Objective(s): The study aims at evaluating pregnancy-related health behaviors for pregnant women, and to identify the association between pregnancy-related health behaviors and their demographic characteristics of pregnant woman’s age, education, employment, residential area and monthly income.
Methodology: A descriptive study is carried out for the period from December 14th, 2020 to June 20th, 2021. This study was conducted through a non-probability (convenience) sample of 150 pregnant women attending, Abo Ghareeb primary health care sector in Abo Ghareeb spend. The sample has been collected by using the instrument to gather data and accomplish the study's objectives. A questionnaire is composed of (29) items and it is divided into two parts. The first part is socio-demographic data of pregnant women which consists of (five) items including age, level of education, occupation, residential area, and monthly income and the second part is items of health behaviors during pregnancy which consists of (29) questions and question number twenty-nine consist of (17) items of changed behaviors. The content validity of the instrument has been determined by panel of experts and the internal consistency reliability is determined by the computation of Cronbach alpha correlation coefficient. The data are collected throughout the use of the questionnaire and the interview technique and analyzed by applying descriptive and inferential statistical data analysis approaches.
Results: Results show that (30%) of the study subjects are (20-24) years old, (88%) housewives, (28.7%) are primary school graduated, 60.7% are rural residents, and (50%) of their monthly income is (300, 000-600,000) ID. The Study results show that there is a high-significant association between the study sample's healthy behaviors and their education, monthly income, and residential area at a p-value less than (0.01). While other demographic characteristics of age and occupation has non-significant association with a p-value of higher than (0.05).
Recommendations: The study recommends that the Iraqi Ministry of Health should print and distribute booklets containing all the health behaviors of pregnant women
Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls sh
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Abstract
For sparse system identification,recent suggested algorithms are 
-norm Least Mean Square ( -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named -ZA-LMS,
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