Objective: To assess the impact of a social support for pregnant women upon their pregnancy outcome Methodology: A descriptive purposive study was used to assess the impact of a social support on their pregnancy outcomes. The study was conducted from (22 \ September \ 2020 to 15 \ February \ 2021). A non-probability sample (purposive sample) was selected from 100 women. Data were collected through an interview with the mother in the counseling clinic, during the third trimester of pregnancy, as well as after childbirth in the labor wards to assess the outcome of pregnancy. Data were analyzed through descriptive statistics (frequency and percentages). Results: The most important thing observed in this study was the positive pregnancy outcome for women with high social support during pregnancy. But the women suffering from a low social support in this study (18%) stillbirth at P. value (.042). Recommendations: social support during pregnancy provides some buffer from its enduring effects. Interventions designed to enhance pregnancy social support may not only improve maternal wellbeing, but may also safeguard infant health. Increased social support may improve maternal mental health during pregnancy and this association should be assessed in longitudinalstudies.
Background :Thalassemia is an autosomal
disease of the haemoglobin. Two types of
thalassemia are recognized: thalassemia major
and thalassemia intermedia.
The most serious cardiac complication in
thalassemia major is due to multiple blood
transfusions rather than the disease itself, which
is due to iron overload.
Cardiomyopathy is the most common cardiac
defect that occurs with iron overload. Pricarditis,
congestive heart failure and arrhythmias are due
to hemosidrosis and chronic aneamia.
Aim of the study: to demonstrate the prevalence
and types of electrocardiographic changes among
thalassemic patients with aged over ten years old.
Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
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Precision irrigation applications are used to optimize the use of water resources, by controlling plant water requirements through using different systems according to soil moisture and plant growth periods. In precision irrigation, different rates of irrigation water are applied to different places of the land in comparison with traditional irrigation methods. Thus the cost of irrigation water is reduced. As a result of the fact that precise irrigation can be used and applied in all irrigation systems, it spreads rapidly in all irrigation systems. The purpose of the Precision Agriculture Technology System (precision irrigation) , is to apply the required level of irrigation according to agricultural inputs to the specified location , by us
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Drought is a natural phenomenon in many arid, semi-arid, or wet regions. This showed that no region worldwide is excluded from the occurrence of drought. Extreme droughts were caused by global weather warming and climate change. Therefore, it is essential to review the studies conducted on drought to use the recommendations made by the researchers on drought. The drought was classified into meteorological, agricultural, hydrological, and economic-social. In addition, researchers described the severity of the drought by using various indices which required different input data. The indices used by various researchers were the Joint Deficit Index (JDI), Effective Drought Index (EDI), Streamflow Drought Index (SDI), Sta
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One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first
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