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 most popular medium that being used by people on the internet nowadays is video streaming. Nevertheless, streaming a video consumes much of the internet traffics. The massive quantity of internet usage goes for video streaming that disburses nearly 70% of the internet. Some constraints of interactive media might be detached; such as augmented bandwidth usage and lateness. The need for real-time transmission of video streaming while live leads to employing of Fog computing technologies which is an intermediary layer between the cloud and end user. The latter technology has been introduced to alleviate those problems by providing high real-time response and computational resources near to the
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Recent research has shown that a Deoxyribonucleic Acid (DNA) has ability to be used to discover diseases in human body as its function can be used for an intrusion-detection system (IDS) to detect attacks against computer system and networks traffics. Three main factor influenced the accuracy of IDS based on DNA sequence, which is DNA encoding method, STR keys and classification method to classify the correctness of proposed method. The pioneer idea on attempt a DNA sequence for intrusion detection system is using a normal signature sequence with alignment threshold value, later used DNA encoding based cryptography, however the detection rate result is very low. Since the network traffic consists of 41 attributes, therefore we proposed the
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This study was carried out to prepare and characterize domperidone nanoparticles to enhance solubility and the release rate. Domperidone is practically insoluble in water and has low and an erratic bioavailability range from 13%-17%. The domperidone nanoparticles were prepared by solvent/antisolvent precipitation method at different polymer:drug ratios of 1:1 and 2:1 using different polymers and grades of poly vinyl pyrolidone, hydroxy propyl methyl cellulose and sodium carboxymethyl cellulose as stabilizers. The effect of polymer type, ratio of polymer:drug, solvent:antisolvent ratio, stirring rate and stirring time on the particle size, were investigated and found to have a significant (p? 0.05) effect on particle size. The best formul
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Globally, the COVID-19 pandemic’s development has presented significant societal and economic challenges. The carriers of COVID-19 transmission have also been identified as asymptomatic infected people. Yet, most epidemic models do not consider their impact when accounting for the disease’s indirect transmission. This study suggested and investigated a mathematical model replicating the spread of coronavirus disease among asymptomatic infected people. A study was conducted on every aspect of the system’s solution. The equilibrium points and the basic reproduction number were computed. The endemic equilibrium point and the disease-free equilibrium point had both undergone local stability analyses. A geometric technique was used
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Voice denoising is the process of removing undesirable voices from the voice signal. Within the environmental noise and after the application of speech recognition system, the discriminative model finds it difficult to recognize the waveform of the voice signal. This is due to the fact that the environmental noise needs to use a suitable filter that does not affect the shaped waveform of the input microphone. This paper plans to build up a procedure for a discriminative model, using infinite impulse response filter (Butterworth filter) and local polynomial approximation (Savitzky-Golay) smoothing filter that is a polynomial regression on the signal values. Signal to noise ratio (SNR) was calculated after filtering to compare the results
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Sentiment Analysis is a research field that studies human opinion, sentiment, evaluation, and emotions towards entities such as products, services, organizations, events, topics, and their attributes. It is also a task of natural language processing. However, sentiment analysis research has mainly been carried out for the English language. Although the Arabic language is one of the most used languages on the Internet, only a few studies have focused on Arabic language sentiment analysis.
In this paper, a review of the most important research works in the field of Arabic text sentiment analysis using deep learning algorithms is presented. This review illustrates the main steps used in these studies, which include
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