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Tegegne</title> <meta name="description" content="Search results for: Firew Tegegne"> <meta name="keywords" content="Firew Tegegne"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Firew Tegegne"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Firew Tegegne</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> A Review on Potential Utilization of Water Hyacinth (Eichhornia crassipes) as Livestock Feed with Particular Emphasis to Developing Countries in Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shigdaf%20Mekuriaw">Shigdaf Mekuriaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Firew%20Tegegne"> Firew Tegegne</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tsunekawa"> A. Tsunekawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Dereje%20Tewabe"> Dereje Tewabe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to make a comprehensive review on the use of water hyacinth (Eichhornia crassipes) as a potential livestock feed and argue its utilization as complementary strategy to other control methods. Water Hyacinth is one of the most noxious plant invaders of rivers and lakes. Such weeds cause environmental disaster and interfere with economic and recreational activities such as water transportation and fishing. Economic impacts of the weed in seven African countries have been estimated at between 20-50 million US$ every year. It would, therefore, be prudent to suggest utilization as a complementary control method. The majority of people in developing countries are dependent on traditional and inefficient crop-livestock production system that constrains their ability to enhance economic productivity and quality of life. Livestock in developing countries faces shortage of feed, especially during the long dry seasons. Existing literature shows the use of water hyacinth as livestock and fish feed. The chemical composition of water hyacinth varies considerably. Due to its relatively high crude protein (CP) content (5.8-20.0%), water hyacinth can be considered as a potential protein supplement for livestock which commonly feed cereal crop residues whose contribution as source of feed is increasing in Africa. Though the effects of anti-nutritional factors (ANFs) present in water hyacinth is not investigated, their concentrations are not above threshold hinder its utilization as livestock feed. In conclusion, water hyacinth could provide large quantities of nutritious feed for animals. Like other feeds, water hyacinth may not be offered as a sole feed and based on existing literature its optimum inclusion level reaches 50%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Africa" title="Africa">Africa</a>, <a href="https://publications.waset.org/abstracts/search?q=livestock%20feed" title=" livestock feed"> livestock feed</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20bodies" title=" water bodies"> water bodies</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20hyacinth%20and%20weed%20control%20method" title=" water hyacinth and weed control method"> water hyacinth and weed control method</a> </p> <a href="https://publications.waset.org/abstracts/43297/a-review-on-potential-utilization-of-water-hyacinth-eichhornia-crassipes-as-livestock-feed-with-particular-emphasis-to-developing-countries-in-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43297.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Automatic Detection and Filtering of Negative Emotion-Bearing Contents from Social Media in Amharic Using Sentiment Analysis and Deep Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Derejaw%20Lake%20Melie">Derejaw Lake Melie</a>, <a href="https://publications.waset.org/abstracts/search?q=Alemu%20Kumlachew%20Tegegne"> Alemu Kumlachew Tegegne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=negative%20emotion" title="negative emotion">negative emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media%20filtering%20sentiment%20analysis" title=" social media filtering sentiment analysis"> social media filtering sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning." title=" deep learning."> deep learning.</a> </p> <a href="https://publications.waset.org/abstracts/191945/automatic-detection-and-filtering-of-negative-emotion-bearing-contents-from-social-media-in-amharic-using-sentiment-analysis-and-deep-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">23</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Biogas Production from Lake Bottom Biomass from Forest Management Areas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dessie%20Tegegne%20Tibebu">Dessie Tegegne Tibebu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirsi%20Mononen"> Kirsi Mononen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ari%20Pappinen"> Ari Pappinen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In areas with forest management, agricultural, and industrial activity, sediments and biomass are accumulated in lakes through drainage system, which might be a cause for biodiversity loss and health problems. One possible solution can be utilization of lake bottom biomass and sediments for biogas production. The main objective of this study was to investigate the potentials of lake bottom materials for production of biogas by anaerobic digestion and to study the effect of pretreatment methods for feed materials on biogas yield. In order to study the potentials of biogas production lake bottom materials were collected from two sites, Likokanta and Kutunjärvi lake. Lake bottom materials were mixed with straw-horse manure to produce biogas in a laboratory scale reactor. The results indicated that highest yields of biogas values were observed when feeds were composed of 50% lake bottom materials with 50% straw horse manure mixture-while with above 50% lake bottom materials in the feed biogas production decreased. CH4 content from Likokanta lake materials with straw-horse manure and Kutunjärvi lake materials with straw-horse manure were similar values when feed consisted of 50% lake bottom materials with 50% straw horse manure mixtures. However, feeds with lake bottom materials above 50%, the CH4 concentration started to decrease, impairing gas process. Pretreatment applied on Kutunjärvi lake materials showed a slight negative effect on the biogas production and lowest CH4 concentration throughout the experiment. The average CH4 production (ml g-1 VS) from pretreated Kutunjärvi lake materials with straw horse manure (208.9 ml g-1 VS) and untreated Kutunjärvi lake materials with straw horse manure (182.2 ml g-1 VS) were markedly higher than from Likokanta lake materials with straw horse manure (157.8 ml g-1 VS). According to the experimental results, utilization of 100% lake bottom materials for biogas production is likely to be impaired negatively. In the future, further analyses to improve the biogas yields, assessment of costs and benefits is needed before utilizing lake bottom materials for the production of biogas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anaerobic%20digestion" title="anaerobic digestion">anaerobic digestion</a>, <a href="https://publications.waset.org/abstracts/search?q=biogas" title=" biogas"> biogas</a>, <a href="https://publications.waset.org/abstracts/search?q=lake%20bottom%20materials" title=" lake bottom materials"> lake bottom materials</a>, <a href="https://publications.waset.org/abstracts/search?q=sediments" title=" sediments"> sediments</a>, <a href="https://publications.waset.org/abstracts/search?q=pretreatment" title=" pretreatment"> pretreatment</a> </p> <a href="https://publications.waset.org/abstracts/34770/biogas-production-from-lake-bottom-biomass-from-forest-management-areas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34770.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Potential Risk Assessment Due to Groundwater Quality Deterioration and Quantifying the Major Influencing Factors Using Geographical Detectors in the Gunabay Watershed of Ethiopia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asnakew%20Mulualem%20Tegegne">Asnakew Mulualem Tegegne</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarun%20Kumar%20Lohani"> Tarun Kumar Lohani</a>, <a href="https://publications.waset.org/abstracts/search?q="></a>, <a href="https://publications.waset.org/abstracts/search?q=Abunu%20Atlabachew%20Eshete">Abunu Atlabachew Eshete</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Groundwater quality has become deteriorated due to natural and anthropogenic activities. Poor water quality has a potential risk to human health and the environment. Therefore, the study aimed to assess the potential risk of groundwater quality contamination levels and public health risks in the Gunabay watershed. For this task, seventy-eight groundwater samples were collected from thirty-nine locations in the dry and wet seasons during 2022. The ground water contamination index was applied to assess the overall quality of groundwater. Six major driving forces (temperature, population density, soil, land cover, recharge, and geology) and their quantitative impact of each factor on groundwater quality deterioration were demonstrated using Geodetector. The results showed that low groundwater quality was detected in urban and agricultural land. Especially nitrate contamination was highly linked to groundwater quality deterioration and public health risks, and a medium contamination level was observed in the area. This indicates that the inappropriate application of fertilizer on agricultural land and wastewater from urban areas has a great impact on shallow aquifers in the study area. Furthermore, the major influencing factors are ranked as soil type (0.33–0.31)>recharge (0.17–0.15)>temperature (0.13–0.08)>population density (0.1–0.08)>land cover types (0.07– 0.04)>lithology (0.05–0.04). The interaction detector revealed that the interaction between soil ∩ recharge, soil ∩ temperature, and soil ∩ land cover, temperature ∩ recharge is more influential to deteriorate groundwater quality in both seasons. Identification and quantification of the major influencing factors may provide new insight into groundwater resource management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=groundwater%20contamination%20index" title="groundwater contamination index">groundwater contamination index</a>, <a href="https://publications.waset.org/abstracts/search?q=geographical%20detectors" title=" geographical detectors"> geographical detectors</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20health%20%C2%B7%20influencing%20factors" title=" public health · influencing factors"> public health · influencing factors</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20water%20resources%20management" title=" and water resources management"> and water resources management</a> </p> <a href="https://publications.waset.org/abstracts/192443/potential-risk-assessment-due-to-groundwater-quality-deterioration-and-quantifying-the-major-influencing-factors-using-geographical-detectors-in-the-gunabay-watershed-of-ethiopia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192443.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">17</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Magnitude and Factors of Risky Sexual Practice among Day Laborers in Ethiopia: A Systematic Review and Meta-Analysis, 2023</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalkidan%20Worku">Kalkidan Worku</a>, <a href="https://publications.waset.org/abstracts/search?q=Eniyew%20Tegegne"> Eniyew Tegegne</a>, <a href="https://publications.waset.org/abstracts/search?q=Menichil%20Amsalu"> Menichil Amsalu</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Derbie%20Habtegiorgis"> Samuel Derbie Habtegiorgis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Because of the seasonal nature of the work, day laborers are exposed to risky sexual practices. Since the majority of them are living far away from their birthplace and family, they engage in unplanned and multiple sexual practices. These unplanned and unprotected sexual experiences are a risk for different types of sexual-related health crises. This study aimed to assess the pooled prevalence of risky sexual practices and its determinants among day laborers in Ethiopia. Methods: Online databases, including PubMed, Google Scholar, Science Direct, African Journal of Online, Academia Edu, Semantic Scholar, and university repository sites, were searched from database inception until March 2023. PRISMA 2020 guideline was used to conduct the review. Among 851 extracted studies, ten articles were retained for the final quantitative analysis. To identify the source of heterogeneity, a sub-group analysis and I² test were performed. Publication bias was assessed by using a funnel plot and the Egger and Beg test. The pooled prevalence of risky sexual practices was calculated. Besides, the association between determinant factors and risky sexual practice was determined using a pooled odds ratio (OR) with a 95% confidence interval. Result: The pooled prevalence of risky sexual practices among day laborers was 46.00% (95% CI: 32.96, 59.03). Being single (OR: 2.49; 95% CI: 1.29 to 4.83), substance use (OR: 1.79; 95% CI: 1.40 to 2.29), alcohol intake (OR: 4.19; 95% CI: 2.19 to 8.04), watching pornographic (OR: 5.49; 95% CI: 2.99 to 10.09), discussion about SRH (OR: 4.21; 95% CI: 1.34 to 13.21), visiting night clubs (OR: 2.86 95% CI: 1.79 to 4.57) and risk perception (OR: 0.37 95% CI: 0.20 to 0.70) were the possible factors for risky sexual practice of day laborers in Ethiopia. Conclusions: A large proportion of day laborers engaged in risky sexual practices. Interventions targeting creating awareness of sexual and reproductive health for day laborers should be implemented. Continuous peer education on sexual health should be given to day laborers. Sexual and reproductive health services should be accessible in their workplaces to maximize condom utilization and to facilitate sexual health education for all day laborers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=day%20laborers" title="day laborers">day laborers</a>, <a href="https://publications.waset.org/abstracts/search?q=sexual%20health" title=" sexual health"> sexual health</a>, <a href="https://publications.waset.org/abstracts/search?q=risky%20sexual%20practice" title=" risky sexual practice"> risky sexual practice</a>, <a href="https://publications.waset.org/abstracts/search?q=unsafe%20sex" title=" unsafe sex"> unsafe sex</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20sexual%20partners" title=" multiple sexual partners"> multiple sexual partners</a> </p> <a href="https://publications.waset.org/abstracts/176247/magnitude-and-factors-of-risky-sexual-practice-among-day-laborers-in-ethiopia-a-systematic-review-and-meta-analysis-2023" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176247.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Groundwater Potential Delineation Using Geodetector Based Convolutional Neural Network in the Gunabay Watershed of Ethiopia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asnakew%20Mulualem%20Tegegne">Asnakew Mulualem Tegegne</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarun%20Kumar%20Lohani"> Tarun Kumar Lohani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abunu%20Atlabachew%20Eshete"> Abunu Atlabachew Eshete</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resources are now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to a lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN-based AUC validation platform showed that 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=geodetector" title=" geodetector"> geodetector</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20influencing%20factors" title=" groundwater influencing factors"> groundwater influencing factors</a>, <a href="https://publications.waset.org/abstracts/search?q=Groundwater%20potential" title=" Groundwater potential"> Groundwater potential</a>, <a href="https://publications.waset.org/abstracts/search?q=Gunabay%20watershed" title=" Gunabay watershed"> Gunabay watershed</a> </p> <a href="https://publications.waset.org/abstracts/192442/groundwater-potential-delineation-using-geodetector-based-convolutional-neural-network-in-the-gunabay-watershed-of-ethiopia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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