Viruses are the most abundant living things and a source of genetic variation. Despite recent research, we know little about their biodiversity and geographic distribution. We used different bioinformatics tools, MG-RAST, genome detective web tools, and GenomeVx, to describe the first metagenomic examination of haloviruses in Wadi Al-Natrun. The discovered viromes had remarkably different taxonomic compositions. Most sequences were derived from double-stranded DNA viruses, especially from Myoviridae, Podoviridae, Siphoviridae, Herpesviridae, Bicaudaviridae, and Phycodnaviridae families; single-stranded DNA viruses, especially from the family Microviridae; and positive-strand RNA viruses, especially from the family Potyviridae. Additionally, our results showed that Myohalovirus chaoS9 has eight Contigs and is annotated to 18 proteins as follows: tail sheath protein, tco, nep, five uncharacterized proteins, HCO, major capsid protein, putative pro head protease protein, putative head assembly protein, CxxC motive protein, terl, HTH domain protein, and terS Exon 2. Additionally, Halorubrum phage CGphi46 has 19 proteins in the brine sample as follows: portal protein, 17 hypothetical proteins, major capsid protein, etc. This study reveals viral lineages, suggesting the Virus’s global dispersal more than other microorganisms. Our study clarifies how viral communities are connected and how the global environment changes.

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

There is no doubt that the traffic problem is one of the problems faced by universities in different intensities for roads, intersections, and parklands. The negative effects of traffic congestion and bottlenecks are clear; they include extension of long waiting lines and increasing the time of transport with the consequences of ill effects. The effective solutions placed to resolve these problems need specialized studies relying on scientific methods for collecting and analyzing relevant data and drawing effective conclusions, recommendations, and solutions. The main purpose of this study is to analyze the problems of traffic congestion in crowded institutions, with realistic application to an anonymous University in Saudi Arabia as a case study and to provide the best solutions to achieve fluent flow of traffic at the present time and in the near and far future. Due to the complexity of the problem and its huge data and to facilitate the analysis process, the problem has been divided into three main sections, and the data is collected in four different ways.

In next-generation smart city infrastructure, monitoring emotional stability in real time is a crucial component. This paper introduces a powerful Edge-AI monitoring system suitable for all children, with a specific focus on those displaying special behavioral patterns or sensory sensitivities. The proposed model combines improved Frilled Lizard Optimization (FLO), the Histogram of Orientated Gradients (HOG), and the Teager–Kaiser Energy Operator (TKEO) to enhance the analysis of spontaneous behaviors. Then, the model is called FLHTKEO. The model demonstrates high accuracy in interpretating children’s facial expressions in real-time recorded datasets. To address the non-stationary noise and varying brightness typical of school bus interior, the TKEO is employed as a preprocessing tuning stage to amplify the immediate energy of features extracted via HOG. Additionally, the proposed model is implemented on a System-on-Chip (SoC) using a Raspberry Pi 4 to achieve real-time monitoring and generate automatic alarms when abnormal or uncertain behaviors are detected among children in the school vehicle. Real-time datasets were collected, simulating diverse school bus scenarios under varying lighting and behavioral conditions. The proposed model achieves a superior AUC-ROC ≅ 0.9613, proving a high degree of the precision recognition of children’s facial expressions. The system will help with some healthy measures during ongoing and future emergencies. Simulations and experimental results indicate that the proposed method is effective for the model, and automatic compliance monitoring systems are set to advance in the future. Finally, it is compared with the last piece of previous work.