Journal of Computing and Electronic Information Management
Journal of Computing and Electronic Information Management (JCEIM) 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 computing, electronics, information management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Graph Convolutional Network-Based Multi-Agent Collaborative Task Allocation Optimization Algorithm
Due to the complex interdependencies between tasks, the dynamic state changes of the tasks, and the lack of flexibility of the traditional static matching method in the collaborative task allocation with multi-agent, this study aims to explore an optimization algorithm for collaborative task allocation driven by a graph convolutional network. The paper details the construction of multi-agent task graphs, the design of node features and edge weights as well as the graph convolutional feature aggregation mechanism. It also introduces updating of dynamic collaborative relationship, construction of allocation utility function and collaborative optimization process. The results of comparative and ablation experiments in a two-dimensional simulation platform show that the algorithm has a 97.4% task completion ratio and an average utility of 86.7 with an excellent allocation efficiency and convergence stability.
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Adaptive Crow Search Algorithm Based on Population Diversity
As an emerging metaheuristic optimization algorithm, the Crow Search Algorithm (CSA) has received widespread academic attention for its intuitive model and concise parameter settings. However, its inherent limitations, including the tendency to fall into local optima and insufficient convergence accuracy in solving complex optimization problems, have severely restricted its popularization and application in practical engineering scenarios. Aiming at the above shortcomings of the basic CSA, this thesis conducts in-depth and systematic research, proposes two targeted improved algorithms, and systematically verifies their theoretical performance and engineering application value.To address the performance bottleneck of the basic CSA, this thesis proposes an Adaptive Crow Search Algorithm (ACSA) based on population diversity. By constructing an adaptive framework with population diversity as the feedback signal, the algorithm achieves three core improvements. First, a dynamic dual-mode guidance mechanism is designed to intelligently switch between global exploration and local exploitation strategies according to the population diversity index. Second, the Sigmoid function is introduced to realize adaptive adjustment of flight length, enabling the parameter settings to match the real-time search state of the algorithm. Third, the golden sine strategy is adopted to optimize the individual position update rule, which is combined with a reflection boundary handling mechanism to enhance the iterative stability of the algorithm.
Research and Implementation of an Agricultural Pest and Disease Detection Algorithm Based on Deep Learning
Food security is increasingly threatened by crop pests and diseases, while conventional field inspection remains labor intensive, experience dependent, and prone to missed detection during early disease stages. To support accurate and real-time pest and disease monitoring in precision agriculture, this study proposes RSC-YOLOv11-EMA, an improved lightweight detection framework for soybean leaf pest and disease images. First, YOLOv11 is selected as the baseline after comparison with YOLOv5 and YOLOv8 on the same dataset. Second, the backbone network is reconstructed with RepVGG so that multi-branch training can be converted into a single-path inference structure, reducing computational cost for edge deployment. Third, SENet channel attention is embedded into multi-scale feature outputs to strengthen discriminative lesion channels and suppress background interference. Fourth, K-means++ is used to analyze the true scale distribution of lesions and guide scale-aware augmentation for small targets. On this basis, an Efficient Multi-scale Attention module, warm-up cosine annealing, and exponential moving average weight updating are introduced to improve spatial sensitivity, convergence stability, and generalization on a small-sample dataset. Experiments show that the final RSC-YOLOv11-EMA model achieves 93.6% precision, 90.4% recall, 91.2% mAP@0.5, and 31.2 FPS, improving mAP@0.5 by 6.5 percentage points over the YOLOv11 baseline while preserving real-time inference. A PyQt5-based detection system is further implemented, with an end-to-end response time of 145 ms on edge equipment. The results demonstrate that the proposed method provides an effective and deployable solution for early agricultural pest and disease detection.
Secrecy Analysis for a Fixed UAV-Assisted DF Relay Network with Hardware Impairments over Nakagami-m Channels
A fixed UAV-assisted decode-and-forward relay network is examined from the perspective of physical-layer security under residual transceiver distortion. The source communicates with the destination only through the UAV relay, and the eavesdropper overhears the relay transmission. Independent Nakagami-m fading is used for the three wireless links so that different UAV propagation conditions can be represented within one model. Based on the SNDRs of the two legitimate hops and the wiretap hop, expressions for SOP and SPSC are derived, and simplified finite-sum integral forms are given for integer fading parameters. The numerical study confirms the analytical results and shows how fading severity, secrecy-rate demand, eavesdropper SNR, and impairment level affect secure transmission.
Design and Simulation of a 24 GHz Microstrip Array Antenna
To meet the demand for high gain and low sidelobe levels of array antennas in radar systems, hydrological monitoring, and next-generation wireless communication systems, this paper proposes a 24 GHz microstrip array antenna based on a series–parallel hybrid feeding network. The proposed design combines the advantages of both series and parallel feeding structures to optimize array performance while maintaining a compact configuration. Simulation results show that the antenna achieves a reflection coefficient better than −10 dB over the frequency range of 23.9–24.3 GHz, with a maximum gain of 19.58 dBi and a sidelobe level close to −20 dB. The results indicate that the proposed antenna can basically meet the design requirements for high gain and low sidelobe performance in radar systems.
Generalized dissipativity state estimation of delayed neural networks under a novel event-triggered strategy
This paper investigates the generalized dissipativity state estimation problem for delayed neural networks (DNNs) under a novel event-triggered strategy. Unlike traditional Lyapunov-Krasovskii Functionals (LKFs) which impose positive definiteness constraints on matrices, this paper constructs a novel delay-dependent LKF. The positive definiteness constraint on the LKF matrix is relaxed via a quadratic inequality, which offers more flexibility in the functional construction. Meanwhile, to guarantee satisfactory estimator performance under limited communication resources, a novel adaptive event-triggered strategy (AETS) with memory-based triggering logic is proposed. Unlike existing event-triggered (ET) strategies, the proposed AETS can dynamically adjust triggering parameters and further reduce communication frequency. Accordingly, two sufficient criteria are derived via linear matrix inequalities (LMIs) to guarantee the dissipativity of the estimation error system. Finally, numerical examples are provided to demonstrate the superiority of the proposed methods.
Generalized dissipativity state estimation
Research on Personalized NIPT Detection Timing and Anomaly Judgment Based on Bayesian Optimization
With rising obesity-related NIPT failures due to insufficient fetal DNA, we introduce a pioneering clinical application of Bayesian optimization—traditionally confined to engineering and machine learning—to personalized prenatal screening. Linear mixed-effects models quantified nonlinear relationships between fetal DNA concentration and gestational age, BMI, and maternal age. Gaussian mixture clustering stratified pregnancies into five BMI-specific subgroups. Uniquely applying Bayesian optimization to determine optimal 10–25-week sampling windows for each subgroup, we minimized weighted risks of early/late testing and low DNA concentration. Results reveal distinct optimal timing across BMI categories that diverge from current uniform guidelines, with higher BMI requiring earlier collection. This innovative integration of machine learning optimization into clinical obstetrics establishes a data-driven 'one person, one time' framework, representing the first utilization of Bayesian methods for individualized NIPT scheduling decisions.
A Hybrid Data-Driven Machine Learning Approach for Anomaly Detection
Anomaly detection is a vital application field of machine learning. Given the widespread problems of extreme class imbalance and complex nonlinearity in high-dimensional data, traditional anomaly detection methods can hardly achieve both high-precision identification and model interpretability. Taking non-invasive prenatal testing data as the research background, this paper proposes a two-stage expert model and constructs an anomaly detection strategy integrating statistical analysis and machine learning. After appropriate data oversampling, the random forest algorithm is used to preliminarily verify the reliability of the results, and the recall rate of each expert model on the test set exceeds 0.9. SHAP is further introduced to analyze the main effects and interaction effects, indicating that the Z-score of each chromosome plays a core role in its corresponding model. Combined with index quality control in NIPT theory, this paper innovatively applies SHAP single-feature dependence analysis to explore the stability of quality control indicators, constructs a standard quality control score, and divides samples into high, medium, and low risk regions, with an abnormality rate of only 7.6% in the low-risk region. On this basis, the results of multi-index collaborative quality control are analyzed and predicted.
AI Empowered Breakthroughs and Innovations in Drug Development
The development of novel drugs has long faced the severe challenge of the "Double Ten Law," with pharmaceutical R&D persistently encountering the "triple dilemma" of prolonged cycles, high costs, and low success rates, falling into the trap of "anti-Moore's Law." The rise of artificial intelligence (AI) technology offers a comprehensive solution to this predicament, achieving efficiency revolutions across all stages—from target discovery and molecular design to clinical trials. To outline the technological evolution of AI in drug discovery and design and analyze its role in driving innovative models for new drug discovery, this study conducts a systematic literature review and case analysis. It focuses on the core pain points of traditional drug development, elaborates on the application pathways and technological innovations of AI throughout the drug development process, presents practical achievements and potential enabled by AI based on real-world data, examines current challenges in data, technology, and talent, and outlines future prospects. The findings aim to provide insights for technological innovation and industrial upgrading in the field of drug development.
Enhancing Engagement in Speech Rehabilitation through Gamified Interaction and Emotional Design
Speech rehabilitation systems often suffer from low user engagement due to repetitive training processes and limited emotional support. To address this issue, this study proposes an integrated interaction design framework combining gamification, emotional design, and multisensory compensation mechanisms for speech rehabilitation. Based on User-Centered Design (UCD) principles and Norman’s emotional design theory, a mobile application named Yinshu was developed, incorporating gamified tasks, adaptive difficulty adjustment, AI emotional companion interaction, and a “visualizing sound” feedback mechanism. To evaluate the system, 40 participants completed a customized short-form User Engagement Scale (UES) questionnaire after using the application. The results showed positive evaluations across all dimensions, with mean scores above 4.0 on a 5-point Likert scale. Gamified rewards improved motivation and self-efficacy, while emotional companion interaction reduced feelings of isolation and enhanced psychological engagement. In addition, the visual feedback system effectively improved users’ understanding of pronunciation accuracy and reduced cognitive barriers during training. The findings suggest that integrating gamification and emotional design can effectively enhance engagement in speech rehabilitation, providing practical insights for future digital rehabilitation system design.
Research on a Complete Set of Intelligent Agricultural Protection Equipment Based on AIoT and Knowledge Graph Technology for Remote Spectral Flight Control
In response to the problems existing in the traditional extensive agricultural protection mode, such as low monitoring coverage, blind pesticide usage, low herbicide efficiency, and poor adaptability to low-growing crops, combined with the demand for fragmented operations in small-scale farmland in China, this paper has developed a set of intelligent agricultural protection equipment for crops based on AIoT and knowledge graph technology. This equipment integrates high-altitude inspection by drones and ground operation by agricultural protection carts, innovatively applying high-spectrum imaging technology to solve the problem of monitoring low-growing crop canopies, adopting a combined structure of physical cutting and chemical inhibition to optimize the control effect of weeds, and completing the parameter selection of power motors and active shaft servo motors through theoretical calculation and the construction of intelligent algorithms, to achieve a closed-loop operation system of "monitoring - analysis - decision - execution". Through multiple sets of repeated field tests, the accuracy rate of disease and pest detection and weed detection of the equipment reached 97.5%, the chemical pesticide saving rate was 87%, the weed removal rate was 88.3%, and the operation efficiency was 62.5% higher than that of manual operations. All performance indicators met the research goals. The research results show that this equipment is suitable for operations in various terrains, has controllable costs, and can provide low-cost and practical intelligent agricultural protection solutions for small-scale farmland, effectively filling the gap in precise agricultural protection technology for low-growing crops, improving the integration application system of AIoT and knowledge graph technology in the field of smart agriculture, and having important theoretical value and practical significance for promoting agricultural cost reduction and efficiency improvement and green sustainable development.
WorldSense: Enabling Safe Autonomous Navigation Under Rare Event Scenarios
Autonomous vehicle (AV) safety in rare or long-tail driving scenarios remains one of the most intractable challenges in modern intelligent transportation research. This paper presents WorldSense, a unified framework that integrates predictive world modeling, temporal anomaly detection (TAD), and safety-aware planning to enable reliable navigation under infrequent but high-risk conditions. WorldSense encodes the driving environment into a compact latent representation using a convolutional neural network (CNN)-based multi-camera perception backbone, predicts future scene evolution through a recurrent gated memory module, and monitors reconstruction divergence in real time to generate a rarity score that triggers conservative trajectory planning when anomalous events are detected. Evaluations conducted on the CARLA simulator using a custom rare event test suite (RETS) and on the nuScenes benchmark demonstrate that WorldSense reduces collision rates by 34.2% and improves route completion by 14.6 percentage points relative to transformer-based planning baselines under rare event conditions. These results establish WorldSense as a principled and scalable framework for safety-critical autonomous navigation in scenarios underrepresented in standard training distributions.
Privacy Preserving Language Models Detect Early Warning Signs in Digital Mental Health
The proliferation of digital communication platforms has generated large-scale behavioral and linguistic data streams that carry meaningful signals about users' mental health trajectories. Deploying language models to analyze such data, however, introduces serious privacy challenges, as mental health-related text constitutes among the most sensitive categories of personal information. This paper proposes an integrated framework combining federated learning and differential privacy mechanisms to enable privacy-preserving language model training for early warning sign detection across depression, anxiety, and suicidal ideation. The architecture allows gradient updates to be computed locally on distributed client nodes and aggregated without centralizing raw user content, while formal DP guarantees bound the statistical influence of any individual's data on the released model. Experimental evaluations demonstrate competitive detection performance, with an area under the receiver operating characteristic curve of 0.847 at epsilon equal to five, approaching the non-private centralized baseline of 0.891. The results confirm that privacy-preserving approaches are technically feasible and clinically viable for real-world digital mental health monitoring applications.
Dynamic Knowledge Graph Augmentation Enhances Factual Accuracy in Retrieval Based Generation
Large language models (LLMs) demonstrate exceptional fluency in natural language generation but remain susceptible to producing factually incorrect outputs due to static parametric knowledge frozen at training time. Retrieval-augmented generation (RAG) partially mitigates this limitation by conditioning generation on externally retrieved evidence, yet conventional RAG systems depend on unstructured, flat document corpora that fail to represent the relational and temporal dynamics characterizing real-world knowledge. This paper proposes Dynamic Knowledge Graph Augmentation (DKGA), a framework that integrates continuously updated knowledge graphs (KGs) with retrieval-based generation pipelines to substantially improve factual accuracy. DKGA employs a graph neural network (GNN) encoder for subgraph-conditioned entity representation learning, a temporal update module for incremental knowledge refresh, and a cross-modal relevance-aware fusion mechanism that jointly conditions the generator on structured KG evidence and unstructured text passages. Experiments on the WebQA and TriviaQA benchmarks demonstrate that DKGA achieves an 11.4% improvement in exact match factual accuracy and an 18.2% reduction in hallucination rate over strong RAG baselines. These results provide compelling evidence that dynamic, structured knowledge representations are a critical and underutilized resource for knowledge-intensive language generation.
Research on 3D Geological Modeling Methods Based on Big Data Analysis
With the advent of the big data era, the scale and complexity of geological data have been continuously increasing. Traditional geological modeling methods can no longer meet the demands for high precision and high efficiency. Therefore, proposing new methods for 3D geological modeling based on big data analysis has become particularly important. This paper focuses on the collection and processing of big data, explores the application of big data analysis technologies in 3D geological modeling, and further proposes a 3D geological modeling algorithm based on big data analysis to improve modeling accuracy and visualization capabilities. Future research can further explore big data analysis methods and technologies in 3D geological modeling to promote development and innovation in the field of geology.
Hierarchical YOLOv5 Detection and ResNet Recognition Pipeline for Degraded Heritage Character Imagery
Character recognition on heavily degraded historical imagery presents three intertwined challenges: heterogeneous noise patterns generated by the underlying physical substrate, scarce labeled training data within the target domain, and small character instances embedded in cluttered backgrounds. This paper develops a four-stage pipeline that integrates adaptive image denoising, hierarchical YOLOv5 detection, multi-scale convolutional feature analysis, and ResNet recognition with cross-domain transfer learning. The denoising stage combines mean filtering with morphological opening to suppress impulse noise and blob-like artifacts, complemented by an attention-based generative adversarial network for residual texture artifacts. The detection stage trains YOLOv5 with initial learning rate 0.01, achieving test-set precision of 64.10% and recall of 49.50%, with the loss function stabilizing at 0.04 after sufficient iterations and outperforming YOLOv2, YOLOv3, Fast R-CNN, and R-CNN baselines on intersection-over-union, recall, and average precision metrics. Multi-scale feature visualization across convolutional output layers confirms that the detection model recovers character locations across 200 benchmark images and produces region coordinate vectors with high coverage. The recognition stage achieves over 70% accuracy under in-domain training, and incorporation of an external auxiliary dataset through transfer learning substantially improves recognition accuracy. Sensitivity analysis under additive noise confirms pipeline robustness across degradation levels.
A Visual SLAM-Based Framework for Indoor Hovering Robots in 3D Rescue Missions with Dynamic Obstacle Avoidance
This paper presents an intelligent and energy-efficient UAV framework for indoor rescue missions in GPS-denied environments. The proposed system integrates visual-inertial SLAM (Simultaneous Localization and Mapping), dynamic obstacle avoidance, and adaptive path planning using lightweight embedded sensors. At its core, the design fuses monocular camera data with inertial measurements through ORB-SLAM3, enhanced by 3D depth sensing from the STMicroelectronics VL53L9 dToF LiDAR. A deep learning module based on YOLOv5 and semantic mapping enables the UAV to detect and navigate around dynamic obstacles, while a novel risk-based path planner evaluates cost maps in real time to optimize safety and energy consumption. The architecture is evaluated in simulated disaster environments, demonstrating improved localization accuracy, reduced power usage, and superior obstacle avoidance over conventional SLAM-based and planning baselines. The results support the framework’s viability for deployment in real-world rescue missions requiring intelligent aerial autonomy.
Structure Enhancement and Cross-Modal Alignment for Open-Vocabulary Semantic Segmentation
This paper proposes a structure-enhanced cross-modal alignment method for open-vocabulary semantic segmentation. Existing methods mostly rely on CLIP’s image-level vision-language alignment capability, but CLIP visual features remain insufficient for modeling fine-grained spatial information such as boundaries, textures, and region structures. Moreover, relying solely on semantic alignment between images and text categories makes it difficult to model fine-grained correspondences between visual and textual information. To address these issues, we design a DINO Structure Enhancement Module and a Cross-Modal Alignment Module (CCMA). The DINO Structure Enhancement Module introduces a parameter-frozen DINO model to extract structural priors and adaptively enhance CLIP visual features, thereby producing structure-aware visual features. CCMA jointly models global visual semantics, local region features, and text semantic prototypes to mine fine-grained vision-language consistency at the region level, thereby strengthening the correspondence between image regions and textual semantics. Experimental results demonstrate that the proposed method effectively improves open-vocabulary semantic segmentation performance.
A weighted average temperature model for the Yunnan-Guizhou region considering multiple factors
Addressing the issue of complex terrain in the Yunnan-Guizhou region and the insufficient applicability of existing weighted mean temperature (Tm) models, this paper constructs a multi-factor, multivariate nonlinear Tm empirical model (YGTm) based on data from six sounding stations spanning 2010 to 2019. Internal consistency, external consistency, and leave-one-out cross-validation were employed to compare the model with Bevis, GPT2w, and HGPT2 models. The results indicate that the YGTm model exhibits a root mean square error (RMS) of 2.52 K, representing a 15%–39% improvement in accuracy compared to the comparative models; the relative error of atmospheric precipitable water vapor (PWV) retrieved based on YGTm is only 0.85% on average. This model can provide high-precision Tm support for GNSS water vapor retrieval in the Yunnan-Guizhou region.
An Enhanced CTNet for Motor Imagery EEG Classification
To address the limited feature discriminability, substantial training fluctuations, and limited decision stability during inference in four-class motor imagery electroencephalogram (MI-EEG) classification, this paper proposes an enhanced CTNet method that integrates lightweight structural enhancement, stability-oriented training, and complementary fusion inference. Built upon CTNet, the proposed method introduces a lightweight token-channel gate to adaptively recalibrate high-level token features on top of the joint modeling of a convolutional front-end and a Transformer encoder, and further adopts a two-layer classification head to enhance final discriminative capability. During training, label smoothing, center loss, exponential moving average of parameters, Top-5 checkpoint averaging, composite-score-based model selection, and lightweight input augmentation are incorporated to reduce model fluctuations under small-sample conditions. During inference, the logits of the CTNet-Repro baseline model and the enhanced model are fused in a weighted manner to exploit their decision complementarity. Under the subject-dependent protocol on the BCI Competition IV 2a dataset, the proposed method achieves average Accuracy, Macro-F1, and Balanced Accuracy of 80.63%, 80.17%, and 80.41%, respectively, across 9 subjects, representing improvements of 4.52, 4.69, and 4.49 percentage points over CTNet-Repro. The results demonstrate that the proposed method can effectively improve discriminative performance, model-selection stability, and test-stage robustness for MI-EEG classification while largely preserving the original CTNet backbone. Highlights: (1) An enhanced CTNet is proposed for four-class MI-EEG classification.(2)A lightweight token-channel gate improves discriminative high-level token features. (3) Stabilized training combines label smoothing, center loss, EMA, and Top-5 averaging. (4) Composite-score-based model selection improves robustness under small-sample settings. (5) Logits-level post-fusion further boosts Accuracy, Macro-F1, and Balanced Accuracy.
Remote Sensing Identification and Spatiotemporal Evolution of Cultivated Land Non-Agriculturalization in Qixian County Based on Google Earth Engine
Cultivated land non-agriculturalization is an important manifestation of county-level land use transition. Timely identification of its spatial distribution and phased evolution is essential for farmland protection and rational land resource allocation. Taking Qixian County in Henan Province, China, as the study area, this paper used Sentinel-2 imagery from 2020, 2023, and 2025 on the Google Earth Engine platform. Percentile compositing was employed to generate multi-temporal feature images, and a random forest classifier was used to extract land use information. A unified cultivated land mask for 2020 was then used as a constraint, and post-classification comparison was applied to identify the conversion of cultivated land to non-agricultural uses. The spatiotemporal evolution of non-agriculturalization was analyzed in terms of area, transition type, and spatial distribution. The results show that the overall accuracy of land use classification in the three periods was higher than 83%, and the Kappa coefficient was above 0.77. The areas of cultivated land non-agriculturalization in 2020–2023, 2023–2025, and 2020–2025 were 54.5241 km², 10.3185 km², and 40.6306 km², respectively. Conversion to water dominated in 2020–2023, whereas conversion to orchard/woodland became dominant later. Spatially, non-agriculturalization was concentrated around the county seat, township centers, and transport corridors.
A concept drift-oriented active learning method
The continuous growth and evolving internal distribution of streaming data were long challenged by the phenomenon of concept drift in classification tasks. In practical applications, labeling costs were prohibitively high, and active learning was frequently employed to mitigate the scarcity of labels. However, in drifting environments, a single sampling strategy was prone to induce selection bias and information redundancy. In this paper, a concept drift-oriented active learning method, termed CDAL, was proposed, which integrated drift detection with a clustering-based representative sampling mechanism. Upon detecting a distribution change, a candidate set was constructed from neighboring samples and structurally partitioned using online clustering. A global average uncertainty was then calculated as a dynamic reference, and the overall uncertainty level within each cluster was compared. Based on this comparison, either a centroid sample or a random sample was adaptively selected from the cluster for labeling. This design eliminated the need for preset fixed thresholds and balanced the information content of samples with their distribution coverage while controlling annotation costs. Experiments conducted on multiple real-world and synthetic datasets demonstrated that CDAL achieved superior cumulative accuracy and related evaluation metrics compared to baseline methods and was capable of rapidly restoring classification performance for new concepts after drift occurred, thereby validating the effectiveness of the proposed strategy.
Optimization and Comprehensive Application of ORB Algorithm and CNN Combined in Feature Extraction
In recent years, an increasing number of studies have attempted to combine ORB with CNN to achieve both efficiency and high accuracy. This survey, based on existing literature, systematically organizes typical fusion methods, application cases, optimization strategies, and challenges of ORB+CNN, and discusses future research trends in this direction. All literature used in this survey comes from published research results, including representative studies of ORB and CNN in fields such as iris recognition, clothing retrieval, vehicle classification, and SDN traffic classification.
Research on Weak Formation Interval Identification in Oil Drilling Based on CNN-BiLSTM-MHA Model
Weak formation intervals in oil drilling are critical sections that may induce complex downhole problems, such as wellbore instability and borehole enlargement. Their accurate identification is of great significance for improving drilling safety and operational efficiency. To address the limitations of traditional weak formation identification methods, including strong dependence on expert experience, high subjectivity, and insufficient utilization of local logging responses and depth-wise sequential dependencies, this paper proposes a weak formation identification method based on a CNN-BiLSTM-MHA model. First, weak formation identification samples are constructed using logging curves and well trajectory parameters, and data preprocessing is performed through missing value handling, outlier correction, standardization, and sliding-window sampling. Then, a convolutional neural network is used to extract local response features, a bidirectional long short-term memory network is employed to model forward and backward dependencies along the depth direction, and a multi-head attention mechanism is introduced to enhance the representation of key intervals and critical features. Finally, comparative experiments and ablation experiments are conducted to verify the effectiveness of the proposed model. Experimental results show that the proposed model achieves an Accuracy, Precision, Recall, and F1-score of 91.52%, 90.28%, 90.15%, and 90.21%, respectively, outperforming comparative models such as SVM, XGBoost, TCN, and Informer. The proposed method can provide methodological support for intelligent identification of weak formation intervals in oil drilling.
Weak formation intervals in oil drilling
Local Elite-Guided Collaborative Optimization Algorithm for Continuous Distributed Constraint Optimization Problems
To address the issues of premature convergence to local optima and slow convergence speed in Continuous Distributed Constraint Optimization Problems (C-DCOPs), this paper proposes a Local Elite-Guided Collaborative Optimization (EGA-LC) algorithm. Utilizing a Breadth-First Search (BFS) pseudo-tree, the algorithm constructs a distributed population structure partitioned into ten sub-populations for parallel search, each further classified into dominant, non-dominant, and local dominant sub-populations based on fitness. Subsequently, a differential evolution strategy is designed to guide individual updates through the synergistic influence of global elites and local dominant solutions, enabling deep mining of high-quality local constraint information. Furthermore, a periodic cross-population recombination and reassignment mechanism is introduced, which leverages historical global optimal solutions to accelerate convergence and prevents evolution stagnation by reallocating sub-populations. Experimental results on four benchmark problem sets demonstrate that EGA-LC significantly outperforms five state-of-the-art algorithms in terms of both solution quality and convergence speed.
Continuous Distributed Constraint Optimization Problems
A Real-Time PCB Defect Detection Framework on Raspberry Pi Based on YOLOv5n
PCB defect detection requires both high accuracy and real-time performance, while small defect sizes and complex backgrounds pose significant challenges for deployment on edge devices. This paper proposes a real-time PCB defect detection method based on the YOLOv5n model and constructs an edge-side detection system on Raspberry Pi 5. To enhance small defect detection capability, dataset integration, image slicing, and data augmentation strategies are employed. The trained model is deployed using the NCNN framework for lightweight inference. Experimental results demonstrate that the proposed method achieves 97.0% mAP50 on the test set and maintains a stable real-time detection speed of approximately 9 FPS on Raspberry Pi 5, showing strong performance on small-scale and sparse defects. The proposed approach provides a feasible solution for low-cost PCB defect detection on edge devices.
A Multimodal Video Anomaly Detection Method Based on Vision-Language Alignment
Existing weakly supervised anomaly detection methods suffer from insufficient semantic alignment of features and a lack of fine-grained localisation capabilities. Furthermore, purely visual models have limitations in understanding semantic information, making it difficult to effectively integrate visual features with semantic information, which results in poor discrimination of multi-class anomalies. To address these issues, this paper proposes a vision-language aligned video anomaly detection model. By introducing a vision-language alignment branch and utilising a pre-trained CLIP model to achieve fine-grained semantic alignment between video features and category text prompts, this model overcomes the limitations of purely visual models in understanding semantic information.It designs an adaptive gated fusion mechanism to dynamically fuse the global anomaly scores from the original visual branch with the semantically guided scores from the alignment branch, combining the complementary strengths of visual pattern recognition and semantic understanding to enhance the model’s ability to distinguish between multiple anomaly categories.And it constructs a multi-task loss function to jointly optimise temporal localisation and fine-grained classification tasks, making full use of video-level weak supervision signals and cross-modal alignment information. Experimental results on the UCF-Crime and XD-Violence datasets demonstrate that this method effectively improves fine-grained anomaly localisation and classification performance, exhibiting significant advantages.
Tuckman Optimization Algorithm: A novel metaheuristic inspired by Tuckman’s Stages of Group Development for solving benchmark and engineering problems
This paper proposes a novel Tuckman Optimization Algorithm (TOA) based on Tuckman’s Stages of Group Development for solving optimization and engineering design problems. TOA is the first to embed this theory into an intelligent optimization framework, simulating team dynamics to guide population search behavior. Its core innovations include a team control parameter and a team structure reconfiguration mechanism. The control parameter dynamically adjusts stage weights and evolutionary strategies based on iteration progress, enhancing adaptability and convergence stability. The team structure reconfiguration mechanism, based on a logistic growth model, replaces inferior individuals to maintain population vitality and avoid premature convergence. Experimental results on the 10-dimensional CEC2017, CEC2020, and CEC2022 benchmark suites show that TOA outperforms three baseline algorithms, ten advanced metaheuristics, and the CEC2017 winner LSHADE SPACMA in both accuracy and robustness. Additional tests on engineering design problems further validate TOA’s effectiveness and broad applicability in real-world optimization scenarios.
Data-driven and algorithm-enabled: The logical evolution, paradigm reconfiguration and practical dimension research of artificial intelligence in sports event prediction
With the exponential growth of sensing technology and computing power, artificial intelligence (AI) is increasingly deeply involved in the field of sports prediction, driving a paradigm shift in sports scientific research from "experience-driven" to "data-driven". This article systematically reviews the evolution logic of AI in sports prediction and analyzes the underlying mechanism of the migration from traditional statistical models to deep learning and reinforcement learning algorithms. The study finds that AI, through the deep mining of multi-source heterogeneous sports data, not only demonstrates remarkable accuracy in predicting competitive performance and assessing the risk of sports injuries, but also achieves functional reconfiguration in the dimensions of sports industry decision support and enhanced spectator experience. This article constructs a technical path model for AI sports prediction and conducts a deep examination of its value spillover and technical limitations in practical applications. The research aims to provide theoretical support for the digital transformation of China's sports industry and offer path references for the optimization and ethical governance of intelligent sports prediction algorithms.
SDAFormer: A Semantic-Guided and Detail-Aware Transformer for Apple Counting in Complex Orchards
Accurate apple counting is crucial for orchard yield estimation and automated management. However, in complex natural agricultural settings, issues such as scale variations, fruit occlusion, and background interference pose significant challenges to existing counting methods. Current mainstream models often struggle to balance global contextual information with local fine-grained features, resulting in inaccurate counts in these areas and difficulty in effectively distinguishing fruits from complex backgrounds. To address the issues of easily disturbed shallow-level details and insufficient coordination between high-level semantics and local structure that apple targets face under varying scales and occlusion conditions in real orchard scenarios, this paper proposes a semantic-guided and detail-aware Transformer-based apple counting method, Named SDAFormer. This method uses the Semantic-Aware Detail Refinement Module (SADRM) to explicitly inject deep semantic information into shallow-level edge, texture, and local structural features, thereby enhancing the feature completeness and discriminative power of occluded apple regions; Through the Coordinate-Aware Multi-scale Module (CAMM), it enhances the position-aware capabilities and multi-scale context modeling during the density map regression stage, thereby improving the model’s counting stability under varying scales and in partially occluded scenarios. Experimental results demonstrate that this method achieves superior counting performance on a self-built apple dataset, with a Mean Absolute Error (MAE) of 3.61 and a Mean Squared Error (MSE) 4.76.