Ethiopian Journal of Science and Technology <p>The <em>Ethiopian Journal of Science and Technology</em> (EJST) publishes high quality original&nbsp;&nbsp; research articles, reviews, short communications, and feature articles on basic and applied aspects of science, technology, agriculture, health and other related fields.</p> <p>Other websites associated with this journal:&nbsp;<a title="" href="" target="_blank" rel="noopener"></a></p> College of Science , Bahir Dar University en-US Ethiopian Journal of Science and Technology 1816-3378 <p>The copyright belongs to the journal.</p><p>The articles in Ethiopian Journal of Science and Technology are Open Access distributed under the terms of the Creative Commons Attribution License (<a title="The articles in Ethiopian Journal of Science and Technology are Open Access distributed under the terms of the Creative Commons Attribution License ( BY4.0)." href="/index.php/ejst/manager/setup/The%20articles%20in%20Ethiopian%20Journal%20of%20Science%20and%20Technology%20are%20Open%20Access%20distributed%20under%20the%20terms%20of%20the%20Creative%20Commons%20Attribution%20License%20(http:/" target="_blank"></a>).</p> Material waste minimization techniques in building construction projects <p>In Ethiopia, the rapid expansion of the construction sector has resulted in the wastage of construction materials that negatively affect the environment, society, and the economy. The reason is inefficient waste management strategies practiced in construction projects. Hence, an adequate material waste management strategy is required. This study was an attempt to identify the key techniques that can help to minimize material wastage in building construction projects. Questionnaire surveys, interviews, and reviews of previous studies and related literature were employed in gathering the relevant data. Seventy of 85 questionnaires administered and distributed to contractors, consultants, and clients were returned. These data were analyzed using the relative importance index method. The results indicated that employing waste management officers for this purpose, using prefabricated or off-site production of components, appropriate on-site waste management, and incorporating a policy of material waste minimization plan were identified as key measures to minimize construction material wastes.</p> Shitaw Tafesse Copyright (c) 2021-01-30 2021-01-30 14 1 1 19 10.4314/ejst.v14i1.1 Voltammetric determination of ascorbic acid using carbon paste electrode in ginger samples from selected areas of Ethiopia <p>Carbon paste electrode was prepared for the determination of ascorbic acid in<em> g</em>inger <em>(Zingiber officinale)</em> samples from three ginger growing areas (Chilga, Tepi and Dale Sadi) in Ethiopia. &nbsp;The effect of pH on the oxidation response of ascorbic acid in 0.1 M phosphate buffer was investigated and pH 2 was chosen as an optimum value. The oxidation response of ascorbic acid was predominantly diffusion-controlled reaction with a better determination coefficient R<sup>2</sup>= 0.9945 on the plot of anodic peak current <em>vs</em> square root of scan rate. A square wave amplitude of 45 mV, step potential of 7.0 mV and frequency of 25 Hz were chosen as optimum values.&nbsp; Linear calibration curve in the range of 0.1 – 8.0 mM of ascorbic acid standard in pH 2 phosphate buffer solution was obtained with a determination coefficient of 0.998. The amount of ascorbic acid detected in ginger samples collected from the three areas, Chilga was 6.85, Tepi was 6.59 and Dale Sadi was 6.54 mg/g of ginger powder. Percentage recovery in the range of 93% and 100% was validated for the applicability of the method for the quantitative determination of ascorbic acid in ginger samples. According to Health Canada dietary reference intakes, the maximum amount of ginger powder recommended intake for adult males in Chilga was 13.1, in Tepi 13.7 and in Dale Sadi 13.8 g/day.</p> Alemu Tesfaye Mulunesh Asaye Copyright (c) 2021-01-30 2021-01-30 14 1 21 37 10.4314/ejst.v14i1.2 Bayesian multilevel model application on determinants of perinatal mortality in Ethiopia using 2011 and 2016 EDHS data <p>Perinatal mortality is the death of a fetus after the age of viability until the 7<sup>th</sup> day of life. Perinatal mortality is estimated by the addition of stillbirths plus the early neonatal mortality, which represents deaths occurring during the first 7 days after delivery. Perinatal mortality remains a great burden in Ethiopia. The purpose of this study was to assess and compare the demographic and socio-economic determinant factors of perinatal mortality in Ethiopia using the 2011 and 2016 Ethiopian Demographic Health Surveys (EDHS). For data analysis, the Bayesian multilevel&nbsp; Model was used in this study. The study revealed that there is a regional variation in perinatal mortality and this variation was high in 2011 EDHS than in 2016 EDHS data. Factors like sex of the child, age of mother, wealth index, family size, birth order, source of drinking water, place of residence, place of delivery, and child twin were found to be the determinant factors of perinatal mortality in both 2011 and 2016 EDHS. In this study, we found that perinatal mortality variation across regions has decreased from 2011 to 2016 surveys which shows the promising progress of health intervention in the country.</p> Berhanu Bekele Debelu Denekew Bitew Belay Nigatu Degu Terye Copyright (c) 2021-01-30 2021-01-30 14 1 39 55 10.4314/ejst.v14i1.3 Prevalence and associated factors of anemia among children aged 6 to 59 months in Ethiopia: Evidence from the Ethiopian demographic and health survey <p>Anemia is one of the most widely spread public health problems, especially in developing countries including Ethiopia. The aim of this study was to assess the prevalence and associated factors of anemia among children aged 6-59 months in Ethiopia. A community-based cross-sectional study (the Ethiopian Demography and Health Survey 2016) was used as a source of data. Participants were 8385 children aged from 6 to 59 months selected in a two-stage stratified cluster sampling. The level of hemoglobin was determined by HemoCue analyzer. The risk factors of anemia were computed by logistic regression (α=0.05). The result revealed that more than half (57.3%) of children aged 6-59 months were found anemic of which 3.1% had severe anemia, 29.2% had moderate and 25% mild anemia. The anemia status of the children’s mother (OR: 3.01, CI: 0.34, 6.75), living in Somali region (OR: 5.73, CI: 1.86, 17.71), living in rural areas (OR: 1.84, CI: 1.38, 2.83), age of study participants (OR: 0.82, CI: 0.29,1.45) among 24-42 months old children (OR: 0.53, CI: 0.16,1.08) among 43-59 months old children, rich and medium parents (OR: 0.29, CI: 0.20,0.73), smoker mother (OR: 0.02, CI: 0.00, 0.05) were the risk factors of anemia among Ethiopian children aged 6-59 months. The overall prevalence of anemia among Ethiopian children aged 6–59 months was high. It is argued that measures that prevent childhood illnesses and maternal anemia need to be put in place in order to reduce anemia among Ethiopian children.</p> Ashenafi Abate Woya Abay Kassa Tekile Copyright (c) 2021-01-30 2021-01-30 14 1 57 70 10.4314/ejst.v14i1.4 A cluster-genetic programming approach for detecting pulmonary tuberculosis <p>Tuberculosis (TB) remains a global health concern. It commonly spreads through the air and attacks low immune bodies. TB is the most common and known health problem in low and middle-income countries. Genetic programming (GP) is a machine learning model for discovering useful relationships among the variables in complex clinical data. It is more appropriate in a circumstance when the form of the solution model is unknown a priori. The main objective of this study is to develop a model that can detect positive cases of TB suspected patients using genetic programming approach. In this paper, Genetic Programming (GP) is exploited to identify the presence of positive cases of tuberculosis from the real data set of TB suspects and hospitalized patients. First, the dataset is pre-processed, and target variables are identified using cluster analysis. This data-driven cluster analysis identifies two distinct clusters of patients, representing TB positive and TB negative. Then, GP is trained using the training datasets to construct a prediction model and tested with a separate new dataset. With the 30 runs, the median performance of GP on test data was good (sensitivity=0.78, specificity=0.95, accuracy=0.89, AUC=0.91). We find that GP shows better performance in predicting TB compared to other machine learning models. The study demonstrates that the GP model might be used to support clinicians to screen TB patients.</p> Adane Nega Tarekegn Tamir Anteneh Alemu Alemu Kumlachew Tegegne Copyright (c) 2021-01-30 2021-01-30 14 1 71 88 10.4314/ejst.v14i1.5