The article discusses exploring the Photovoltaic (PV) emulator for simulating photovoltaic systems for Renewable Energy Analysis (REA) focuses on creating and applying a PV emulator to simulate photovoltaic systems and concludes by emphasizing the value of precise simulation tools in comprehending and enhancing photovoltaic system performance. The first portion of the article emphasizes how renewable energy sources especially solar energy are becoming more and more important in tackling the world's energy problems. As a result, the pv output along with the voltage and current have been presented

This paper employs Monte Carlo Simulation (MCS) to assess how randomly power flow is distributed in Vehicle-to-Grid (V2G) operations. V2G technology facilitates a bidirectional flow of electricity by allowing Electric Vehicles (EVs) to feed excess power back into the grid in addition to drawing power from it. The researchers used Monte Carlo simulation as a computational technique that involves generating random samples to analyze the behaviour of a system, to assess the impact of random variations in power flow on V2G operations. They considered various factors such as EV charging and discharging patterns, grid conditions, and user behaviour to create a realistic simulation environment. The main objective function in this study wants to achieve is to reduce the dependency on the grid and develop a stochastic framework for the efficient management of microgrids with the presence of EVs. reduce cost. As a result, the analysis of the MCS shows the performance of the bidirectional operation, and the output power of the integrated sources is obtained.

Buildings exert a profound influence on the environment, with the design phase recognized as the pivotal determinant of a building’s overall performance. Green building design, in particular, introduces heightened complexity, where the attributes of the design team play a pivotal role in shaping performance outcomes. Consequently, the characteristics of the design team emerge as crucial factors in the enhancement of both green building design performance and client attributes. This study aims to empirically examine a model formulated to gauge the extent to which Effective Design Team Attributes contribute to the enhancement of performance in designing green buildings and influencing client attributes. To achieve this objective, a comprehensive questionnaire survey was administered to professionals within the architecture and engineering domains actively engaged in the design and consulting sectors of the building industry. The collected data underwent meticulous scrutiny for authenticity and dependability using the WINSTEPS 5.2.5 software before undergoing subsequent analysis. Statistical analyses were conducted using SPSS version 19, with Principal Components Analysis (PCA) and the Structural Equation Modeling (SEM) approach implemented through Amos version 18 to derive the most robust model. The findings underscore the pivotal role of an adeptly managed design team in significantly improving both the performance of green building designs and the qualities of clients. Rasch’s analysis confirmed the validity of our 5-point Likert scale for Design Green Building Performance (DGBP), Effective Design Team Attributes …

ABSTRACT

This article presents another method to improve the performance

and efficiency of a gas-fired power plant in Al-Zawia, Libya. In

order to improve power plant efficiency and increase power, the

principle of a heat recovery steam generator (HRSG), which is

mainly based on the waste heat energy of exhaust gases generated

by a simple gas turbine unit, is a method that can be used for

improvement. The present system was simulated using Aspen

HYSYS software. For validation purposes, the simulation results

were compared with the actual operating data, net power and

thermal efficiency of the cycle under the same conditions. These

results show a high level of agreement between theoretical and

practical data. This agreement enables and provides good

implementation to use this software to simulate an entire combined

cycle. In addition, operating parameters that influence power plant

performance were examined. The results show that the combined

cycle improved the thermal efficiency and net power output by

49.8% and 50%, respectively, under the same operating conditions.

The results also showed that compressor pressure ratio and inlet air

temperature have significant effects on thermal efficiency and net

power produced in both simple and combined power plants

Alzheimer's disease (AD) is a complex neurological illness that has several deep reasons. According to recent research, the use of machine learning techniques (ML) on MRI images can assist in identify the brain regions and the connections between them that are implicated in dementia. The study aims to review literature from 2017 to 2023 on the use of machine learning algorithms to identifying and categorize AD. The precision of each machine learning model is assessed, and mind map models are employed to illustrate the study and compare the outcomes.

Abstract. One innovative initiative that shows how technology and creativity can save lives in dire circumstances is the creation of a smart autonomous drone system for Search and Rescue Operations in Libya. The Search and Rescue Drone is a ray of hope that is intended to transform rescue operations by offering a quick and effective way to find persons who are in trouble or who may have been lost in Libya's desert or Mediterranean Sea. A Raspberry Pi, a Pixhawk flying controller, the Internet of Things, and a specially created mobile application are the main parts of the study. With the help of the YOLOv4-tiny module and object detection algorithms, the system enables users to operate the drone and quickly and accurately identify those who go missing in challenging environments. By fusing technological innovation with a humanitarian goal, this paper paves the way for future search and rescue operations in Libya and other nations to be safer and more responsive. The work shows how technology can save lives in dire circumstances and serves as an example of how it can benefit humanity at its most vulnerable times.

ABSTRACT: As more due to the obvious quickening pace of the digital transformation revolution, information and cyber security are

becoming major challenges in industries like banking, telecommunications, and energy. As a branch of cyber security,

network security is concerned with organizing and putting into practice safeguards against software and network hacking and illegal access. To guarantee security, the CIA security tried confidentiality, integrity, and availability must exist. Malicious actors frequently exploit distributed denial of service (DDoS) attacks to compromise availability. When it comes to DDoS detection and prevention, open-source software is thought to be the most economical option. The purpose of this study is to investigate the efficacy and potential of open-source DDoS detection and prevention techniques. To ascertain the best open-source solution, how to launch attacks, and how to measure system performance metrics like CPU, RAM usage, packet loss, and delay, an experimental testbed was set up and assessed. Because PfSense firewall includes widely used intrusion detection/prevention systems (IDS, IPS) packages (such as Snort and Suricata), they are deployed to verify the system performance.

This study aims to evaluate the performance of a simple cycle gas turbine power plant by analysing the effect of different operating parameters. These operating parameters include compressor pressure ratio and compressor & turbine isentropic efficiencies. The study quantitatively assesses the exergetic efficiency and the exergy destruction of each unit in the cycle, as well as the power used or produced by the cycle. Any change in these parameters can significantly impact the power plant's overall performance through a specific unit in the cycle. For instance, increasing the compressor pressure ratio can reduce the temperature difference across the combustor, lessening the exergy destruction and improving the cycle’s overall performance. However, any decline in the compressor or the turbine isentropic efficiency results in an increase in the exergy destruction of the affected unit and can result in a decrease in the overall cycle performance. This is due to either an increase in power required by the compressor or a decrease in power produced by the turbine. The analysis suggests that the turbine isentropic efficiency has a greater impact on the net power generated than the compressor isentropic efficiency. Additionally, the turbine inlet temperature is a dependent variable as operating at different compressor pressure ratios and compressor isentropic efficiencies lead to varying turbine inlet temperatures. Therefore, increasing the turbine inlet temperature does not always lead to improved performance.

This paper presents the development of an advanced system designed to efficiently process Arabic queries in today’s data-driven landscape. By using the T5 sequence-to-sequence model developed by Google and fine-tuning it specifically for transforming textual and verbal input into SQL queries, the system becomes adept at accurately comprehending and interpreting Arabic queries.

The resulting system serves as a user-friendly tool that automates the generation of SQL queries. Users can input their queries in written or recorded Arabic utterances, eliminating the need for manual translation and query construction.

This involved fine-tuning T5 models on a SQL dataset, splitting the dataset, tokenizing it, and setting training parameters. The implementation phase included loading the fine-tuned model, which incorporated PICARD to generate valid queries effectively. This paper also explores the impact of Arabic translation on the performance and accuracy of the model.

The optimal testing on test set accuracy achieved was 63.11%, the real-world testing accuracy: T5-base without PICARD scored 56.96% and 51.36% on English and Arabic questions, respectively, while T5-large with PIARCD scored 77.74% and 70.34% on English and Arabic questions.

Abstract—Fault detection and classification is critical to the reliability of modern control systems in different industries, where detecting and classifying faults in operational processes are very important things while failure to detect and classify them, may cause irreparable damage. In this paper, fault detection and classification approach is presented. The first step, multi stage recursive least squares parameter estimation approach for controlled autoregressive autoregressive moving average systems (CARARMA) is developed with a view to estimate the parameters of the system, additionally, improve the effectiveness of the computation. By means of multi stage approach, the (CARARMA) system is decomposed into three simple identification models, and the parameters of each simple model is identified one by one. These parameters estimated by this approach are referred to as features, and not all of them have the same useful data about the system. In order to select the valuable features and improve a classification accuracy, the Linear Discriminant Analysis (LDA) approach based on scattering matrices is applied for dimension reduction. The classification between these reduced classes is done based on the Naive Bayes classifier. Finally, the obtained results explain the performance of this proposed approach.