This paper examines on the effect of the reservoir rock permeability on gas injection by using reservoir simulation. This task will be performed by using reservoir simulation software (Eclipse). This injection interacts with CO2 to create conditions favorable for oil recovery. The main target of this project is to investigate the effect of the reservoir rock permeability on gas injection and the optimum injection rate to get the optimum recovery. The problems statement of this study is: As the oil and gas in a formation is produced, the hydrocarbons remaining in the reservoir may become trapped because the pressure in the formation has lessened, making production either slow dramatically or stop altogether. Climate change refers to long-term shifts in temperatures and weather patterns. Burning fossil fuels generates greenhouse gas emissions that act like a blanket wrapped around the Earth, trapping the sun’s heat and raising temperatures. Examples of greenhouse gas emissions that are causing climate change include carbon dioxide and methane. The result of effect of the reservoir rock permeability on gas injection by using reservoir simulation shows that with the increase in the permeability of reservoir rock, the rate of gas production increases. The greater the permeability of rocks, the rate of water production increases, which is a direct method relationship between water production and permeability. We note after this evaluation that the cumulative oil, water, and gas production increases with the increase in rock permeability.

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 …

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.

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.