The network connectivity is often lost during some situations such as in war zones, battle fields or in regard to natural disasters like floods or earthquakes. Therefore, the connection becomes difficult for rescuing teams to locate people. At the same time, mobile nodes are difficult to be tracked/located. Therefore, flying ad-hoc networks (FANETs) is the solution which provides a way to tackle this situation without the need for any central infrastructure with the use of flying objects (nodes). FANTEs are frequently formed of tiny unmanned aerial vehicles (UAVs) which are flexible, affordable, and rapid to deploy. UAV as a router and without complex hardware deployment, provides an effective real-time communication solution for multiple UAV systems. FANETs can also be used to provide connectivity in such areas in which flying nodes are utilized. In this paper, we discuss the importance of the FANETs and present a routing protocol comparison that depends on the link budget to identify the optimized packet route for the data to be transmitted. The simulation design results show that a throughput enhancement of 0.62Gbps is achieved in comparison to the distance-based routing protocol. The results also confirm the efficiency of the optimized link state routing (OSLR) protocol over the other. The performance of FANETs can be optimized further by using the OLSR protocol.

The aim of the present study was to evaluate the variations in the phytochemical compositions and the antimicrobial activities of Syzygium grande and Oenanthe javanica leaf extracts against Bacillus sp. The antibacterial activity of the methanol, 70% methanol, ethyl acetate, and hexane extracts of both plant species were examined in vitro, against various Bacillus species. In addition, the 1H Nuclear Magnetic Resonance (1H NMR) spectroscopy-based metabolomics was employed to gain insights into the correlations between the chemical constituents of the bioactive extract to the biological activity. The hexane extract of S. grande leaves and methanolic extract of O. javanica leaves showed more potent anti Bacillus activity, among the test extracts. Principal component analysis (PCA) successfully differentiated the extracts on the basis of their metabolite profiles. The results of the Partial Least Square (PLS) analysis showed that the non-polar compounds of the hexane extract of S. grande (alanine, betulin, β-sitosterol, β-caryophyllene, acetic acid and 3-hydroxybutyric acid) were strongly correlated to its anti-Bacillus activity. On the other hand, choline, ellagic acid, and gallic acid, the metabolites present in the methanolic extract of O. javanica, were strongly correlated to its anti-Bacillus activity. On this basis, it was therefore concluded that these conmpounds could be the potential bioactive constituents, contributing to the anti-Bacillus activity of the individual plant species. Further in-depth investigations into the potential utilization of the two plants for useful applications in managing and control of Bacillus sp., will help pave the way towards their valorization.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Counting the number of people in many estimation systems, such as still images or video frames, is a buoyant research area that is challenging in the field of computer vision. It plays a considerable role in a variety of applications, such as security, management, education, and commerce. The purpose of this paper is to suggest a system to estimate the number of people in digital still images based on the Face Detection method. This system composed of two parts: face detection and counting of detected faces. In the detection step, the Skin Color Face Detection method was applied on the input of a digital still image. In the counting part, the obtained detected faces by the Skin Color Face Detection method have counted to estimate the number of people in an input color image with simple software and simple low-cost hardware. The skin color face detection algorithm was tested using 133 images from the People Image Groups dataset, which contains about 2573 color images of people, to test the proposed system. based on the obtained results, the best precision achieved of the proposed Skin Color face detection algorithm was 85%

Traditional education depends on a set of basic elements, the most important of which are the educational curriculum, the lecturer, and the student, but these basic elements have been added to a new element in distance education, which is the electronic environment. During the COVID19 pandemic that began at the end of 2019 and the rapid transformation from traditional education to distance education and the involvement of distance education in implementing pre-prepared educational plans. This rapid transformation, in which the elements of education were not developed and not prepared in advance, led to the emergence of some problems in the educational outputs. Distance education, although its basic elements are the same as those of traditional education, the method of implementing it differed by introducing a new essential element, which is the electronic environment through which educational plans are implemented. In this research paper, the most important and influential actors in the educational process of distance learning and the problems resulting from this rapid transformation will be studied, then find appropriate solutions to keep pace with the development in the educational process, through an evaluation study of the experience of the Faculty of Science at Gharyan University. The results of this research will be useful to implement changes in the educational system and to meet the requirements of modern educational standards for higher education.