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Proton Exchange Membrane Fuel Cells (PEMFCs) offer a clean and sustainable alternative to traditional engines. PEMFCs play a vital role in progressing hydrogen-based energy solutions. Accurate modeling of PEMFC performance is essential for enhancing their efficiency. This paper introduces a novel reinforcement learning (RL) approach for estimating PEMFC parameters, addressing the challenges of the complex and nonlinear dynamics of the PEMFCs. The proposed RL method minimizes the sum of squared errors between measured and simulated voltages and provides an adaptive and self-improving RL-based Estimation that learns continuously from system feedback. The RL-based approach demonstrates superior accuracy and performance compared with traditional metaheuristic techniques. It has been validated through theoretical and experimental comparisons and tested on commercial PEMFCs, including the Temasek 1 kW, the 6 kW Nedstack PS6, and the Horizon H-12 12 W. The dataset used in this study comes from experimental data. This research contributes to the precise modeling of PEMFCs, improving their efficiency, and developing wider adoption of PEMFCs in sustainable energy solutions.

Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources.

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

This study aimed to demonstrate the potential benefits of donepezil (DPZ) and vitamin D (Vit D) in combination to counteract the neurodegenerative disorders induced by CuSO intake in experimental rats. Neurodegeneration (Alzheimer-like) was induced in twenty-four male Wistar albino rats by CuSO supplement to drinking water (10 mg/L) for 14 weeks. AD rats were divided into four groups: untreated AD group (Cu-AD) and three treated AD groups; orally treated for 4 weeks with either DPZ (10 mg/kg/day), Vit D (500 IU/kg/day), or DPZ + Vit D starting from the 10th week of CuSO intake. Another six rats were used as normal control (NC) group. The hippocampal tissue content of β-amyloid precursor protein cleaving enzyme 1 (BACE1), phosphorylated Tau (p-tau), clusterin (CLU), tumor necrosis factor-α (TNF-α), caspase-9 (CAS-9), Bax, and Bcl-2 and the cortical content of acetylcholine (Ach), acetylcholinesterase (AChE), total antioxidant capacity (TAC), and malondialdehyde (MDA) were measured. Cognitive function tests (Y-maze) and histopathology studies (hematoxylin and eosin and Congo red stains) and immunohistochemistry for neurofilament. Vit D supplementation alleviated CuSO -induced memory deficits including significant reduction hippocampal BACE1, p-tau, CLU, CAS-9, Bax, and TNF-α and cortical AChE and MDA. Vit D remarkably increased cortical Ach, TAC, and hippocampal Bcl-2. It also improved neurobehavioral and histological abnormalities. The effects attained by Vit D treatment were better than those attained by DPZ. Furthermore, Vit D boosted the therapeutic potential of DPZ in almost all AD associated behavioral and pathological changes. Vit D is suggested as a potential therapy to retard neurodegeneration.

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems’ constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function.

The worldwide crises from multi-drug-resistant (MDR) bacterial infections are pushing us to search for new alternative therapies. The renewed interest in medicinal plants has gained the attention of our research group. Tamarindus indica L. (T. indica) is one of the traditional medicines used for a wide range of diseases. Therefore, we evaluated the antimicrobial activities of ethanolic extract of T. indica. The inhibitions zones, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and fractional inhibitor concentration indices (FICI) against Gram+ve and −ve pathogens were detected. The bioactive compounds from T. indica extract were identified by mass spectroscopy, thin-layer chromatography, and bio-autographic assay. We performed scanning electron microscopy (SEM) and molecular docking studies to confirm possible mechanisms of actions and antivirulence activities, respectively. We found more promising antimicrobial activities against MDR pathogens with MIC and MBC values for Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), i.e., (0.78, 3.12 mg/mL) and (1.56, 3.12 mg/mL), respectively. The antimicrobial activities of this extract were attributed to its capability to impair cell membrane permeability, inducing bacterial cell lysis, which was confirmed by the morphological changes observed under SEM. The synergistic interactions between this extract and commonly used antibiotics were confirmed (FICI values < 0.5). The bioactive compounds of this extract were bis (2-ethylhexyl)phthalate, phenol, 2,4-bis(1,1-dimethylethyl), 1,2-benzenedicarboxylic acid, and bis(8-methylnonyl) ester. Additionally, this extract showed antivirulence activities, especially against the S. aureus protease and P. aeruginosa elastase. In conclusion, we hope that pharmaceutical companies can utilize our findings to produce a new formulation of T. indica ethanolic extract with other antibiotics.

Sunitinib has been associated with several cardiotoxic effects such as cardiac fibrosis. The present study was designed to explore the role of interleukin (IL)-17 in sunitinib-induced myocardial fibrosis (MF) in rats and whether its neutralization and/or administration of black garlic (BG), a form of fermented raw garlic (Allium sativum L.), could extenuate this adverse effect. Male Wistar albino rats received sunitinib (25 mg/kg three times a week, orally) and were co-treated with secukinumab (3 mg/kg, subcutaneously, three times total) and/or BG (300 mg/kg/day, orally) for four weeks. Administration of sunitinib induced significant increase in cardiac index, cardiac inflammatory markers, and cardiac dysfunction that were ameliorated by both secukinumab and BG, and to a preferable extent, with the combined treatment. Histological examination revealed disruption in the myocardial architecture and interstitial fibrosis in cardiac sections of the sunitinib group, which were reversed by both secukinumab and BG treatments. Both drugs and their co-administration restored normal cardiac functions, downregulated cardiac inflammatory cytokines, mainly IL-17 and NF-κB, along with increasing the MMP1/TIMP1 ratio. Additionally, they attenuated sunitinib-induced upregulation of the OPG/RANK/RANKL axis. These findings highlight another new mechanism through which sunitinib can induce interstitial MF. The current results propose that neutralizing IL-17 by secukinumab and/or supplementation with BG can be a promising therapeutic approach for ameliorating sunitinib-induced MF.

A novel method to improve the robustness of steel end plate connections is presented in this paper. Existing commonly adopted techniques alter the stiffness of the beam or the end plate to improve the connection’s robustness. In this study, the robustness is enhanced by improving the contribution of the bolts to the rotational capacity of connections; the higher the bolts’ elongation, the higher the rotational capacity that can be achieved. However, the brittleness of the bolt material, combined with its small length, results in negligible elongation. Alternatively, the load path between the end plate and the bolts can be interrupted with a ductile element to achieve the required elongation. This can be achieved by inserting a steel sleeve with a designated length, thickness, and wall curvature between the end plate and the washer. The proposed sleeve should be designed so that its ultimate capacity is less than the force in the bolt at failure; accordingly, the sleeve develops a severe bending deformation before the failure of any connection components. Using a validated finite element model, end plate connections with various parameters are numerically investigated to understand the performance of the sleeve device. The proposed system substantially enhances the rotational capacity of the connections, ranging between 1.37 and 2.46 times that of the standard connection. It is also concluded that the sleeved connections exhibit a consistent elastic response with the standard connections, indicating the proposed system is compatible with codified elastic design approaches without modification. Furthermore, for a specific connection, various ductile responses can be achieved without altering the connection capacity nor configuration.