Showing papers by "Adama University published in 2023"

Analysis of Bagasse Cellulose-Based Hydrogel for Methylene Blue Removal from Textile Industry Wastewater

Abstract: The textile industry is one of the biggest water consumption production areas and its waste is essential to cause ecological contamination as they deliver questionable color, weighty metal, and degradable natural and inorganic results whenever arranged without treatment. The natural treatment strategy is not broadly drilled because of its intricate method. In the adsorption method, for example, actuated carbon was limited by prudence of its significant expense and low adsorption limit. This study has completed the combination and portrayal of bagasse cellulose-based hydrogel for the expulsion of methylene blue color from textile industry wastewater. The study furnishes a major critical contributing option for biodegradable adsorption material by supplanting the conventional color evacuation method with a nonconventional one that showsthe attainability of horticultural waste for a union of the hydrogel as opposed to noninexhaustible petrochemical based and show the capability of involving hydrogel for the expulsion of harmful contamination from the textile industry. The hydrogel was combined utilizing free extreme polymerization that can utilize acrylic corrosive (AA) and citrus extract as cross-connecting specialists and monomers individually. FTIR, XRD, and conduct metric titration are the primary hardware utilized for the portrayal of the hydrogel. The cycle boundaries that can influence the color evacuation proficiency of hydrogel, for example, pH, contact time, and temperature have been examined. A focal composite plan by rotatable component is the technique used to browse reaction surface strategies to control the tests with the communication of cycle boundaries.

The 2D resistivity measurement eror and its effect on the model accuracy

Abstract: We examine the measurement noise of electrical resistivity tomography and assess its effect on the inverted results. The observed and numerically simulated resistivity datasets are analyzed regarding noise distributions. We evaluate and present the contact resistance, reciprocal and repeating errors, potential noise, artificial effect on 2D resistivity measurement, inversion misfit, and model accuracy. The result shows considerable measurement noise variation for dry and wet conditions. This study uses a 3% repeatability error cut-off, and about 3.2% of the dry season and 0.83% of the wet season datasets are above cut-off values.  The result also exhibits an inverse relationship between the precipitation and reciprocal error. The resistivity measurement in dry conditions generally indicates high contact resistance, repeatability error, and reciprocal errors, resulting in significant data discarding. We also reveal the misfit between observed and model-predicted resistivity data; a high discrepancy is exhibited for noisy data, leading to substantial model error. The depth of investigation (DOI) threshold depth decrease with increasing measurement noise. This study will give insight into measurement noise evaluation, allow cut-off value, assess data noise propagation and its effects on the data misfits and inverted models, and reduce model misinterpretation.

Hydrothermal Synthesis of Nitrogen and Phosphorus Codoped Carbon Dot Interfacial Modification Layer for Efficient Charge Transfer between ZnO Electron Transport Layer and PTB7:PC70BM Active Layer

Abstract: The interface between the active layer and the charge-transporting layer is critical for performance improvement in polymer solar cells (PSCs). The use of zinc oxide (ZnO) as an electron transport layer (ETL) in PSCs was limited due to inherent defects in the surface of ZnO prepared by the sol-gel method, mismatched energy bands with the photoactive layer, and incompatibility between the photoactive layer and ZnO ETL. In this study, nitrogen and phosphorus codoped carbon dots (N & P-CDs) were prepared from Ensete ventricosum (false banana) and used as an interfacial modification layer for ZnO ETL. The inverted devices with structures ITO/ZnO/N & P-CD/PTB7:PC70BM/Al were fabricated to investigate the charge transfer dynamic between the active layer and ETL interface modification with N & P-CDs. We have observed that the interfacial modification between the ZnO ETL and the active layer, using N & P-CDs, improves the charge transfer between ZnO ETL and PTB7:PC70BM active layer. The obtained result shows that the ETL/BHJ interface resistance of the devices with ZnO:undoped CDs, ZnO:N-CDs, ZnO:P-CDs, and ZnO:N & P-CD ETLs decreases dramatically from 103.4 to 84.04, 78.16, 37.88, and 28.9 Ω, respectively. This is due to the improvement of charge extraction efficiency by smoothing ZnO surface defects and minimizing the band mismatch between the active layer and ZnO using N & P-CDs. The results indicate that the water-soluble N & P-CDs developed in this study have the potential to be used for efficient free charge carrier extraction for PSCs.

Overall performance evaluation of an urban water supply system: a case study of Debre Tabor Town in Ethiopia

Abstract: Abstract Urban water utilities in Ethiopia, including Debre Tabor Town, commonly suffer from an intermittent water supply, water quality issues, poor service delivery, and other problems. Thus, the main focus of this study was to evaluate the actual performance level of the water supply system of the town based on hydraulic efficiency, quality, cost recovery and customer satisfaction. The water distribution system status was measured by using reliability, resilience, and vulnerability as performance indicators. Weightage Arithmetic Water Quality Index (WAWQI) and household-based questionnaires were used to evaluate the water quality and customer satisfaction, respectively. Pressure and velocity-based sustainability index of 0.614 and 0.132 showed acceptable and unacceptable water supply status, respectively, and overall moderate sustainability. Results of the WAWQI revealed that more than half of the sampled tap waters were either poor or unfit for drinking purposes. The comparison of income collected from customers and the water production costs of the utility showed that only 34.31% of production cost is covered by customers. Generally, 62.6% of the society confirmed that they are unsatisfied with the existing water supply system. Thus, to improve the performance, it is recommended to address all the major social, economic, environmental and technical problems.

Building a Named Entity Recognition model for Ethiopian Languages: a comparative analysis of composite feature embedding

Abstract: Abstract Named Entity Recognition (NER) has become a critical and essential step in information extraction, machine translation, and question-and-answering systems in a different language. One of the most important factors which directly and significantly affects the quality of the NER is the selection and encoding of the input features to generate rich semantic and grammatical representation vectors. However, the existing NER models are insufficient to handle new and unseen entity types from the growing Amharic digital data, and the development of more effective and accurate NER models is being widely researched. In this regard, herein, we propose a deep learning NER model that effectively represents word tokens through the design of a combinatorial feature embedding; and performed a comparative analysis with the existing models for Ethiopian languages. The word vectors built for all tokens using an unsupervised learning algorithm was merged with a set of specifically developed language-independent features and together fed to the neural network model to predict the classes of the words. Empirical results over the Ethiopian language dataset show that the use of character-level word embeddings in conjunction with other features in BiLSTM-CRF models leads to comparable state-of-the-art performance. Besides just showing the ability of our model to generalize to different languages, we evaluated the model and obtained state-of- the-art performances: 92.88%, and 82.35% of accuracy on AM_NER and Oro_ NER datasets, respectively.