Highlights in Science, Engineering and Technology
Highlights in Science, Engineering and Technology is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of science, engineering and technology.
Research on Employee Scheduling in Sorting Centers Based on NSGA-III Algorithm
This article studies the attendance situation of employees and temporary workers in the sorting center in the future, and ensures a balance between attendance rate and actual hourly efficiency. Establish a multi-objective linear programming model and list multiple constraints, such as personnel non negative integer constraints, task quantity compliance constraints, formal worker quantity constraints, attendance rate constraints, continuous attendance days constraints, and maximum work efficiency constraints. In order to obtain the optimal solution, the NSGA-III algorithm was used for optimization and solving. The paper elaborates on the establishment process of a multi-objective linear programming model, clarifies decision variables and objective functions, and introduces objectives such as minimizing the total number of people, balancing actual worker performance, and balancing attendance rates. In addition, the paper introduces the basic principles of the NSGA-III algorithm and the model solving process based on this algorithm, demonstrating some of the prediction results. This study has important guiding significance for managers to optimize work arrangements and improve work efficiency.
NSGA-III Algorithm, Multi-objective Planning, Employee Scheduling.
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Exploring the Application of Brain-Computer Interface Technology in Stroke Rehabilitation
As a serious neurological disease, stroke can cause irreparable impact on patients' daily lives, and also cause serious damage to patients' language functions, motor functions and other neurological functions. As science and technology have advanced, so too has the possibility for using brain-computer interface technology in the field of rehabilitation. However, BCI technology still faces many challenges in clinical applications, such as low accuracy of signal acquisition, complex signal processing process, and poor portability of BCI devices. The use of BCI technology in stroke rehabilitation is supported theoretically and technically in this paper, along with suggestions for future research. It also talks about how BCI technology is currently being used in stroke rehabilitation and goes into great detail about the basic ideas, classifications, and research advances in the field of rehabilitation using BCI technology. Future research can further optimize signal processing algorithms, improve the accuracy and stability of BCI system signal acquisition, explore safer and more portable equipment designs, strengthen multidisciplinary cooperation, and promote the clinical transformation of BCI technology, which will bring new breakthroughs in the field of stroke rehabilitation.
Research on Olympic Medal Prediction and Influencing Factors Based on Multi-Dimensional Feature Fusion and Machine Learning
To address the limitations of existing Olympic medal prediction models—which over-rely on macroeconomic indicators and lack interpretability—this study proposes a hybrid machine learning framework integrating multi-dimensional feature engineering and model interpretability. This article construct a 19-feature system incorporating historical medals, athlete gender ratios, elite coach attributes (simulated via Monte Carlo), and host-nation effects. Three synergistic models are developed: (1) A multiple linear regression predicting 2028 medal distributions (RMSE=0.25); (2) A LASSO-Logistic regression identifying first-time medal-winning nations (e.g., UAE and Samoa, probability >0.5); (3) A Gradient Boosting Tree with SHAP interpretability quantifying elite coaches’ contribution (SHAP mean=0.217, *p*<0.001). Hyperparameter optimization via RandomizedSearchCV achieves high accuracy (gold medal MAE=0.09, total medals MAE=0.25). Key innovations include dynamic feature fusion and micro-level coach impact quantification, providing actionable insights for Olympic resource allocation.
Multiple Linear Regression Model, Gradient Boosting Model, SHAP Analysis, LASSO-Logistic Regression, Olympic Medal Prediction.
Functional Multicore Gaussian Process Regression Modeling Based on Kalman Filtering
Aiming at the regression problem in which the response variable is scalar and the predictor variable is functional-type variable, this paper innovatively proposes a functional-type multicore Gaussian process regression model based on Kalman filtering. Firstly, a functional principal component base expansion method is applied to extract features of functional predictor variables to realize the approximate characterization of functional data; then, the potential function between each principal component score and the response variable is assigned to the Gaussian process a priori and multiple Gaussian process regression submodels are constructed using different kernel functions for fitting; finally, the Kalman filtering algorithm is utilized to dynamically integrate the results of the submodels to obtain the final Prediction results. Compared with the traditional integration learning algorithm, this model integrates the uncertainty estimation into the integration framework, fully considers the adaptability of different regenerative kernel Hilbert space from the probability level, and significantly improves the robustness and generalization ability of the model. Empirical studies on meat and maize near-infrared spectral analysis datasets show that the model in this paper exhibits superior prediction performance in both MSE and MAE metrics compared to benchmark methods such as traditional machine learning models: XGBoost, single-kernel Gaussian process regression (GPR): using RBF kernel function. In addition, the construction of differentiated sub-models by adjusting the number of principal component truncations in functional principal component analysis provides a new path for model performance optimization.
Functional regression, Gaussian process, functional principal component analysis, Kalman filter algorithm.
Research on Intelligent Garbage Classification Device Based on the Fusion Technology of Differential Classification and Artificial Intelligence Recognition
To solve the problem of the "last mile" in garbage classification, this study designs an intelligent garbage classification device based on artificial intelligence, machine vision and automation technologies. By constructing a multi-modal garbage identification model (YOLOv5s) and integrating it with a differential sorting mechanism, the functions of garbage type identification, sorting and compression are achieved. The experimental results show that the device's recognition accuracy rate is ≥98%, the single-piece processing cycle is ≤300ms, the compression rate of recyclable waste is ≥70%, the operating cost is reduced by 60%, and the sorting rate of hazardous waste is increased to 99%. This device overcomes the bottlenecks of traditional classification efficiency and accuracy, providing core infrastructure support for the construction of "waste-free cities" and contributing to the achievement of the "dual carbon" goals.
Intelligent garbage classification, differential sorting technology, AI visual recognition, YOLOv5s model, automated device.
Optimization of Audio Coding Parameters and Adaptive Denoising Using a Convolutionally Enhanced Transformer Framework
With the rapid advancement of digital audio technology, this study proposes an intelligent audio processing framework to address two key challenges: storage optimization and adaptive denoising. By modeling the trade-off between sampling rate, bit depth, and compression algorithm, the system recommends optimal encoding parameters for speech and music to balance file size and audio quality. For denoising, an adaptive algorithm based on time-frequency analysis is introduced, which applies targeted strategies according to identified noise types—Wiener filtering for background noise, median filtering with spectral subtraction for burst noise, and band-stop filtering with spectral smoothing for narrowband interference. Experiments on public datasets using ΔSNR, PESQ, and STOI metrics show that the method improves both noise suppression and audio fidelity, with SNR gains of up to 5.11dB. Subjective listening confirms enhanced clarity, and robustness tests reveal stable performance under moderate noise. Overall, the framework outperforms traditional fixed-parameter methods in both efficiency and quality.
Audio Processing, Storage Optimization, Adaptive Coding, Noise Removal, Time-Frequency Analysis.
Research on Dynamic Path Planning and Real-Time Collision Detection for Bench Dragon Based on Multi-Algorithm Collaborative Optimization
The dynamic and complex nature of traditional dragon dance performances, especially the multi-segmented "Bench Dragon" presents considerable challenges in real-time collision detection and path optimization. This study introduces an integrated optimization framework that combines an enhanced golden section search algorithm, dynamic collision detection based on the Separation Axis Theorem (SAT), and a genetic algorithm to tackle these challenges. The enhanced golden section algorithm effectively reduces computational complexity by efficiently narrowing the search space, while SAT ensures collision-free motion through real-time projection analysis. Additionally, genetic algorithms optimize the turnaround path by minimizing the total length under geometric and continuity constraints. Experimental results demonstrate the framework's effectiveness: an optimal pitch of 0.4529 m facilitates smooth coiling, with the total turnaround path length reduced to 13.6206 m without any collisions. The algorithm maintains high accuracy while ensuring computational efficiency, as validated through simulations of the dragon's movement across critical time intervals (0–300 s). This research offers a robust solution for coordinating large-scale rigid-chain movements, with potential applications in swarm robotics and automated logistics. However, limitations include idealized assumptions of rigidity and 2D motion, underscoring the need for future research into real-time dynamic modeling and multi-physics constraints.
Bench Dragon, Collision Detection, Simulated Annealing Algorithm, Genetic Algorithm.
Research on Olympic Medal Table Prediction Based on Graph Convolutional Neural Network
As a global top sports event, Olympic medal table prediction is of great significance to the development of national sports strategies. In this study, we break through the limitations of traditional regression analysis by integrating historical medal data, host country effects, athletes' characteristics and other sources of data, and innovatively constructing a hybrid feature system that includes time series features and dynamic graph structure. Through the graph convolutional neural network modeling the time evolution characteristics of inter-country competition, combined with Bootstrap resampling technology to construct a probabilistic prediction model, to achieve the dynamic prediction of the medal list of the 2028 Los Angeles Olympic Games, and concluded that the United States with a probability of 85.7% to become the most progressive country, and Russia with a probability of 70.5% to become the most regressive country. This study establishes a multi-dimensional spatio-temporal correlation feature system and develops a hybrid GCN-Bootstrap architecture to realize the probabilistic inference of the Olympic medal distribution for the first time, and reveals the deep influence mechanism of geopolitical factors on competitive sports.
2028 Olympic Games, Medal Prediction, GCN, Time Series.
Research on Multi-objective Planning Optimization Algorithm Based on Cluster Analysis
AI has introduced new solutions to multi-objective decision-making in agricultural resource optimization. Still, the current methods struggle with handling complicated constraints and heterogeneous resource types effectively. This study proposes an integrated approach combining linear programming (LP) with K-means++ clustering to enhance the adaptability in diverse agricultural contexts. Specifically, the linear programming (LP) model firstly optimizes resource allocation according to real-world constraints, while K-means++ clustering groups crops with similar characteristics, selecting representative crops from each cluster, and allows simplification of crop structure and enhancing resilience. Compared to traditional K-means, K-means++ improves the stability of the initialization, ensuring more reliable crop clustering results, and largely avoids local minima. This approach has been applied to a case study in a village of Northern China, demonstrating its effectiveness. Using the LP model alone increases annual revenue from 5.66 to 6.58 million yuan annually, while combining with K-means++, revenue increases further to 7.89 million yuan, exceeding 2.23 million yuan compared to the original. This research also integrates risk modeling, simulating disruptions from the environment and market, demonstrating strong robustness in maintaining revenue stability in different scenarios. This hybrid model not only improves profitability but also provides a scalable and robust strategy for sustainable agricultural planning.
Linear programming, K-means++, Multi-objective optimization, Robustness.
Research on the Impact of AIGC on Traditional Media Audiences and Symbiotic Paths Based on Logistic Regression Model
Under the background of the rapid development of artificial intelligence technology and the unstoppable wave of digitalization, AIGC has impacted the mode and core position of "gathering, editing and broadcasting" of traditional media. In order to explore the future development direction of the media industry, this paper quantifies the impact of AIGC on audience acquisition and retention of traditional media by constructing a logistic regression model, and analyzes the differences between them in content production and broadcast output by using radar chart and comparison chart. This paper analyzes the relationship between AIGC and traditional media in a comprehensive manner, taking into account their complementary advantages, diversity of audience needs, policy support and industry trends. The research shows that AIGC has impacted traditional media in terms of audience, editing content production and broadcast output, and traditional media is faced with technical shortcomings and talent shortage. However, the two have complementary advantages. With the support of policies, integration is an inevitable trend in the future development. Through symbiotic development paths such as collaboration in content creation, sharing of communication channels and cooperation in talent training, they can achieve complementary advantages, meet the diverse needs of audiences, and promote social and economic development and cultural prosperity.
AIGC, Traditional media, Symbiotic development, Impact, Fusion path.
Estimating the Demand for Spotify Premium Plans: Evidence from Pricing and Plan Characteristics in India
This paper investigates consumer demand for Spotify’s premium subscription plans in India, focusing on how price and plan features influence user choice. Using a discrete choice framework and individual-level survey data, this paper estimates a conditional logit model to quantify the roles of price and account-sharing attributes (e.g., Duo and Family plans) in shaping subscription decisions. To address potential price endogeneity—where firms set prices in response to unobserved demand—this study employs an instrumental variable approach using Spotify’s premium plan prices in other geographic markets (U.S. and Brazil). The results reveal substantial price sensitivity: after instrumenting, the estimated effect of price becomes more negative, suggesting that uncorrected models understate true demand elasticity. While the estimated impact of features such as the ability to share plans with one or more additional users is economically meaningful, statistical significance is limited—likely due to the constrained variation in available plan choices. The study contributes to the growing literature on digital subscription platforms and provides a flexible empirical framework for future research in emerging markets and multi-tier pricing environments.
Demand Model, Subscription, Conditional Logit Model, Instrument.
Optimization and evaluation of traffic based on network models
This paper constructs a network model optimization function and corresponding indicators, and based on network model analysis, proposes targeted optimization recommendations. The aim is to meet the transportation needs of different stakeholders and analyze the impact of bridge collapses on different stakeholders. A network model optimization function and corresponding indicators are constructed, and the impact of bridge collapses on different stakeholders is analyzed. The study employs the Voronoi polygon method to delineate transportation analysis zones, combines ArcGIS tools for spatial analysis and visualization, and quantifies transportation accessibility metrics (such as the average area of transportation analysis zones, the number of transportation analysis zones per square kilometer, and accessibility indices) to provide scientific basis for identifying weak links in the transportation network. A topology-based traffic flow network model further analyzed the connectivity between nodes and edges, and combined with traffic flow data (such as AADT, AAWDT) to optimize network weights, providing precise support for traffic planning.
Optimization of the transport system, Optimization Model, Network Structure, Bridge collapse, ArcGIS.
Prediction on Olympic Medal Based on Random Forest and Logistic Regression
The Olympic Games, as a highly renowned global sports event, attract significant attention, and the Olympic medal table is a focal point of public interest. The medal table not only showcases athletes' exceptional competitive abilities but also reflects a nation's overall strength. In this paper, a Random Forest model was developed to predict the medal rankings for the 2028 Olympic Games. The model innovatively considers not only economic factors but also athlete performance, the number of events participated in, and host nation advantages. By integrating these comprehensive factors, it forecasts that the United States, China, and the United Kingdom will secure the top three positions. Additionally, the study focuses on countries that have never won Olympic medals—groups that receive little attention—using Logistic Regression to predict that nations like Tuvalu have a 3.530% probability of gaining their first Olympic medals in 2028. This study not only provides forecasts for the Olympic medal table but also offers a scientific basis for countries to develop sports strategies and optimize resource allocation, which is significant for enhancing international sports.
Olympic Medal, Random Forest, Logistic Regression, Prediction Model.
Olympic Medal Prediction Based on Random Forest Modeling
The Olympic Games are the most influential sporting event in the world, conveying the idea of higher, faster, and stronger. It promotes the pursuit of excellence in sportsmanship and the dedication to sportsmanship. At the same time, the results of the Olympic Games also reflect the level of sports of each country, so the number of medals is the focus of attention, and the factors that affect the number of medals predicted are very complex, usually based on the historical data of each country, the strength of the players, etc. This paper proposes a random forest prediction model based on multiple linear regression and regression to predict the interval of each country's medal count at the Los Angeles 2028 Olympic Games, as well as the ranking of each country. At the same time, this study provides reference opinions for the prediction of the medal table of the next Olympic Games.
Random Forest, Interval Estimation, Hypothesis Testing, Factor Analysis.
Research on the Optimization Model and Algorithm for the Movement Path of the "Bench Dragon"
To enhance the organizational efficiency and cultural presentation effectiveness of the folk activity "Bench Dragon", this paper focuses on the dynamic optimization problem of the Bench Dragon's "panlong" (coiling) movement. A chain path optimization modeling method based on plane geometric recursion and kinematic constraints is proposed: First, an Archimedean spiral model is constructed to characterize the movement trajectory of the dragon head. The spatiotemporal coordinates and velocity distribution of each dragon body segment are systematically derived using differential-integral methods. For collision constraints during movement, a safety distance verification model in polar coordinate space is established. Combined with the bisection method and iterative algorithms, the critical coiling time is accurately solved. Under the constraint of turn space, the minimum safe pitch is determined through parameter optimization. The quantitative model system constructed in this study provides a scientific dynamic analysis framework and optimization strategy support for improving the coordination and spectacle of the dragon dance performance.
Path Optimization, Bisection Method, Iterative Method, Arc Differential, Polar Coordinates.
Olympic Medal Prediction Analysis Based on The Random Forest Model
During the 2024 Paris Olympics, both individual events and the overall medal tally have garnered significant attention. As a key indicator of a nation's competitive strength, the number of Olympic medals serves as a critical reference for countries preparing for the Games. This paper addresses three core questions: how to predict the medal rankings for the 2028 Los Angeles Olympics, the likelihood of countries that have never won medals securing their first, and the impact of Olympic events on medal rankings. By combining the Random Forest algorithm and the LSTM model to predict feature variables (such as gender ratio and number of participants), a high-precision medal prediction model was constructed, achieving an R² score of 0.929 and an MSE of 1.78098. For countries that have never won a medal, nine countries were selected, and their probability of winning a medal in 2028 was predicted. Additionally, the Random Forest Regressor model was used to analyze the importance of sports events, identify key events for each country, and reveal the strategic impact of host country event selection. The conclusions indicate that this model provides a scientific basis for countries to optimize their Olympic training strategies and resource allocation.
Random Forest Model, LSTM, Olympic Medal Prediction.
Research On Stock Index Prediction Based on CGAF Hybrid Deep Learning Model
This article focuses on the prediction difficulties caused by the inherent high-frequency noise, strong nonlinearity, and complex dynamic characteristics of stock index time series, and proposes an innovative hybrid deep learning model - CGAF (CNN-GRU Attention FFN). The CGAF model integrates one-dimensional convolutional neural networks to extract multi-scale local features, gated recurrent units to capture temporal dependencies, multi head self attention mechanism to dynamically focus on key time step information, and feedforward network to deepen nonlinear feature transformation. Using the closing prices of the Shanghai Stock Exchange Composite Index from March 3, 2023 to March 31, 2025 as empirical data, the model was trained and evaluated for performance after linear interpolation, minimum maximum normalization, and sliding window processing. The research results indicate that the CGAF model is significantly superior to traditional time series models such as ARIMA, ARCH, GARCH, multiple benchmark deep learning architectures such as CNN, CNN-LSTM, CNN-LSTM Attention, CNN-GRU-MAHA, and models referenced in the literature in key evaluation indicators such as RMSE, MAE, and MAPE. The original scale RMSE, MAE, and MAPE of the CGAF model on the test set were 24.7868, 18.1145, and 0.54%, respectively, demonstrating excellent predictive performance and goodness of fit. This study not only provides an effective tool for financial market forecasting, but also verifies the progressiveness and potential of the proposed hybrid architecture in processing complex time series data, and the modular design also has good portability.
Stock Index Prediction, Deep Learning, Hybrid Models, CGAF, Attention Mechanism, Time Series Prediction.
Olympic Medal Count Prediction Model Based on Xgboost
Accurately predicting Olympic medal counts for each country is of significant value for shaping sports policy, optimizing resource allocation, and advancing the sports industry. This study presents a predictive model based on the Extreme Gradient Boosting (XGBoost) algorithm to estimate the number of gold, silver, and bronze medals awarded to each country in the Olympics. The validity of the model is demonstrated by predicting the medal counts for the 2024 Olympics. Relevant data from 1896 to 2024 were collected and preprocessed. Five key features were extracted for each Olympic edition: the host country, the number of gold, silver, and bronze medals, the total medal count, the number of athletes per country, the gender distribution of athletes, and the sports events in which each country participated. Data from 1896 to 2012 were used for training, while data from the 2016 and 2020 Olympics were used as the test set. The model was trained using the XGBoost algorithm, and optimization was performed by minimizing the root mean square error (RMSE). Four features for the 2024 Olympics—host country, number of participating athletes, gender distribution of athletes, and the sports events contested—were used to predict the medal counts for each country. By comparing the predicted results with the actual data, the RMSE values for gold, silver, bronze, and total medals were calculated to be 0.6462, 0.5547, 0.2965, and 0.3922, respectively. These results validate the exceptional performance of the XGBoost model in predicting Olympic medal counts, providing effective and forward-looking strategic insights for the optimization of sports resource allocation and the setting of competitive goals across countries.
Olympic Games, Olympic Medal Prediction, XGBoost, RMSE.
Monte Carlo Simulation-Based Production Decision Optimization Research in Uncertain Environments
Facing intensified competition in electronics manufacturing, this study develops an integrated decision-making framework to optimize inspection strategies and defect rate control. Leveraging binomial distribution, hypothesis testing, and multi-objective optimization, a hierarchical cost model is constructed for components, semi-finished products, and finished goods. Genetic algorithms identify an optimal solution—inspecting critical component 8 and semi-finished product 2 while eliminating redundant inspections and disassembly—reducing total costs by 18.7%. To address process variability, Monte Carlo dynamically assesses batch-specific defect rate fluctuations across six intervals, enabling adaptive re-optimization for Scenarios 2 and 3. Results show that tiered detection strategies (prioritizing key components 1/4 and enforcing full finished-product inspection) cut quality loss costs by 23.4% while preventing over-inspection waste. This work advances a data-driven theoretical foundation for intelligent quality-cost synergies in electronics production, offering actionable insights for sustainable manufacturing competitiveness.
Genetic Algorithm, Dynamic Programming, Monte Carlo Simulation.
Research On Olympic Medal Prediction Based on Random Forest-ARIMA Combined Model
The prediction of Olympic medals is complex due to the multi-dimensional influencing factors and the non-linear feature relationships. Therefore, a combined model capable of handling both non-linear feature relationships and capturing time series trends is required. Specifically, this study predicts the medal table for the 2028 Los Angeles Olympics based on a Random Forest-ARIMA combined model. After cleaning and integrating past data using non-negative least squares in a "property division" manner, the United States, China, and Japan are predicted to rank first, second, and third respectively, with a total of 92, 61, and 33 medals, and 37, 28, and 14 gold medals respectively. The United States, China, and Brazil are expected to continue their upward trends, while the United Kingdom will maintain a stable number of medals. Australia, Italy, and Japan have experienced greater medal fluctuations in recent Olympics and are expected to continue this trend. SAM is most likely to win its first medal, with a probability of approximately 72.3%, and three countries are expected to win their first medals.
Random Forest-ARIMA Hybrid Model, Medal Tally Prediction, Non-negative Least Squares Method,
Research On Optimal Allocation of Hu Sheep Breeding Based on Stochastic Difference Equations
This study addresses the dynamic resource allocation problem in large-scale Hu sheep farming by establishing a production cycle model based on stochastic difference equations. Through systematic analysis of seven production stages (mating period, pregnancy confirmation, etc.), we develop a population dynamic system incorporating multi-dimensional state variables. Three critical stochastic factors—conception success rate, gestation duration variance, and lambing number variation—are innovatively integrated using composite probability models for stochastic process characterization. An improved Genetic Algorithm (GA) is employed to optimize multiple parameters including ram-to-ewe ratio, lactation duration, and sheep pen allocation, constructing a nonlinear programming model with dynamic pen utilization cost functions and production constraints. Simulation experiments demonstrate that optimized key production parameters significantly reduce spatial utilization costs while maintaining annual yield targets, validating the model's effectiveness in balancing production efficiency and cost control. This research provides a decision-making framework for large-scale breeding operations through synergistic optimization of stochastic process modeling and intelligent algorithms, offering substantial practical guidance value.
Difference equations, Hu sheep breeding, Genetic Algorithm, Stochastic processes.
Semi-Parametric Functional Kriging Regression Model with L1 Penalty
Partial functional linear models are widely studied and applied models, where the response variable is related to both general random variables and functional random variables. However, with the increasing application of data scenarios involving functional and vector-valued covariates and scalar responses in modern science, this paper proposes a partial functional regression model based on Gaussian processes. On the one hand, the proposed method can flexibly fit the nonlinear connection relationship between the functional covariates and the scalar responses by assuming the existence of a Gaussian process prior between them. On the other hand, for vector-valued covariates, in this paper, while constructing the linear relationship between them and scalar responses, the LASSO regularization technique is used to achieve the purpose of variable selection. Furthermore, in this paper, functional principal component analysis is used as the regularization strategy to approximate the distances between random functions, thereby achieving the approximate calculation of the kernel function matrix. The simulation experiment analysis indicates that the proposed method has higher prediction accuracy compared with the benchmark model and can effectively identify irrelevant variables. The actual data analysis also confirmed the comprehensive performance of the proposed method.
Functional data regression, Variable selection, Gaussian process, L1 regularization.
Prediction Of Medals Based on Machine Learning and OLS Statistical Regression Models
This paper constructs a predictive model that integrates variables such as the number of athletes and the growth rate of medals. It employs regression and classification techniques from the field of machine learning, utilizing algorithms including Random Forests, Support Vector Machines, and Logistic Regression and the performance of random forest in the last three Olympic Games was compared to determine the optimal performance of random forest in the classification task. Additionally, it applies Bayesian Change Point Detection and Ordinary Least Squares statistical regression models to analyze the performance trajectories of the Chinese women’s volleyball team, the United States women's volleyball team, and the Romanian gymnastics team. Based on the data predicted by the model, the study proposes to increase investment in sports science research, attract talent, strengthen logistical support, and develop special policies. At the same time, error analysis and sensitivity analysis were carried out in this study, and the limitations in the data and model and the sensitivity of the model to certain features were pointed out.
Olympic Games; Medal Prediction; Machine Learning Models; OLS Statistical Regression Model.
Research On Twin Support Vector Regression by Hiker Optimization Algorithm
This paper presents an enhanced twin support vector regression (TSVR) model integrated with the hiker optimization algorithm (HOA) and successive 0ver-relaxation (SOR) method to address challenges in regression accuracy and computational efficiency. HOA is employed to optimize hyperparameters, while SOR accelerates the solution of quadratic programming problems by reducing memory usage for sparse matrices. Experimental results on 8 synthetic test functions with diverse noise types and 18 benchmark datasets demonstrate that the proposed HOA-TSVR significantly outperforms traditional models. However, performance fluctuations on high-dimensional small-sample datasets highlight sensitivities to data distribution and feature redundancy. The study concludes that the hybrid approach enhances generalization and robustness, offering a promising framework for complex data modeling, with future research directions focusing on adaptive feature selection and multi-scenario applications.
Twin Support Vector Regression, Successive Over-Relaxation, Hyperparameter Optimization.
Research On Multi-Objective Optimization of Complex Systems Based on A Feedback-Driven Structure-Preserving Optimization Architecture and An Improved NSGA-II Algorithm
The complex systems typically exhibit multiple objectives, multiple constraints, and dynamic structural characteristics, which make their multi-objective optimization and structural adaptability problems highly challenging. To address these challenges, this paper proposes a Structure-Preserving Optimization Architecture (SPOA) to systematically describe the nonlinear interaction process between input stimuli, constraint boundaries, and response structures. In terms of algorithm implementation, SPOA employs an improved NSGA-II algorithm to approximate the Pareto optimal solution set in a non-convex and constrained high-dimensional decision space. Finally, simulation experiments are conducted using a tourism system as an example scenario, validating the effectiveness of the proposed model. The results show that the model demonstrates excellent performance in terms of convergence stability, parameter transferability, and robustness to structural perturbations, and exhibits broad applicability in complex systems.
Optimization, Multi-objective Optimization, NSGA-II, Feedback Mechanism, Complex Systems.
A Study on Cybercrime Policy Analysis Based On K-Means++ Clustering and TOPSIS Modeling
As modern technology advances, more and more of our world is interconnected through the Internet, but it has also increased our individual and collective risk of cybercrime. This paper uses modelling to find policies that are effective in curbing cybercrime. Firstly, this article utilized the K-Means++ clustering algorithm to classify countries into five categories based on the cybercrime rates, then this article uses Logistic regression to study the impact of prosecution and reporting on the cybercrime situation. Secondly, in order to explore the model that can identify the effectiveness of relevant policies, this article constructed a Combined Empowerment - TOPSIS model for assessing the effectiveness of the policies and finally get the most effective policies to solve cybercrime in different categories of countries. After that a Pearson correlation analysis of the different policies is performed to explore synergies between policies. Finally, in order to make theory complete and scientific, this article looked for four demographic features: internet coverage (%), wealth (GDP per capita), education level, and population happiness index, predicted the above four indicators as well as the cybercrime rate through the LSTM algorithm, and combined them with the historical data to perform a comprehensive Spearman correlation analysis, and then united the results of the analysis into theory.
K-Means++ Clustering, Logistic Regression, TOPSIS, LSTM, Correlation Analysis.
Cybercrime And Policy Insights Based on Time-Series Forecasting and Multiple Regression
With the rapid development of global information technology and the internet, cybercrime has become increasingly rampant, posing severe threats to the economy, society, and national security of various countries. This study aims to construct mathematical models to analyze the global distribution and influencing factors of cybercrime and evaluate the effectiveness of national cybersecurity policies. We utilized data visualization techniques to illustrate the frequency of cybercrime and its economic impact across countries. Additionally, a time-series forecasting model predicts a steady rise in cybercrime frequency over the next three years(2023-2025), underscoring the importance of continuously enhancing cybersecurity policies. Moreover, a decision tree model identified internet penetration and per capita GDP as the most crucial statistical variables in explaining variations in cybercrime rates.
Cybercrime, Decision tree, Time-series forecasting model, Neural Network.
Research On Complex Sequence Prediction and Interpretation Based on Adversarial Learning Over Precise Networks and Heuristic Optimization Optimization
In the field of high-dimensional dynamic system modeling, achieving high-precision prediction of target variables, interpretability of model structures, and quantification of output uncertainty has always been a key focus of intelligent system research. Traditional methods encounter bottlenecks in terms of feature heterogeneity, multi-dimensional interactions, and prediction credibility. This paper proposes a multi-objective sequence modeling framework that integrates generative adversarial learning, simulated annealing optimization, and a game-theoretic interpretation mechanism. This framework first cleans, standardizes, and imputes missing values in the original structured data, and then reconstructs the input tensor using a BP neural network. Subsequently, a prediction model based on GAN is constructed, and SA is used for global optimization of key hyperparameters. During the prediction phase, a Monte Carlo perturbation strategy is introduced to quantify uncertainty. Finally, the importance of features is analyzed based on the SHAP method to achieve causal transparency. This method outperforms mainstream baseline models such as LSTM and Transformer in multiple metrics, demonstrating good generalization ability and interpretability. It is suitable for prediction and auxiliary decision-making in complex engineering systems in fields such as finance and industry, and has broad application prospects and significant practical value.
Generative Adversarial Networks, Sequence Modeling, Simulated Annealing, Model Interpretability, Feature Contribution Degree.
Olympic Medal Count Prediction Research Based on A Hybrid ARIMA-Xgboost-Lightgbm Model
This study proposes an integrated ARIMA–XGBoost–LightGBM model to predict both medal counts and medal-winning probabilities for the 2028 Los Angeles Olympic Games. The framework begins by applying the ARIMA model to forecast time-series features, such as the number of athletes and participating countries. These features are then fed into an XGBoost regressor to estimate gold, silver, bronze, and total medals per country. Additionally, a LightGBM classifier is utilized to predict the probability that a nation will win at least one medal. Model performance was evaluated using ten-fold cross-validation, with R² values exceeding 0.84 across all medal categories, demonstrating high accuracy and robust generalization ability. Notably, six countries are projected to win their first Olympic medals based on the predicted probabilities. This ensemble approach effectively combines time series forecasting with machine learning algorithms, showcasing its potential in supporting strategic sports planning and medal outcome prediction. It provides valuable forecasting capabilities for complex, dynamic events such as the Olympics.
Olympic medal prediction, ARIMA, XGBoost, LightGBM.
Effects Of Lamprey Sex Ratio on Ecosystem Stability Based on Graph Neural Network and Deep Learning
The purpose of this study was to explore the impact of the dynamic adjustment of the sex ratio of lamprey on the stability of the ecosystem. The ecological effects of the sex ratio under different resource conditions were analyzed by constructing logistic model, genetic algorithm and deep learning prediction model combined with graph neural network. The study found that the dynamic adjustment of gender ratio can significantly improve the robustness of the ecological network, and its stabilizing effect can be reflected through the network centrality and the optimization of community structure. The results of data visualization show that the coupling effect of gender ratio change and resource availability can be visualized through dynamic thermal map and three-dimensional interactive map. The innovation of this study is to introduce graph neural network and deep learning technology into ecological research, which effectively makes up for the shortcomings of traditional models in dealing with the dynamics of complex ecological networks, and provides a new analytical framework for the ecological mechanism of gender ratio regulation. In addition, the research and development of prediction tools based on deep learning has high application feasibility, which can provide accurate decision support for ecosystem management and help the sustainable management and protection of ecosystems.
Gender ratio, Ecosystem stability, Graph neural network, Deep learning, Data visualization.