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.

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.

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.

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: 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.

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.

Abstract: Network operators are confronted with the demanding requirements resulting from the evolution of IP networks. As a result, it has become necessary to provide rigorous Service Level Agreements (SLAs) that are in line with these requirements. However, traditional IP networks lack the necessary flexibility, scalability, and manageability to meet these demands. In order to address these limitations, the segment routing (SR) architecture has been developed. SR is based on source-routing and tunneling paradigms, which enable IP/MPLS and IPV6 networks to operate in a simplified and more scalable manner. The focus of this paper is on network protection (resiliency) using Topology Independent Loop-Free Alternatives Fast Re-Route (TI-LFA FRR) using MPLS as the underlying technology. SR overcomes the limitations of previous network protection mechanisms in terms of coverage and optimal path selection. To show the effectiveness of SR TI-LFA in comparison to its predecessor, Remote Loop-Free Alternate (rLFA), we have implemented various scenarios. These scenarios are designed to highlight the superior capabilities of TI-LFA. 

Net Zero Energy (NZE) buildings play a crucial role in meeting the Sustainable Development Goals (SDG) and creating environmentally friendly residential areas. These buildings are designed to generate as much energy as they consume, resulting in a net balance of zero energy consumption from the grid. By integrating innovative technologies and sustainable design principles, NZE buildings minimize their carbon footprint and contribute to a more sustainable future. The acquired result has been presented and discussed. The concept of Net Zero Energy Buildings (NZEBs) has gained significant attention in recent years as a crucial strategy for achieving sustainability in the built environment. NZEBs are designed to produce as much energy as they consume, resulting in a net energy balance of zero over a specified period.

The classical Model Predictive Control (MPC) is still considered in many industrial applications, although advanced control methods have seen  significant development over the last few years. However, a lot of MPC strategies still suffer from  robustness to cope with a variety of process dynamics. For the MPC controller to work effectively,  it must be properly tuned. However, MPC is challenging and there is no ideal analytic method to  obtain exact solutions that result in the best desired responses. Based on that, this paper  investigate the robustness performance for a MPC tuning strategy. Three formulae were derived  (Proposed , Proposed  and Proposed ). These formulae are used for calculating the suppression coefficient. The three  formulae were derived by fitting the optimal empirical suppression coefficients for varies process  dynamics commonly found in industry. Simulation results show that the use of the proposed strategy results in good performance compared to other strategies previously proposed. The proposed   strategy also demonstrated a robust performance with respect to modelling errors of process  parameters. The results demonstrate the effectiveness and validity of proposed strategy when  compared with conventional strategies.