Path planning is a core technology for mobile robots. However, existing state-of-the-art methods suffer from issues such as excessive path redundancy, too many turning points, and poor environmental adaptability. To address these challenges, this paper proposes a novel global and local fusion path-planning algorithm. For global path planning, we reduce path redundancy and excessive turning points by designing a new heuristic function and constructing an improved path generation method. For local path planning, we propose an environment-aware dynamic parameter adjustment strategy, incorporating deviation and avoidance dynamic obstacle evaluation factors, thus addressing issues of local optima and timely avoidance of dynamic obstacles. Finally, we fuse those global and local path-planning improvements to form our fusion path-planning algorithm, which can enhance the robot’s adaptability to complex scenarios while reducing path redundancy and turning points. Simulation experiments demonstrate that the improved fusion path-planning algorithm not only effectively addresses existing issues but also operates with higher efficiency.

The COVID-19 pandemic, as well as the complex response techniques employed to halt its spread, are both detrimental to mental and emotional well-being. Students’ lives have been damaged by social alienation and self-isolation. These effects must be detected, analyzed, and dealt with to make sure the well-being of individuals, specifically students. This research examines the impact of parent–child relationships, parental autonomy support, and social support on enhancing students’ mental well-being using data collected from post-COVID-19. The Potential participants were students from several universities in Pakistan. For this reason, we chose Pakistan’s Punjab province, with 8 prominent institutions, as the primary focus for data collection. A questionnaire was created to gather information from 355 students. For descriptive statistics, SPSS was used, while AMOS structural equation modeling was used to test hypotheses. The findings revealed that social support on mental well-being (standardized β = 0.43, t = 7.57, p < 0.01) and parental autonomy support was significant and positively related to mental well-being (standardized β = 0.31, t = 5.016, p < 0.01), and predicted parent–child relationships. Furthermore, the parent–child relationship strongly mediated the association between social support, parental autonomy support, and students’ mental well-being. This research proposes that good social support and parental autonomy support improve parent–children relationships and contribute to students’ mental well-being.

Micro-expression recognition (MER) is inherently challenging due to the difficulty of extracting subtle, localized changes in micro-expressions (MEs). Various optical flow-based methods have been proposed for MER, as optical flow can effectively suppress facial identity information while capturing the movement patterns of MEs. However, these methods, characterized by simple architectures, often fail to extract discriminative features, resulting in suboptimal performance. In this paper, we propose an Enhanced Micro-expression Recognition Network with attention and distance correlation (EMRNet) for MER. EMRNet consists of three key phases: First, we introduce a novel channel-wise region-aware attention mechanism within two identical Inception networks, designed to extract global and local expression features in parallel, based on the optical flow input of the same ME. Second, to enhance ME representations, we propose a regularized dilated loss function incorporating distance correlation, which improves the information entropy transferred between the two branches. Last, emotion categories are predicted by fusing the expression-dilated features in the classification branch. Extensive experiments conducted on the composite database from the MEGC 2019 challenge demonstrate the effectiveness of EMRNet under both leave-one-subject-out (LOSO) cross-validation and the composite database evaluation (CDE) protocol. The results show that our approach successfully generates discriminative features, achieving substantial performance gains. Furthermore, EMRNet outperforms existing single-stream and dual-stream models, delivering superior results in MER.

Micro-expressions have the potential to reveal true emotions in fields such as public safety and healthcare. However, due to their small movement and short duration, their spotting has always been a significant challenge. To address the limitations of existing methods in feature extraction and representation, this paper proposes a micro-expression spotting method that combines Optical Flow features with Categorical Text information (OFCT). First, facial alignment is performed, and optical flow features in both the main and secondary directions are extracted from 13 regions of interest to capture subtle movements more comprehensively. Then, the micro-expression category text information is vectorized and fused with the main and secondary direction optical flow features, enhancing the spotting ability at the semantic level. In the micro-expression segment spotting phase, low-pass filtering and empirical mode decomposition are used to smooth and denoise the optical flow curves, preserving key change trends. Finally, high-precision micro-expression segments are obtained by combining threshold determination with non-maximum suppression. Experimental results show that the proposed method outperforms existing mainstream methods on the CAS(ME)² and SAMM-LV databases. Additionally, its F1-score on the MEGC2022 test set also exceeds the best result at the time, demonstrating the effectiveness and robustness of integrating category text information with main and secondary direction optical flow features in micro-expression spotting.

To address the issue of missed detection for small-scale occluded pedestrians in dense scenes, this paper proposes an improved YOLOv8 detection algorithm named Dense-YOLOv8. Firstly, to resolve the difficulty of extracting features from small-scale pedestrians in dense environments, a backbone network enhanced with deformable convolution and dynamic convolution is adopted to improve feature extraction capabilities. Simultaneously, a multi-scale linear spatial attention module is designed to amplify features of visible parts of occluded pedestrians while suppressing interference from complex backgrounds. Secondly, a small-scale pedestrian detection head is introduced in the neck of the YOLOv8 network to enhance detection performance for diminutive pedestrians. Finally, to improve training efficiency, a novel weighted loss function named DFL-SIoU is developed to accelerate model convergence. Experimental results demonstrate that the proposed algorithm achieves superior performance on two challenging dense pedestrian datasets, CrowdHuman and WiderPerson, significantly enhancing detection capabilities in dense scenarios. Comparative evaluations with other state-of-the-art pedestrian detection models further confirm the strong competitiveness of the proposed model.

Facial expression recognition (FER) remains a challenging task due to subtle variations in facial details and unconstrained conditions such as changes in head posture, illumination, and occlusion. Current FER approaches primarily focus on capturing discriminative facial features in vision manner, often neglecting the rich semantic information available in textual modalities. Additionally, these methods typically rely on generic classification templates, which fail to capture instance-specific features, resulting in inadequate representation and fine-grained discrimination ability. To tackle the above issues, we propose a novel emotion-aware adaptation framework that integrates the pre-trained CLIP model for FER, leveraging both visual and textual modalities to enhance representation learning and capture fine-grained emotional details. Specifically, we introduce the Expression-aware adapter module to capture emotion-specific facial representations through task-specific fine-tuning while preserving the generalization capabilities of the CLIP model. Furthermore, the instance-enhanced expression classifier module is proposed to enhance textual descriptors with instance-specific visual embeddings using spherical linear interpolation, creating a more precise and discriminative classifier. Extensive experiments on three in-the-wild FER benchmarks demonstrate superiority of our proposed approach.