The direct current (DC) microgrid is one of the key research areas for our advancement toward carbon-free energy production. In this paper, a two-step controller is designed for the DC microgrid using a combination of the deep neural network (DNN) and exponential reaching law-based global terminal sliding mode control (ERL-GTSMC). The DC microgrid under consideration involves multiple renewable sources (wind, PV) and an energy storage unit (ESU) connected to a 700 V DC bus and a 4–12 kW residential load. The proposed control method eliminates the chattering phenomenon and offers quick reaching time by utilizing the exponential reaching law (ERL). In the two-step control configuration, first, DNNs are used to find maximum power point tracking (MPPT) reference values, and then ERL-based GTSMC is utilized to track the reference values. The real dynamics of energy sources and the DC bus are mathematically modeled, which increases the system’s complexity. Through the use of Lyapunov stability criteria, the stability of the control system is examined. The effectiveness of the suggested hybrid control algorithm has been examined using MATLAB simulations. The proposed framework has been compared to traditional sliding mode control and terminal sliding mode control to showcase its superiority and robustness. Experimental tests based on the hardware-in-the-loop (HIL) setup are then conducted using 32-bit TMS320F28379D microcontrollers. Both MATLAB and HIL results show strong performance under a range of environmental circumstances and system uncertainties.

In this work, we present a framework that facilitates the sharing of EHRs among the community of health-care providers (HCPs). However, the sharing might be obstructed by patients’ privacy and the controlling legislation. Nevertheless, our sharing scheme of EHRs strives to respect patients’ privacy and comply with relevant legislator guidelines, e.g., HIPPA. The proposed work introduces two services while sharing the EHRs: privacy and non-repudiation. To this end, we introduce the partners and the role of each during the course of exchanging of EHRs. The principle of sharing EHRs among HCPs has to be reinforced to save patients’ lives while cryptographic primitives have to be employed to serve this purpose. In this paper, we are motivated to introduce the notion of non-repudiation private membership test (NR-PMT). In NR-PMT, we help patients receive medical services with great flexibility while maintaining their privacy and thwarting all possible threats to disclose their identities. In addition, a formal security analysis based on Kailar’s accountability framework has been used to analyze the proposed framework. Also, a complexity analysis has been conducted.

In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an Integer Programming solver. The model utilizes the potential of graph neural networks, which have the ability to capture complex relationships and preferences among nodes. While the graph neural network forms node embeddings that are fed as input into the next layer of the model, the introduced MILP solver works to solve the team formation problem. Finally, our experimental work shows that the outcome of the model is balanced teams.

Complex event detection over data streams has become ubiquitous through the widespread use of sensors, wireless connectivity and the wide variety of end-user mobile devices. Typically, such event detection is carried out by a data stream management system executing continuous queries (CQs), registered by the users. In this paper, we consider the situation where the results of the CQs, which are in the form of individual data streams, are disseminated to the users' hand-held, battery-operated devices over a shared broadcast medium. In order to reduce the overall energy consumption of the mobile devices, we propose Bose*, a power-aware query operator placement algorithm that determines which part of a CQ plan should be executed at the data stream management system and which part should be executed at the mobile device. Bose*'s effectiveness in reducing energy consumption, as well as response time under specific conditions, is evaluated using simulation, driven by parameters measured on real mobile devices.