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Mobile robots play a pivotal role in advancing smart manufacturing technologies. However, existing Obstacle avoidance path Planning (OP) algorithms for mobile robots suffer from low stability and applicability. Therefore, this paper proposes an enhanced Secret Bird Optimization Algorithm (SBOA)-based OP algorithm for mobile robots to address these challenges, termed AGMSBOA. Firstly, an adaptive learning strategy is introduced, where individuals enhance the diversity of the algorithm’s population by summarizing relationships among candidates of varying quality, thereby strengthening the algorithm’s ability to locate globally optimal obstacle avoidance path regions. Secondly, a group learning strategy is incorporated by dividing the population into learning and teaching groups, enhancing the algorithm’s exploitation capabilities, improving the accuracy of obstacle avoidance path planning, and reducing actual runtime. Lastly, a multiple population evolution strategy is proposed, which balances the exploration/exploitation phases of the algorithm by analyzing the nature of different individuals, improving the algorithm’s ability to escape suboptimal obstacle avoidance path traps. Subsequently, AGMSBOA was used to solve the OP problem on five maps and two OP problems in real-world environments. The experiments illustrate that AGMSBOA achieves more than 5% performance improvement in path length and a 100–win rate in runtime metrics, as well as faster convergence and stability of the solution. Therefore, AGMSBOA proposed in this paper is an efficient, robust, and robust OP method for mobile robots.

In the evolving digital landscape, network flow models have become integral to various sectors, including supply chain management. This research develops a robust network flow model for semiconductor wafer supply chains, optimizing resource allocation and addressing maximum flow challenges in production and logistics. The model incorporates the stochastic nature of wafer batch transfers and employs a dual-layer optimization framework to reduce variability and exceedance probabilities in finished goods. Empirical comparisons reveal significant enhancements in cost efficiency, productivity, and resource utilization, with a 20% reduction in time and production costs and a 10% increase in transportation and storage capacities. The model’s efficacy is underscored by a 15% decrease in transportation time and a 6700 kg increase in total capacity, demonstrating its capability to resolve logistical bottlenecks in semiconductor manufacturing. This study concludes that network flow models are a potent tool for optimizing supply chain logistics and offer a 23% improvement in resource utilization along with a 13% boost in accuracy. The findings provide valuable insights for supply chain logistics optimization.

Extreme Multi-Label Text Classification (XMTC) is a crucial task in natural language processing, aiming to assign the most relevant subset of labels to an input text from an extremely large label set. Existing deep learning models often neglect the correlation information among labels when addressing XMTC, resulting in limited prediction performance. This paper proposes the Label Correlation Enhancement Network (LCENet), a pluggable modular architecture capable of effectively capturing and utilizing inter-label correlation knowledge without modifying the original model structure. The LCENet module introduces a bottleneck layer and residual connection mechanism, transforming raw label predictions into correlation-enhanced outputs. The bottleneck design reduces parameter complexity from [Formula: see text] to O(RL), effectively addressing computational feasibility under extreme-scale label spaces. We integrate the LCENet module into various mainstream deep XMTC models-including CNN-, BERT-, and RNN-based architectures-and conduct comprehensive evaluations on three benchmark datasets: EUR-Lex, AmazonCat-13K, and Wiki-500K. Experimental results demonstrate that LCENet significantly improves the performance of baseline models, achieving consistent gains across multiple metrics such as Precision@k and nDCG@k, with a maximum increase of 5.22 percentage points in P@1, while also accelerating model convergence. Ablation studies further verify the effectiveness of key components, including the bottleneck layer, residual connections, and nonlinear activation functions. Training curve analysis shows that LCENet provides stronger learning signals through label correlation constraints, alleviating the early-stage stagnation observed in some baseline models (Fig. 5) and reducing the required convergence steps by nearly half. This study presents a practical and effective enhancement framework for deep multi-label learning, offering excellent scalability and practical applicability. The core idea of LCENet can be extended to other structured prediction tasks such as multimodal learning and sequence labeling.

Human motion prediction and action recognition are critical tasks in computer vision and human-computer interaction, supporting applications in surveillance, robotics, and behavioral analysis. However, effectively capturing the fine-grained semantics and dynamic spatiotemporal dependencies of human skeleton movements remains challenging due to the complexity of coordinated joint and part-level interactions over time. To address these issues, we propose a spatiotemporal skeleton modeling framework that integrates a Part-Joint Attention (PJA) mechanism with a Dynamic Graph Convolutional Network (Dynamic GCN). The proposed framework first employs a multi-granularity sequence encoding module to extract joint-level motion details and part-level semantics, enabling rich feature representations. The PJA module adaptively highlights critical joints and body parts across temporal sequences, enhancing the model’s focus on salient regions while maintaining temporal coherence. Additionally, the Dynamic GCN dynamically constructs and updates inter-joint spatial relationships based on temporal feature similarities, facilitating effective spatiotemporal reasoning. Extensive experiments on the Human3.6M dataset demonstrate that our method consistently outperforms strong baselines across various prediction horizons. Specifically, it achieves a Mean Per Joint Position Error (MPJPE) of 10.2 mm at 80 ms and 57.5 mm at 400 ms, outperforming the best baseline by 9–12 percentage relative improvement across diverse actions. These results indicate the proposed method’s ability to accurately capture both subtle and large-scale human motions while maintaining temporal stability. This work advances the development of interpretable and precise skeleton-based motion modeling and can benefit broader domains such as real-time human-robot interaction, intelligent surveillance, and behavior recognition in practical environments.

In the field of financial technology, stock prediction has become a popular research direction due to its high volatility and uncertainty. Most existing models can only process single temporal features, failing to capture multi-scale temporal patterns and latent cyclical components embedded in price fluctuations, while also neglecting the interactions between different stocks–resulting in predictions that lack accuracy and stability. The StockMixer with ATFNet model proposed in this paper integrates both time-domain and frequency-domain features. By fusing information from both domains, the deep neural network significantly improves prediction accuracy and reliability. While temporal feature analysis is common, frequency-domain features, derived via spectral analysis (e.g., Fourier Transform), can reveal latent periodicities and seasonality patterns in price movements. This study employs an adaptive fusion approach to allow the two types of features to complement and enhance each other. The main innovations of this model are reflected in three aspects: (1) Construction of a time-channel hybrid model (MultTime2dMixer) to decouple the temporal evolution and inter-channel interactions of multivariate time series. (2) A novel non-graph-based stock relation modeling approach (NoGraphMixer) is proposed, which employs a learnable attention-based mapping mechanism to dynamically capture cross-stock dependencies without relying on pre-defined or static graph structures–thereby overcoming the inflexibility of conventional graph-based relation encoders. (3) Integration of a frequency-domain complex attention model (ATFNet) to model discontinuities in both the time and frequency domains, providing a strong supplement to time-domain modeling. At the implementation level, the original stock sequences are subjected to bidirectional feature extraction along both time and channel dimensions. NoGraphMixer is then used to construct implicit stock correlations. ATFNet is applied to map time-series data into both the temporal and frequency domains, extracting spectral features. Finally, a fusion mechanism integrates multimodal information to achieve effective fusion of multi-source data. Experimental results show significant improvements in classification evaluation metrics (Accuracy, Precision, Recall, F1-score) for predicting price movement direction, as well as in metrics assessing the ranking ability of return predictions and backtesting performance–IC, RIC, Prec@N, and Sharpe Ratio (SR).

This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system’s scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety.

Autonomous path planning in obstacle-dense environments remains challenging for swarm intelligence methods due to infeasible initialization, insufficient exploration–exploitation balance, and poor trajectory smoothness for real-robot execution. To address these issues, this paper proposes a Kinematic-Aware Improved Hippo Optimization algorithm (KA-IHO) for mobile robot path planning. The proposed method integrates four components: an elite safety pool initialization strategy to improve feasible solution generation in dense maps, a hierarchical elite-scout update mechanism to better balance global exploration and local exploitation, anti-stagnation mechanisms including a Population Stagnation Restart strategy and a 10-Direction Radial Micro-Search to guarantee high feasibility rates across all map complexities, and a late-stage Laplacian Line-of-Sight Ironing Operator to reduce path redundancy and improve trajectory smoothness. Comparative experiments are conducted on five reproducible grid maps with different complexity levels (40×40 and 80×80), where KA-IHO is evaluated against six representative algorithms, including HO, SBOA, PSO, GWO, ARO, and INFO, over 20 independent runs. The results show that KA-IHO consistently achieves collision-free planning and obtains lower mean fitness values with smaller standard deviations than the compared methods, indicating improved robustness and solution quality. In addition, hardware closed-loop experiments on a differential-drive mobile robot demonstrate that the planned paths can be executed reliably in real environments, with trajectory tracking errors controlled within ±4 cm.

This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. The R-CSAN adopts an encoder-decoder architecture, where the encoder extracts feature correlations from historical data through multiple layers of channel-spatial attention modules, and the decoder incorporates a masking mechanism to prevent future information leakage and introduces a cross-attention mechanism to model inter-market correlations. Experiments conducted on four cross-market stock datasets, including Amazon, Maotai, Ping An, and Vanke, demonstrate that R-CSAN significantly outperforms not only traditional baseline models such as ARIMA, LSTM, and CNN-LSTM, but also recent Transformer-based approaches like Informer, Autoformer, and iTransformer on metrics including RMSE, MAE, MAPE, , and return on investment. The model reduces RMSE by 17.3–49.3% compared to traditional methods and 6.2–11.6% compared to Transformer variants, with the highest reaching 93.17% and an increase in return on investment to 482.64%. Ablation experiments confirm the critical contributions of each component, with the temporal module removal causing an average increase of 38.6% in RMSE and channel-spatial attention removal resulting in a 21.3% increase. Moreover, the model provides an interpretative analysis of features and temporal dimensions through attention weight visualization, offering insights into both indicator importance and critical time periods for prediction. In practical applications, R-CSAN’s outputs can be integrated into quantitative trading strategies including breakout trading, moving average crossover signals, and portfolio allocation optimization, providing a new paradigm for robust prediction in highly volatile markets.

Diabetes mellitus is one of the major issues in global public health and its cardiovascular complications are the primary cause of death in patients. Traditional risk assessment models, such as the Framingham Risk Score and the UKPDS Risk Engine, are built primarily on linear hypotheses, so they fail to capture complex non-linear relationships and show poor generalization across different populations. In this study, a multi-index dynamic weighted ensemble model was built by innovatively integrating TCM tongue diagnosis indexes with modern medical biomarkers. Specifically, adopting a cross-sectional design, this study initially enrolled 3,111 Type 2 diabetes patients, of which 2,895 were included in the final analysis after excluding 216 participants with excessive missing data ( ), built base models using Random Forest (RF), Gradient Boosting Decision Tree (GBDT), K-nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) algorithms, and developed a dynamic weighted ensemble framework for optimizing model integration. According to the study results, the ensemble model achieved an accuracy of 95.68%, a sensitivity of 94.92%, and a specificity of 96.21%, remarkably outperforming the existing models. The SHAP analysis revealed that indexes such as chest tightness, ESR, and tongue purple had a significant non-linear influence on diabetes risk. Moreover, the ablation test results further proved the superiority of the ensemble framework over single-algorithm frameworks. This study effectively integrates TCM diagnosis indexes with Western medical indexes, provides a reliable decision-making tool for early screening and personalized intervention in diabetic complications, and shows great values for clinical application and transformations.

This study presents an advanced conveyor belt inspection system that integrates laser scanning technology with deep learning to achieve high-precision micro-damage detection. The system is designed to overcome two major challenges in industrial inspection: ensuring reliable operation under harsh environmental conditions such as dust, vibration, and low illumination, and enabling the early identification of subtle belt defects that are often overlooked by conventional approaches. To this end, an innovative hardware platform was developed, combining laser-based illumination with high-speed imaging to enhance defect visibility. On the algorithmic side, an improved You Only Look Once (YOLO)v7 model was proposed, incorporating four enhancements-funnel rectified linear unit (F-ReLU) activation, spatial pyramid pooling fast cross stage partial convolution (SPPFCSPC) module, efficient intersection over union (EIoU) loss function, and squeeze-and-excitation (SE-Net) attention mechanism. A comprehensive dataset was constructed from both laboratory test benches and field-collected samples in a coal coking plant, ensuring robustness across diverse operating conditions. Experimental results demonstrate that the improved YOLOv7 achieves a mean average precision (mAP@0.5) of 96.6%, significantly surpassing the baseline YOLOv7 (90.7%) and outperforming recent detectors such as DETR and RT-DETR in both accuracy and efficiency. Moreover, long-term reliability tests, including 72-hour continuous operation and low-light industrial deployment, validated the system’s stability and adaptability. These contributions highlight not only the technical novelty of combining laser-enhanced imaging with deep learning, but also the practical value for predictive maintenance, safe production, and sustainable operation. This work offers a robust and scalable inspection framework, advancing the digitalization and intelligent automation of conveyor systems in line with Industry 4.0 principles.