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The release of high amount of nitrogen and phosphorous from agricultural, municipal and industrial sewage into natural water body is the major source of eutrophication in aquatic ecosystems. This leads to the depletion of oxygen, cyanobacteria bloom and proliferation of aquatic weed plants, and destruction of water ecosystem. Therefore, effective treatment of municipal wastewater is crucial prior to discharge into natural water bodies in order to prevent pollution, and to meet the wastewater acceptable limits set by government and Environmental Protection Agencies. The present study evaluates the performance of varying weight of S. molesta plants in phytoremediation of treated domestic wastewater samples for 14 days at 24 h retention time. Physicochemical analysis such as turbidity, phosphate, ammoniacal nitrogen and nitrate tests were conducted on the wastewater samples according to spectrophotometric methods. Also, S. molesta plants were harvested once in a week and the relative growth rate (RGR) was calculated. The outcome of the study indicated that the treated domestic wastewater from the sewage treatment plant contains pollutants that stimulates the growth of S. molesta plants. Similarly, the highest reduction efficiency of up to 97.7%, 99.7%, 99% and 90.6% was observed for turbidity, phosphate, ammoniacal nitrogen and nitrate, respectively. In addition, the outcome of the study also demonstrated that S. molesta plants with the highest weight (280 g) were more efficient in removal of the excess nutrient present in the influent samples. Furthermore, the free energy content in the wastewater provided a suitable consortium for the plant cultivation and growth. Therefore, this method can be used for lab-scale production of wastewater-based biomass for the generation of biofuels.

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Digital education in schools through artificial intelligence, big data and other technologies is conducive to improving teaching efficiency and promoting teaching progress. Unfortunately, many schools lack a sense of social responsibility when applying digital education. Students are online more than ever before. There are the potential for increased exposure to risks such as exposure to harmful content, cyberbullying, age-inappropriate advertising and data misuse, which affect students’ well-being and undermine their right to privacy. Schools should bear the overall social moral level of social responsibility. This paper focuses on the social responsibility of schools under the new crown epidemic and holds that implanting social responsibility into the value system of the current digital education reform, which can promote the sustainable development of education. This is also a relatively neglected problem in the practice of educational reform. European standard: ISO26000 is adopted as a CEN-standard. 21 October 2020 the stakeholder consultation process in the European Committee for Standardization (CEN) decided that ISO 26000 is approved as a CEN-standard. For businesses and organizations committed to operating in a socially responsible way, there’s ISO 26000. It provides guidance to those who recognize that respect for society and environment is a critical success factor. This paper highlights the school how to take social responsibility by using ISO 26000. Based on the seven subjects of ISO 26000, this paper constructs the social responsibility of digital education frame, focuses on analyzing the main content and core issues of social responsibility of digital education, puts forward some improved strategies and suggestions for the more prominent problems at this stage through the examination and reflection of reality.

The adherence to planned budget and time schedule of engineering construction projects is an important indicator for the successful project planning and implementation. Most of the Palestinian governmental and municipal organizations face technical and financial challenges during the planning, bidding, and execution phases of their projects. The paper investigates and assesses the cost changes and delays for a sample of forty-six municipal road projects that were executed as part of the Municipal Development Program, financed through a multi-donor fund, administered by the Municipal Development and Lending Fund, and followed up by local consultants. It supports small- to medium-sized municipal projects, mainly roads, and has restrictions on budget and time changes. The findings indicate that there are limited cost deviations with an average of about 5% and that about 8% of the projects suffered from unjustified delays. The paper analyzes the reasons behind the noticed cost and/or time deviations, which include the underestimation of the project budget, delay by the contractors, lack of deterrent measures, and addition of new items or making changes in the scope of the project that result in variation orders. Recommended actions are identified, which are expected to contribute to reducing the consequences of the indicated challenges.

Global challenges of climate change, environmental control, and energy conservation are persisting. In museum buildings, facing these challenges is prominent to achieve the sustainability of our cultural heritage. Controlling the microclimate of the indoor environment in exhibitions where artefacts are conserved, stored and exhibited is a critical challenge that faces museums not only in Egypt but worldwide as well. The aim of this paper is to analyze the main operational practices for rational environmental control that consider preservation and conservation requirements. In order to achieve the objective of this paper, a literature review of recent papers discussing this problem has been performed and analyzed. Then, a survey was conducted to analyze the operation practices in museums in Egypt. At last, environmental assessment criteria were suggested to manage the museum indoor environment to conserve energy and preserve artefacts. The findings of this paper could guide those involved in decision making and setting legislations in Egyptian museums.

Smart Grid technology has been considered an attractive research issue due to its efficiency in solving energy demand, storage, and power transmission. The integration of IoT technology in the Smart Grids is a critical way to accelerate the digitization of the power grid and is useful for the efficient performance of the energy grid infrastructure. For efficient real-time data analysis and decision-making, the Internet of Things will incorporate various communication systems seamlessly. To achieve efficient communication between all Internet of Things, devices are expected to use multiple means, including smart sensors, cable and wireless communication. Improved Internet of Things sensor technologies and connectivity could theoretically prevent or minimize the potential to natural disaster transmission lines, improve transmission power capacity and reduce economic losses. A smart grid is a variety of sensors, devices, and data sets that continuously capture high-resolution data equal to individual IoT conditions. A vast amount of data is one of the biggest challenges on the Internet of Things. Edge Computing is trying to process data close to linked sensors to address this problem, where the data is gathered and processed. This paper aims to investigate the edge computing solutions for the smart grid. A comprehensive review of both emerging issues and edge computing in the Smart Grid environment is discussed and explained. There are two primary components to the energy sharing process among Prosumers: information/digital technologies and Artificial Intelligence Scheduling Techniques. Each of them is mentioned in detail to discuss the Prosumer smart Grid. Furthermore, Edge Computing and classifications (cloudlet, Fog computing and Multi-Access) are among the suitable network methods mentioned in this paper. Some techniques and methodologies have been extensively covered to improve reader awareness of the Prosumer smart grid system.

Buildings' facades significantly impact human thermal comfort and the energy budget of buildings. It is estimated that 73% of heat losses or gains are immediately linked to the configuration and types of materials in the buildings' envelope. Building styles in the Kingdom of Saudi Arabia witnessed a large and growing expansion in curtain walls, glass facades, metal cladding, and precast concrete. It is striking that terracotta cladding is most often associated with school building facades, especially the government ones, with very limited applications in other building types. This paper investigates the energy-related budget of using Aluminum and terracotta cladding in an educational facility in KSA. The article follows the experimental approach. Data about both cladding materials' specifications were obtained and input into the virtual model building in DesignBuilder software for simulation and analysis. The comparative analysis of energy consumption during the building's operational phase in both cases revealed that more energy budget was needed in the case of Aluminum cladding to provide thermal comfort to the occupants. The study concludes that the efficiency of thermal insulation of Terracotta is better than the Aluminum sheets. On the other hand, and in the long run, the study underscores the preference of using Aluminum cladding for its extensive reuse and recycling potential. Brittleness of Terracotta limits its prospects for recycling; therefore, it might be more energy-consuming when frequently reproduced for new applications or maintenance purposes throughout the building life cycle.

This paper presents Water Cycle Algorithm (WCA) to solve the Optimal Power Flow (OPF) problem with and without renewable energy sources. WCA has been tested on IEEE 30, 57, and 118-bus transmission networks to find minimum fuel cost. Main parameters of WCA have been set to different values for surveying the real performance and the best settings of the parameters have led to better results than those of previous methods. In addition, the best appropriate parameters have also supported WCA in finding the best location of a Photovoltaic system (PVS) in the first network. WCA could reach less power loss than two other metaheuristics, especially reaching much less power loss than the case without PVS. Consequently, WCA with the most appropriate parameter settings is really a powerful search method in terms of quick convergence speed and high-quality global optimum for the OPF problem with large-scale power networks.

In the real world, the demands for some specific goods may increase in some special occasions. At such conditions, in-time delivery of the desired goods and minimization of the traveling costs will be found crucial importance for the customers and the suppliers, respectively. In this paper, a new mathematical model consisting of two objectives: minimizing traveling costs and maximizing customers’ demand satisfaction was proposed through a multi-objective optimization approach. The nonlinear equations were converted to linear equations and the proposed model was then solved using CPLEX solver by GAMS23.6.3 software. Two approaches were proposed to solve the problem based on the NSGA-II algorithm with different modifications in the mutation operator. A two-row structure was also employed for the chromosomes. This research contributes to vehicle routing literature considering time windows, multiple demands, and two conflict objectives and proposes two metaheuristic algorithms. The algorithm results were also compared with two criteria: covered non-dominated solutions and spread solutions. The results show that the improvements were made with the variation in the mutation operator in both criteria. In summary, the developed vehicle routing mathematical model works effectively for some real-world logistic problems with occasional goods, and the customer satisfaction in due time is incorporated in the mathematical model.

In this paper, a new classification approach based on swarm-optimization is introduced to investigate the various effects of the ammonia concentration on the protein level and bioactivity that directly affect the Egyptian Nile’s fish health and mortality rate (i.e. Tilapia fish “O. Niloticus”). This approach enhances the Support Vector (SVM) Machines to classify the fish based on the protein level by Moth-Flame Optimization (MFO) algorithm. The experiment was divided into sub-phases: lab experiments and computational experiments. The primary purposes of the proposed approach, guiding decision-makers to review the pathophysiological status of the fish. The proposed MFO-SVM approach utilizes physical and chemical measurements to finally show revolutionary advances against the classic SVM and other well-known optimizers and classifiers. By achieving 99.983% of classification accuracy, the proposed approach outperforms other machine learning approaches on the same dataset. We believe that such an approach could be useful for many other real-world challenging tasks.

This paper aims to evaluate educators' and students’ preferences for either computer-aided design (CAD) or traditional hand drafting in working drawings coursework (assignments, term projects and exams). (1) Background: Working drawings are considered an advanced level of technical drawings for the architecture discipline. (2) Material and Methods: A pilot study with three focus groups was conducted in the architecture department of a governmental male university in Saudi Arabia. According to the notes collected from the previous literature review and pilot study, a questionnaire was designed. This questionnaire was given to 21 educators and 59 students. (3) Results: Educators' and students' results were almost the same, showing a preference for using the CAD method compared to the hand drafting method in achieving the working drawing characteristics of accuracy, neatness, pen assignment adjustments, material presentation, ease of modification, saving the drawing for later review, and overall quality. CAD is also preferred due to saving exam time and its cost efficiency. However, the respondents confirmed the negative impact of CAD copy commands from previous projects on the understanding of working drawings and their preference to use the hand drafting drawing method for drawing notes on site visits. (4) Conclusions: The working drawings coursework should combine the CAD & hand drafting methods to utilize the advantages of both in order to improve the course learning outcome.

Blockchain is one of the most powerful and promising technologies nowadays in the IT industry, One of Blockchain’s main features is the presence of a Consensus Algorithm, which is responsible for maintaining the security and integrity of the entire blockchain network where all the nodes participating in the network reach a certain agreement. However, some algorithms like the Proof-of-Work require very high energy consumption to reach a single agreement by solving a puzzle and provides a low throughput (3-7 transactions/second), thus, it may not be very reliable in blockchain solutions. In this paper, we aim to provide a reliable choice for different business use-cases by proposing a modification in the Istanbul Byzantine Fault Tolerance voting-based algorithm that provides a higher throughput (up to 1140 tx/s), which will be very important to be used in the use case called a letter of credit which is a part of trade finance (relation between exporter, importer and the bank institutions).

Nowadays; the New Administrative Capital (NAC) is being established in Egypt; NAC project is considered the 1st smart city in Egypt where thousands of SBP are being constructed. During establishing SBP in Egypt many challenges evolve; those challenges include, but not limited to lack of guidelines, vague objectives, ignoring life cycle costs, lack of framework, etc. The objective of the paper was fulfilled first; through analysis of literature sources in order to identify the main risks and challenges that face international SBP and second; through a questionnaire survey that targeted some design managers, project managers, facility managers, architects and developers who are involved in the field of SBP in Egypt. Finally; a comparison was conducted between the challenges of SBP that were extracted from the analysis of literature sources, and those that were determined through application of the questionnaire survey.

Nowadays, the subject of delays is considered one of the most challenging problems for construction projects, particularly in Egypt. Delays occurrence normally leads to an increase in the project’s time and so its cost. Analyzing the factors of construction project delays in Egypt is a matter of crucial importance; thus, the reason why a systematic processing framework is developed. Considered as one of the main elements for this framework, a survey questionnaire was conducted among the key parties in the Egyptian construction industry. The aim is to assess the relevance and impact of the various delay causes on construction projects, to identify the responsible party as well as to evaluate the effect of using the Building Information Modeling (BIM) on delays. Thus, this paper promotes the use of a systematic processing framework to outline a course for examining delay factors of construction projects in Egypt while validating the outcomes through a survey questionnaire. This leads to prioritize delay causes and hence to identify the most suitable approach for dealing with construction projects’ delays and their associated cost impact. Also, it is worth mentioning that this framework is of major use to construction projects with similarity nature such as residential buildings, hotels, etc. This study opens the doors for further development of parametric models which will help in effectively treating this subject.

The height obtained through the GNSS method is the ellipsoidal height and to have an efficient application in surveying it has to be converted into orthometric height. This paper aims to determine a local geoid model for a part of Duhok region based on the known orthometric height using GPS/levelling and assessing the precision performance of the two Earth gravitational models (EGM1996 and EGM2008) in the study area. The EGMs were adjusted based on root mean square errors, calculated from the differences between two geoid heights, GPS/levelling and EGMs. The Kriging interpolation method was used for creating a local geoid model based on GPS/levelling. The result from the adjusted geoid models to the survey data at 54 points revealed that the mean accuracy of geoidal heights at 0.08 m level is attainable. The precision performance of EGM96 is more precise than EGM08 by 14 mm in the area of interest.

Renewable energy through the use of fuel cells and solar cells is one of the popular developments in recent days that produce electricity. Accurate modelling of fuel cell and solar cells are essential in simulation and analysis of energy systems with these sources. However, the systems are extremely nonlinear and complicated. The model needs to be optimized under distinct operating circumstances. Enhanced and streamlined Improved Estimation of Distribution (IED) Algorithm is suggested in this paper to estimate the parameter through optimization for solar cell models and fuel cell models. This is accomplished through the introduction of an ideal approach to improve population quality and the use of a local search to improve the efficiency of the finest global solution further. The design of an IED algorithm is much more straightforward and search efficiency is greatly improved compared with the fundamental optimization techniques from the literature.

This paper presents an adaptive load frequency control technique in both of isolated microgrid power system and two interconnected microgrids (MGs). A modified Jaya optimization method is used to make online tuning of the area controller. The Balloon Effect (BE) modification is designed to increase the sensitivity of the Jaya algorithm to both of the system load disturbance and parameters variations. In Jaya with BE technique, the objective function (OF) is affected by the on-time system problems. Communication delay time is considered in the dynamic model of interconnected MGs. System with proposed adaptive control method has been tested in several scenarios including step and variable load demand and parameters variations. Simulation results using Matlab software show that the proposed adaptive controller tuned by Jaya + BE technique provides better performance of frequency control than other methods such as conventional controller or adaptive one tuned by classical Jaya algorithm.

Building footprint has a significant role in urban design and management but, extracting the building feature is still challenging due to significant variations in scale and shape. Convolution neural networks (CNN) have shown effective results in extracting the high-level and discriminative urban features from high-resolution remote sensing imagery. The traditional CNN architecture has been modified two-scale input based architecture, known as Dual-scale CNN (Du-CNN). However, the repetitive usage of pooling operation leads to a fuzzy boundary of the extracted features. This paper has proposed the MRF-based fusion strategy to take full use of the blurred boundary and define the conditional relationship between classification maps. In the Preliminary stage, we have designed a Du-CNN framework to extract the building feature at different scales. Du-CNN contains a multiple-size asymmetric filter to provide the variation in the receptive field and the depth-wise convolution in the skip connection block for efficient computation. Finally, the classification maps of both CNN sub-architectures are fused using a Modified Markov random field (MMRF). The proposed MMRF technique inherits the properties of Classical MRF with the extension of overlap potential for associating additional terms between the inter-decision layer. The model was tested on Inria Aerial image labeling dataset and Mnih dataset. Experimental result has shown the superior performance of the proposed strategy for extracting the complex building architecture.

The presence of openings greatly affects the response of unreinforced masonry (URM) walls. This topic greatly attracts the attention of many researchers. Perforated unreinforced masonry (PURM) walls under in-plane loads through the truss discretization method (TDM) along with several machine learning approaches such as Multilayer perceptron (MLP), Group of Method Data Handling (GMDH), and Radial basis function (RBF) are described in this paper. A new method named Multi-pier (MP) that is fast and accurate, is used to determine the behavior of PURM walls. The results of the MP method are expressed as a ratio of lateral load-bearing capacity and initial stiffness of PURM walls to the solid wall in order to generalize the obtained results to other PURM walls. The outcomes of the MP method are employed to predict the behavior of PURM walls using various machine learning approaches. Using the validated network with suitable accuracy, empirical functions and curves are presented in an effort to provide a simplified and practical approach to assess the reduction in the load-bearing capacity and initial stiffness of PURM walls. Results indicate that the adjacent piers of opening have a remarkable impact on the overall response of the PURM wall. Finally, the ability of the MP method to conduct stochastic analysis is evaluated. Moreover, the effect of randomness in the mechanical characteristics and their spatial variation within the PURM wall is presented.

Nowadays, photovoltaic (PV) are considered a promising alternative solution as a clean energy source. The power output from the solar PV module is proportional to the amount of solar radiation received by its surface. Hence, the determination of the most convenient locations of PV systems is crucial to maximize their output power. In this paper, the most suitable sites of PV farms in Egypt are identified based on the multi-criteria decision-making (MCDM) technique using the geographic information system (GIS) software. Four categories have been taken into acount; location, environment, meteorology, and climatology, to select the suitable locations. Based on experts’ knowledge ten criteria belong to the main categiories have been weighted using the analytical hierarchy process (AHP) and combined in PV land suitabliltiy index which maped using GIS techniques. The conducted suitability map could be used to guiding installation of renewable energy projects in the future.

This paper suggests a new fuzzy method for active power (AP) and reactive power (RP) control of a power grid that includes wind turbines and Doubly Fed Induction Generators (DFIGs). A Recurrent Type-II Fuzzy Neural Networks (RT2FNN) controller based on Radial Basis Function Networks (RBFN) is applied to the rotor side converter for the power control and voltage regulation of the wind turbine equipped with the DFIG. In order to train a model, the voltage profile at each bus, and the reactive power of the power grid are given to the RT2FNN as the input and output, respectively. A wind turbine and its control units are studied in detail. Simulation results, obtained in MATLAB software, show the well performance, robustness, good accuracy and power quality improvement of the suggested controller in the wind-driven DFIGs.

Big data systems are sufficiently stable to store and process a massive volume of rapidly changing data. However, big data systems are composed of large-scale hardware resources that make their subspecies easily fail. Fault tolerance is the main property of such systems because it maintains availability, reliability, and constant performance during faults. Achieving an efficient fault tolerance solution in a big data system is challenging because fault tolerance must meet some constraints related to the system performance and resource consumption. This study aims to provide a consistent understanding of fault tolerance in big data systems and highlights common challenges that hinder the improvement in fault tolerance efficiency. The fault tolerance solutions applied by previous studies intended to address the identified challenges are reviewed. The paper also presents a perceptive discussion of the findings derived from previous studies and proposes a list of future directions to address the fault tolerance challenges.

Improving the operation of wind energy generation system is a big challenge especially when it operates under unstable weather conditions. Therefore, installing a tracker for monitoring the maximum power expected to be generated from the wind energy system is essential, this controller is known as maximum power point tracker (MPPT). Hill climbing based approaches were applied for simulating the tracker, however they have limitations in tracking speed and efficiency. This paper proposes a recent efficient approach of Archimedes optimization algorithm (AOA) for simulating MPPT installed with the wind energy generation system. The constructed system composes wind turbine (WT) connected to a permanent magnet synchronous generator (PMSG), the AC output power from the generator is converted to DC via 3-phase rectifier, the DC voltage is the input of boost converter which is controlled via its MOSFET duty cycle. The designing process is presented as an optimization problem considering the electrical output power from the system as the target. The proposed AOA tunes the converter duty cycle to maximize the output power. Three scenarios are followed in this work which are fixed wind speed, variable wind speed, and real wind speed recorded at four sites in Saudi Arabia (Sakaka, Riyadh, Jeddah, and Jizan). The obtained results via the proposed approach are compared with the results obtained via cuckoo search (CS), grasshopper optimization algorithm (GOA), and electric charged particle optimization (ECPO). The results confirmed the robustness of the proposed AOA-MPPT in achieving the best performance of wind energy generation system.

Usually, electric vehicle systems are based on various modules that should ensure the high power and stability of the vehicle on the track. The majority of these components are linked to the charging mechanism. In this regard, dynamic wireless power transfer is a practical method to solve electric vehicle range anxiety and reduce the cost of onboard batteries. Wireless recharging has long been common with pure electric vehicles and is designed to allow charging even when the vehicle is in motion. However, it is difficult to analyze this method since its operating philosophy is complex, particularly with the existence of several variables and parameters. Also, the state of the vehicle, whether it is in motion or not, defines several parameters such as the vehicle speed as well as the sizes and dimensions of the coil receivers. This paper presents a novel method to improve the performance of the dynamic wireless recharge system. In the proposed system, receiver coils have been added to maximize charging power by offering a dynamic mathematical model that can describe and measure source-to-vehicle power transmission even though it is in motion. In the proposed mathematical model, all physical parameters describing the model were presented and discussed. The results showed the effectiveness of the proposed model. Also, the experimental tests confirmed the validity of the simulation results obtained by providing two coil receivers under the vehicle.

This paper presents a detailed analytical and non-linear mathematical model describing the I − V characteristics of submicron silicon Carbide (SiC) Metal semiconductor field effect transistor (MESFETs). Silicon Carbide (SiC) MESFET is a wideband device capable to handle high power at microwave frequencies. It is shown that our proposed model can predict the output characteristics of the device both under normal conditions as well as at relatively high drain bias. A comparison of our proposed model is made with Riaz, Khan.M, Shamsir, Kompa, Zhao, Raffo, Josef, Guidry, Mishra and recently reported 4H-silicon carbide (SiC) MESFET large signal I-V models by using the Particle Swarm Optimization (PSO) process. A full SiC MESFET analysis is provided and using experimental data, the validity of the proposed technique is demonstrated. The results show that the new model has the advantages of high accuracy, easily making initial value and robustness over other models. Our proposed technique improves accuracy in predicting the I-V characteristic and output characteristic of SiC MESFETs device. Silicon Carbide (SiC) MESFET is a wide band platform capable of handling microwave frequencies with high efficiency. SiC MESFETs operations are typically conducted under high bias conditions, in which the response is modified from normal. The proposed technique is useful for evaluating the output of high power microwave SiC MESFETs over a wider bias range.

A two-stage power converter for PV applications is developed in this paper. In the first stage, a multiple-input high step-up dc/dc converter is proposed. The second stage is the five-level dc/dc inverter with boosting functionality. The proposed two-stage converter harnesses the superior features of WBG devices to build a converter with high efficiency and high-power density. The high switching speed of WBG power devices allow both stages operating at high switching frequency, hence, large size film capacitors can be replaced by small size multilayer ceramic capacitors, furthermore, the high operating temperature of WBG devices will help to shrink the cooling size. In additions, a zero current-switching technique is implemented in order to reduce the switching loss and electromagnetic interferences. Prototype with 300 W is built and experimental results are presented with a peak efficiency of 98.3%.

Hybrid micro-grid systems (HMGS) are small scale power system where the energy sources are installed to supply local customers. These systems may be considered as promising energy solution to meet the increased in energy demand and traditional sources depletion. Cost of electricity, system reliability, and environmental impacts of the system are three design criteria that must be considered in obtaining the accurate parameters of hybrid renewable energy system components. In this paper, hybrid micro-grid renewable energy system includes photovoltaic system, (PV) wind energy system, (WES) battery bank, (BB) and conventional diesel generator (DG) are proposed to meet the energy requirements in remote area, located in Red Sea called city of Bernice, Egypt, at 23° 54′ 31″ N, 35° 28′ 21″ E. Optimization of Cost of Electricity (COE), Renewable Factor (RF), and Loss of Power Supply Probability (LPSP) are main objective of the designing process of the hybrid system considered as the objective functions. Then, Multi-objective multi-verse optimization (MOMVO) algorithm is used with considering two scenarios, the first one is renewable sources and the second is renewable/diesel energy source. All the possible HMGS configurations namely: PV/battery, wind/battery, PV/wind/battery and PV/battery/diesel, wind/battery/diesel, PV/wind/battery/diesel are studied and analyzed. Moreover, one year hourly meteorological weather data for case study are recorded. Reverse osmosis desalination (ROD) is considered in conjunction with the residential load. The proposed power management strategy is used to manage the system operation when supplying the load. A linear fuzzy membership function is used for purpose of decision making. The simulation results show that MOMVO produces appropriate components size and the PV/wind/battery/diesel is the optimum configuration with values of COE = 0.2720$/KWh, LPSP = 0.1397, and RF = 92.37% at w1 = 0.5, w2 = 0.3, and w3 = 0.2. Sensitivity analysis is performed to show the effect of changing system parameters on the objective functions. It is also shown that the techno-economic feasibility of using HMGS for rural electrification systems and enhance energy access.

This paper explores a recent application of the computational fluid dynamics technique “CFD” for wind analysis and its comparisons with the conventional wind tunnel experimentations. This study is centered on the wind response of square and corner cut-shaped building models and its optimization caused by the variation of the wind incidence angle. Extensive rigid model experiments of two building models of length scale 1:100 have been performed in the boundary layer wind tunnel. The numerical analysis has been carried out with the standard k-e turbulence model to evaluate the force coefficients, base moments, power spectra, external surface pressure coefficients, and flow field characteristics of the models with variable wind angles of incidence. The comparisons between experimental results and CFD analysis suggest the computational approach's viability in wind analysis of tall structures efficiently and accurately. A case study of aerodynamic mitigation by corner cut suggests minor modification techniques performance, efficiency, and limitations.