Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.

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When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms-such as perturb and observe (P&O) and hill-climbing (HC)-are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithmssuch as an artificial neural network (ANN) and fuzzy logic control (FLC)-have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB R is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.INDEX TERMS Adaptive jaya (Ajaya), maximum power point tracking (MPPT), metaheuristic algorithms, conventional algorithms, photovoltaic (PV).

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High voltage conversion dc/dc converters have perceived in various power electronics applications in recent times. In particular, the multi-port converter structures are the key solution in DC microgrid and electric vehicle applications. This paper focuses on a modified structure of non-isolated fourport (two input and two output ports) power electronic interfaces that can be utilized in electric vehicle (EV) applications. The main feature of this converter is its ability to accommodate energy resources with different voltage and current characteristics. The suggested topology can provide a buck and boost output simultaneously during its course of operation. The proposed four-port converter (FPC) is realized with reduced component count and simplified control strategy which makes the converter more reliable and costeffective. Besides, this converter exhibits bidirectional power flow functionality making it suitable for charging the battery during regenerative braking of an electric vehicle. The steady-state and dynamic behavior of the converter are analyzed and a control scheme is presented to regulate the power flow between the diversified energy supplies. A small-signal model is extracted to design the proposed converter. The validity of the converter design and its performance behavior is verified using MATLAB simulation and experimental results under various operating states.INDEX TERMS Multi-port converter, electric vehicle, bidirectional dc/dc converter, battery storage, regenerative charging