Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.

This review provides the mechanism, simulation, chemistries and processes employed in the plasma etching of silicon-based materials. Current applications and research prospects in plasma etching for micro and nanofabrication are discussed.

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation.

In response to critical security threats such as data tampering, identity impersonation, and channel eavesdropping in Vehicle-to-Grid (V2G) networks, numerous charging authentication schemes have been proposed. However, these schemes either lack sufficient anonymity, physical security, or electricity consumption data aggregation for electricity dispatch. In light of these considerations, we propose a comprehensive solution—a physically secure and privacy-preserving charging authentication framework with data aggregation, comprising two foundational schemes. The first scheme introduces a fully anonymous authentication system. In this approach, an Electric Vehicle (EV) seeking charging generates a random signature for its charging request. Subsequently, a Charging Station (CS) verifies the signature, granting charging services upon successful validation. Notably, this process guarantees the EV’s real identity remains undisclosed, even to the Control Center (CC). Moreover, this scheme also addresses potential physical attacks through the incorporation of a physical unclonable function. The second scheme involves a privacy-preserving data aggregation scheme, aggregating total electricity consumption of CSs in a given area while simultaneously preserving individual CSs’ electricity consumption data from potential leakage. Subsequently, the aggregated electricity consumption data is transmitted back to the CC, enabling efficient electricity coordination. A detailed security and privacy analysis demonstrates that our proposed framework meets intended security and privacy objectives. The final performance evaluation underscores the advantages of our proposed framework in comparison with related work.

Vehicular platoon is an evolved driving pattern in Internet of Vehicles (IoV) that consists of a platoon head (PH) vehicle and several member vehicles to reduce fuel consumption and enhance communication efficiency. In such vehicular platoon system, PH vehicles' reputation is calculated according to feedback reports provided by its member vehicles. Hence, it is essential to calculate a truthful reputation for PH vehicle without revealing the privacy and weight information of member vehicles. Inspired by this, we explore homomorphic encryption, data perturbation, and truth discovery techniques to design a privacy-preserving and reliable trust-aware platoon recommendation scheme. Compared with existing schemes, this article features the following advantages. The feedback reports collection process and never reveals the privacy and weight information of member vehicles. Besides, PH vehicles' reputation is iteratively calculated by fog node and cloud service provider without the involvement of member vehicles, which considers high-speed mobility and equipment limitations of vehicles. The security analysis illustrates that our scheme fulfills security requirements of IoV. Further, we deployed Network Simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO) for simulations, which demonstrated that our scheme reduces the computation burden by at least $\text{42.62{\%}}$ , communication burden by at least $\text{2{\%}}$ , and storage burden by at least $\text{45.95{\%}}$ , compared with related schemes.