Journal of Computer Science and Artificial Intelligence
Journal of Computer Science and Artificial Intelligence (JCSAI) 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 computer science and artificial intelligence across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
A Statistical Comparison of Multi-Architecture Models for Retention-Period Classification of Chinese Electronic Official Documents
Electronic document archiving requires automatic retention-period classification (permanent/30-year/10-year). To evaluate architectures on weakly structured Chinese official texts—texts lacking clear semantic boundaries whose key cues appear as scattered keywords—we benchmark six models across four paradigms: Qwen3 (0.6B/1.7B/4B) with LoRA, BERT-base-Chinese, FastText, and BERT-Embedding-TextCNN (frozen BERT character embeddings + TextCNN). On 2,118 test samples we conduct pairwise McNemar exact binomial tests with Benjamini-Hochberg correction over the 15 comparisons (FDR q=0.05). BERT-Embedding-TextCNN (75.50%) and FastText (75.07%) achieve the highest accuracy, ahead of Qwen3-4B (73.94%), but their Wilson 95% confidence intervals (approximately ±1.9 percentage points) overlap substantially. After correction, only two differences remain significant: BERT-Embedding-TextCNN and FastText each outperform Qwen3-0.6B (adjusted p=0.002 and p=0.006); the other 13 pairwise differences do not reach significance. The accuracies thus form descriptive clusters rather than statistically verified tiers. A leakage-controlled evaluation on the 1,690 test documents that are neither exact nor near-duplicates of training documents preserves this ranking and both significant differences, with accuracies 1.7–2.5 percentage points lower across models. Feature-weight inspection shows that archival keywords (e.g., bidding and state-asset terms, which skew toward permanent retention) are important predictive features for FastText, supporting a keyword-driven explanation of task success. For resource-constrained enterprise deployment, FastText offers near-top accuracy with CPU-only inference at roughly 26,800× the throughput of the 4B LLM under the evaluated hardware configurations.
Copyright (c) 2026 Journal of Computer Science and Artificial Intelligence https://creativecommons.org/licenses/by-nc/4.0
Financial Fraud Data Analysis Based on Genetic Neural Networks
It is used the Guotai An violation database to select 75 listed companies that were punished for financial fraud from 2011 to 2020 as fraud samples, and uses Beasley's matching principle to select 75 non fraud companies that match the fraudulent companies as matching samples. Among the 21 preliminary indicators, SPSS was used for significance testing, and factor analysis was used to reduce the significance indicators. Finally, 5 variables were selected as input variables for the data mining model, while fraud (marked as 2 for fraud and 1 for non fraud) was used as the output variable for identifying financial fraud. Using a GA-BP neural network algorithm to train and model the training group sample data, the accuracy rate reached 87.16%.
Design and Implementation of the "Deep Eye Intelligent Control" Campus Security Management System
To solve the problems of low efficiency, poor real-time performance and high labor cost in traditional campus security management, this paper designs and implements an intelligent campus security control system named "Shenmou Zhikong". The system integrates the improved YOLOv3-tiny target detection model, AAGC-LSTM spatiotemporal behavior recognition algorithm and multi-modal data collaborative analysis technology, and adopts the architecture of "video perception-algorithm analysis-multi-terminal linkage" to realize accurate identification and hierarchical early warning of abnormal behaviors, dangerous goods (such as knives and open flames) and traffic risks (such as fake license plate vehicles and speeding vehicles) in campus scenarios. To improve the adaptability of the system in complex weather, wavelet fusion algorithm is used to enhance the detection accuracy in foggy days. At the same time, memory mapping technology is adopted to realize continuous video frame analysis, and 5G network is used to support concurrent processing of 200 cameras. The experimental results show that the system has a foggy day detection accuracy (mAP) of 82%, a license plate detection accuracy of 94.47%, and a fight recognition response time of 0.8 seconds, which can improve the efficiency of security incident handling by 60%. It forms a full-chain security closed loop of "real-time monitoring-intelligent analysis-accurate disposal".
A Combined Prediction Model of Rainfall and Flowering Period Based on GRU and Random Forest for Qingming Festival Tourism
Aiming at the uncertainty of rainfall and flowering period during the Qingming Festival, which directly affects the experience of spring outing and flower viewing activities, this paper proposes a combined prediction framework based on machine learning and phenology models. First, a rainfall discrimination standard for the “continuous drizzle” scenario is defined, and a prediction model based on GRU and SMOTE oversampling is established to forecast the rainfall probability and precipitation of typical cities during the Qingming Festival in 2026. Second, a flowering period prediction model integrating growing degree day (GDD) and random forest regression is constructed to predict the initial, full, and end flowering stages of rapeseed, cherry blossoms, and peony. The proposed method is verified on historical meteorological and phenological datasets. The results show that the rainfall prediction accuracy reaches 87.45%, and the mean absolute error (MAE) of the flowering period prediction is less than 2.5 days. The predicted results match the actual distribution of flowering periods in the middle and lower reaches of the Yangtze River and northern regions. The proposed model can provide reliable data support for tourism route planning, and has practical significance for the development of flower viewing tourism and the extension of cultural tourism industry chains.
Reflections on Practical Paths of Empowering Embodied AI Development via Computer Vision in Robotic Systems
Currently, most commercial robots rely on fixed programs to perform repetitive tasks. The visual modules equipped on these devices have limited anti-interference capabilities, making it difficult to adapt to complex structures and variable environments, which in turn limits the practical effectiveness of robotic intelligence deployment. Drawing from hands-on experience in robot debugging and project implementation, this paper explores the integration of computer vision and embodied intelligence. Based on common application scenarios such as industrial sorting, power inspection, and intelligent services, it analyzes specific methods by which visual technologies assist robots in environmental perception, intelligent decision-making, motion adjustment, and model updating. The study identifies several common challenges encountered during industry deployment, including insufficient stability in recognizing complex scenes, difficulty balancing computational power with recognition accuracy, significant gaps between simulation training and real-world conditions, and a lack of unified application standards across industries. Addressing these practical issues, the paper proposes feasible improvement strategies from four perspectives: algorithm enhancement, computational power upgrade, simulation optimization, and the establishment of industry standards. The conclusions drawn from this research offer practical insights for advancing robot vision technology, expanding application scenarios, and promoting standardized development within the industry.
Research on Agentic AI-Driven Automation and Collaboration in Full Architectural Design Workflow for Personalized Spatial Generation Based on User Behavior Perception
Architectural design has long relied on experience, and there is often a gap between the design intent and the actual implementation of the project. This study focuses on two main lines of autonomous intelligent AI intervention in architectural design, namely full-process collaboration and personalized space generation, and examines the possible paths for reconfiguring the design process. It reviews the application of generative adversarial networks, diffusion models, and large language models in architectural planning, scheme layout, and structural optimization, and combines the practical cases of domestic architectural design enterprises from 2024 to 2026 to discover that the AI integration system based on BIM as the data hub is bridging the information gap between concept generation and construction drawing output. Generative AI enables space design to gradually break free from the limitations of standardized modular constraints, and user intentions can directly participate in the iterative generation of spatial schemes through natural language interaction. The promotion of technology also faces problems such as inconsistent data standards, insufficient algorithm interpretability, and a shortage of interdisciplinary talent. It can be promoted collaboratively from three aspects: institutional norms, technological research and development, and talent cultivation, to provide a reference for the intelligent transformation of the architectural design industry.
Machine Learning for Performance Prediction of Ultra-High Performance Concrete
Ultra-high performance concrete (UHPC) has emerged as a revolutionary cementitious composite with exceptional mechanical properties and durability, yet its mixture design remains challenging due to complex nonlinear interactions among numerous constituents. Traditional experimental approaches are time-consuming, costly, and inefficient for optimizing UHPC formulations. Machine learning (ML) has recently gained significant attention as a powerful alternative for predicting UHPC performance and optimizing its mixture designs. This paper provides a comprehensive review of ML applications in UHPC, focusing on the prediction of compressive strength, flexural strength, workability, and durability properties. Various ML algorithms—including artificial neural networks (ANN), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBoost), CatBoost, and Bayesian neural networks—are critically examined. The review synthesizes findings from over 200 published studies and multiple publicly available datasets comprising more than 2,000 UHPC mix designs. Key challenges, including data quality, model interpretability, and external validation, are discussed. Future research directions, such as physics-informed neural networks, generative AI for data augmentation, and explainable AI frameworks, are proposed to advance the field toward reliable and interpretable UHPC design.
Research on the Improvement Path of Security Governance Capability of Distribution Network Driven by Intelligent Technology
This article proposes a systematic improvement path for the security governance capability of distribution networks driven by intelligent technology. Realize global state awareness through IoT sensors and edge computing; Based on AI algorithms for fault diagnosis and situation deduction, combined with digital twin platform optimization decision-making; Constructing self-healing control system based on intelligent terminal and virtual power plant technology. The technical level focuses on cloud-side collaborative architecture and deep learning algorithm, the management level promotes cross-departmental collaboration and process standardization, and the mechanism level improves the standard specification and dynamic evaluation mechanism. Taking a demonstration area as an example, after the implementation, the fault location time is reduced from 42 minutes to 2 minutes, the annual power outage events are reduced by 78%, and the voltage qualification rate is increased to 99.71%, which verifies the effectiveness of the closed-loop system of "perception-analysis-decision-execution". The research shows that the integration of intelligent technology, management innovation and institutional guarantee can improve the operation toughness and governance efficiency of distribution network.
Research on a Transformer Algorithm with Mixture-of-Experts for 3D Human Pose Estimation
Monocular 3D Human Pose Estimation Technology has been widely applied in the field of intelligent rehabilitation devices. However, existing Transformer-based methods typically adopt a uniform parameter structure, making it difficult to effectively capture the significant pose feature differences among various action categories. This leads to suboptimal performance when handling complex or unseen actions. In recent years, the Mixture-of-Experts (MoE) architecture has achieved breakthrough progress in large-scale models, offering new insights for human pose estimation. Inspired by DeepSeekMoE, this paper proposes a novel 3D human pose estimation framework, PoseMoE Former, which significantly enhances the model’s generalization ability and modeling efficiency across different action categories by integrating both shared expert and routing expert mechanisms. Specifically, our model comprises three core modules: static and dynamic routing mechanisms that enable expert selection; shared expert and routing expert modules that capture general low-level features and category-specific features; and a loss system including the main task loss, dynamic loss, and load balancing loss, which alleviates issues of unbalanced expert utilization. Experiments on Human3.6M demonstrate that PoseMoE Former outperforms mainstream baseline methods, particularly excelling in complex action categories. Ablation studies further confirm the rationality of each module’s design, showing notable academic value and practical application potential.
Preliminary Study on Quality Control Method for Hydrological Station Precipitation Data in Haidong City, Qinghai Province
The accuracy of precipitation observations from hydrological stations is critical for multi-source fusion-based nowcasting products, particularly in the complex terrain of the Qinghai-Tibet Plateau. However, precipitation data from hydrological stations, which are managed by external departments, often suffer from inconsistent observation standards and variable data quality, limiting their direct applicability in operational meteorological services. This study proposes a collaborative quality control (QC) framework specifically designed for hourly precipitation data from hydrological stations in Haidong City, Qinghai Province. The methodology integrates three independent QC modules: (1) a climatological and spatiotemporal consistency check using adjacent meteorological stations, (2) a radar-based quantitative precipitation estimation (QPE) validation with dynamic rainfall-rate-dependent thresholds, and (3) a synergistic verification combining FY-4B geostationary satellite infrared brightness temperature (TBB) and weather radar composite reflectivity data. A weighted comprehensive scoring system based on grey relational analysis is then applied to derive final QC flags (correct, suspect, or erroneous). The proposed method was evaluated using data from a typical precipitation event on July 10, 2023, across 42 hydrological stations in Haidong. Results indicate that the collaborative approach effectively identifies anomalous observations, with the radar-reflectivity contradiction model capturing precipitation–reflectivity mismatches, and the satellite–radar synergy successfully flagging cases where zero precipitation coincides with deep convective clouds (TBB < 220 K) or measurable rainfall occurs under warm-cloud conditions (TBB > 265 K). The comprehensive scoring system reduced the proportion of erroneous stations from 4.8% to 0.7% after QC. This study demonstrates that multi-source collaborative QC significantly enhances the reliability of hydrological station precipitation data, providing a practical and transferable solution for improving multi-source fusion products in data-sparse plateau regions.
satellite infrared brightness temperature
A Review of Recent Advances in Control Algorithms for Stewart Platforms
The Stewart platform is a typical six-degree-of-freedom parallel mechanism characterized by high load-carrying capacity, high structural stiffness, a short error propagation chain, and fast dynamic response. It has been widely used in motion simulation, ship motion compensation, space payload pointing, precision machining, and multidimensional vibration control. Because the moving platform is cooperatively driven by six kinematic chains, its control system exhibits pronounced nonlinearity, strong coupling, and multivariable characteristics, and is affected by factors such as mechanical clearance, load variations, actuator saturation, sensor noise, and model parameter errors. Although conventional proportional-integral-derivative (PID) control has a simple structure and is easy to implement in engineering applications, it often struggles to simultaneously achieve high tracking accuracy, fast response, and stable operation under complex attitude variations and persistent external disturbances. In recent years, methods such as model predictive control, sliding mode control, adaptive control, robust control, neural networks, and reinforcement learning have gradually been introduced into Stewart platform systems. This review examines recent control strategies for Stewart platforms against four practical criteria: model requirements, disturbance rejection, constraint handling, and computational demand. The discussion also covers the validation methods and application conditions reported in marine compensation, motion simulation, aerospace positioning, precision machining, and vibration isolation. Particular attention is given to unresolved issues in modeling accuracy, sensing delay, real-time implementation, safety, and the generalization of learning-based controllers.
Reverse-Engineering Fan Votes in Dancing with the Stars: Rule-Consistent Inference, Fairness Analysis, and Mechanism Design
This paper develops a data-driven framework to infer confidential audience voting and to evaluate voting aggregation mechanisms in Dancing with the Stars under rule changes and structural bias. We propose a two-stage inverse approach. In Stage 1, a Bradley Terry style model is fitted to historical elimination sequences to estimate each contestants latent fan strength, forming a prior preference distribution. In Stage 2, we reconstruct weekly audience vote shares by solving a constrained inverse problem: among all vote allocations consistent with the show’ s elimination mechanism (including percentage-based elimination and the bottom-two variant), we select the distribution closest to the prior via convex quadratic programming (with discrete handling for bottom-two weeks). Uncertainty is quantified through posterior sampling. Across 34 seasons, the reconstructed votes reproduce 77.0% of weekly eliminations, achieve a 0.953 Spearman correlation with final rankings, and match champions 58.8% of the time, indicating strong explanatory power despite unobserved votes. Using the inferred votes, we compare historical aggregation rules and find the percentage-based rule aligns substantially better with observed eliminations than the rank-based rule in their respective eras (78.6% vs. 56.0 weekly agreement), while avoiding excessive amplification of judge influence. We further quantify structural fan advantage related to celebrity background and propose a debiasing weight to correct audience shares. Building on this correction, we introduce a Fairness-Adjusted Hybrid Rule (FHR) that blends judge shares with debiased audience shares. FHR is designed as a forward-looking mechanism: it improves end-of-season technical ordering (finals rank correlation 0.325 vs. 0.271 under the base- line percentage rule) at the cost of lower week-by-week elimination consistency, and its debiasing strength can be tuned to match production priorities. We recommend retaining the percentage-based rule as the default for stability and historical continuity, while piloting the FHR as an optional fairness overlay with transparent parameter tuning and periodic audits.
An Adaptive Portrait Image Enhancement Algorithm Based on Facial and Scene Statistics in Complex Lighting
Portrait image quality in user-provided photographs is frequently compromised by highly variable capture conditions, including underexposure, facial shadows cast by side lighting, and color casts introduced by indoor illuminants. This paper presents an interpretable, fully ablatable workflow for portrait-perception-based color normalization. The algorithm builds a multidimensional statistical profile of the face and the surrounding scene in the LAB and HSV color spaces, and uses this profile to drive adaptive color-cast correction with a dead-zone-and-damping mechanism, localized skin-tone equalization through large-scale Gaussian diffusion, nonlinear luminance mapping with highlight suppression, and detail-aware texture restoration. By separating luminance from chrominance, the method avoids the color crosstalk typical of RGB-space adjustments. On a dataset of 120 portraits covering 12 typical complex-lighting scenarios, our method raises the central L-mean from 144.64 to 165.55 while constraining the overall highlight ratio to 0.72%. In the overexposed group, the method suppresses the highlight ratio to 1.12%, avoiding the failure state (8.57%) produced by conventional brightness enhancement, and in the low-light group it lifts the central L-mean from 128.37 to 170.05. Ablation experiments confirm that the color-normalization module is the dominant contributor to these gains. The workflow offers a robust, reproducible, and interpretable alternative to black-box enhancement filters for portrait normalization under complex lighting.
A Review of Environmental Microplastic Detection Methods
Microplastic detection provides an essential basis for investigating environmental contamination, elucidating transport patterns, assessing human exposure, and evaluating ecological risks. In recent years, research has progressed from early visual screening and single-particle identification to an integrated analytical framework that combines sample collection, matrix separation, chemical composition confirmation, particle counting, polymer mass quantification, and automated data processing. This review summarizes recent advances in microplastic detection over the past five years and discusses the collection and pretreatment of samples from water, sediments, soils, organisms, and air. Particular attention is given to the principles, applicability, and limitations of micro-Fourier transform infrared spectroscopy, micro-Raman spectroscopy, pyrolysis-gas chromatography-mass spectrometry, scanning electron microscopy, fluorescence staining, and flow cytometry. Particle number concentration and polymer mass concentration, minimum detectable particle size, analytical throughput, degree of automation, and applicability to complex matrices are further compared. Current studies show that micro-infrared and micro-Raman techniques are suitable for particle-level identification and morphological analysis, whereas pyrolysis mass spectrometry is more appropriate for polymer mass quantification in complex matrices. Fluorescence-based and flow-cytometric methods also show potential for rapid screening and high-throughput detection. Future work should further standardize sampling, pretreatment, blank control, recovery assessment, and result reporting, and establish a complementary multi-technique system that provides comparable data and is suitable for routine monitoring.
micro-Fourier transform infrared spectroscopy
Noise in Rolling Bearing Fault Diagnosis: From Characterization to Robust Solutions
Rolling bearing fault diagnosis is essential for the reliability and safety of rotating machinery, yet its effectiveness in industrial practice is fundamentally challenged by noise. This narrative review argues that noise in bearing fault diagnosis should not be treated as a single additive disturbance, but rather as a collection of distinct phenomena — environmental and background signal noise, sensor and acquisition noise, operating-condition-induced domain shift, label noise, and compound noise scenarios — each affecting different stages of the diagnostic pipeline and demanding differentiated strategies. This review develops a noise taxonomy for rolling bearing fault diagnosis and systematically examines how each noise type degrades signal processing, feature extraction, and diagnostic decision-making. It surveys denoising strategies, noise-robust feature engineering approaches, and noise-aware diagnostic models through this noise-type lens, identifying which strategies are appropriate for which noise conditions. A standardized evaluation and benchmarking protocol is proposed to address the inconsistency that currently prevents meaningful comparison of noise-robustness claims across studies. The central synthetic contribution is a noise–method matching matrix that maps each noise category to appropriate combinations of denoising, feature, model, and evaluation strategies, providing a structured framework for method selection and future research design. The review concludes that robust bearing fault diagnosis cannot be achieved through a single universal model. The more realistic path forward requires noise-type-aware strategies, standardized multi-noise evaluation, and methods co-designed for industrial deployment constraints.
Multi-Period Crop Planting Strategy Optimization Based on Multi-Objective Programming and Monte Carlo Simulation
This study addresses the optimization of crop planting arrangements for the period from 2024 to 2030, with the objective of maximizing the cumulative economic return. A dynamic programming framework is adopted as the primary methodology, while the decision-making logic of the greedy algorithm is incorporated to update the planting strategy year by year. Specifically, the planting decision for each year is determined on the basis of the forecast results and operational outcomes of the preceding year. For the two possible disposal strategies of unsold agricultural products, separate multi-stage optimization models are established and solved, thereby generating corresponding optimal planting plans for the entire planning horizon.In addition, the model takes into account a range of uncertainties associated with agricultural production and market conditions, including historical sales performance, expected demand for different crops, yield per unit area, cultivation costs, and fluctuations in selling prices. These factors are assumed to vary over time rather than remain constant. To evaluate the influence of such uncertainties, Monte Carlo simulation and probabilistic statistical analysis are employed to generate numerous possible scenarios. The expected performance of each planting strategy is then estimated through repeated random sampling, which improves the robustness and risk resistance of the final decision scheme.
Application Analysis of Cryptographic Algorithms in Smart Home Data Protection
This study focuses on sensitive data security challenges in smart home systems within the context of the Internet of Things (IoT), exploring the application value of cryptographic algorithms in data protection. The paper systematically reviews four cryptographic algorithm types suitable for smart homes: symmetric, asymmetric, hash, and lightweight algorithms. It analyzes specific implementation methods of these algorithms in data collection, transmission, storage, and user authentication processes, while conducting experimental tests to compare efficiency, resource consumption, and security performance of four commonly used algorithms including AES-128 and RSA-2048. Research findings reveal significant performance disparities among algorithms, necessitating rational selection based on device resources and application scenarios, with hybrid encryption schemes being the most widely adopted approach. This study provides practical references for algorithm selection in smart home data protection, lays the foundation for subsequent optimization of lightweight algorithms and post-quantum cryptography exploration, and holds significant implications for building secure smart home data protection systems.
Multimodal Large Model Guided Diffusion Model for Transformer Oil Leakage Image Generation
Transformer oil leakage is a critical defect in power equipment inspection, yet its automatic detection remains challenging because real leakage samples are scarce, highly irregular in shape, variable in color, and easily confused with water stains, rust, shadows, and complex structural backgrounds. To alleviate the data scarcity problem and improve downstream segmentation performance, this paper proposes a multimodal semantic-heuristic diffusion framework, termed MSH-Diff, for automatic transformer oil-leakage image synthesis. The proposed framework exploits the visual reasoning capability and prior industrial knowledge of a multimodal large language model to identify potential leakage-prone components, such as flanges, valves, and radiator fin roots, through expert-role prompt engineering. The recognized component semantics are further mapped to spatial Gaussian anchors by analyzing cross-attention responses in the latent diffusion model, enabling leakage-center localization without manually drawn masks or key-point annotations. In addition, MSH-Diff automatically constructs dense scene descriptions involving illumination, viewpoint, and surface material, which are encoded as semantic constraints to enhance structural consistency and physical realism during diffusion sampling. Experimental results demonstrate that MSH-Diff achieves competitive image quality and diversity, with an FID of 38.14 and an IC-L score of 0.29. When the generated samples are incorporated into downstream semantic segmentation training, the mIoU of DeepLabV3+ increases from 60.09% to 62.31%, confirming the effectiveness of the proposed framework for industrial defect data augmentation.
Load Balancing Techniques in Multi-Sink Wireless Sensor Networks
Wireless sensor networks (WSNs) rely on battery-powered sensor nodes, making load balancing essential for prolonging network lifetime and ensuring reliable data delivery. Multi-sink WSNs can mitigate the hot-spot problem of single-sink architectures by distributing data collection points, but they also introduce complex imbalance among nodes, paths, cluster heads, and sink service regions. This paper reviews load balancing in multi-sink WSNs by first defining major load types and balancing objects, including communication, energy, path, cluster-head, and sink-region loads. Then, this paper analyzes imbalance formation from network topology, sink placement, node-sink association, routing strategy, traffic dynamics, and residual energy variation. Existing methods are classified into sink deployment, node association, load-aware routing, clustering, mobile sink scheduling, and intelligent optimization approaches, with their advantages and limitations compared. Finally, key challenges are discussed, including dynamic load awareness, low-overhead state maintenance, multi-objective trade-offs, static-mobile sink coordination, and lightweight adaptive mechanisms for practical deployment.
Event-Triggered Control for Course Tracking of Underactuated Ships Considering Actuator Faults
Addressing the course-keeping control problem of underactuated surface vessels subject to strong unknown environmental disturbances and asymmetric actuator saturation, this paper proposes an event-triggered (ETC) adaptive control scheme. A Radial Basis Function (RBF) neural network is constructed to approximate external disturbances and model uncertainties. To handle the impact of non-smooth asymmetric saturation nonlinearities, a continuously differentiable model based on the Gaussian error function is employed. An ETC is introduced to significantly reduce the frequency of controller updates, and a trajectory tracker is designed utilizing the backstepping method. The stability of the closed-loop system is theoretically proven via Lyapunov stability analysis. The control algorithm is simulated within the MATLAB environment to evaluate tracking performance. Simulation results demonstrate that the controller achieves precise and stable tracking, with the number of updates reduced by 1,056—amounting to a 44.0% reduction in communication resource utilization. This research provides a theoretical reference for the trajectory tracking of underactuated vessels under event-triggered mechanisms and asymmetric actuator constraints, with potential for further application in practical engineering.
Fan Vote Share Estimation Based on Constrained Inversion and Bayesian Validation
This study develops a fan vote share estimation model for competition settings in which weekly audience support is not directly observable. The model uses known judge scores, elimination outcomes, and rule mechanisms to infer contestant-level fan vote shares through constrained nonlinear optimization. The framework defines active contestants by elimination week, aggregates multi-judge scores, standardizes judge-score shares, and combines judge scores with estimated fan vote shares under percentage-based scoring rules. To address the non-uniqueness of inverse estimation, the model introduces temporal smoothness and maximum entropy regularization, while hard elimination constraints ensure that inferred results reproduce observed eliminations. The SLSQP algorithm is used for sequential season-week optimization, and Bootstrap perturbation provides confidence intervals and certainty indices. Verification results include champion prediction, cross-validation, residual analysis, and Bayesian latent popularity validation. The model explains cases in which low judge scores coexist with high fan support and shows strong consistency between constrained inversion and Bayesian posterior estimates.
Extended Horizon Model Predictive Control for Cooperative Encirclement of Unmanned Surface Vehicle Swarm
Cooperative encirclement of moving targets using unmanned surface vehicle (USV) swarms plays a vital role in maritime patrol, active interception, and autonomous cluster confrontation missions. Conventional finite-horizon model predictive control strategies often suffer from short-sighted control decisions and gradual performance degradation in long-duration cooperative tasks, which may cause unstable formation evolution and potential constraint violations. To address these limitations, this paper proposes an extended horizon model predictive control (EH-MPC) method for USV swarm cooperative encirclement. By extending the optimization prediction horizon, the proposed method endows the controller with long-term decision-making capability, effectively optimizing the swarm formation evolution while strictly maintaining multiple physical constraints, including inter-vehicle safety distance and formation centroid tracking. Comprehensive numerical simulations demonstrate that the EH-MPC strategy can achieve smooth and stable encirclement trajectory evolution, sustained collision-free performance, and high-precision centroid tracking during long-time marine missions. The results verify that the proposed method possesses excellent stability, constraint adaptability, and cooperative consistency for USV swarm encirclement applications.
Research on the Mechanism of Artificial Intelligence in Tourism Management
With the rapid development of information technology, the application of artificial intelligence in various industries is becoming increasingly extensive, and the tourism industry is also facing the opportunities and challenges of digital transformation. Against this background, this study aims to deeply analyze the mechanism of the role of artificial intelligence in tourism management. Through methods such as literature research, case analysis and empirical research, a systematic study is carried out on the application of artificial intelligence in various links such as tourism resource planning, tourist service, marketing management and security assurance. The research finds that artificial intelligence has significantly improved the efficiency of tourism management and the tourist experience through mechanisms such as technology empowerment, data-driven and collaborative optimization. However, in the process of its application, it also faces problems such as data privacy and security, technology cost and accessibility, and shortage of talents. This study provides a theoretical basis and practical guidance for the rational use of artificial intelligence technology in the tourism industry and promoting innovative development, and is of great significance for promoting the intelligent transformation of the tourism industry.
The Application of the Function and Equation Thought in Middle School Mathematics Problem-Solving
The idea of functions and equations is a core approach running through algebra, geometry, probability and other knowledge modules in middle school mathematics. Its value in cross-module problem-solving and connection with curriculum standards has become increasingly prominent. Taking sequences, trigonometric functions and solid geometry as research objects, this study systematically analyzes the problem-solving logic of the function and equation idea, so as to provide empirical references for front-line teaching to implement curriculum standards and improve students’ key mathematical competencies. This paper adopts literature review, case analysis and classification research. First, it defines the connotation and relationship between function thought and equation thought, clarifies their core feature of “interconversion”, and meets the curriculum standard requirement of “understanding the essence of mathematical thought”. Second, based on recent college entrance examination questions and typical examples, it analyzes the application paths of these thoughts by module. Finally, it summarizes their general problem-solving logic and puts forward teaching and problem-solving suggestions in line with curriculum standards. The research shows that the function and equation idea permeates middle school mathematics and is crucial for developing core competencies such as mathematical abstraction and logical reasoning. It is often tested with the combination of numbers and shapes, classified discussion in the college entrance examination, reflecting the orientation of "emphasizing literacy and application". It simplifies complex problems and improves students’ problem-solving efficiency and thinking quality.
Standardized Entry and Three-Dimensional Visualization Management of Basic Data for Coal Reserves
With the continuous advancement of intelligent coal mine construction and refined resource management, coal reserve management has imposed higher requirements on the standardized organization of basic data, intuitive expression of spatial objects, rapid query of results, and traceability of management processes. In response to problems such as diverse data sources, inconsistent formats, and the dispersed management of two-dimensional drawings and statistical results, this paper constructs a workflow for standardized data entry and three-dimensional visualization management around data sorting, standardized database entry, two-dimensional drawing extraction, three-dimensional visual expression, and result version management in reserve management. Taking boreholes, boundaries, faults, protective coal pillars, reserve blocks, and parameter tables as the main data objects, the workflow realizes the standardized organization of basic coal reserve data through field unification, coordinate checking, object classification, file archiving, attribute association, and version recording. On this basis, functional modules such as two-dimensional drawing preview, three-dimensional model browsing, result query, and version tracking are designed to form a management loop from data preparation to result display. Application using coal mine data shows that the proposed workflow can improve the standardization and traceability of reserve data management, enhance the spatial expression capability of reserve management objects, and provide basic support for the informatization of coal reserve management.
A Review of State Estimation for Time-Delay Neural Networks
In practical engineering, neural networks inevitably suffer from time-delay phenomena caused by signal transmission, hardware response, and data communication. Time-delay will degrade the dynamic performance of neural networks, even lead to oscillation, chaos, and instability. Meanwhile, affected by sensor constraints and external interference, only a small number of system states can be measured directly, which makes state estimation become one of the key technologies in the analysis and application of time-delay neural networks. State estimation refers to reconstructing the real full state of the system through measurable output data, which provides the state basis for stability analysis, fault detection, and feedback control. In recent years, state estimation of time-delay neural networks has become a research hotspot in the fields of control science, intelligent systems, and dynamical systems. This paper systematically summarizes the research results of state estimation for time-delay neural networks. Firstly, the basic model, delay types, and activation function constraints are introduced. Secondly, the core theoretical tools including Lyapunov-Krasovskii functional method, various integral inequalities, reciprocally convex combination lemmas, S-Procedure, and switched system methods are summarized. Thirdly, the research progress of H∞ state estimation, L₂-L∞ state estimation, dissipative state estimation, and event-triggered state estimation is reviewed in detail. Finally, the existing problems and challenges in this field are summarized, and the future development directions including low conservatism, intelligence, network security, and practical application are discussed.
IIR Digital Filter Implementation via Red-billed Blue Magpie Optimization Algorithm
To overcome the insufficient stop-band attenuation and poor stability of conventional IIR filter design, this paper introduces the Red-billed Blue Magpie Optimization (RBMO) algorithm for the synthesis of 8th-order infinite-impulse-response digital filters. By emulating the collective intelligence of red-billed blue magpies, RBMO integrates a tri-modal “hop-walk-fly” search strategy with an “information-sharing & food-storing” cooperation mechanism, achieving an effective balance between global exploration and local exploitation. A pole-radius constraint is embedded to guarantee BIBO stability, while a unified fitness function that simultaneously penalizes pass-band magnitude error, peak stop-band ripple, and weighted transition-band deviation converts the design task into a single-objective optimization problem. Extensive simulations on low-pass, high-pass, band-pass and band-stop specifications demonstrate that the proposed scheme significantly reduces pass-band peak ripple, stop-band amplitude and transition-band error. The optimized coefficients, visualized through magnitude–frequency curves and pole-zero maps, corroborate the feasibility and practical potential of RBMO in high-performance IIR filter design.
Red-billed Blue Magpie Optimization (RBMO)
Visible Watermark Detection Based on an Improved YOLOv8 Model
With the widespread dissemination of digital images on the internet, issues related to image copyright protection and content security have become increasingly prominent, making visible watermarks a common method of copyright marking. To address the frequent inaccuracies in visible watermark detection under complex background conditions, this paper proposes an improved deep learning-based visible watermark detection model that achieves high-precision detection and localization of watermark regions. structural optimization is conducted based on the YOLOv8 object detection framework. Considering the characteristics of visible watermarks, such as large scale variations, uneven transparency, and strong coupling with background textures, an attention mechanism module is introduced into the backbone network to enhance feature representation of critical regions. In addition, a watermark feature enhancement module is designed to strengthen multi-scale feature fusion. Furthermore, the improved YOLOv8 model is fused with the RT-DETR model, combining the local feature extraction capability of convolutional neural networks with the global modeling ability of the Transformer architecture to improve detection accuracy and localization robustness. Experimental results demonstrate that the proposed method achieves an mAP50 of 98.9% and an mAP50:95 of 97.6% on the LVW dataset, outperforming multiple mainstream detection models and effectively balancing detection accuracy with efficiency.
A Study on the Efficiency of Regional Water Resource Utilization in China: Based on the Three-Stage Data Envelopment Analysis (DEA)-Malmquist Method
To objectively evaluate the efficiency of water resource utilization in 31 provinces (municipalities and autonomous regions) of China from 2015 to 2024 and effectively eliminate the effects of environmental factors and random disturbances, this study employs a three-stage data envelopment analysis (DEA) model and the Malmquist index method to construct an evaluation system for water resource utilization efficiency from an input-output perspective. The results show that: (1) environmental factors and random disturbances exert significant effects on the measured efficiency of water resource utilization, and the traditional DEA model overestimates efficiency in the eastern region while underestimating it in the central and western regions; (2) from 2015 to 2024, the overall efficiency of water resource utilization in China exhibited a fluctuating upward trend, with marked regional disparities, the highest efficiency in the eastern region, and relatively lower efficiency in the central and western regions; (3) technological progress is the core driver of efficiency improvement, whereas managerial inefficiency is the key factor constraining efficiency gains in some provinces; and (4) per capita water resources and industrial structure exert heterogeneous effects on water resource utilization efficiency. Accordingly, it is essential to further upgrade the structure of the industry, strengthen interregional diffusion and coordinated application of technologies, and implement differentiated water resource management policies to promote efficient water use and sustainable development.
Design and Implementation of an Automatic Vehicle License Recognition System for End-of-Life Vehicle Dismantling Scenarios
The accuracy and efficiency of vehicle license information entry directly determines whether such operations can truly implement the Standards for Motor Vehicle Recycling and Dismantling Operations and further advance the digitalization of compliant supervision in this sector. To address challenges in this scenario, such as severely damaged documents, complex shooting conditions, and strong field logic, this paper designs and implements an automatic vehicle license information recognition system for end-of-life vehicle dismantling enterprises. The system first employs a YOLOv8 model combined with a multi-frame stability criterion to achieve robust detection of the vehicle license region and capture clear images under complex shooting environments. Subsequently, PaddleOCR is utilized for text extraction. Finally, a multi-layer post-processing engine integrating regular expression matching, semantic analysis, and business rules corrects and structures the raw OCR results, outputting standardized vehicle information. Experiments demonstrate that the designed post-processing rule engine effectively corrects the majority of OCR recognition errors. Ultimately, the system is integrated into a desktop application featuring data visualization and browser automation form-filling capabilities, achieving an end-to-end automated process from physical document scanning to business system entry, thereby enhancing operational efficiency and data accuracy.