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Due to the depletion of traditional energy resources, emissions of greenhouse gases, climate change, etc., renewable energy resources (RER) based power generation is becoming the main source of the present and future power sector. The major RERs, including solar, wind, and small hydro, may provide reliable and sustainable solutions in the smart grid environment. Solar and wind energy-based power generation is more prevalent but varies in nature and is not even very predictable very efficiently. Therefore, it has become necessary to integrate two or more RER and develop a hybrid energy system (HES). The HESs provide a cost-effective and reliable power supply with reduced and/or almost negligible greenhouse gas emissions as well. Due to economic and power reliability concerns, the optimal sizing of components is necessary for the development of an optimum HES. In recent years, metaheuristic evolutionary algorithms have been widely used for optimal sizing of HES. Harris hawk’s optimizer (HHO) is a recently devised metaheuristics search method that has the ability to discover global minima and maxima. However, due to its weak exploitation capacity, the basic HHO algorithm’s local search is pretty slow and has a slow rate of convergence. Thus, to boost the exploitation phase of HHO, a new approach, random exploratory search centered Harris hawk’s optimizer (hHHO-ES), has been developed in the present work for optimal sizing of HES. The suggested approach is validated and compared to existing optimization approaches for a variety of well-known benchmark functions, including unimodal, multimodal, and fixed dimensions. Following this, it is used to develop HES, which will be capable of providing power to remote areas where grid supply is scarce. The objective function is formulated using net present cost (NPC) as a prime function under a set of constraints such as bounds of system components and reliability. The obtained results are compared with those from harmony search (HS) and particle swarm optimization (PSO) and found to be better.

As the price of renewable electricity continues to plummet, hydrogen (H₂) production via water electrolysis is gaining momentum globally as a route to decarbonize our energy systems. The requirement of high purity water for electrolysis as well as the widespread availability of seawater have led significant research efforts in developing direct seawater electrolysis technology for H₂ production. In this Perspective, we critically assess the broad-brush arguments on the research and development (R&D) needs for direct seawater electrolysis from energy, cost and environmental aspects. We focus in particular on a process consisting of sea water reverse osmosis (SWRO) coupled to proton exchange membrane (PEM) electrolysis. Our analysis reveals there are limited economic and environmental incentives of pursuing R&D on today’s nascent direct seawater electrolysis technology. As commercial water electrolysis requires significant amount of energy compared to SWRO, the capital and operating costs of SWRO are found to be negligible. This leads to an insignificant increase in levelized cost of H₂ (<0.1 $/kg H₂) and CO₂ emissions (<0.1%) from a SWRO-PEM coupled process. Our analysis poses the questions: what is the future promise of direct seawater electrolysis? With an urgent need to decarbonize our energy systems, should we consider realigning our research investments? We conclude with a forward-looking perspective on future R&D priorities in desalination and electrolysis technologies.

Abstract Energy resources in outer space, also known as space energy, has been recognized as a promising supplement to conventional energy supplies on Earth, as well as an irreplaceable energy provision for future space explorations. A critical review is conducted in this paper, to identify the most potential space energy resources, to conclude on the current exploitation technologies and to suggest on the challenges and future directions. Space solar power station, also known as SSPS, is presented first as a well-known utilization of space energy, and we go through the international progress, evolution of the collection systems and the thermophotovoltaic systems. The main technical gaps hampering the practical application of SSPS is concluded then to inspire future investigations. Energy on Mars is presented afterwards as a representative ISRU(In Situ Resource Utilization)-type energy resource, and we select three potential resources on Mars worth exploitation: solar energy, geothermal energy and wind energy. A model describing the global solar irradiance on Mars is concluded, typical applications of geothermal energy is analyzed, the phase equilibrium of geothermal fluids is established and the wind turbine is designed. Furthermore, the review on energy on Moon is started with the discussion on lunar geology relevant with energy resources, and an example of feature detection using Convolutional Neural Networks is illustrated as an example to demonstrate the application of deep learning techniques in space energy exploitation. Solar energy is always taken into account in space activities, and we are more focusing on the discussion of Helium-3, a promising resource for nuclear fusion. The material for nuclear fission, Uranium, has also been detected on Moon. A summary is provided in the end with concluding remarks.

The study investigated the effect of newly synthesized benzimidazole derivatives against ethanol-induced neurodegeneration. According to evidence, ethanol consumption may cause a severe insult to the central nervous system (CNS), resulting in mental retardation, neuronal degeneration, and oxidative stress. Targeting neuroinflammation and oxidative stress may be a useful strategy for preventing ethanol-induced neurodegeneration.Firstly, the newly synthesized compounds were subjected to molecular simulation and docking in order to predict ligand binding status. Later, for in vivo observations, adult male Sprague Dawley rats were used for studying behavioral and oxidative stress markers. ELIZA kits were used to analyse tumour necrosis factor-alpha (TNF-), nuclear factor-B (NF-B), interleukin (IL-18), and pyrin domain-containing protein 3 (NLRP3) expression, while Western blotting was used to measure IL-1 and Caspase-1 expression.Our findings suggested that altered levels of antioxidant enzymes were associated with elevated levels of TNF-α, NF-B, IL-1, IL-18, Caspase-1, and NLRP3 in the ethanol-treated group. Furthermore, ethanol also caused memory impairment in rats, as measured by behavioural tests. Pretreatment using selected benzimidazole significantly increased the combat of the brain against ethanol-induced oxidative stress. The neuroprotective effects of benzimidazole derivatives were promoted by their free radical scavenging activity, augmentation of endogenous antioxidant proteins (GST, GSH), and amelioration of lipid peroxide (LPO) and other pro-inflammatory mediators. Molecular docking and molecular simulation studies further supported our hypothesis that the synthetic compounds Ca and Cb had an excellent binding affinity with proper bond formation with their targets (TNF-α and NLRP3).It is revealed that these benzimidazole derivatives can reduce ethanol-induced neuronal toxicity by regulating the expression of cytokines, antioxidant enzymes, and the inflammatory cascade.

In today’s world, the development and continuation of life require energy. Supplying this energy demand requires careful and scientific planning of the energy provided by a variety of products, such as oil, gas, coal, electricity, etc. A new study on the operation of energy carriers called Energy Commitment (EC) is proposed. The purpose of the EC is to set a pattern for the use of energy carriers to supply energy demand, considering technical and economic constraints. EC is a constrained optimization problem that can be solved by using optimization methods. This study suggests the Following Optimization Algorithm (FOA) to solve the EC problem to achieve technical and economic benefits. Minimizing energy supply costs for the total study period is considered as an objective function. The FOA simulates social relationships among the community members who try to improve their community by following each other. Simulation is carried out on a 10-unit energy system supplied by various types of energy carriers that includes transportation, agriculture, industrial, residential, commercial, and public sectors. The results show that the optimal energy supply for a grid with 0.15447 Millions of Barrels of Oil Equivalent (MBOE) of energy demand costs 9.0922 millions dollar for a 24-h study period. However, if the energy supply is not optimal, the costs of operating energy carriers will increase and move away from the optimal economic situation. The economic distribution of electrical demand between 10 power plants and the amount of production units per hour of the study period is determined. The EC outputs are presented, which include an appropriate pattern of energy carrier utilization, energy demand supply costs, appropriate combination of units, and power plant production. The behavior and process of achieving the answer in the convergence curve for the implementation of FOA on EC indicates the exploration and exploitation capacity of FOA. Based on the simulated results, EC provides more information than Unit Commitment (UC) and analyzes the network more efficiently and deeply.

Slime Mould Algorithm (SMA) is a newly designed meat-heuristic search that mimics the nature of slime mould during the oscillation phase. This is demonstrated in a unique mathematical formulation that utilizes adjustable weights to influence the sequence of both negative and positive propagation waves to develop a method to link food supply with intensive exploration capacity and exploitation affinity. The study shows the usage of the SM algorithm to solve a non-convex and cost-effective Load Dispatch Problem (ELD) in an electric power system. The effectiveness of SMA is investigated for single area economic load dispatch on large-, medium-, and small-scale power systems, with 3-, 5-, 6-, 10-, 13-, 15-, 20-, 38-, and 40-unit test systems, and the results are substantiated by finding the difference between other well-known meta-heuristic algorithms. The SMA is more efficient than other standard, heuristic, and meta-heuristic search strategies in granting extremely ambitious outputs according to the comparison records.