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.

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.

Sarcasm is often used to convey the opposite of what is actually said (positive words, negative meaning). It is a type of humor that relies on the listener or reader to understand the intended meaning of the words. Sarcasm can be difficult to detect in written text, especially on social media platforms where people often use abbreviated language and emojis to convey their messages. The sarcasm detection can be a crucial role in refining Automated Sentiment (SA). In order to enhance the effectiveness of data-driven approaches for customer service and product development. Furthermore, the existing research on sarcasm detection in Arabic is limited. Also, there is a noticeable absence of studies in particular, the sarcasm detection in Libyan Arabic dialects. This study utilizes a data-set of 5082 collected from Facebook platform, labeled by team annotators experienced in the Libyan dialects as (sarcastic or non-sarcastic), and extracting some lexical and syntactic features which used to training Support Vector Machine (SVM) model. The results show commendable performance with an accuracy of 79.15%, precision of 79.3%, recall of 79.7%, and an F1 score of 79.5%.

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.