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.
MATLAB-Simulation-Based Analysis of Free RLC Oscillations
Targeting the LC oscillation commonly taught in high school, this study models a resistive RLC circuit using university-level circuit theory and ODE tools. We compare time responses and energy dissipation under under-, critical-, and over-damping, and visualize charge, current, and total energy via MATLAB. The work offers a quantitative, reproducible framework to bridge high-school and university instruction and to tune experimental parameters, while clarifying often-confused issues (e.g., trends of the Q factor). Innovations: (1) a classroom-oriented simulation pipeline with ready-to-use plots; (2) a unified, dimensionless and energy-based formulation covering all three damping regimes; (3) practical guidance on selecting R, L, and C to achieve target behaviors.
LC oscillation; RLC model; damping characteristics.
Copyright (c) 2026 Transactions on Computer Science and Intelligent Systems Research https://creativecommons.org/licenses/by-nc/4.0
A Quantitative Study on the Impact of National Demographic Characteristics on Cybercrime Distribution Based on Spearman and XGBoost Models
This study focuses on quantitatively analyzing the relationship between national demographic characteristics and the distribution of cybercrime, aiming to identify the most influential macro-factors. The research first employed Spearman's rank correlation analysis to assess the linear correlations between twelve national indicators—encompassing economic, social, educational, and internet usage metrics—and the cybercrime factor. The results revealed strong linear correlations for GDP per capita, GDP, aging rate, and three internet-related data points. Subsequently, to incorporate both linear and non-linear relationships, this study utilized the XGBoost machine learning model for in-depth analysis.Feature importance analysis from the XGBoost model indicated that GDP per capita is the most influential national characteristic affecting cybercrime distribution, with a correlation coefficient of 0.24633. The number of secure internet servers and GDP were also identified as high-impact indicators. The findings suggest that while regions with high GDP per capita host active online financial activities and advanced technology—potentially offering more criminal opportunities—overall, economically developed areas may exhibit lower rates of cybercrime incidence. In contrast, the unemployment rate demonstrated the lowest impact, with a coefficient of only 0.01276. The model achieved a mean squared error (MSE) of 0.339 and a coefficient of determination (R²) of 0.744, demonstrating strong explanatory power and predictive accuracy regarding the factors influencing cybercrime distribution.
Cybercrime distribution; National characteristics; XGBoost model.
Application of Machine Learning Methods in Stroke Prediction
Stroke is currently one of the largest causes of death and disability worldwide, hence it needs effective strategies to detect it early. The paper discusses the role that ML techniques may play in predicting stroke risk, along with methodologies, performance metrics, and their clinical implications. There are benefits associated with integrating ML into stroke prediction models: large dataset processing, improved accuracy, and timely risk assessment. In this paper, we have considered five distinct ML models: Linear Regression, Logistic Regression, Support Vector Machines, Random Forest, and Neural Networks. All these models provide different perspectives and insights about stroke risk analysis. In this work, vigorous analytical methods will be used that involve correlation analysis, SVM, and logistic regression. All these analyses will source a comprehensive stroke dataset from Kaggle. The results show that parameters like age, heart disease, glucose level, and hypertension are important indices that could predict the risk of stroke. Notwithstanding the odds of challenges that may result from data imbalance and bias, our study is going to implement various strategies aimed at mitigating these issues to provide precision and accuracy in our findings. We conclude from the study that, indeed, ML algorithms, especially deep neural networks, are capable of providing effective improvement in the prediction of long-term outcome of patients who have ever suffered from ischemic stroke. This current study joins other efforts in progress geared toward improving early detection and, consequently, treatment outcomes for stroke patients.
Machine Learning; Data Visualization; Stroke; Disease Prediction.
Empirical Analysis and Effect Quantification of Investment and Financing Demands for Rural Revitalization Based on Multimodal Data Fusion
This study employs natural language processing, Kalman filtering, and Gaussian process regression to construct an NLP-KF-GPR multimodal data fusion model, conducting empirical analysis across 31 provinces and municipalities nationwide. Focusing on quantifying the synergistic effects of policy instruments and nonlinear responses to environmental constraints, the findings reveal significant regional heterogeneity in the transmission mechanisms of rural revitalization investment and financing. Analysis of spatial differentiation in regional policy elasticity coefficients shows that the eastern region exhibits an average coefficient of 0.42, with significantly shorter policy transmission lags compared to the western region. The median policy transmission time in the west reaches 8.3 months, reflecting gradient differences in infrastructure conditions and policy implementation efficiency. Nonlinear validation of the environmental-economic threshold effect reveals an inverted U-shaped relationship between <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>CO</m:r></m:e><m:sub><m:r>2</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: 12.0pt; mso-text-raise: -12.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"> emissions and investment-financing demand, with an inflection point at 527,000 tons, providing a basis for dynamically adjusting ecological compensation standards. Furthermore, empirical evidence confirms the significant dampening effect of policy semantic stability on market volatility: a 10% increase in the semantic coherence index reduces market expectation dispersion by 14.8%. Model performance evaluation indicates that the NLP-KF-GPR framework demonstrates outstanding capability in investment and financing demand forecasting, achieving a low normalized root mean square error of 0.124, showcasing excellent predictive accuracy and robustness.
Multimodal Data Fusion; Investment And Financing Demand Forecasting; Policy Elasticity Coefficient.
Predicting PM2.5 Concentrations Based on Vehicle Ownership Using Big Data Models and Machine Learning
This study integrates multi-source satellite-remote-sensed PM2.5 data and provincial vehicle ownership data in China (1998-2023) to construct a high-precision prediction model. We developed a spatiotemporally coupled hybrid model that fuses time series analysis (Prophet) with graph convolutional networks, and incorporates a reinforcement learning framework for dynamic feature optimization. The model achieves an R² of 0.95, with a mean absolute error of 1.97 <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>μ</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;"> g/m³ and a root mean square error of 2.67 <!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>μ</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;"> g/m³ on the 2022-2023 test set, representing a 12% improvement over single models. Analysis reveals significant regional heterogeneity, indicating a stronger PM2.5 suppression effect from new energy vehicles in eastern coastal areas compared to inland regions. This study provides a robust quantitative tool for vehicle pollution control and offers a methodological framework for multi-scale environmental modeling.
PM2.5 Prediction; Spatiotemporal Coupling Model; Reinforcement Learning.
Research on Numerical Solution Optimization of Partial Differential Equations Based on Physics-Informed Neural Network (PINN)
Partial differential equations (PDEs) are core tools for characterizing physical laws and are widely used in fluid mechanics, power systems, additive manufacturing, and other fields. However, traditional numerical methods are limited by mesh partitioning, leading to a trade-off between accuracy and efficiency in complex geometric domains and high-dimensional problems. While Physically Informed Neural Networks (PINNs) achieve meshless PDE solutions by embedding prior physical knowledge, they still face challenges such as insufficient accuracy, low training efficiency, and poor stability. This study aims to address the core bottlenecks of PINN in solving PDEs by proposing a systematic optimization strategy to improve its numerical solution accuracy, efficiency, and stability. Methodologically, firstly, a composite loss function containing PDE residuals and boundary initial condition constraints is constructed based on automatic differentiation techniques to ensure that the network satisfies physical laws. Secondly, the PINN training system is optimized, including using Neural Architecture Search (NAS-PINN) to automatically match the optimal network structure, designing an adaptive data sampling strategy to improve data utilization, and introducing parallel computing and hardware acceleration techniques to reduce training time. Finally, the effectiveness of the strategy is verified through classical PDEs and engineering problems. The results show that the optimized PINN achieves high-precision numerical approximation in classical PDE solutions and can efficiently solve forward design and backward parameter inversion problems in engineering scenarios. Compared with traditional methods, it exhibits stronger adaptability in complex geometric domains and high-dimensional problems, while improving training efficiency by more than 30% and significantly enhancing stability. This research not only enriches the optimization theory of PINN and provides an efficient new path for PDE numerical solutions, but also promotes the practical application of PINN in engineering fields such as fluid mechanics, additive manufacturing, and power systems, possessing significant theoretical value and application significance.
Partial Differential Equations; Physically Informed Neural Networks; Numerical Solution; Deep Learning; Optimization Strategies.
Natural Gas Flow Metering Error Compensation Model Based on XGBoost-Stacking Ensemble Learning
With the rapid growth of natural gas consumption in my country, flow metering accuracy is crucial for energy trade settlement and transmission and distribution loss control. However, metering errors caused by multiple factors, such as liquefied natural gas vaporization, metering equipment failures, and improper parameter settings, pose challenges to traditional compensation methods that rely on simple mathematical models. This study aims to construct a high-precision natural gas flow metering error compensation model to intelligently improve metering accuracy and reduce economic losses caused by errors. Based on multi-source data from the natural gas pipeline network, after data cleaning and standardization preprocessing, this study combines the advantages of XGBoost in handling nonlinear relationships with the Stacking ensemble strategy, supplemented by Bayesian optimization and random search for parameter tuning. Ultimately, an XGBoost-Stacking ensemble learning error compensation model is constructed. Experimental results show that the proposed model achieves a mean absolute error of 0.0298, a root mean square error of 0.0451, and a coefficient of determination of 0.9432, significantly outperforming traditional regression models and single XGBoost models. The compensation accuracy meets the industry requirement of ≤0.5%, and generalization capability is stable. This model provides an effective technical path for resolving natural gas metering errors, reducing transmission losses, ensuring trade fairness, and promoting the intelligent development of the natural gas industry.
Natural Gas Flow Measurement; Error Compensation; XGBoost; Stacking; Ensemble Learning.
Study on the Spatial Correlation between Artificial Intelligence Patent Growth and Regional Innovation Capabilities Based on the Spatial Durbin Model and Geographically Weighted Regression Model
This study aims to deeply analyze the complex spatial linkage mechanism between the growth of artificial intelligence patents in China and regional innovation capacity. Based on panel data from Chinese provinces and municipalities from 2019 to 2023, this research comprehensively employs two spatial econometric methods: the Spatial Durbin Model (SDM) and the Geographically Weighted Regression Model (GWR). The study first reveals significant spatial dependence in regional innovation factors through the Moran's I index. Subsequently, SDM results demonstrate that R&D investment exerts a significant positive direct effect on local patent output, yet its spatial spillover effects exhibit substantial fluctuations—shifting from a negative "siphon effect" in 2019 to positive spillovers in subsequent years, reflecting dynamic changes in interregional competition and cooperation. GWR model analysis further confirms that the impact of university and scientific personnel concentration on R&D intensity exhibits significant spatial heterogeneity, highlighting uneven regional innovation capacity development. The ultimate objective is to provide theoretical support and decision-making basis for governments to formulate scientifically sound regional innovation policies and optimize artificial intelligence industry distribution.
AI Patents; Regional Innovation Capacity; Spatial Econometric Models.
Optimization of Non-Invasive Prenatal Testing Timing and Quantification of Influencing Factors Based on Gradient Boosting Trees and Survival Analysisa
To enhance the accuracy of non-invasive prenatal testing and safeguard maternal health, this study mathematically models and investigates the relationship between fetal Y chromosome concentration and maternal characteristics, aiming to provide optimal testing timing recommendations. First, exploratory data analysis was conducted to examine the correlation between fetal Y chromosome concentration and maternal gestational age, BMI, and age. The Shapiro-Wilk normality test confirmed non-normal distribution, prompting the use of Spearman's correlation coefficient. Results revealed a weak positive correlation between Y chromosome concentration and gestational week at testing, and weak negative correlations with maternal BMI and age. Subsequently, a gradient-boosted decision tree was employed to construct a relational model. The F-test confirmed that gestational week, maternal BMI, and age all exerted statistically significant effects on Y chromosome concentration. Model evaluation revealed comparable mean squared errors across training and testing datasets, indicating no significant overfitting. To determine the optimal NIPT timing based primarily on maternal BMI, K-means clustering divided pregnant women carrying male fetuses into three BMI clusters: Cluster 0, Cluster 1 , and Cluster 2. Kaplan-Meier survival analysis and log-rank tests confirmed that gestational age significantly influenced Y-chromosome concentration Cluster 1, and Cluster 2. Kaplan-Meier survival analysis and log-rank tests confirmed significant differences in the distribution of Y chromosome detection timepoints across clusters. Ultimately, by constructing a risk-cost function and optimizing its solution, the optimal NIPT testing time points for each cluster were determined: 12.8 weeks for Cluster 0, 13.0 weeks for Cluster 1, and 16.2 weeks for Cluster 2. Sensitivity analysis demonstrated the model's robustness to detection errors, providing scientific evidence for clinical NIPT timing decisions.
Gradient Boosting Trees; K-means Clustering; Survival Analysis.
Optimization of the optimal time point for NIPT detection in pregnant women with male fetuses based on FCM fuzzy clustering and TOPSIS
In response to the clinical pain point of insufficient accuracy of the unified detection time point due to the difference in body mass index (BMI) of pregnant women in non-invasive prenatal testing (NIPT) for male fetuses, this paper proposes an optimization scheme for the optimal detection time point of NIPT combining FCM fuzzy clustering and TOPSIS methods. Firstly, the original NIPT data of 1082 male pregnant women with fetuses were preprocessed (abnormal samples of BMI and gestational weeks were cleaned, and gestational weeks were converted to continuous values), and valid samples were retained. FCM fuzzy clustering (fuzzy coefficient m=2) is adopted to achieve flexible grouping of BMI, solving the problem of misjudgment of sample boundaries in traditional hard clustering. Then, the "compliance rate - risk - cost" decision matrix is constructed through the TOPSIS method, the optimal detection time points of each group are screened, and the influence of detection errors on the results is analyzed. The results show that FCM can achieve reasonable grouping of BMI, and TOPSIS can effectively determine the optimal detection time point for each group. When the detection error is ≤5%, the stability of the grouping results is good, and the compliance rate of each group's detection meets the clinical needs. There is a correlation between BMI and the optimal detection time point. This scheme can match the NIPT testing needs of pregnant women with different BMI levels, providing reliable support for the timing planning of clinical NIPT testing.
FCM Fuzzy Clustering; TOPSIS Method; Non-Invasive Prenatal Testing (NIPT); Optimal Testing Time Point; BMI Grouping.
A Quantitative Association Model for Male Fetal Y Chromosome Concentration Based on Random Forest Regression and SHAP Interpretability
This study aims to clarify the quantitative association patterns between maternal biological indicators and fetal Y chromosome concentration in male fetuses. First, comprehensive preprocessing was performed on 549 male fetal detection datasets, including key missing value deletion, Z-score outlier handling, and construction of derived features such as BMI grouping and gestational age segmentation, to ensure data quality and applicability for modeling. Spearman's rank correlation analysis confirmed a significant monotonically negative relationship between maternal BMI and Y chromosome concentration. Subsequently, a random forest regression model was established to capture the nonlinear relationship between features and Y concentration. Model parameters were optimized via grid search and 5-fold cross-validation, ultimately determining the number of decision trees T=800. On the test set, the model demonstrated excellent performance with a coefficient of determination R² and a mean absolute error MAE of 0.0183 ng/mL. Further SHAP interpretability analysis quantified feature contributions, revealing that X chromosome concentration, number of blood draws, and Y chromosome Z-score were the top three factors influencing Y concentration prediction. Additionally, SHAP dependency plots clearly demonstrated a sharp increase in the positive contribution of gestational age during weeks 12–15, while high BMI systematically reduced Y concentration.
Random forest regression; Y chromosome concentration; SHAP interpretability.
Prediction and Strategic Analysis of Olympic Medals Based on Hybrid Modeling Method
This study developed a hybrid prediction framework to address the issues of Olympic medal prediction and strategic analysis, with a focus on model architecture, integration, and validation. A combination of linear regression and random forest algorithm was proposed, which was dynamically weighted through grid search optimization. This model combines athlete performance indicators such as participation frequency, historical medal count, national strength indicators, host country advantages, and sports specific dynamics, and rigorously preprocesses the data to address inconsistencies in event classification and historical country codes. Construct a prediction interval through error distribution analysis, and achieve a coverage rate of 91.4-93.2% through the country specific chi storial error quantile. The entropy weighted evaluation of medal concentration, competitiveness, and stability is used for the first time, and the constrained model prioritizes medal prediction for highly probable sports (athletics, swimming, shooting, boxing). This article establishes three indicators to evaluate the importance of different sports to a country: medal concentration, international competitiveness, and time stability. The entropy weight method is used to determine the weights of indicators. At the same time, the changes in medals won by the host country in additional events were counted. For the "Great Coach" effect, this article specifically counted the medals won by Lang Ping and Bella Karoli in volleyball and gymnastics. Research has found that the 'big coach' effect does indeed exist.
Medal prediction; Linear regression; Random forest; Entropy weight method.
Quantitative Study on the Correlation Between Fetal Y Chromosome Concentration in NIPT and Maternal Indicators Based on Multiple Regression and Quantile Regression, and the Optimal Detection Timing
This study focuses on the quantitative association analysis between fetal Y chromosome concentration and maternal indicators, and the determination of the optimal timing for non-invasive prenatal testing (NIPT) based on BMI grouping. The aim is to optimize NIPT strategies and minimize potential risks to pregnant women. Regarding the association characteristics between Y chromosome concentration and maternal indicators such as gestational age and BMI, Pearson correlation analysis first revealed a negative correlation between Y concentration and BMI (-0.1683) and a positive correlation with gestational age (0.09842), though both linear relationships were weak. Building upon this, a multivariate polynomial regression model incorporating quadratic and interaction terms was constructed, with parameters fitted using least squares. F-test validation confirmed the model's overall statistical significance (p<0.001). To determine the optimal testing time point, considering that K-means clustering might cause sample size imbalance, this study employed quartile grouping to reasonably categorize the BMI of pregnant women carrying male fetuses, ensuring an even distribution of sample sizes across groups. Subsequently, a quantile regression model was established with the objective of minimizing the potential risk to pregnant women. The results revealed optimal NIPT testing timepoints of 12.59 weeks, 12.87 weeks, 13.06 weeks, and 13.31 weeks for the four BMI groups, respectively. Detection error increased with rising BMI.
Multivariate polynomial regression; Quantile regression; Optimal timing for NIPT.
Dynamic Analysis of Multi-dimensional Sustainable Development of Tourist Destinations Based on Dynamic System Modeling
In response to the problems of environmental degradation, social pressure and economic imbalance caused by excessive tourism in tourist destinations, this paper takes Juneau, Alaska, USA as the research object and constructs a multi-dimensional dynamic analysis model for sustainable tourism development based on dynamic system modeling technology. The number of tourists, the quality of life of residents, carbon emissions and infrastructure pressure were selected as the core indicators. By constructing the differential dynamic equations of each indicator, based on the actual data of Juno from 2014 to 2023 (such as 1.6 million tourists and 375 million US dollars in tourism revenue in 2023), the key parameters (positive feedback coefficient α=0.0723 for the number of tourists, negative feedback coefficient λ=-0.00503 for the quality of life of residents, etc.) were calibrated to quantify the coupling feedback relationship among the indicators. The results show that the model can effectively predict the dynamic trends of various indicators in the next ten years. The growth of the number of tourists has a significant positive driving effect on carbon emissions (γ=0.46314 t CO₂eq/tourists), and environmental protection expenditure can significantly suppress carbon emissions (δ=0.02647 t CO₂/US dollars). The Juno load threshold is the total infrastructure capacity K=3,358,064 units per year. This model provides quantitative tools and scientific basis for the dynamic regulation of the sustainable development of tourist destinations.
Dynamic system modeling; Sustainable tourism; Multi-dimensional indicators; Parameter calibration; Tourism destination management.
Prediction and Strategic Analysis of Olympic Medals
The Olympic medal table showcases the competitive level of each country, and establishing a medal prediction model based on historical data is crucial for accurately predicting each country's performance at the 2028 Los Angeles Summer Olympics. This article first preprocesses data from the past 10 Olympic Games, selecting 11 features Xi (such as total number of participants, total number of events, etc.) and 4 predictor variables Yi (such as whether medals were won, total number of medals, etc.), and encodes the classification features. Subsequently, five cross validation fits were performed on the data using random forest, LightGBM, and XGBoost models to establish the relationship between features and predictor variables. Next, the stacked model is used to integrate the prediction results of these three models, assign different weights, and finally construct an Olympic medal prediction model. On the training set (80%), the model scored 0.823, and on the testing set (20%), the model scored 0.806. Next, use ARIMA and grey prediction models to predict the feature variable Xi of the 2028 Olympics, and substitute it into the medal prediction model to obtain the medal situation Yi of each country in 2028. According to the model, China and the United States may perform better in 2028, while Japan and France may perform worse; The probabilities of Monaco, Bahamas, and Brunei winning medals for the first time are 83%, 81%, and 76%, respectively; Diving, weightlifting, shooting, and gymnastics will have a significant impact on China's total medal count; As the host country, there was a significant change in the medal count of Chinese gymnastics in 2008.
Olympic Medal Prediction Model; Random Forest; Lightgbm; XGBoost; Stacked Model.
End-to-End Computer Vision Driven by Python: Research, Education, and Practical Innovation
Nowadays, with the development of artificial intelligence technology, image processing is widely used in many areas such as industry or education field. Python is becoming the de facto standard of choice when developing computer vision software mainly due to its cross platform capability, simple programming style as well as a rich set of open source third party libraries such as OpenCV [1], TensorFlow [2], and PyTorch [3]. Unfortunately, today’s programming practices require tedious coding and cumbersome task coordination and a disconnect between teaching methods and real-world application settings. In this work, we introduce the EECVF vision system by using visual programming methods for providing an intuitive, easy-to-use interface. By dividing the program flow to independent working processes and offering simplified operation steps the model facilitates efficient data-driven control of processes; it simplifies the end-to-end workflow starting with data management and design of algorithms through deployment; It lowers the technological barrier of implementation so that it is more feasible to implement as an assignment for university courses; and enables us to perform further studies with higher fidelity methods like MD simulations or explainable ML, while having many shortcomings for example lack of interactivity and dependence on specific computers; future releases will remedy these problems. In the future we plan to deploy this platform using a server in the cloud and load balancing techniques and the use of GPUs. Given its generality, it is likely that this system could be used more widely where we expect to see a growing interest and applications for example in the following fields.
Python; End-to-End Computer Vision; Education and Research; EECVF.
A Physical–Economic Coupling Modeling and Weighted Bi-Objective Optimization Framework for Large-Scale Transportation Systems
This paper constructs a physical-economic coupling model integrating space elevators with traditional rockets for multi-billion-ton Earth-Moon material transport missions. Within a unified framework, it delineates the intrinsic relationships among transport capacity, time cycles, and economic costs. Based on dynamic analysis and energy consumption calculations, a dual-objective planning model targeting total project duration and total cost is established by incorporating unit cost functions and annual transport capacity constraints. A time-cost Pareto frontier is constructed using a weighted summation method. The model achieves multi-year optimized resource allocation through capacity constraints and cumulative demand conditions. It also designs a dynamic allocation mechanism prioritizing full elevator utilization with flexible rocket compensation, enabling synergy between low-cost baseline transport capacity and high-mobility acceleration capabilities. Results demonstrate that this method systematically reveals economies of scale and marginal cost differences across transport architectures, forming a continuously adjustable time-cost tradeoff range. This provides a universally applicable optimization framework and theoretical foundation for comprehensive decision-making in large-scale deep-space engineering.
Physical–Economic Coupling Model; Multi-Objective Programming; Multi-Stage Resource Allocation.
Inverse Preference Inference via Approximate Bayesian Computation and Counterfactual Simulation for Rule Evaluation in Ranking Systems
This paper addresses the issue of unobservable audience voting data in real-world competition scenarios by proposing a unified modeling framework based on inverse inference. Treating judges' scores and elimination outcomes as observable constraints, the study constructs a Bayesian inverse problem model incorporating ranking rules by introducing a social attention prior. An approximate Bayesian computation method is employed to achieve stable estimation of the underlying voting distribution. Building upon this foundation, the paper further proposes statistical metrics to characterize result anomalies and validates the model's reliability under sparse information conditions using multi-season data. Centered on the inference results, the study establishes a counterfactual simulation and fairness evaluation system, systematically comparing the structural differences in fairness and engagement across various scoring mechanisms. Furthermore, the paper designs an automated competition mechanism requiring no subjective intervention. By employing algorithmic immunity strategies, it significantly enhances fairness while maintaining commercial activity. This method operates independently of domain-specific assumptions, making it applicable to diverse complex systems involving implicit preferences and ranking decisions. It provides a universally applicable, data-driven approach for rule design and mechanism optimization.
Inverse Inference; Approximate Bayesian Computation; Counterfactual Simulation.
A Study on Optimal NIPT Timing Selection and Intelligent Fetal Abnormality Dectection Based on Machine Learning
To address the challenges of uncertain testing time points and the difficulty in determining abnormalities due to individual variations in non-invasive prenatal testing (NIPT), this paper introduces an integrated machine learning framework that amalgamates prediction, optimization, and classification. The framework is designed to identify key factors influencing the quality of testing data and, based on these factors, to offer personalized optimal testing time points and highly accurate solutions for abnormality determination. Initially, the study utilizes an XG-Boost regression model to elucidate the complex nonlinear relationships among critical quality control indicators and factors such as gestational age at testing and maternal BMI. Building upon this, a novel approach that integrates weighted K-means clustering with nonlinear programming is employed to ascertain the optimal testing window for groups of pregnant women with varying physiological characteristics. Finally, to address the challenge of classifying rare abnormal samples, an adaptively weighted and optimized random forest model is developed. Experimental results indicate that this framework effectively handles the data nonlinearity and individual heterogeneity. The final classification model achieves a high recall rate of 85% and a precision of 97%, underscoring the advanced nature of the proposed method and its potential for clinical application.
XG-Boost regression; nonlinear optimization; random forest classification; K-means clustering; fetal abnormality detection.
Analysis of Factors Influencing Fetal Y Chromosome Concentration Based on a Linear Mixed-Effects Model
The accuracy of Non-Invasive Prenatal Testing (NIPT) relies heavily on sufficient fetal Y chromosome concentration in maternal blood. Identifying key influencing factors is critical for optimizing testing timing and improving diagnostic reliability. This study employed multiple linear and polynomial regression models to explore relationships between Y concentration, gestational age, and maternal BMI. To address repeated measurements and significant individual variations, a linear mixed-effects model was introduced, incorporating fixed effects for population-level trends and random effects for individual-specific variations. Gestational age demonstrated a significant positive effect on Y chromosome concentration (p < 0.001), while BMI showed a weak negative correlation (p < 0.05). However, all models exhibited low goodness-of-fit (R²≈0.04–0.07), indicating substantial unexplained individual variability. Discussion and Innovation: The study highlights the necessity of personalized approaches in NIPT timing, as population-level factors alone are insufficient for accurate prediction. The application of a mixed-effects model provides a robust framework for handling hierarchical data and isolating individual differences, offering a methodological foundation for future personalized prenatal testing strategies.
Linear Mixed-Effects Model; Y Chromosome Concentration; Gestational Age.
Performance characterization and structural optimization of a fluid diode
As a passive unidirectional flow control device, Tesla valves demonstrate broad application prospects in microfluidics, thermal photovoltaics, and biomedical equipment due to their advantages of no moving parts, easy processing, and scalability. However, current research faces two major bottlenecks: first, the lack of unified performance evaluation metrics, where parameters such as flow rate ratio and pressure drop ratio are often used interchangeably, making results difficult to compare horizontally; second, structural optimization relying on empirical trial-and-error methods, where complex coupling of flow channel parameters results in low optimization efficiency, hindering deeper engineering applications. To address these issues, this paper systematically conducts research on the performance characterization and structural optimization of Tesla valves, aiming to establish a standardized evaluation system and provide efficient optimization methods. This study first established single-section and 1-8-section series models based on ANSYS Fluent, using the rectification ratio (forward/reverse flow velocity/pressure drop ratio) as the core evaluation metric. It was found that the rectification ratio of single-section valves ranges from 1.34 to 1.40 under 1000-3000Pa pressure differentials, while the 8-section series configuration achieves 1.5 times the rectification ratio of single-section valves, with improvement slowing after 4 sections. Building on these simulations, this paper innovatively introduces circuit analogy theory, mapping fluid pressure differentials, flow rates, and flow resistance to circuit voltage, current, and resistance respectively. This establishes a Tesla valve "flow-pressure" voltage-current characteristic model.
Thermal Valve, Computational Fluid Simulation, Circuit Analog, Structural Optimization.
Physical Model Construction and Robust Parameter Inversion for Infrared Interferometric Spectroscopy Measurement of Silicon Carbide Epitaxial Layer Thickness
This paper establishes a physical model and designs a robust inversion algorithm for the precise measurement of silicon carbide epitaxial layer thickness. First, under the dual-beam interference assumption, a physical dual-beam interference model for silicon carbide epitaxial layer thickness was developed. This model, based on optical path difference and phase analysis, dynamically calculates refractive index using the Sellmeier dispersion model. It quantifies beam amplitude differences through Fresnel reflection coefficients, constructing a complete total internal reflection model that satisfies energy conservation. Subsequently, a dispersion-coupled nonlinear inversion and reliability assessment scheme for epitaxial layer thickness was designed for measured spectral data. This scheme employs data preprocessing to construct a nonlinear least-squares model targeting minimization of residuals between measured and modeled reflectance. The TRF algorithm and robust loss function are then jointly optimized to determine thickness and Sellmeier parameters. Ultimately, the optimal thickness estimate was obtained. The reliability of the results was comprehensively evaluated using Bootstrap resampling, error analysis, and multi-angle consistency verification. This research provides a highly accurate and reliable measurement framework, which is crucial for quality control and process optimization in the manufacturing of high-performance silicon carbide semiconductor devices.
Infrared Interferometry; Sellmeier Dispersion Model; Nonlinear Least-Squares Optimization.
Design and Implementation of a Motion Control System for an Omnidirectional Mobile Platform Based on a Microcontroller Microprocessor
This paper details the design and implementation of an omnidirectional mobile platform motion control system built upon a microcontroller-based microprocessor. The mechanical framework employs low-hardness, lightweight aluminum extrusions as base materials, featuring a pentagonal chassis structure integrated with omnidirectional wheels. This configuration ensures agile movement while enabling on-the-spot rotation and high-speed cornering. The execution system innovatively incorporates an electric screw-driven parallel mechanical gripper for severing fruit stems and grasping objects. Hardware design centers on the selection of the main control board (ROBOMASTER Development Board Type C, utilizing a high-performance STM32 chip) and motor selection (M3508 geared motors for the chassis, 57 stepper motors for the robotic arm). On the software side, the system employs semi-automatic control to adapt to complex environments. Key implementations include DBUS protocol parsing for receiving remote controller commands and CAN communication protocol for efficient control of multiple M3508 motors. Additionally, the software design incorporates PWM signal control and PID closed-loop speed control system design with parameter tuning. Through the seamless integration of mechanical, hardware, and software components, this solution successfully establishes a complete control system with high real-time performance and stability.
Omnidirectional mobile platform; Microcontroller control; CAN bus.
Optimization of Structural Parameters for Robotic Arm to Enhance Load-Bearing Performance Based on MOPSO Algorithm
The enhancement of mechanical arm load-bearing performance under constrained operational conditions represents a persisting conundrum in mechanical engineering, given the profound influence of structural parameters on stress distribution and displacement profiles. In response to this requirement, this investigation introduces a comprehensive optimization paradigm. Initially, computational simulations are executed via ANSYS Workbench, wherein cross-sectional geometry, area proportion, and support configuration are designated as control variables, while mean stress intensity and displacement magnitude serve as pivotal response metrics. Leveraging regression analysis techniques, empirical models are constructed to elucidate the quantitative correlations between structural attributes and mechanical performance. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm is employed to systematically search the solution space, with the objective of minimizing stress concentration and displacement variance. The proposed methodology yields a set of optimal structural parameters and a validated predictive model, facilitating expedient assessment of load-bearing capacity and informed design decisions. The study identified a balanced optimized structure: a rectangular cross-section combined with a medium-high area ratio (0.95-1.0) and a middle support position (1.5-1.8 meters), and clarified the risk control areas for different cross-sectional shapes. This optimization framework not only significantly augments the structural efficiency of mechanical arms but also offers actionable insights for the rational design of robotic manipulators.
Robotic Arm; Structural Parameter Optimization; MOPSO Algorithm; Load-Bearing Performance.
Analysis of Strategies for Improving the Performance of SiC Semiconductor Devices
Due to its wide bandgap, high thermal conductivity, and high-temperature, high-frequency, and high-power characteristics, silicon carbide (SiC) has been widely applied, especially in the field of electric vehicles, power conversion and photovoltaic inverters. However, there are still some defects in the utilization of SiC-based semiconductor devices, such as lattice mismatch, material defects, and interface defects. Enhancing the stability and reliability of SiC devices themselves is an urgent issue to be solved in order to improve their performance. This study proposes four strategies to enhance the performance of SiC semiconductor devices, with a focus on improving material quality, optimizing device structure, and addressing thermal management challenges. These strategies have made significant progress in the reliability and efficiency of silicon carbide devices, especially for high-power, high-frequency applications such as electric vehicles and photovoltaic systems. Moreover, a liquid-cooled heat sink design based on topology optimization is adopted to improve the heat dissipation efficiency. Benefiting from its wide bandgap (3.26eV), high thermal conductivity (~4.9 W·cm-¹·K-¹), and strong breakdown field (~3MV/cm), SiC is inherently suited for high-temperature, high-frequency, and high-power operation. The strategies proposed in this study further leverage these advantages to enhance device performance and reliability.
PVT; MOS Structure; Inverted Trigger Electrode; Photoconductive Semiconductor Switch; SiC.
A Survey on Deep Learning Methods for UAV Frequency-Hopping Signal Detection
To address the security threats posed by unauthorized UAV operations, commonly known as "black flights," traditional frequency-hopping signal detection methods often exhibit low accuracy and poor adaptability in environments characterized by low signal-to-noise ratios and complex electromagnetic conditions. This paper provides a systematic review of deep learning-based methods for detecting UAV frequency-hopping signals, followed by a detailed comparative analysis of three representative approaches. First, we introduce a detection method that synergizes compressed sensing with multi-level deep learning, which achieves the highest reported detection accuracy (≥99.3%) in controlled settings. Next, we describe an improved YOLOX-tiny model that incorporates an attention mechanism and a lightweight architecture, achieving a real-time processing rate of 57 FPS in mobile scenarios with a 40.6% reduction in parameters. Finally, a two-stage anti-interference framework named YOLOv3-CNN, which is designed for complex electromagnetic environments and demonstrates remarkable robustness (≥96% accuracy) under non-line-of-sight and strong interference, is analyzed. The results confirm that deep learning effectively breaks through the limitations of traditional methods in complex settings. Multi-scenario generalization, low-cost hardware adaptation, and integrated multi-task system development emerge as three key directions for future research to pave the way for the technology's transition from lab to field.
UAV Frequency-Hopping Signal; Deep Learning; Signal Detection; Anti-Interference; Real-Time Processing.
Preface: 7th International Conference on Electronic Science and Automation Control (ESAC 2026)
The 2026 7th International Conference on Electronic Science and Automation Control (ESAC 2026) was held during February 27-28, 2026 in Bangkok, Thailand. The objective of this conference was to provide a common platform to engineers, practitioners, scientists and researchers across the globe for exchanging their knowledge and views to deal with global challenges. The theme of this conference was not only the fundamental technology in the fields of electronic science, information technology, signal processing, communication technology, mechanical automation, and control technology, but also can change our society by exploring new horizons and continuous progress. This scientific event brings together more than 30 international researchers. The conference model was divided into three sessions, including keynote sessions, presentations and Q&A discussion. All accepted papers were strictly selected based on originality, significance, relevance, and contribution to the area after being peer-reviewed. We would like to acknowledge all of those who supported ESAC 2026. The help and contribution of each individual and institution was instrumental in the success of the conference. The ESAC Scientific Committee Bangkok, Thailand