Objective(s): To assess women’s knowledge about health promotion after a cesarean delivery and to determine the association between women’s knowledge and their demographic data of age, level of education, and monthly income. Methodology: A descriptive design is carried out to assess women’s knowledge about health promotion after cesarean delivery at Maternity and Pediatric Hospital in Al-Samawa City. This study starts from 26th of September 2020 up to 16th March 2021. Sample of (100) woman who are at reproductive age, pregnant (prime or multipara) who have planned to have birth by elective cesarean section or had previous elective caesarian section without medical indication or women who had cesarean section with medical indication or emergency. Results: Results of the study show moderate mean of scores for most questions are moderate significance and others questions shows low mean of scores for most questions. Recommendations: The study recommends providing instructions for pregnant women by nurses and midwives about promotion of health after a cesarean delivery. Apply knowledge for gestation women will be counseled by nurses and midwives during the antenatal cycle about childbirth procedures, symptoms, characteristics, and risks, allowing women to make their own decisions through primary health care centers.
This study examines the removal of ciprofloxacin in an aqueous solution using green tea silver nanoparticles (Ag-NPs). The synthesized Ag-NPs have been classified by the different techniques of SEM, AFM, BET, FTIR, and Zeta potential. Spherical nanoparticles with average sizes of 32 nm and a surface area of 1.2387m2/g are found to be silver nanoparticles. The results showed that the ciprofloxacin removal efficiency depends on the initial pH (2.5-10), CIP (2-15 mg/L), temperature (20-50°C), time (0-180 min), and Ag-NPs dosage (0.1-1g/L). Batch experiments revealed that the removal rate with ratio (1:1) (w/w) were 52%, and 79.8% of the 10 mg/L of CIP at 60, and 180 minutes, respectively with optimal pH=4. Kinetic models for adsorpti
... Show MoreBearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us
... Show MoreA fixed callus weight of 150 mg was induced from immature embryos of three bread wheat Triticum aestivum L. genotypes (Tamos 2, El-izz and Mutant 1) cultured on nutrient medium {MS) containing Polyethylene glycol (PEG-6000) supplemented with concentrations (0.0, 3.0, 6.0, 9.0 or 12.0%) to evaluate their tolerance to water stress. Cultures were incubated in darkness at temperature of 25?1 ?C. Callus fresh and dry weights were recorded and soluble Carbohydrate and the amino acid Proline concentrations were determined. Results showed that there were significant differences in studied parameters among bread wheat genotypes of which Tamos 2 was higher in callus average fresh and dry weights which gave 353.33 and 38.46 mg/cultured tube respecti
... Show MoreConditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.
Background: Diabetes mellitus consists of a group of diseases characterized by abnormally high blood glucose levels. Glycated haemoglobin (HbA1c) is a form of haemoglobin used to identify the average concentration of plasma glucose over prolonged periods of time. It is formed in a non-enzymatic pathway by normal exposure of hemoglobin to high levels of plasma glucose, The main alterations observed in the saliva of Type 1 diabetic patients are hyposalivation and alteration in its composition, particularly those related to the levels of glucose. The aim of the present study was to assess the effect of Glycated haemoglobin level on the level of salivary glucose which may have an effect on oral health condition. Materials and methods
... Show MoreThis study was conducted to test the effectiveness of Agaricus bisporus inoculums (spawn) in the ratio of (0.25, 0.5 and 1%) v/v to control Pythium aphanidermatum fungus the causal agent of damping- off disease of cucumber plant. results showed the ability of A. bisporus fungus to protect the seedlings from incidence by P. aphanidermatum . all treatments of edible fungus inoculums were significantly different from pathogen treatment after 15 day of planting and there was no significant difference found from control treatment (without pathogen) . the successful of A. bisporus was continued to protect the seedlings after 30 and 45 day after planting. The numbers of seedlings were (8, 7.25 & 7.25) respectively compared to 5.5 seedlings in con
... Show MoreCodes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de
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