Mathematical Modeling and Algorithm Application
Mathematical Modeling and Algorithm Application (MMAA) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of mathematical modeling, algorithm application, theoretical and applied mathematics across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Research on Generative Design for Packaging of Shanxi Mature Vinegar Based on Artificial Intelligence
In the context of fast development of artificial intelligence technology, the design industry is currently actively involved in the process of AI integration and use. Nevertheless, systematic design approaches and studies focused on generative AI are comparatively limited in packaging design. This paper uses the case study of Shanxi Mature Vinegar a traditional Chinese product to discuss the new opportunities of applying generative AI in the cultural and creative sector through innovative application channels. The first suggestion made in the paper is an integrated AI-based methodology of packaging design. With the help of AI-assisted tools, data mining and analysis of consumer preferences, market trends, and regional cultural factors can be done to make intelligent initial researches of design needs. Then, according to the results of the research, style positioning and element extraction are carried out with the help of generative AI multi-round design generation and iterative optimization. Lastly, representative solutions are chosen and optimized by means of extensive assessment and contemplation on various dimensions such as cultural relevance, aesthetic expressiveness, and market adaptability. The importance of this study can be seen in two points, First, it offers a human-machine collaborative innovation model of packaging design of traditional products that have deep-rooted cultural implications, including Shanxi Mature Vinegar, which improves the efficiency of design and accuracy of cultural expression. Second, by actualizing design practice, it develops a transferable AI-assisted packaging design procedure and method. It provides theoretical basis and practical examples of how to further develop the profound implementation of AI technology in the cultural and creative field, helping traditional industries to realize dual goals of brand upgrading and cultural value diffusion in the digital age.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Application Analysis of Intelligent Construction Technology in Fully Cast-in-Situ Concrete Structure Residential Buildings
The construction industry's accelerating digital transformation has made Intelligent Construction Technology essential for improving efficiency and quality in cast-in-situ concrete residential projects. This paper examines BIM-based integrated approaches combined with IoT and AI across design, construction, and operations phases. Using a Wuhan residential project as case study, we investigate BIM applications in clash detection and formwork design; IoT sensor deployment for concrete curing and structural monitoring; and AI-driven equipment maintenance and energy optimization. The findings aim to accelerate adoption of Intelligent Construction in cast-in-situ concrete residential buildings.
Housing Prices in Fujian Province: A Linear Regression Analysis of Fuzhou
This study used multiple linear regression to investigate the key points affecting housing prices in Fuzhou, Fujian Province. According to data from 20 residential communities, 5 variables are examined: building area, building age, distance to the nearest metro station, distance to the central business district (CBD), and proximity to key schools. The results show that metro distance has the strongest effect: one kilometer closer to a metro station increases price by 4,856 RMB/m². School proximity adds 8,923 RMB/m², while each additional year of building age reduces price by 413 RMB/m². The model explains 71.2% of price variation (R² = 0.712), and all variables are statistically significant. These findings support land value capture for metro funding and education equity policies to reduce housing pressure. Limitations include a small sample size (20 communities) and cross-sectional data. Future research should expand the number of the samples and include more detailed values especially variables.
Prediction of the Household Consumption in China by Linear Regression
Household consumption plays a crucial role in economic growth and accounts for a large proportion of GDP. So, making accurate predictions of household consumption trends is highly important to predict macroeconomic activity. This paper estimates the impact of disposable income per head and the Gini coefficient on household consumption in China by a multiple linear regression equation. It is constructed based on annual data coming from the CEI website during the recent 20 years. The multiple linear regression equation is calculated by the Excel software to get the coefficients of each determinant. The paper also predicts the disposable income per head using Log-linear Trend Model, which is used to input into the multiple linear regression equation and get the final prediction of household consumption. Moreover, a VIF method is used to prove the effectiveness of the multiple linear regression equation. The results show that both variables have significant positive effects and the household consumption will have a slight increase in 2025.
Lightweight Design of New Energy Vehicle Wheels Using ANSYS Workbench
Against the backdrop of an intensifying global energy crisis and the ongoing advancement of the “dual carbon” goals, vehicle lightweighting has emerged as a key technological direction for improving driving range and energy efficiency, with wheel lightweighting playing a particularly crucial role in this effort.This paper focuses on the wheel hub of a specific vehicle model. Adopting a lightweight structural design approach, the authors used Creo to perform 3D modeling and structural optimization of the wheel hub, resulting in a lightweight design solution;and perform finite element simulations of the component under various operating conditions, including radial and bending loads, using ANSYS.The test results show that the hub we designed has a significantly reduced weight, and its strength, hardness and fatigue resistance can all meet the requirements of actual use.This study provides technical guidance and practical insights for the lightweight design and performance validation of new energy vehicle wheel hubs.
Polynomial Analysis of U.S. Real Wage using U.S. CPI and Weekly Earnings Data
The United States has been concerned with inflation ever since early 2019. Inflation is a rise in prices that reduces purchasing power. When the nominal wage growth fails to keep up with price growth, it weakens the real purchasing power and puts pressure on the living standards of households. This paper utilized official data from the U.S. Bureau of Labor Statistics, including monthly CPI-U figures and quarterly median weekly income statistics. Two quantitative techniques were used: an exact sequence model for measuring cumulative inflation and purchasing power loss, and a polynomial least squares fit to measure the shape of price and wage movements over time. The results indicate that the cumulative CPI-U inflation is about 26.1% from December 2019 through December 2025, increasing to about 27.2% in February 2026. During that same time, nominal weekly earnings increased meaningfully, while real weekly earnings only increased marginally and fell during the inflation spike.
On the Arithmetic of x3+y3=nz3 : Complex Multiplication and Asymptotic Distributions
This expository article reviews the arithmetic of the Fermat elliptic curve <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1846063905"> </o:OLEObject> </xml><![endif]-->, which admits complex multiplication by the Eisenstein integers <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1846063906"> </o:OLEObject> </xml><![endif]-->. We outline the computation of Frobenius traces over finite fields via cubic residue characters and Jacobi sums, explicitly linking local Diophantine data to the associated Grössencharakter over <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1027" DrawAspect="Content" ObjectID="_1846063907"> </o:OLEObject> </xml><![endif]-->. Following Deuring's theorem, we detail the identification of the Hasse-Weil L-function with a Hecke L-function and review the standard formulation of the Birch and Swinnerton-Dyer conjecture. Finally, we present a linear-time lattice algorithm for computing the sequence of local traces and derive an asymptotic expansion for the mean number of affine solutions.
Bootstrap-Based Confidence Intervals: Methods, Applications, and Challenges
Bootstrap methods offer a flexible and generally applicable framework for statistical inference, especially when classical parametric assumptions such as normality and large-sample approximations are violated. This paper offers a systematic review of confidence intervals constructed via bootstrap methods, including their conceptual foundations, methodological variants, and applications. After presenting an overview of the motivation for bootstrap confidence intervals and their advantages over classical analytical methods, three major methods are reviewed and discussed: the percentile method, the bias-corrected and accelerated method, and the bootstrap-t method. These methods are compared and evaluated according to their bias correction, complexity, and accuracy. Some applications of these methods, such as group comparisons, correlation coefficient estimation, and mediation effects, are reviewed and discussed to illustrate their applicability and effectiveness under non-normal and complex data conditions. Finally, some methodological challenges and limitations, such as small sample size, method selection, and computational issues, are reviewed and discussed. By synthesizing recent research and applied studies on confidence intervals via bootstrap methods, this review article attempts to highlight the applicability and relevance of confidence intervals via bootstrap methods as a robust and flexible statistical tool for modern statistical practices.
A Proxy-Based, Entropy-Weighted Sustainability Scoring Framework for Mega Sporting Event Host Selection: Evidence from the 2029 Super Bowl and the 2029 Copa América
Sports mega-events provide economic and social benefits; however, they can also concentrate environmental impacts, including (but not limited to) electricity demand, greenhouse gas emissions, venue waste (e.g., catering), and water demand. We develop a proxy-based, environment-focused host-selection framework. Using the entropy weight method, we compute objective weights for four burden categories based on prior research on Super Bowl events and similar single-day championship events. We then score and rank candidate hosts using matched city-level sustainability proxies and a weighted-sum model. In a 2029 Super Bowl scenario, Stanford ranks first among 19 candidate cities under the stated boundary conditions. We also extend the same pipeline to a multi-match tournament setting (2029 Copa América) using an expanded proxy set, under which Canada ranks first among candidate countries.
The Impact of R&D Investment on the Innovation Capacity of China's Electrical Machinery Manufacturing Industry
China's machinery manufacturing industry is an important part of the country's manufacturing and processing industry. The improvement of technological research and development capabilities must be premised on the continuous increase of research and development funds and the number of R&D technical personnel, and then further influence the enhancement of innovation capabilities and core competitiveness. This study focuses on China's electrical machinery manufacturing industry, utilizing financial statement data and R&D investment data collected from 315 listed companies in the sector between 2019 and 2024 through the use of the Wind and CSMAR databases. Through the construction of a panel data model, from the perspective of R&D investment, analyze the main factors influencing the innovation capabilities of listed electrical machinery manufacturing enterprises in China. The fixed effects panel data analysis results indicate that explanatory variables, including R&D expenditure, firm size, and cash input capacity, exhibit a statistically significant and positively correlated relationship with enterprise innovation output. The correlation between the input of R&D personnel and the innovation ability of enterprises is not obvious, while the asset-liability ratio of enterprises shows a strong negative correlation with the innovation ability. According to the results of the empirical analysis, this paper further puts forward measures and suggestions for enhancing the innovation capacity of China's electrical machinery manufacturing industry.
Dynamic Hyper-Parameter Adjustment in Fedprox for Performance Optimization in Federated Learning
This paper investigates the application of dynamic hyper-parameter adjustment strategies to improve the performance of the FedProx algorithm in federated learning (FL). FedProx addresses the challenges posed by data heterogeneity across clients, a common issue in FL that hinders convergence. While FedProx improves upon the basic FedAvg algorithm by introducing a proximal term to stabilize model updates, the hyper-parameter μ that controls the impact of the proximal term is sensitive and needs careful tuning. An inappropriate μ can degrade performance, making dynamic adjustment crucial. The study introduces two dynamic μ adjustment strategies. The first strategy, called ExponentialDecay, progressively reduces μ exponentially by multiplying it with a constant factor. The second strategy, AdaptiveLoss, adjusts μ based on changes in the loss value, decreasing it when the loss decreases and increasing it when the loss increases, offering a more responsive approach to the model’s performance. The effectiveness of both strategies is evaluated on the EMNIST dataset, which simulates real-world non-IID data distributions. Results show that both dynamic strategies outperform the static μ setting in terms of model accuracy, loss reduction, and training speed, particularly in scenarios with highly heterogeneous data. Specifically, the AdaptiveLoss strategy achieves the best performance when the data is extremely non-IID, reducing loss by 10.2% and improving accuracy by 2.5% compared to the static μ approach. These dynamic strategies not only enhance FedProx’s stability but also accelerate the convergence of the global model.
Research and Analysis on the Mechanism of Suppressing Large Model Hallucination Based on Modular RAG Architecture
Large Language Models (LLMs) perform remarkably well in knowledge-intensive tasks, yet they are difficult to deploy in high-stakes scenarios due to 'hallucinations'—outputs that contradict factual information. Although Retrieval-Augmented Generation (RAG) is widely regarded as a mainstream approach to mitigate hallucinations, most existing studies treat it as a black box and rarely analyze the heterogeneous functions of its internal modules. To address this, we propose a 'functionally decomposed' modular RAG taxonomy, dividing the entire process into three stages: retrieval, refinement, and generation, from which three technical pathways are derived: Direct Injection RAG (DI-RAG), Relevance-Focused RAG (RF-RAG), and Fact-Checking RAG (FC-RAG). Utilizing the large-scale real-world Q&A dataset MS MARCO, we constructed four benchmarks simulating high-risk scenarios such as information noise, knowledge conflicts, and outdated knowledge. Using Qwen1.5-7B-Chat as the generative backbone, we systematically evaluate the marginal benefits of the three architectures in suppressing hallucinations and quantify the contributions of components like re-rankers and multi-query verifiers in specific hallucination scenarios, providing actionable empirical guidance and optimization pathways for building high-fidelity RAG systems.
Collaborative Model of Business Analysis and Data Analysis in the Era of Big Data-Taking Retail Industry as an Example
The retail industry is closely related to people's lives and is also a driving force for the development of the national economy. This article focuses on studying the dynamic changes of the retail industry in the commercial market under the premise of rapid economic and social productivity development. In the past, people usually used past experience to predict the future market, but there was no definite support, and there would often be large errors, as well as face some unavoidable crises. However, in the current era of data informatization, relatively accurate predictions of the future market can be made through a large amount of data, while avoiding losses caused by some crises. This study employed various analytical methods to analyze the past and current situation of the retail market. This article further reveals the three major problems existing in traditional retail analysis: the separation of data analysis and business needs; the lack of data support for experience-based decision-making; and severe departmental data barriers. Finally, implementation suggestions are proposed from three aspects: data governance, talent cultivation, and organizational optimization, providing a systematic solution for the retail industry to enhance its decision-making ability and market competitiveness.
Adaptive Confidence-Driven Sample Selection with Alignment Loss for Efficient Federated Active Learning
Federated Active Learning (FAL) aims to reduce labeling costs by selecting the most informative samples for annotation while preserving strong model performance. However, current FAL methods struggle with the challenge of client heterogeneity in non-IID environments, often leading to suboptimal sample selection and increased computational burden due to repeated inference on unlabeled data. To address these issues, we propose a novel approach called Adaptive Confidence-Driven Sample Selection (ACdSS), which combines the CHASe strategy with Alignment Loss to improve sample selection efficiency. ACdSS leverages dynamic confidence scores to assess uncertainty in unlabeled data and identifies samples with fluctuating predictions between training rounds, which are more likely to be near the decision boundary. This approach balances exploration and exploitation by adapting the sample selection strategy throughout the training process, minimizing inference costs while maintaining high selection effectiveness. Experimental results on benchmark datasets demonstrate that ACdSS outperforms existing FAL techniques, delivering superior accuracy with reduced annotation costs. This framework offers a practical solution for managing client heterogeneity in FAL and is well-suited for real-world federated learning systems, offering a significant reduction in computational overhead while improving model performance.
Referring Image Segmentation via Register-Aware Feature Selection and Adaptive Adaptation
Referring Image Segmentation (RIS) aims to segment target regions in an image at pixel level based on natural language descriptions. This paper proposes RAFS (Register-Aware Feature Selection and Adaptation), a parameter-efficient framework built upon DINOv3. Three lightweight adapter modules are designed: MLFS for adaptive multi-level feature aggregation via learnable Gaussian weighting, SCSA for efficient cross-modal fusion via depthwise separable convolutions and cross-modal attention, and RAFF for leveraging register tokens’ global context to enhance local features. With only 6.31M additional parameters, RAFS achieves competitive performance on RefCOCO, RefCOCO+, and G-Ref benchmarks.
Copyright (c) 2026 Xiaozhen Gao https://creativecommons.org/licenses/by-nc/4.0
Research on Urban Distribution Route Optimization Based on Time-Sliced OD Travel Time Matrices
Urban distribution operations are significantly affected by time-varying traffic conditions. If Euclidean distance or static shortest paths are still used as the travel cost between nodes, the actual distribution process under real road network conditions cannot be accurately represented. To address this issue, this study investigates a vehicle routing problem with soft time windows based on time-sliced travel times derived from a real road network. Travel time data between nodes in the core urban area of Zhengzhou are collected through an open map platform, and the service period is divided into multiple consecutive time intervals to construct time-sliced OD travel time matrices with directional and temporal heterogeneity. On this basis, a time-dependent vehicle routing optimization model is established with the objective of minimizing total distribution cost, in which time window deviations are incorporated into the objective function as penalty costs. Considering that local route adjustments may lead to subsequent changes in time states, an improved adaptive large neighborhood search algorithm is designed, together with a local time update mechanism to reduce repeated computations. A distribution network with 69 nodes in an urban area of Zhengzhou is used as the test instance, and the proposed method is compared with a genetic algorithm, ant colony optimization, simulated annealing, tabu search, and adaptive large neighborhood search. The results show that the proposed method can effectively reflect travel cost differences across time periods and achieves a relatively balanced performance in both total distribution cost control and computational efficiency.
Copyright (c) 2026 Zhiwei Tuo, Chengming Zhu https://creativecommons.org/licenses/by-nc/4.0
Research on Heterogeneous Transport Capacity Coordination Modeling and Comprehensive Evaluation for Large-Scale Earth-Moon Material Transfer Systems
Addressing the requirement to transport 100 million tons of materials for establishing a 100,000-person lunar settlement by 2050, this paper establishes a quantitative evaluation framework encompassing traditional rocket networks, pure space elevator systems, and hybrid transport solutions integrating both. The study first establishes a dynamic growth model based on technology learning curves, capturing the exponential enhancement of space elevator payload capacity over time. Simultaneously, for traditional rocket networks, a nonlinear decreasing model for per-launch costs with increasing reuse cycles is derived, incorporating reusability technology evolution trends. To achieve optimal allocation of transport resources during long-term construction phases, this study innovatively introduces a sigmoid function as a dynamic allocation weighting mechanism. This enables the system to fully leverage mature rocket technology during the initial construction phase while smoothly transitioning to the high-potential space elevator solution in the later stages. Quantitative evaluation using the Analytic Hierarchy Process (AHP) across two core dimensions—transportation time and comprehensive cost—reveals that the hybrid approach achieves a significantly superior composite score of 0.932 compared to pure rocket and pure elevator solutions. This study not only demonstrates the hybrid transport model's superiority in shortening project cycles and controlling long-term expenditures but also provides a scientific decision-making benchmark for strategic planning of large-scale deep-space logistics systems.
Copyright (c) 2026 Jinyu Zhang, Yifan Zhao https://creativecommons.org/licenses/by-nc/4.0
Synergistic Optimization of Competitive Performance and Commercial Profitability in Professional Women's Basketball
Addressing the operational challenge faced by professional women's basketball clubs in balancing competitive performance and commercial profitability under salary cap constraints, this study constructs an integrated multi-model dynamic decision-making system. Using the Connecticut Sun as a case study, the research first employs the Entropy Weighted Topix method to establish a player evaluation system encompassing competitive contribution, commercial value, and dynamic risk factors, enabling precise stratification of player value. Subsequently, a BP neural network is utilized for nonlinear forecasting of ticket, sponsorship, and merchandise revenues, combined with a Markov model to simulate the cross-seasonal evolution logic of team status. The core process employs multi-objective optimization using the NSGA-II algorithm to identify Pareto-optimal operational solutions, ensuring synergistic enhancement of competitive strength and economic returns while maintaining a 90.01% salary cap utilization rate. Addressing external uncertainties, the study quantifies potential financial risks from key player injuries and macroeconomic fluctuations via Monte Carlo simulations, while graph theory analyzes the win rate enhancement effects of team collaboration networks. Empirical results indicate that retaining the current 12 players yields an annual net profit of approximately $2.29 million and a projected 60% win rate under the baseline scenario. Through data-driven modeling, this study provides robust quantitative recommendations for resource allocation in professional sports.
Copyright (c) 2026 Haojun Jiang https://creativecommons.org/licenses/by-nc/4.0
Prediction of The Earliest Detection Time of Fetal DNA Concentration in Noninvasive Prenatal Testing Based on Multivariate and Logistic Regression Model
Fetal DNA concentration (FF) is a critical indicator affecting the accuracy and stability of non-invasive prenatal testing (NIPT). An excessively low FF may lead to test failure or false negatives. To address the lack of quantitative prediction for the earliest safe testing time at which clinical thresholds (FF ≥4%) are met, this study constructed multivariate linear regression and Logistic regression models based on publicly available sample data. The models analyzed the influence of gestational age and maternal body mass index (BMI) on FF levels and the probability of meeting the threshold, and introduced a gestational age quadratic term to establish a nonlinear model. The results demonstrated that BMI was a significant negative predictor of both FF and the probability of meeting the threshold. Further, the study derived the earliest gestational age at which the probability of meeting the threshold ≥95% for different BMI stratifications and validated model stability using the Bootstrap method. This research provides a quantitative basis for individualized NIPT testing timing.
Copyright (c) 2026 Jia Zhou, Huipeng Li https://creativecommons.org/licenses/by-nc/4.0
Optimization Study of Dual-Path Logistics Model for Interstellar Material Transport Integrating Space Elevators and Chemical Rockets
Addressing the extreme logistical challenge of transporting 100 million tons of materials across planets for the lunar colony construction mission launching in 2050, this paper establishes a dual-path logistics modeling framework integrating path deconstruction, parameter correction, and multi-objective optimization. The study categorizes transport routes into traditional rocket-based and space elevator-based modes. By introducing a geocorrection factor, it precisely quantifies the impact of different latitude launch sites on rocket payload capacity. Additionally, it derives the lunar soft-landing payload rate for space elevators using the Tsiolkovsky equation. At the optimization decision level, a nonlinear programming model balancing time and cost was established. Utilizing a heuristic algorithm based on a greedy strategy and grid search, the study compared the performance differences among pure rocket, pure elevator, and hybrid transport scenarios. To address real-world random uncertainties, disturbance terms such as equipment failures and maintenance costs were incorporated. The system's robustness was evaluated by constructing a set of near-optimal solutions. Simulation results confirm that the hybrid transport strategy optimally resolves the conflict between rockets' high initial costs and space elevators' limited early-stage capacity, achieving global benefit maximization under specific parameter constraints. This research provides a scientific quantitative basis and decision template for logistics planning in large-scale interstellar colonization.
Copyright (c) 2026 Bochen Tian, Shunqi Ji, Tianxiang Mo https://creativecommons.org/licenses/by-nc/4.0
Research on Transportation System Planning for Lunar Colonization Based on Pareto Optimization and Monte Carlo Simulation
Amid Earth’s worsening ecological pressure and resource conflicts, the Moon is humanity’s top choice for the first extraterrestrial colony. The 2050-launched 100,000-person lunar colony requires transporting 100 million tons of construction materials and securing long-term stable water supply. This paper builds a multi-model collaborative framework to evaluate lunar transportation schemes from cost, time, reliability and environmental impact perspectives. A weighted bi-objective optimization model for minimizing cost and time is constructed, and Pareto frontier analysis yields an optimal hybrid transportation ratio with a total cost of $1.08×10¹² and a 148.98-year duration. Monte Carlo simulation quantifies four failure scenarios’ impacts: rocket failures barely affect transportation time, while elevator and extreme failures cause severe delays and performance decline, requiring a 30-year buffer and a $221.4 billion contingency budget. Additionally, a regression forecasting-MOEA/D integrated framework estimates the colony’s annual water demand at 270,000 tons, verifying that elevator-dominated transportation is more economically efficient and operationally reliable for long-term lunar water supply.
Copyright (c) 2026 Haoyu Wang https://creativecommons.org/licenses/by-nc/4.0
A Training Strategy–Driven YOLOv11s Method for Traffic Sign Detection
Traffic sign detection is a fundamental component of intelligent transportation systems, where both detection accuracy and real-time performance are critical for practical deployment. Although deep learning–based object detectors, particularly single-stage YOLO-based methods, have achieved promising results in traffic sign detection, their performance remains limited when dealing with visually similar categories, small-sized targets, and complex traffic environments. Moreover, many existing approaches rely on architectural modifications or additional modules, which may increase model complexity and hinder real-time applicability. To address these challenges, this paper proposes a training-strategy-driven traffic sign detection method based on YOLOv11s, termed TS-YOLOv11s. Without modifying the original network architecture or loss function formulation, the proposed method improves detection performance by optimizing the training process. Specifically, loss weight adjustment is employed to enhance fine-grained category discrimination, label smoothing is introduced to mitigate overconfidence caused by limited samples, and data augmentation strategies tailored for small objects and complex scenes are applied to improve robustness and generalization. Extensive experiments conducted on the Chinese Traffic Sign Dataset demonstrate that the proposed method achieves high detection accuracy while maintaining real-time inference efficiency. The results indicate that TS-YOLOv11s provides an effective balance between performance and computational cost, highlighting the potential of training strategy optimization as a general and practical approach for traffic sign detection in real-world intelligent transportation systems.
Copyright (c) 2026 Zhuya Qi, Yiran Han, Gelin Zeng https://creativecommons.org/licenses/by-nc/4.0
Patent Value Score Prediction Based on BERT-XGBoost-Stacking with Late Fusion
Predicting the value scores of high-value patents is essential for evaluating technological innovation, managing intellectual property, and promoting industrial development. However, existing methods still face challenges in effectively fusing multimodal data and achieving high prediction accuracy. To address this issue, we propose a late fusion model based on BERT-XGBoost-Stacking to improve the accuracy and robustness of patent value assessment. This approach leverages BERT to extract deep semantic features from patent texts, employs XGBoost to model structured numerical features, and optimizes the fusion strategy through the Stacking framework. Experimental results on patents in China's new energy vehicle sector demonstrate that the proposed method outperforms single-modal models in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), indicating higher prediction accuracy and stability. This study not only enriches quantitative methods for patent valuation but also provides new insights for the application of artificial intelligence in patent analysis, thereby supporting technological innovation and high-quality industrial development.
Copyright (c) 2026 Wenjuan Li https://creativecommons.org/licenses/by-nc/4.0
Research on a Lightweight and Efficient Vehicle Detection Approach Based on YOLO Algorithm
In recent years, intelligent transportation systems have developed rapidly, and autonomous driving systems, as key solutions to traditional traffic problems, have received increasing attention. Vehicle and pedestrian detection serves as a fundamental component within these systems. However, in practical application scenarios, it is challenging to achieve a good balance between algorithm accuracy and resource consumption; furthermore, accuracy degrades under complex conditions such as occlusion and illumination variations. To address these challenges, this paper proposes an efficient and lightweight vehicle and pedestrian detection model named YOLO-MDT, based on the YOLOv8n architecture. Firstly, we introduce a Feature Dynamic Task-Sharing Detection Head (DT-Head). This head reduces the number of parameters through weight sharing and enhances detection performance by facilitating feature interaction between classification and localization tasks. Secondly, we design a C2f-MEE module to replace the original C2f module. This new module improves multi-scale feature extraction and edge awareness capabilities, thereby boosting object detection performance. Finally, DySample upsampling and the Focal-PIoUv2 loss function are employed to replace the original upsampling method and loss function, respectively. This improves model convergence speed, enhances bounding box accuracy, and reduces computational overhead. The proposed model was validated on the SODA10M and KITTI datasets. It achieves a parameter count that is only 70% of the original network and reduces computational complexity by 0.1 GFLOPs. Significant improvements in mean Average Precision (mAP) were observed: mAP50 increased by 4.3% and 3.1%, while mAP50-95 increased by 2.9% and 3.2% on the SODA10M and KITTI datasets, respectively.
Copyright (c) 2026 Chengfeng Su, Qiang Xiang https://creativecommons.org/licenses/by-nc/4.0
Development and Empirical Study of an Intelligent Car's Line-Following and Obstacle-Avoiding Control System Based on the STC89C52 Microcontroller
With the development of the electronics industry, intelligent small vehicles not only have been widely used in industrial intelligence, but also the products used in smart homes are increasingly favored by people. This group's design is an intelligent small vehicle based on STC89C52. It uses an infrared sensor module for tracking and an ultrasonic sensor module for obstacle avoidance, and transmits the measured data to the embedded microcontroller. The single-chip microcontroller processes the data and gives responses to control the operation of the motor to achieve the functions of tracking and obstacle avoidance. This automatic tracking and obstacle avoidance intelligent small vehicle uses the STC89C52 single-chip microcontroller as the microprocessor, adopts the TCRT5000 infrared sensor module for infrared tracking, adopts the HC-SR04 ultrasonic sensor module for ultrasonic obstacle avoidance, uses the L298N motor driver board and two DC speed motors to form the motor drive module, and uses four South Power batteries to form the power circu. This automatic tracking and obstacle avoidance intelligent small vehicle uses the STC89C52 single-chip microcontroller as the microprocessor, adopts the TCRT5000 infrared sensor module for infrared tracking, adopts the HC-SR04 ultrasonic sensor module for ultrasonic obstacle avoidance, uses the L298N motor driver board and two DC speed motors to form the motor drive module, and uses four South Power batteries to form the power circuit. Through experiments, the intelligent car can successfully follow black lines and avoid obstacles within a 20cm range, achieving the expected design goals.
Copyright (c) 2026 Gang Qin, Yan Pan https://creativecommons.org/licenses/by-nc/4.0
PF-LSTM Reinforcement Learning Enhanced Hybrid Acoustic-Optical Adaptive Collaborative AUV Localization Algorithm
Cooperative localization of autonomous underwater vehicles (AUVs) is widely used in fields such as ocean exploration and environmental monitoring. However, its effectiveness highly depends on precise position estimation and clock synchronization mechanisms. Clock offset and drift, signal multipath attenuation, and dynamic interference in the underwater medium significantly constrain localization accuracy in anchor-free environments. Although existing cooperative algorithms have proposed solutions like TDOA/TDOC to address asynchrony, they still face challenges such as error accumulation, slow convergence, and energy consumption imbalance. To this end, this paper proposes a Potential Field-LSTM reinforced hybrid acoustic-optical adaptive AUV cooperative localization algorithm (PF-LSTM-QHAACL). The algorithm introduces a reinforcement learning decision framework incorporating Potential Field-based dense reward and LSTM temporal memory modules, thereby accelerating the learning process and improving localization accuracy. Simultaneously, to tackle clock asynchrony dynamics and acoustic-optical channel fluctuations, PF-LSTM-QHAACL employs a DQN-like mode switching mechanism for real-time channel assessment and adaptive training, further optimizing system stability and energy utilization. Furthermore, the algorithm integrates a hybrid ranging strategy combining Time Difference of Arrival (TDOA) and Received Signal Strength (RSS), effectively suppressing the impact of asynchronous bias on position estimation. Simulation results demonstrate that the PF-LSTM-QHAACL algorithm significantly enhances underwater localization accuracy and success rate in highly asynchronous scenarios.
Copyright (c) 2026 Qiankun Fu https://creativecommons.org/licenses/by-nc/4.0
Research on Power Generation Forecasting for Photovoltaic Power Plants Based on Spatio-Temporal Multidimensional Feature Decomposition and Deep Learning Architecture
Against the backdrop of energy transition driven by the “dual carbon” goals, this study addresses the challenges posed by the fluctuating power output of photovoltaic (PV) generation to grid stability by establishing an integrated forecasting framework combining physical modeling and deep learning. First, a theoretical irradiation and power generation model based on geographic location information was established as an ideal benchmark for deviation analysis. The STL decomposition method was applied to decompose the power sequence into seasonal, trend, and random components, while the K-means clustering algorithm quantified intraday power fluctuation characteristics. Analysis reveals a 55.30% deviation rate between actual and theoretical summer power, highlighting environmental uncertainty impacts. Subsequently, a “month-day-hour” three-dimensional tensor-based data reconstruction technique was introduced. By characterizing multi-scale features through seasonal indices and intraday distribution factors, the performance of LSTM and Transformer models in day-ahead power forecasting was comparatively validated. Experimental results demonstrate that the LSTM model exhibits significant advantages in capturing power variation trends, achieving a low root mean square error of 3.7229%. This effectively addresses the technical challenge of low prediction accuracy under complex meteorological conditions, providing reliable data support for grid dispatch.
Copyright (c) 2026 Yan Pan, Gang Qin https://creativecommons.org/licenses/by-nc/4.0
Cryptocurrency Market Behavior: A Data Analytics Approach to Price Prediction
With the rapid development of digital finance, cryptocurrencies have become an important component of the global financial market, and their price prediction has become a research hotspot with both theoretical and practical value. The high volatility, non-linearity and multi-factor driving characteristics of cryptocurrency prices make traditional financial prediction models difficult to achieve satisfactory results. This paper focuses on the research of cryptocurrency price prediction based on data analytics methods: first, it analyzes the multi-dimensional influencing factors of cryptocurrency market behavior, including on-chain data, macroeconomic indicators and social sentiment signals; second, it sorts out the application of mainstream data analytics models in price prediction, such as time series analysis, machine learning and deep learning algorithms; finally, it discusses the technical challenges faced by current prediction research, such as data noise interference and market black swan events, and proposes future optimization directions combined with emerging technologies such as federated learning and graph neural networks. This paper provides a systematic methodological framework for the research of cryptocurrency price prediction and the risk management of digital asset investment.
Copyright (c) 2026 Jiayu Zhang https://creativecommons.org/licenses/by-nc/4.0
Research on Intelligent Detection of Container Surface Damage Based on Deep Residual Networks
Addressing the issues of low efficiency and high subjectivity in container damage detection during automated port operations, this study employs deep residual networks to construct a high-precision system for determining the presence or absence of container damage. Based on a dataset of 3,713 images, the approach achieves the goal of extracting key damage features from complex port backgrounds by converting original YOLO annotations into binary classification labels. Targeted data augmentation strategies, including geometric and illumination transformations, were employed to address challenges such as reflections, shadows, and multi-scale damage in real-world scenarios. The model architecture incorporates three residual blocks, global average pooling, and a Sigmoid output layer, with skip connections effectively mitigating gradient vanishing during deep training. Experiments employed 3-fold hierarchical cross-validation and ensemble learning strategies, incorporating label smoothing and early stopping mechanisms to optimize training. Results demonstrate the model achieves 99.39% accuracy, 100% precision, and 99.39% recall on the validation set, while maintaining only 2.81 million parameters. This significantly enhances detection robustness and stability, providing reliable technical support for smart port development.
Copyright (c) 2026 Haoyang Zhu https://creativecommons.org/licenses/by-nc/4.0
Group Theory and Patterns Generation in High Order Rubik Cubes
This paper explores the relationship between group theory and Rubik’s Cube operations and explains how mathematical structures can be used to generate patterns on higher-order cubes. The operations of a Rubik’s Cube can be modeled by a finite permutation group, where each move corresponds to a permutation of edge and corner blocks. By analyzing the locations and orientations of these blocks, the paper explains why repeated operations eventually return the cube to its original state. The total number of valid configurations of a 3×3×3 Rubik’s Cube is derived using permutation constraints on edge and corner positions and orientations. Examples such as the operations U, RRUU, and FR are used to illustrate how permutation cycles determine the order of operations and the resulting cube patterns. The study further extends these ideas to higher-order cubes and demonstrates methods for generating visual patterns on a 7×7×7 cube using structured sequences of layer rotations.
Copyright (c) 2026 Qiyuan Sun https://creativecommons.org/licenses/by-nc/4.0