study helps to understand the evolution of AI innovation collaborative networks and provides theoretical support for the allocation of innovation resources and the formulation of relevant policies.
AI collaborative innovation network; Temporal Exponential Random Graph Model (TERGM); network evolution mechanism; peer effect.
The Architecture of Resonance: Decoding How an Acoustic Cavity Shapes Sound Pressure Levels
This study investigates the acoustic performance of microspeakers across various cavity configurations using coupled electromagnetic-mechanical-acoustic (E-M-A) simulations and experimental validation. A modular test setup was developed to accommodate interchangeable microspeaker modules with distinct cavity designs, including front-firing, side-firing, and back cavity geometries. The results demonstrate that side-firing cavities induce a distinct Helmholtz resonance peak in the mid-frequency range, while the back cavity contributes additional acoustic compliance, effectively shifting the system’s resonant frequency. Experimental measurements show strong agreement with simulation results, confirming the effectiveness of the coupled E-M-A method for optimizing microspeaker-cavity systems to achieve desired frequency responses.
Electroacoustic; Multi-physics coupling; Acoustic cavity; Sound pressure level.
Comparative Evaluation of Machine Learning Models for Cardiovascular Disease Prediction
Cardiovascular diseases (CVDs) persist as a critical global health burden, accounting for over 30% of annual mortality worldwide. While machine learning approaches show promise in early-stage risk identification, existing research predominantly focuses on isolated model validation without systematic comparative analysis. To address this gap, our study conducts a rigorous multi-algorithm evaluation, leveraging Logistic Regression (LR), Random Forest (RF), and XGBoost classifiers trained on clinically validated CVD datasets. Methodologically, this study implement stratified 5-fold cross-validation with adaptive class-weight balancing to mitigate data imbalance issues, while embedding recursive feature elimination for optimal predictor selection. Performance benchmarking across three critical dimensions-precision, recall (sensitivity), and ROC-AUC-reveals RF’s consistent superiority in both discriminative power and operational stability. Specifically, RF generates the most favorable sensitivity-specificity trade-off curve and attains significantly higher diagnostic sensitivity compared to LR and XGBoost, essential for minimizing false negatives in clinical screening scenarios. The validated model establishes a scalable risk stratification pipeline for community healthcare systems, enabling timely interventions while demonstrating methodological replicability for predictive epidemiology in resource-constrained primary care settings. This framework bridges a critical feasibility gap in translating algorithmic innovations into deployable clinical tools.
Random Forest; Logistic Regression; XGBoost; Cardiovascular Diseases.
End-to-End Neural Calibration of Stochastic Volatility Models via Option Panels
We propose an end-to-end neural architecture that calibrates stochastic volatility models directly from high-dimensional option panels. A 3-D CNN–Transformer hybrid learns the inverse mapping from implied-volatility surfaces to latent Heston parameters in milliseconds while enforcing no-arbitrage constraints via a differentiable penalty layer. Closed-form gradients of the Fourier pricing formula enable stable training on synthetic and market data. Extensive experiments on SPX and EURO STOXX 50 options show sub-0.3 % RMSE and a 100× speed-up over L-BFGS-B, with calibration times under 2ms on GPU.
Stochastic volatility calibration; option panels; deep learning; Heston model; no-arbitrage constraints; Fourier pricing.
Design and implementation of campus social blogging culture circle based on multi-end convergence
In the context of the deep penetration of the mobile Internet into campus life, in response to the problems of traditional campus communities such as scattered functions, information silos, and insufficient interactivity, this study takes Dalian Maritime University as a pilot, and designs and implements the ‘Ocean Wave Bay’ multi-terminal interconnected campus social blogging and culture circle platform, which adopts multi-terminal convergence architecture, covering WeChat The platform adopts a multi-converged architecture, covering WeChat, applets, web pages and background management system, and adopts SpringBoot, WeChat applets, vue and other technology stacks to build the system, adopts a layered architecture and a hybrid storage mode to integrate AI intelligent recommendation, blockchain depository function, covering the three core modules of cultural dissemination, community interaction, and service integration. The core requirements of users (students, teachers, administrators) are clarified through demand analysis, and tests and pilot applications show that the average daily activity of platform users reaches 35%, the efficiency of campus activities organisation is improved by 91.7%, and user satisfaction reaches 82%, which effectively solves the pain points of traditional campus platforms.
Campus Social; Multi-End Convergence; Intelligent Recommendations; Digital Platforms.
Research on Influencing Factors of Elderly Care Model Selection for the Silver - Haired Population Based on Traditional Statistical Analysis and Machine Learning Models
To address global population aging, this study innovatively integrates statistics and machine learning, analyzing multidimensional data, including economic status, health conditions, family support, and social environment. Logistic regression is employed to elucidate the influencing mechanisms of elderly care model choices, while decision tree and random forest models significantly boost the prediction accuracy to 85%. Key findings indicate that financial capabilities and family support are the core determinants, highlighting machine learning's superiority in identifying crucial influencing factors. This research provides a scientific foundation for optimizing the allocation of elderly care service resources and designing services. Emphasizing the potential of interdisciplinary research, it aims to fundamentally transform gerontology and create a supportive aging future. The developed model can be tailored to specific regional needs, enhancing its applicability and effectiveness, and it also promotes cross - cultural exchanges of elderly care experiences, thus contributing to building a society that enables the elderly to age with dignity and receive adequate support.
Silver haired population; Elderly care model; Traditional statistical analysis; Machine learning.
Research on a Modular Intelligent Prediction Framework Based on Structural Recognition and Temporal Steady-State Fusion
In multivariate predictive modeling, traditional models are often difficult to balance prediction accuracy, robustness and interpretability in the face of challenges such as high dimensionality, strong noise, nonlinear coupling and structural uncertainty. To this end, this paper proposes a new modular intelligent modeling framework - KFPS-Net. The framework integrates four key functional modules: temporal denoising, nonlinear feature modeling, causal structure exploration and integration fusion, corresponding to sequence signal stabilizer, feature perception modeler, structure explorer and integrated prediction engine, respectively. Through the collaborative work between sub-modules, KFPS-Net effectively realizes the closed-loop optimization of the system from data preprocessing to structural interpretation to the final prediction output. The experimental part was evaluated by using multiple high-dimensional multivariate time series datasets, and multiple sets of baseline models were set up for comparative analysis with ablation experiments. The results show that KFPS-Net is significantly better than the existing mainstream methods in terms of MAE, RMSE and R², and still maintains strong stability under noise disturbance and structural change scenarios. At the same time, the causal structure output further enhances the interpretability of the model and provides a theoretical basis for reliable prediction and systematic decision-making. This work provides a unified and scalable solution for building an intelligent prediction system with robustness and structural cognition.
Modular Modeling Framework; Intelligent Forecasting; KFPS-Net; Causal Structure Modeling; Integrated learning.
Research on Strategy Optimization for the Snakes and Ladders Game Based on Graph Theory and Markov Chains
The research on the strategy optimization of the“Snakes and Ladders”game offers a practical case for applying mathematical and algorithmic theories to real - world decision - making scenarios. It enriches the research methods in the field of game - based decision - making and provides new ideas for solving complex problems in other related fields.This paper addresses the strategy optimization problem in the Snakes and Ladders game by systematically solving four core issues through the integration of graph theory, Markov chains, backtracking, and greedy algorithms. First, the game board grid is abstracted into graph nodes, and the breadth-first search (BFS) algorithm is employed to determine the shortest path and the minimum number of grids traversed. Subsequently, an optimized BFS algorithm is designed for special dice to obtain the minimum number of throws and corresponding paths. Next, a hybrid strategy combining hierarchical greedy and backtracking algorithms is proposed to solve the resource allocation problem for three game pieces. Finally, a Markov chain model is used to quantify the probability distribution of rows and columns after 30 dice throws, validating the model's effectiveness and providing a universal method for board game strategy analysis.
Snakes and Ladders Strategy; Graph Theory; Markov Chains; Dynamic Programming; Greedy Algorithm.
Research on production decision of electronic products based on genetic algorithm
This paper focuses on the production decision optimization problem of electronic products, using a variety of models and algorithms to carry out research. Assuming that the defect rate of electronic products follows Bernoulli distribution, a hypothesis testing model is constructed with binomial distribution and normal approximation, and the minimum sample size formula is derived to determine the minimum detection scheme. The 0-1 variable is introduced to construct the unconstrained decision optimization model, and the geometric series summation model is used to describe the correlation of the decision process. Genetic algorithm is used to solve the optimal decision strategy in various scenarios. The algorithm simulates Darwinian natural selection theory and iteratively optimizes a series of possible solutions through selection, crossover and mutation operations. The minimum sample size under different conditions and the production strategy and maximum profit of different cases were calculated, which revealed the key mechanism of production decision optimization and provided theoretical basis and method support for production process quality control optimization decision.
Production decision; Hypothesis testing; 0-1 Variable optimization model; Genetic algorithm.
Research on the Spatial Distribution Characteristics of Global Cybercrime and Prevention Strategies Based on Geographically Weighted Regression
Against the backdrop of deep global digital integration, the transnational nature and spatial heterogeneity of cybercrime have intensified governance challenges. This study employs a Geographically Weighted Regression (GWR) model to integrate cybercrime data from 129 countries (including 12 indicators such as crime incidents, success rate, and prevention rate) from the International Telecommunication Union (ITU) 2023 Global Cybersecurity Index (GCI) and the VERIS Community Database (VCDB), constructing a three-dimensional analytical framework of "Spatial Distribution - Influencing Factors - Policy Efficacy". The results show that four countries including the United States and the United Kingdom are high-incidence target countries for cybercrime (crime incidents ≥ 138 times), while five countries such as Singapore and South Korea achieve a crime prevention rate of over 89%. Economic level (GDP), internet penetration, and cybersecurity policy efficacy (GCI index) exhibit significant spatially heterogeneous impacts on crime distribution, with well-developed and strictly enforced legal policies accounting for 80% of the crime inhibition effect. Geospatial proximity and technical investment intensity show positive inhibitory effects, and regional collaboration mechanisms can enhance prevention efficiency by over 30%. The study reveals the dual laws of cybercrime—"economic centers attracting crime" and "spatial differentiation of policy efficacy"—providing data support for optimizing global cybersecurity policies.
Cybercrime; Geographically Weighted Regression (GWR); Spatial Heterogeneity; Transnational Prevention; Policy Efficacy.
Research on Public Perception of Autonomous Driving Taxis Based on Web Text Analysis: A Case Study of Apollo Go in Wuhan
In the context of rapid artificial intelligence advancement, this study explores public perception of autonomous driving technology, exemplified by Baidu's " Apollo Go " autonomous driving taxis in Wuhan. Given the discrepancy between governmental/enterprise optimism and public skepticism, this research aims to analyze public sentiment and concerns towards autonomous vehicles to mitigate risks associated with AI adoption in intelligent driving. Utilizing web text analysis, 2,181 valid Weibo comments were collected and analyzed using ROST CM6 software. Results reveal that while 81.50% of the public expresses positive emotions, primarily driven by technological progress and service experience, significant concerns persist regarding safety (15.38% negative sentiment) and socio-economic impacts, such as potential job displacement. The study innovatively employs social media data for a nuanced understanding of public perception, providing valuable insights for policymakers and industry stakeholders to enhance autonomous vehicles social compatibility and facilitate its safe, orderly, and extensive deployment.
Public Perception; Apollo Go; Autonomous Taxi.
Research on a Time Series Forecasting Model Based on Multiple Regression and Polynomial Fitting
To address the inherent limitations of traditional time series forecasting models in handling complex real-world scenarios characterized by nonlinear dynamics and multivariate interactions, this paper proposes a novel hybrid prediction framework that synergistically integrates multivariate regression analysis with adaptive polynomial fitting mechanisms. First, outliers and missing values are processed through data cleaning technology. A feature system of many parameters such as event focus, new event participation rate, and host advantage growth coefficient is constructed. A prediction model based on polynomial regression is designed. And the parameter combination is optimized through cross-validation. The experimental results show that the mean square error of the model on the standard data set is lower than that of the traditional method, and the mean absolute error is reduced. The research results verify the effectiveness of the multi-model fusion strategy in nonlinear time series prediction and provide a new method for processing time series data.
time series forecasting; formatting; multivariate regression analysis; polynomial approximation.
Research on the Prediction of Event Medal Distribution Based on XGBoost and Dynamic Markov Chain
With the rapid development of information technology, machine learning and artificial intelligence have achieved significant breakthroughs in the field of data analysis and prediction, demonstrating great application potential. For example, in the field of sports, ordinary prediction methods can no longer meet the increasingly complex global sports events with a surge in data volume. Taking sports event prediction as a specific application scenario, this paper proposes an advanced integrated prediction system based on the XGBoost algorithm. It combines a dynamic Markov prediction framework and a comprehensive evaluation model based on multi-dimensional features to help predict the number of medals in major events such as the Olympic Games. Firstly, through multi-level feature engineering and model optimization, we achieved high-precision prediction of the number of medals. This paper innovatively introduces a dynamic weight adjustment mechanism and a multi-dimensional feature interaction analysis to improve the prediction accuracy. Secondly, when predicting the medal-winning countries, we employed the Bootstrap resampling and cross-validation methods to enhance the stability of the prediction. Finally, when analyzing the impact of events on medals, we developed a comprehensive model to evaluate the dynamic influence of event changes on the medal distribution.This model not only demonstrates remarkable prediction ability and robustness in Olympic events but also can be extended to the prediction of other large-scale sports events, making new contributions to the sports undertakings of various countries.
Medal Prediction Algorithm; Dynamic Markov chain; XGBoost algorithm.
An Improved ARIMA-Neural Network Fusion Model for Multivariate Time-Series Prediction with External Factor Integration
This study aims to solve the difficult problem of sports performance prediction and improve the accuracy and reliability of prediction. The improved ARIMA and neural network fusion technology was used to clean and standardize the sports data first, and then the improved ARIMA model was used to capture the time series features, and external factors such as athletes' physical fitness and training level were included in the model, and special cases were predicted by combining Bayes theory. Finally, neural network is used to further optimize the prediction results. The experimental results show that the fusion technology effectively integrates the advantages of the two models, and the prediction error is significantly reduced compared with the single model. The research conclusion is that the improved ARIMA and neural network fusion technology is feasible and effective in the field of sports performance prediction, which provides an innovative method for subsequent sports-related research and practice, and strongly promotes the development of sports data prediction technology.
ARIMA Model; Neural Network; Bayesian theory; Fusion Model.
Multi-Objective System Predicting Based on Hybrid LSTM-Markov and GSRF Models
The prediction of multi-objective complex systems has always been one of the significant challenges in academia. The multi-dimensional structure, diverse distribution, and complex temporal dependencies of the data make it difficult for conventional mathematical models to perform effective analysis. This study explores the application of hybrid machine learning algorithms in predicting multi-objective systems. This paper developed an LSTM-Markov model which uses the Markov chain to optimize the preliminary predictive results obtained by LSTM. A Grid Search-optimized Random Forest (GSRF) model was integrated with the LSTM-Markov model to address the zero-inflated and long-tailed characteristics of the chosen datasets. The GSRF model was a powerful non-linear classifier that could resolve the zero-inflated outputs. Using Olympic medal counts prediction as the case, the final evaluation metrics of LSTM-Markov revealed that the MSE and R-squared value of the LSTM-Markov model were 0.12 and 0.89 respectively, indicating significant improvements upon baseline methods. The classification accuracy of GSRF achieved 95.52%, demonstrating promising performance in classification and forecasting. This study provides an innovative approach to solving the multi-target predicting problems of complex time series systems. The method also holds significant application potential in areas such as financial risk prediction and disease transmission modeling.
Multi-objective system; LSTM, Markov chain; Zero-inflation; Grid Search Random Forest.
Prediction of sports strength based on linear regression and random forest model
This paper constructs a dynamic weighted comprehensive model that integrates linear regression and random forest algorithms. This model is used to predict the distribution of competitive performance indicators in international comprehensive sports events and analyze the relevant influencing factors. The model innovatively integrates multiple factors. It incorporates historical data and uses a mechanism to assign higher weights to recent performances, so as to better reflect the current situation. The model also takes into account geographical advantages, which may affect a country's performance in certain sports. Additionally, it includes the effectiveness of training strategy decision - making entities and the scale of participants. In the quantification process, the model first uses linear regression to predict the number of entities that are likely to achieve the best results for the first time. Then, it quantifies the competitiveness index of each project. Specifically, competitive achievements are converted into scores, and a dynamic weighting method is adopted to adjust the scores according to the time when the achievements are obtained, with more emphasis on recent achievements. Finally, the random forest algorithm is used to comprehensively consider various factors and predict the final results of participating units. This model has high application feasibility. Its unique dynamic weighting mechanism and multi - factor integration make its predictions more accurate. The model also predicts potential new - achiever units and evaluates prediction uncertainty. Future research could incorporate real - time individual data and socio-economic variables to enhance the model.
Linear Regression; Random Forest Model; Dynamic Weighted Model; Exponential Decay Coefficient.
Medal Prediction Models Based on LASSO Regression and Random Forest Algorithm
Medal prediction serves as a critical research direction in sports science and data analysis, holding significant implications for optimizing resource allocation and strategic decision-making in competitive sports. This study proposes an innovative hybrid predictive model that integrates hierarchical clustering, LASSO regression, and random forest algorithms. By constructing a purely competition-endogenous multidimensional competitiveness indicator system, the model overcomes the limitations of conventional approaches that rely heavily on external factors. The methodology begins with establishing feature-based indicators to categorize participating nations into three distinct clusters through hierarchical clustering, reflecting their respective stages of sports development and establishing an optimized differentiated modeling framework. For countries at different developmental stages, LASSO regression and random forest algorithms are strategically applied, achieving both model robustness and systematic exploration of feature importance. Empirical results demonstrate the model's capability to accurately forecast medal distributions for the 2028 Los Angeles event, with predictions aligning closely with historical trends and prediction errors confined within a margin of 2 medals. This research provides a quantifiable decision-making tool that substantially enhances the scientific basis for event resource allocation and policy formulation in competitive sports systems.
hierarchical clustering; lasso regression; random forest.
Research on Multi-Agent Task Scheduling Optimization Based on Deep Reinforcement Learning
For the task scheduling problem in multi-agent systems, this paper proposes a collaborative optimization method based on Graph Neural Network and Reinforcement Learning. Firstly, a heterogeneous graph structure is constructed to uniformly model the temporal dependencies, resource competition, and agent capability differences among tasks, and multi-dimensional node features are designed to fully describe the scheduling state. Secondly, the Proximal Policy Optimization algorithm is adopted to achieve efficient training and stable convergence of the policy network based on graph embedding, supporting rapid decision-making for large-scale instances. To verify its effectiveness, 30 test cases are generated for each of the three scales, totaling 90 cases. It is compared with Genetic Algorithm and Gurobi's exact solver. Through a large number of simulation experiments, the effectiveness and advantages of this method in solving the studied problem have been verified.
Multi-agent scheduling; Graph neural network; Reinforcement learning.
Review on the Application of Reinforcement Learning in Distribution Network Voltage Control
Based on the core of Survey on the Application of Reinforcement Learning in Distribution Network Voltage Control, this study systematically organizes RL's application system in distribution network voltage control: it establishes MDP and POMDP modeling frameworks, clarifying the design logic of state, action, and reward functions; classifies and reviews core schemes of value function-based, policy gradient-based, and residual RL, quantitatively comparing algorithm performance via unified benchmarks and evaluation metrics; analyzes engineering implementation challenges (safety, scalability, interpretability) and proposes solutions (hierarchical control, Sim-to-Real transfer, human-machine collaboration); finally, it outlines short-term engineering paths and medium-term technical directions, providing references for intelligent voltage control of distribution networks in the new power system.
Reinforcement Learning; Distribution Network; Voltage Control; DistFlow Model; Multi-Agent Collaboration; Residual Learning; Renewable Energy with High Penetration Rate.
Fringe Projection-Based 3D Surface Measurement System
Three-dimensional measurement technology is widely used in scientific research and industrial fields. With the advancement of the era, traditional contact measurement methods, due to their low imaging accuracy, slow measurement speed, and limited applicability—being unable to measure elastic or deformable objects—are gradually failing to meet the demands of modern industrial development. As a novel 3D imaging approach, non-contact optical three-dimensional measurement technology offers the advantages of high precision, large field of view, and non-destructive inspection without requiring physical contact with the object. This project designs and implements a high-speed 3D surface measurement system based on fringe projection, utilizing non-contact optical three-dimensional measurement technology. By combining spatial and frequency domain analysis, the study investigates the reconstruction effects of the four-step phase-shifting method and Fourier transform profilometry on the 3D surface morphology of objects. Based on this, an independent system algorithm is developed, ultimately achieving an integrated system design. The proposed system not only meets the practical application requirements of intelligent manufacturing and cultural relic preservation but also serves as an optical experimental teaching platform, helping students better understand fringe projection, phase retrieval, and their real-world applications.
Fringe projection; Three-dimensional imaging; Structured light measurement; Interferometric recording.
Deep Learning Architectures for EEG: from CNN to Transformers
This review provides a comprehensive overview of the advancements in deep learning for electroencephalography (EEG) analysis. EEG offers a non-invasive means to monitor brain activity, with applications in brain-computer interfaces (BCIs), clinical diagnostics, emotion recognition, and sleep monitoring. We trace the historical progression from early convolutional neural networks (CNNs) in the mid-2010s to the recent emergence of transformer-based models. Key CNN architectures, such as EEGNet, are examined for their efficiency in feature extraction from raw EEG data. Transformer and hybrid models are discussed, highlighting their ability to model long-range dependencies and achieve state-of-the-art performance across diverse tasks like motor imagery, seizure detection, and sleep staging. The review also addresses self-supervised and unsupervised learning methods to mitigate data scarcity. Challenges including inter-subject variability, noise artifacts, and model interpretability are analyzed, alongside future directions such as transfer learning, synthetic data generation, domain adaptation, and multi-modal fusion. By synthesizing these developments, this work aims to guide future research toward more generalizable and practical EEG deep learning systems.
Electroencephalography (EEG); convolutional neural networks (CNNs); transformer; brain-computer interfaces (BCIs).
Feasibility of Machine Learning and Algorithms in Real World Stock Market Applications
The stock market is one of the most important parts of the global economy, and predicting its movements has long been a challenge. Traditional methods relied on mathematical models and historical data, but advances in computing have introduced machine learning and deep learning approaches. This paper reviews four common methods used in stock prediction: Random Forest, XGBoost, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. Each method has strengths, such as Random Forest’s stability, XGBoost’s strong pattern recognition, CNN’s ability to process visual patterns, and LSTM’s capability to capture temporal dependencies. However, challenges remain, including limited interpretability, poor generalization to new markets, and reliance on single-modality data. To address these issues, future research should focus on combining machine learning with expert systems for better interpretability, applying domain adaptation for cross-market generalization, and using multimodal learning to integrate numerical, textual, and sentiment data. These strategies may help improve the feasibility of machine learning in real-world stock market applications.
Stock market prediction; machine learning; deep learning.
Research on the Global Distribution of Cybercrime Base on Machine Learning and Data Mining
With the acceleration of the global informatization process, cybercrime has increasingly become a serious challenge for the world. The transnational nature of cybercrime makes the response of a single country inadequate, and there is an urgent need for global policy coordination and optimization. Through an in-depth analysis of VCDB data, this paper reveals the distribution characteristics of global cybercrime, and points out the significant differences between developed and developing countries in terms of cybersecurity incident reporting and transparency. Then, use the Grey Relational Analysis method and the five key indicators of the Global Cybersecurity Index (GCI)to evaluate the effectiveness of their national security policies. The results show that technical capability and organizational management are the core factors affecting the effectiveness of cybersecurity policies. In addition, this paper uses the entropy-weighted TOPSIS method to further analyze the relationship between demographic data and network security indicators to verify the previous conclusions. By fostering a unified approach, the international community can better address the evolving landscape of cybercrime and safeguard the digital ecosystem.
Cybercrime; Cybersecurity Policy; Grey Relational Analysis; Entropy-Weighted TOPSIS; Global Cybersecurity Index Introduction.
Advancing Analytic Halo Mass Function Models Through Large-Scale Cosmological Simulations
We develop an analytic halo mass function (HMF) framework calibrated on the comprehensive Abacus-Summit simulations, achieving 2–3% accuracy for <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>Λ</m:r></m:oMath><![endif]--> <span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 12.0pt; line-height: 115%; font-family: 等线; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 12.0pt; mso-text-raise: -12.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> CDM (<!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>≲</m:r></m:oMath><![endif]--> <span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 12.0pt; line-height: 115%; font-family: 等线; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 12.0pt; mso-text-raise: -12.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> 5% for extended cosmologies with massive neutrinos, evolving dark energy, and modified power spectra) across <!-- [if gte msEquation 12]><m:oMath><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>10</m:r></m:e><m:sup><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>11</m:r></m:sup></m:sSup><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>--</m:r><m:sSup><m:sSupPr><m:ctrlPr></m:ctrlPr></m:sSupPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>10</m:r></m:e><m:sup><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>15</m:r></m:sup></m:sSup><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>M</m:r></m:e><m:sub><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/><m:nor/></m:rPr>⊙</m:r></m:sub></m:sSub></m:oMath><![endif]--> <span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 12.0pt; line-height: 115%; font-family: 等线; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 14.0pt; mso-text-raise: -14.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> and <!-- [if gte msEquation 12]><m:oMath>0<z<3</m:oMath><![endif]--> <span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 12.0pt; line-height: 115%; font-family: 等线; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 12.0pt; mso-text-raise: -12.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> . Unlike universal forms limited to <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>Λ</m:r></m:oMath><![endif]--> <span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 12.0pt; line-height: 115%; font-family: 等线; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 12.0pt; mso-text-raise: -12.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> CDM, our MCMC-optimized model explicitly incorporates redshift and cosmology dependence while maintaining physical interpretability. The analytic formulation enables application to new physics without costly retraining, complementing more precise but less flexible emulator-based approaches. We quantify systematic uncertainties from resolution effects, sample variance, and mass definitions, and outline future improvements through symbolic regression. This work provides a robust tool for cosmological tests with galaxy clusters and large-scale structure at percent-level precision.
Cosmology; Dark Matter; Galaxy Cluster; Halo Mass Function; Analytic modeling.