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Renewable energy systems are at the core of global efforts to reduce greenhouse gas (GHG) emissions and to combat climate change. Focusing on the role of energy storage in enhancing dependability and efficiency, this paper investigates the design and optimization of a completely sustainable hybrid energy system. Furthermore, hybrid storage systems have been used to evaluate their viability and cost-benefits. Examined under a 100% renewable energy microgrid framework, three setup configurations are as follows: (1) photovoltaic (PV) and Battery Storage System (BSS), (2) Hybrid PV/Wind Turbine (WT)/BSS, and (3) Integrated PV/WT/BSS/Electrolyzer/Hydrogen Tank/Fuel Cell (FC). Using its geographical solar irradiance and wind speed data, this paper inspires on an industrial community in Neom, Saudi Arabia. HOMER software evaluates technical and economic aspects, net present cost (NPC), levelized cost of energy (COE), and operating costs. The results indicate that the PV/BSS configuration offers the most sustainable solution, with a net present cost (NPC) of$2.42M and a levelized cost of electricity (LCOE) of $ 0.112/kWh, achieving zero emissions. However, it has lower reliability, as validated by the provided LPSP. In contrast, the PV/WT/BSS/Elec/FC system, with a higher NPC of$2.30M and LCOE of $ 0.106/kWh, provides improved energy dependability. The PV/WT/BSS system, with an NPC of$2.11M and LCOE of $ 0.0968/kWh, offers a slightly lower cost but does not provide the same level of reliability. The surplus energy has been implemented for hydrogen production. A sensitivity analysis was performed to evaluate the impact of uncertainties in renewable resource availability and economic parameters. The results demonstrate significant variability in system performance across different scenarios.

Providing access to clean, reliable, and affordable energy by adopting hybrid power systems is important for countries looking to achieve their sustainable development goals. This paper presents an optimization method for sizing a hybrid system including photovoltaic (PV), wind turbines with a hydroelectric pumped storage system. In this paper, the implementation of different optimization techniques has been investigated to achieve optimal sizing of grid-connected hybrid renewable energy systems. A comprehensive study has been carried out between Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Salp Swarm Algorithm (SSA), and Grey Wolf optimizer (GWO) to validate each one. Moreover, the optimal sizing of the system’s components has been studied using real-time information and meteorological data of Ataka region located in Egypt. The purpose of the optimization process is to minimize the cost of energy from this hybrid system while satisfying the operation constraints including high reliability of the hybrid power supply, small fluctuation in the energy injected to the grid, and high utilization of the photovoltaic and wind complementary properties. MATLAB software package has been used to evaluate each optimization algorithm for solving the considered optimization problem. Simulation results proved that WOA has the most promising performance over other techniques.

Maximum Power Point Tracking (MPPT) is a promising technology for extracting peak power from single or multiple solar modules for improving Photovoltaic (PV) system performance and satisfying economic operation. The tracker should continuously follow the MPP of the PV module at all operating and weather conditions. The Particle Swarm Optimization (PSO) algorithm represents a powerful optimal MPP tracker due to its simplicity and has enhanced greatest exploration characteristics. This article proposes a new technique based on PSO enhanced with Quasi-Newton local search for improving power quality while minimizing oscillation. This tracking process is making the MPPT comparable between high accuracy and fast tracking speed. MPPT proposal algorithm results are compared to the results of the hybrid PSO-P&O algorithm at different operating conditions. The proposed algorithm results show that MPP extraction has been done with a high-speed response and the best efficiency. Moreover, the PSO is enhanced with a Quasi-Newton (QN) local search method for tuning the optimal MPP.

Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. The key implications of integrating RDGs include the improvement of voltage profiles and the minimization of power losses. Various optimization techniques, namely Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA), have been applied to achieve optimal allocation and sizing of RDGs in radial distributed systems (RDS). The present paper is structured in two phases. In the initial phase, the Loss Sensitivity Factor (LSF) is employed to identify the most suitable nodes for integrating RDGs. In the second phase, within the selected candidate nodes from the first phase, the optimal location and capacity of RDGs are determined. Additionally, a comprehensive comparison of the proposed optimization methods is conducted to select the most effective solutions for the allocation of units of RDGs. The efficacy of the utilized techniques is validated through testing on two distinct networks, namely the IEEE 33 and 69 buses RDS in MATLAB, with attainments compared against other techniques. Moreover, a larger RDS system of 118- bus IEEE system has been considered in order to enhance its power quality indices. Moreover, a real case of study from Egypt of 15 bus has been considered and evaluated with considering the applied techniques. The results show the enhancement of the voltage profile and decreasing the power losses of the tested system with the DG systems with the superiority of the MPA and SSA algorithms.

This paper proposes a wideband ultra-compact implantable antenna for a wireless body area network (WBAN). The proposed patch antenna works in the industrial, scientific, and medical (ISM) bands. The proposed patch antenna with an ultra-compact size (5 × 5 × 0.26 mm3) was designed with 29% wide bandwidth (about 670 MHz). This wide bandwidth makes the antenna unaffected by implantation in different human body parts. The miniaturization process passed many steps by adding many slots with different shapes in the radiating element as well as in the ground plane. A 50 Ω coaxial feeding excites the antenna to maintain matching and low power loss. The specific absorption rate (SAR) was calculated for health considerations. The result was within the standard limits of IEEE organizations and the International Commission on Non-Ionizing Radiation Protection (ICNRP). The antenna was tested in tissues with multiple layers (up to seven layers) and at various depths (up to 29 mm). The link margin was calculated, and the proposed antenna enables 100 Kbps of data to be transferred over a distance of 20 m and approximately 1 Mbps over a distance of 7 m. The proposed antenna was fabricated and tested. The measured S11 parameters and the simulated results using the Computer Simulation Technology (CST Studio) simulator were in good agreement.

Sentiment analysis is becoming an essential tool for analyzing the contents of online customer reviews. This analysis involves identifying the necessary labels to determine whether a comment is positive, negative, or neutral, and the intensity with which the customer’s sentiment is expressed. Based on this information, service companies such as airlines can design and implement a communication strategy to improve their customers’ image of the company and the service received. This study proposes a methodology to identify the significant labels that represent the customers’ sentiments, based on a quantitative variable, that is, the overall rating. The key labels were identified in the comments’ titles, which usually include the words that best define the customer experience. This database was applied to more extensive online customer reviews in order to validate that the identified tags are meaningful for assessing the sentiments expressed in them. The results show that the labels elaborated from the titles are valid for analyzing the feelings in the comments, thus, simplifying the labels to be taken into account when carrying out a sentiment analysis of customers’ online comments.

Antipathogenic drugs are a potential source of therapeutics, particularly following the emergence of multiple drug-resistant pathogenic microorganisms in the last decade. The inhibition of quorum sensing (QS) is an advanced antipathogenic approach for suppression of bacterial virulence and dissemination. This study aimed to investigate the inhibitory effect of some Egyptian medicinal plants on the QS signaling system of Pseudomonas aeruginosa . Among the tested plants, Mangifera indica exhibited the highest quorum sensing inhibition (QSI) activity against Chromobacterium violaceum ATCC 12472. Four pure compounds were extracted and identified; of these, methyl gallate (MG) showed the most potent QSI. MG had a minimum inhibitory concentration (MIC) of 512 g/mL against P. aeruginosa strains PAO1, PA14, Pa21, Pa22, Pa23, Pa24, and PAO-JP2. The virulence factors of PAO1, PA14, Pa21, Pa22, Pa23, and Pa24 were significantly inhibited by MG at 1/4 and 1/2 sub-MICs without affecting bacterial viability. Computational insights were performed by docking the MG compound on the LasR receptor, and the QSI behavior of MG was found to be mediated by three hydrogen bonds: Trp60, Arg61, and Thr75. This study indicates the importance of M. indica and MG in the inhibition and modulation of QS and QS-related virulence factors in P. aeruginosa .

A Kalman filter approach for combining the outputs of an array of high-drift gyros to obtain a virtual lower-drift gyro has been known in the literature for more than a decade. The success of this approach depends on the correlations of the random drift components of the individual gyros. However, no method of estimating these correlations has appeared in the literature. This paper presents an algorithm for obtaining the statistical model for an array of gyros, including the cross-correlations of the individual random drift components. In order to obtain this model, a new statistic, called the “Allan covariance” between two gyros, is introduced. The gyro array model can be used to obtain the Kalman filter-based (KFB) virtual gyro. Instead, we consider a virtual gyro obtained by taking a linear combination of individual gyro outputs. The gyro array model is used to calculate the optimal coefficients, as well as to derive a formula for the drift of the resulting virtual gyro. The drift formula for the optimal linear combination (OLC) virtual gyro is identical to that previously derived for the KFB virtual gyro. Thus, a Kalman filter is not necessary to obtain a minimum drift virtual gyro. The theoretical results of this paper are demonstrated using simulated as well as experimental data. In experimental results with a 28-gyro array, the OLC virtual gyro has a drift spectral density 40 times smaller than that obtained by taking the average of the gyro signals.

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently.

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.