Disasters, crises and wars are a serious and unforeseen threat. The capacity of the early warning system to monitor such crises is therefore crucial. The ability to make quick decisions in a short time is necessary to prevent crises from occurring. Here, the role and effectiveness of the early warning system emerges through its ability to monitor, record and analyze signals. It can also be evidenced by its ability to immediately convey these indicators to the concerned authorities to take measures that ensure these conflicts and disasters do not worsen. The system’s ability to detect disasters and crises, identify the crisis and its type, and use the scientific method and common sense to deal with it is something that contributes to findi

This study focuses on producing wood-plastic composites using unsaturated polyester resin reinforced with Pistacia vera shell particles and wood industry waste powder. Composites with reinforcement ratios of 0%, 20%, 30%, and 40% were prepared and tested for thermal conductivity, impact strength, hardness, and compressive strength. The results revealed that thermal conductivity increases with reinforcement, while maintaining good thermal insulation, reaching a peak value of 0.633453 W/m·K. Hardness decreased with increased reinforcement, reaching a minimum nominal hardness value of 0.9479. Meanwhile, impact strength and compressive strength improved, with peak values of 14.103 k/m² and 57.3864568 MPa, respectively. The main aim is to manu

An essential issue in obstetrics is the prevalence of maternal and fetal complications in pregnant women with polycystic ovary syndrome (PCOS). The purpose of the present study was to investigate the prevalence of pregnancy complications among various phenotypes of pregnant women with PCOS.

This research explores the concept of cruel optimism in the context of challenging patriarchal, cultural, and social traditions in Ayad Akhtar’s The Who and the What. Cruel optimism, a term coined by Lauren Berlant, refers to the paradoxical attachment to positive aspirations that may ultimately obstruct personal fulfillment and well-being. This study examines how individuals who resist patriarchal norms and entrenched cultural traditions often face significant emotional, psychological, and social challenges. Through a multidisciplinary approach, including literary analysis, sociological perspectives, and psychological insights, the research delves into the lived experiences of those who strive for autonomy, equality, and self-realization

Bipedal robotic mechanisms are unstable due to the unilateral contact passive joint between the sole and the ground. Hierarchical control layers are crucial for creating walking patterns, stabilizing locomotion, and ensuring correct angular trajectories for bipedal joints due to the system’s various degrees of freedom. This work provides a hierarchical control scheme for a bipedal robot that focuses on balance (stabilization) and low-level tracking control while considering flexible joints. The stabilization control method uses the Newton–Euler formulation to establish a mathematical relationship between the zero-moment point (ZMP) and the center of mass (COM), resulting in highly nonlinear and coupled dynamic equations. Adaptiv

Background: In Iraqi communities, the workers considered the largest population groups, so increasing their dental education by increasing the care for their dental health knowledge and behavior is very important, the present study was aimed to evaluate the gingival health and oral hygiene in relation to knowledge and behavior among a group of a workers selected randomly from Al Fedaa company in Baghdad city. Materials and methods: A sample of 110 workers (65 men and 45 women) included in this study, a questionnaire used to evaluate their oral health knowledge and behavior. The gingival health condition of the workers was examined by using Loe and Silness index (1963), Silness and Loe index (1964) was used to asses plaque quantity, and Ramf

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne