Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare’s reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI’s logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather than replace clinical judgment.

Multi-server wireless multihop networks (MWMNs) are critical for modern communication systems, enabling efficient data transmission between devices and servers. However, the complexity of determining optimal server selection and multihop path planning in such networks often results in high interference, high network latency, low network capacity, and reduced network performance. To address these challenges, this paper proposes a two-stage network optimization scheme for MWMNs, using Broad Learning System and Q-learning, called BLSQ. First, the Broad Learning System (BLS) is employed to allocate servers to devices based on their location and computational requirements. Second, a Q-learning algorithm is introduced to optimize multihop path selection, aiming to maximize network capacity while minimizing interference. The proposed approach is evaluated based on different path selection methods in extensive simulations. Results demonstrate that our method significantly reduces network interference, increases network capacity, and achieves lower transmission time, providing a possible approach for optimizing wireless in MWMNs.

With the rapid evolution towards Beyond 5G and future 6G networks, multi-access edge computing (MEC)-enabled wireless networks are expected to support massive device connectivity, ultra-low latency, and high network capacity. However, meeting these stringent requirements in multi-server wireless multihop networks essentially requires the joint orchestration of server selection, multihop routing, and interference management. This paper develops a novel three-stage optimization scheme named broad learning system with Q-learning (BLSQ), consisting of a broad learning system-based server allocation stage, a signal-to-interference-plus-noise ratio-driven Q-learning-based multihop path selection stage, and a consensus transmit power control stage for adaptive interference mitigation. Furthermore, a consensus transmit power control mechanism is incorporated to adaptively adjust the transmit power of user devices, aiming to balance interference mitigation and throughput enhancement. The proposed scheme is particularly suitable for various mission-critical and dynamic scenarios, such as emergency communication in disaster-stricken areas, multihop data exchange between rescue teams and command centers, and flexible network deployment in large-scale events using unmanned aerial vehicles. Extensive simulation results demonstrate that the proposed BLSQ schemes outperforms existing related approaches in terms of network capacity, task completion time, interference management, and quality of servers, validating the superiority and robustness of our design for future MEC-enabled wireless networks.

A wireless multihop network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any packets via intermediate nodes by a multihop fashion. In the WMN, there are still some issues that need to be resolved, like due to any source node may choose an uncertainty path to send their packets through the multihop fashion and this leads to the performance of network capacity can degrade drastically. To solve this problem, in this research, we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from any source node to a destination node with highest end-to-end (E2E) throughput. Besides that, a factor graph (FG) approach and a nested lattice code (NLC) representation are used to reduce the computation time. According to the numerical studies with the NLC is applied, our simulation results reveal that the proposed NLPS and INLPS algorithms can improve the overall average network capacity up to 3.1 times and 10.5 times compared to FG, respectively. However, the overall average computation time are highly increased for NLPS and INLPS, i.e., about 0.627 s and 1.221 s, respectively compared to FG, which is about 0.006 s. In other words, both NLPS and INLPS algorithms can achieve high network capacity and moderate computation time.

Wireless Multihop Network (WMN) is set of wirelessly connected nodes without an aid of centralized infrastructure that can forward any message via relaying nodes by multihop fashion. In WMN, there are still some issues that need to be resolved, like due to the uncertainty of source node choosing a path to send the message and the nature of multihop fashion, the performance of network capacity can degrade drastically. To solve these problems, in this research we propose two novel path selection algorithms called SNR-based learning path selection (NLPS) algorithm and SINR-based learning path selection (INLPS) algorithm, which are incorporated with the deep reinforcement learning (DRL) to select the best multihop path from source node to destination node with highest endto-end throughput. Factor graph (FG) representation is used to reduce the computation time. Our simulation results reveal that both NLPS and INLPS can achieve high network capacity and moderate computation time. Meanwhile, nested lattice code (NLC) is used in compute-and-forward strategy to reduce the time slots. As a result, the network capacity can increase more.

Abstract Corona Virus disease 2019 (COVID‐19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non‐pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID‐19 in some countries, it has been criticized as inefficient or ineffective, because of the time‐delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA‐ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID‐19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID‐19 dataset from Johns Hopkins University, the MAPE of the authors’ model is 7.79%, which corroborates the superiority of the model to state‐of‐the‐art methods.