Objectives: The study aims to (1) Assess the parents' efficacy for child healthy weight behavior. (2) Identify the difference in parents’ efficacy for child healthy weight behavior between the groups of parent’s gender, family’s socioeconomic status, child’s gender, and child’s birth order, (3) Find out the relationship between parents’ age, child’s age, child’s body mass index, family’s socioeconomic status, the number of children in the family and parents’ efficacy for child healthy weight behavior.
Methodology: A descriptive correlational study is conducted for the period from November 11th, 2018 to March 25th, 2019 to assess the parents' efficacy for child healthy weight behavior. The study was carried-out in (30) primary schools that were selected through a simple random sampling of (125) schools from Hilla City. The instruments was composed of two parts , the first part was the demographic data and the second part was the Parent Efficacy for Child Healthy Weight Behavior (PECHWB) Scale, it consists of 41 items based on Australian guidelines for healthy weight behaviors. The validity of the instrument was achieved by eleven experts. Data were collected for the period from January 10th to March 5th, 2019. Data were analyzed using the statistical package for social sciences (SPSS) version 24.
Results: The study results revealed that most of pupils eat three or more serves of fruit and vegetables per day, minimize high fats and sugar intake, engaging in one hour of physical activity per day, and being no more than two hours in sedentary behavior per day on holidays/vacations and on weekends. Furthermore, they minimize high fats and sugar intake and eat healthy snacks on their demands/request. Moreover, they do not minimize high fats and sugar intake and eat healthy snacks when they are stressed or in bad mood and when they complain.
Recommendations: The researcher recommends establishing health activities that aim to raise the public’s awareness of fostering healthy lifestyle and behaviors for their children
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The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI genera
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Aim: This study aimed to investigate the impact of rabbit serum on skin wound healing with the help of histological examination. Materials and Methods: A total of ten indigenous rabbits were used in this study. The animals were divided into two groups: control and serum- treated. The histological assessment was done with a paraffin embedding technique and the histological sections were stained with H&E stain. Results: Severe infiltration of polymorphonuclear leukocytes with severe fibrin deposits were seen in serum treated group at 2 days post-injury; at 7 days post-injury the changes revealed moderate fibroplasia, fibrin deposit and severe infiltration of both mononuclear and polymorphonuclear leukocytes; at 14 days post-inju
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Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiment
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The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr
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In this paper, the characteristics of microstrip monopole antennas are studied firstly in free space. Secondly, the effects of the human body on the studied antenna's performance are investigated for wearable communications. Different patch shapes of microstrip monopole antenna are chosen to operate at two bands: industrial scientific and medical band (ISM) and ultra-wideband (UWB) for wearable applications. The studied antenna consists of a radiating element on one side of the substrate and a partial ground plane on the other side. The antenna is supposed to fabricate on cloth fabric whose relative dielectric constant is Ɛr =1.7. At the same time, the pure copper could be used as the conducting part representing both t
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