ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency module to capture complex spatiotemporal relationships in healthcare demand and resource allocation patterns, combined with centralised training and a decentralised execution approach. The system is modelled as a Markov game and solved using a deep reinforcement learning algorithm. Extensive simulations demonstrate that HealthNet outperforms eight state‐of‐the‐art baseline methods across multiple network configurations and evaluation metrics. In a 4 × 4 grid network, HealthNet reduces average waiting times by 47.6% compared to model predictive control and 22.1% compared to the best‐performing baseline. Traffic congestion rates are reduced to 16.7% compared to 42.3% for the worst baseline and 23.1% for the best baseline. Under irregular network topologies with stochastic disruptions, including demand surges and vehicle unavailability, HealthNet maintains superior performance with 42.1% lower average waiting time and 51.1% improvement in peak response times compared to competing approaches. These findings indicate that HealthNet can enhance both efficiency and resilience in healthcare transportation systems, potentially improving patient outcomes in complex urban environments.

ABSTRACT A formation inversion algorithm with real‐time performance and accuracy is crucial for natural gamma logging while drilling (LWD). However, traditional inversion algorithms are often limited by high computational resource consumption and insufficient accuracy. To address these issues, an improved forward method for natural gamma LWD is proposed. The inverse problem is subsequently modelled using the proposed forward method through which the search methodology and region of formation information are determined. On this basis, a collaborative fuzzy gradient neural dynamics (CFGND) algorithm is proposed, which combines the advantages of the collaborative mechanism in swarm intelligence algorithms and fuzzy gradient neural dynamics (FGND) to improve its accuracy and real‐time performance. Specifically, the collaborative mechanism is applied to conduct a global search using all possible formation information. Concurrently, the FGND algorithm initiates a local search from each particle and dynamically and intelligently adjusts the learning rate of the neural dynamics through a fuzzy logic system during the process to achieve rapid and stable local convergence. The CFGND algorithm subsequently updates its globally optimal solution using the optimal solution obtained from the FGND algorithm. This iterative process continues until the termination condition is met. Theoretical analysis proves the existence of an optimal solution for the inverse problem and the convergence of the CFGND algorithm. The results of simulations and experiments demonstrate that the proposed formation inversion algorithm features high accuracy and sufficient real‐time performance.

ABSTRACTIn the field of infrared small target detection (ISTD), the ability to detect targets in dim environments is critical, as it improves the performance of target recognition in nighttime and harsh weather conditions. The blurry contour, small size and sparse distribution of infrared small targets increase the difficulty of identifying such targets in cluttered backgrounds. Existing methodologies fall short of satisfying the requisites for the detection and categorisation of infrared small targets. To address these challenges and to enhance the precision of small object detection and classification, this paper introduces an innovative approach called location refinement and adjacent feature fusion YOLO (LA‐YOLO), which enhances feature extraction by integrating a multi‐head self‐attention mechanism (MSA). We have improved the feature fusion method to merge adjacent features, to enhance information utilisation in the path aggregation network (PAN). Lastly, we introduce supervision on the target centre points in the detection network. Empirical results on publicly available datasets demonstrate that LA‐YOLO achieves an impressive average precision (AP) of 92.46% on IST‐A and a mean average precision (mAP) of 84.82% on FLIR. The results surpass those of contemporary state‐of‐the‐art detectors, striking a balance between precision and speed. LA‐YOLO emerges as a viable and efficacious solution for ISTD, making a substantial contribution to the progression of infrared imagery analysis. The code is available at https://github.com/liusjo/LA‐YOLO.

ABSTRACT Existing weakly supervised semantic segmentation (WSSS) methods based on image‐level labels always rely on class activation maps (CAMs), which measure the relationships between features and classifiers. However, CAMs only focus on the most discriminative regions of images, resulting in their poor coverage performance. We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process. To address the aforementioned issues, we propose to construct selective multiple classifiers (SMC). During the training process, we extract multiple prototypes for each class and store them in the corresponding memory bank. These prototypes are divided into foreground and background prototypes, with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels. As for the inference stage, multiple prototypes are adaptively selected from the memory bank for each image as SMC. Subsequently, CAMs are generated by measuring the angle between SMC and features. We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image, while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes. Furthermore, SMC can be integrated into other WSSS approaches to help generate better CAMs. Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.