Habesha kemis, an Ethiopian attire traditionally donned by women belonging to the Habeshacommunity, has undergone variations of designs over time. Initially, it comprised a lengthy dresswith a fitted bodice and sleeves extending to the ankles. In the Amhara region, various ethnic groupssuch as Gojjam, Gondar, Shewa, Agew, and Wollo uphold their distinct cultural customs. While theseHabesha garments may appear similar outwardly, their embroidered motifs exhibit unique patterns,shapes, and hues, symbolizing the rich cultural legacy of Gojjam, Gondar, Shewa, Agew, and Wollo.The study aimed to identify the most appropriate model for recognizing and classifying the quality ofHabesha kemis embroidery design. Digital image processing methods and CNN models incorporatingVGG16, VGG19, and ResNet50v2 classifiers were used. Following the gathering of datasets,image preprocessing and segmentation were employed to enhance the model’s performance. Insegmentation, we used canny edge detection, local binary pattern, and dilation with contour detectionfor segmenting and automatically cropping each habesha kemis. After applying the segmentationprocess, the individual habesha kemis and foreign matters are placed in a folder based on theircorresponding categories. This resulted in 320 images before augmenting for each class amountrepresentative. The performance of VGG16, VGG19, and ResNet50v2 for Agew, Gojjam, Gonder,Shewa, and Wollo was evaluated. This process resulted in an image size of 224 × 224 in the CNNmodel with a VGG16 architecture and a SoftMax classifier of course we try also 64 × 64 and 128 × 128.Augmentation techniques were applied to increase the dataset size from 1600 to 3,270. Finally, themodel was evaluated and achieved an accuracy of 95.72% in test data and 99.62% in training datacompared to the VGG19 and ResNet50v2 models.Keywords Ethiopian cultural cloth, Habesha kemis, Embroidery design, Shemma

Background: Digital health data hubs contribute significantly to finding the right solutions to health problems, which forms the basis for achieving sustainable development goals. However, in Ethiopia, the health system has been coming to one central hub for all data, there is limited evidence of health professionals' intentions to use these systems. Understanding their intentions is crucial, as this can significantly improve the advancement of digital health in healthcare organizations. This study assessed health professionals' intention to use digital health data hubs in hospitals in East Gojjam, northwest Ethiopia, in 2024.
Methods: A cross-sectional study design was used to conduct the study. Eleven hospitals were included in the study area. Using an a priori structural equation modeling sample size calculator, the total sample size was 616. Stratified proportional allocation sampling was performed. The study participants were selected using a systematic sample. Structural equation modeling (SEM) was used for the analysis. Because it is a more powerful multivariate technique for testing and evaluating multivariate causal relationships. The assumptions of SEM-like normality, average variance extracted (AVE), composite reliability (CR), Cronbach's alpha, confirmatory factor analysis (CFA), and model specifications were checked using Amos and Stata version 16.
Results: This study was conducted with a sample size of 616 healthcare professionals; 591 (95.94%) responded to the survey. The results showed that 57.69% (n = 341) of the healthcare professionals intended to use the digital health data hub. Further analysis showed that perceived usefulness (PU: β = 0.576, p = 0.000), perceived trust (PT: β = 0.116, p = 0.022), and attitude (β = 0.143, p = 0.043) significantly and positively influenced health professionals' intention to use digital health data hubs.
Conclusion: Overall, the findings showed that 42.31% of health professionals have low intention to use digital health data hubs. These shall be needed to improve their intentions to use digital health data hubs through targeted interventions. Therefore, focusing on critical factors, such as perceived usefulness, trust, and attitude are crucial factors to reinforce their intention to use the system. Additionally, overcoming implementation challenges and building trust is critical to the successful integration and use of digital health data hubs.

Introduction: Promoting healthy lifestyle behaviors during pregnancy is a crucial health promotion strategy that could reduce pregnancy-related complications that may harm women and their fetuses. However, very few studies have assessed the prevalence of health-promoting lifestyle behaviors among pregnant women in Ethiopia. This study aimed to evaluate the extent and associated factors of health-promoting lifestyle behaviors among pregnant women in public health institutions in Debre Markos, northwest Ethiopia.
Methods: An institutional-based cross-sectional study was conducted among 275 pregnant women who were recruited using a systematic random sampling technique from 19 April to 19 May 2021. A face-to-face interview-administered questionnaire was used to collect the data. The data were analyzed using SPSS version 25. Multivariable binary logistic regression was used to identify the factors associated with the outcome variable. adjusted odds ratio (AOR), with a 95% confidence interval (CI) were used to measure the strength of the associations at a p-value

ABSTRACT: β-Lactam antibiotics are employed to treat bacterial illnesses.Despite a high level of clinical success, they have encountered serious resistancethat demands a high-dose regimen and a new pharmacokinetic combination. Thisrequires continuous monitoring of their levels in pharmaceutical and biologicalsamples. In this study, an electrochemical sensor was developed for thedetermination of cephalexin (CLN) in pharmaceutical formulations and biologicalfluid samples. The sensors were developed by modifying a glassy carbon electrode(GCE) using a conducting polymer (dipicrylamine) by potentiodynamicelectropolymerization. Characterization (using cyclic voltammetry and electronimpedance spectroscopy) results revealed modification of the electrode surface,leading to an enhanced effective electrode surface area and their conductivity. Theappearance of an irreversible oxidative peak at much-reduced potential with 5-foldcurrent enhancement at a poly(dipicrylamine)-modified glassy carbon electrode(poly(DPA)/GCE) verified the electrocatalytic role toward CLN. Under optimizedconditions, a wider linear concentration range (5 × 10−8 to 3.0 × 10−4 M), lowest limit of detection (LoD) (2.5 nM), detectedamount of each tablet brand above 97.00% of the labeled value (showing excellent agreement between the detected amount andcompany label), and excellent % recovery results in pharmaceutical and biological samples were obtained with an excellentinterference recovery error of less than 4.05%. Its excellent accuracy, selectivity, reproducibility, and stabilities and only requiring asimple electrode modification step combined with its readily available and nontoxic modifier, which sets it apart from mostpreviously reported methods, have validated the present method’s potential applicability for determining CLN in biological andpharmaceutical samples.

Accurate regulation of the liquid level in a quadruple tank system (QTS) is not easy and imposes higherrequirements on control strategies, so the design of controllers in these systems is challenging dueto the difficulty of dynamic analysis of its nonlinear characteristics and parametric uncertainties.To overcome these problems in liquid level regulation and increase the robustness to the pumpcoefficients, this article proposes and investigates the use of an optimal hybrid fractional-ordertype-2 fuzzy-PID (OH-FO-T2F-PID) regulator using a combination of two bio-inspired evolutionaryoptimizers, namely augmented grey wolf optimizer and cuckoo search optimizer, which gives rise tothe new hybrid A-GWOCS algorithm. This control mechanism was chosen to facilitate the convergenceof the water liquids in the two tanks as quickly as possible to the corresponding required values. Inaddition, a collaborative optimization technique with several objectives is used to adjust the regulatorparameters. The capability and efficiency of the suggested regulator is first investigated throughcomputer simulation results and then confirmed by real-time control experimental results on the QTSbased on dSPACE 1104 computation engine. The findings showed that the suggested OH-FO-T2F-PIDregulator significantly outperformed both the optimized ADRC and the OH-FO-T1F-PID regulators.Specifically, it reduced the rising time by 17.02% and 95.21%, respectively, and the settling time by25.13% and 74.28%. Additionally, the designed OH-FO-T2F-PID regulator successfully eliminatedthe steady-state error and overshoot, enabling precise regulation of the QTS, and maintenance theliquid level at the desired set point under a wide range of working situations. The robustness of therecommended regulator is also studied by considering − 50% disturbance in the QTS parameters, andthe findings showed that the OH-FO-T2F-PID regulator is less susceptible to variations in parameters.Keywords: Quadruple tank system (QTS), Optimal hybrid fractional order type 2 fuzzy PID regulator,Hybrid A-GWOCSO algorithm, Multi-objective optimization, dSPACE 1104 computation engine

Background: Community-based health insurance (CBHI) is an emerging form of microhealth insurance that relies on theprinciple of solidarity, with community members pooling money to help with medical expenses. The level of household heads’satisfaction with CBHI schemes is more likely to affect their decision to remain enrolled and the entrance of new members.However, studies regarding household heads’ satisfaction with the CBHI schemes are scarce in Ethiopia. Therefore, this studyaimed to determine the level of satisfaction with CBHI schemes and associated factors among heads of households inNorthwest Ethiopia.Methods: A community-based cross-sectional study was conducted from March 1–30, 2022. A stratified random samplingtechnique with multistage sampling was used to select 604 study participants. A face-to-face interview was conducted using apretested structured questionnaire. Both bivariable and multivariable logistic regression analyses were conducted. An adjustedodds ratio (AOR) with 95% confidence intervals (CIs) was computed to evaluate the strength of the association, and variableswith a p value < 0 05 at a 95% CI were considered statistically significant.Results: This study found that about 56.1% of household heads were satisfied with the CBHI schemes. Being older age(AOR = 1 85; 95% CI: 1.17, 2.94), rural residence (AOR = 4 13; 95% CI: 2.24, 7.62), visited only health center (AOR = 0 34;95% CI: 0.20, 0.55), distance from a health facility (AOR = 3 18; 95% CI: 1.82, 5.55), agreement with prescribed drugs(AOR = 2 31; 95% CI: 1.36, 3.92), friendliness with healthcare provider (AOR = 3 65; 95% CI: 2.18, 6.10), and had a goodknowledge of benefit packages (AOR = 3 00; 95% CI: 1.93, 4.67) were significantly associated with household head satisfaction.Conclusion: The overall satisfaction of household heads with the CBHI schemes was good. The type of health facility visited,residence, age, distance from health facilities, relationship with healthcare providers, agreement with prescribed medications,and knowledge of community based health insurance were significantly associated with participants’ satisfaction. Thus, thesefindings suggest that improving access to healthcare services, fostering better relationships between healthcare providers andbeneficiaries, and enhancing awareness of CBHI benefits could further increase satisfaction levels among households.Keywords: community-based health insurance (CBHI); Ethiopia; household; satisfaction

This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR)prediction models. The prediction is ensured for a period ranging from a few hours to several days ofthe year. These models are derived from four machine learning methods, namely the Feed-forwardBack Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method,Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combinesConvolutional Neural Networks and Long Short-Term Memory networks. This combination results inthe CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root MeanSquared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), andNormalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by eachmodel above and the actual output, pre-recorded in the experimental trial. The experimental resultsconsistently show the power of the CNN-LSTM model compared to the remaining models in terms ofaccuracy and reliability. This is due to its lower error rate and higher detection coefficient (R2 = 0.99925).Keywords: Artificial neural networks, Convolutional neural network, Convolutional feed-forward backpropagation, Deep learning, Feed-forward back propagation, Long short-term memory, Solar radianceforecasting

Hate speech has been disseminated more frequently on social media sites like Facebook in recent years. OnFacebook, hate speech can proliferate through text, image, or video. We suggested a deep learning approach toidentify offensive memes posted on Facebook in case of Amharic language’. The research process commenced bymanually gathering memes posted by Facebook users. Next came textual data extraction, annotation, pre-processing, splitting, feature extraction, model development and assessment Amharic OCRs were employed toextract textual data. Character normalization, stop word removal, and unnecessary character removal make upthe text-preprocessing step. Using Stratified KFold the textual dataset is split into the train set (80 %), thevalidation set (10 %) and the test set (10 %). Vectors are created from the preprocessed texts using the Bog ofwords (BOW), TFIDF and word embeddings. Following that, the vectors are fed into Machine learning algo-rithms: NB, DT, RF, KNN, LSVM and LR, and deep learning models that are based on Dense, BiGRU, and BiLSTMalgorithms. The model with the optimal parameters is chosen after numerous experiments. With an accuracy rateof 94 %, the BiLSTM + Dense model, the suggested technique identified nasty meme posts on Facebook written inAmharic.
Keywords: Deep learning, BILSTM, BIGRU, Amharic language hate speech

Abstract: Understanding how land use and land cover (LULC) changes affect soil erosion is essentialfor effective management of watershed areas. This study used Geographic Information Systems(GISs) and the Revised Universal Soil Loss Equation (RUSLE) model to analyze the impact of LULCchanges on soil erosion in the Dondor Watershed. Remote sensing data, including Landsat andSentinel-2 satellite images, alongside field surveys, topographic data, rainfall, and soil data wereused. The results showed agricultural land as the primary LULC type, increasing from 43.49% in2002 to 59.10% in 2023. Forest and built-up areas also expanded, while grassland decreased. Soilerosion estimates revealed that more than 85% of the watershed experienced very slight erosionthough the average annual soil loss increased from 4.98 t ha−1 year−1 in 2002 to 7.96 t ha−1 year−1in 2023. Agriculture and built-up areas were identified as the primary contributors to erosion. Thisstudy underscores the importance of monitoring LULC dynamics for responsible land managementand conservation efforts in the watershed.Keywords: Dondor watershed; land use land cover change; soil erosion; RUSLE

Teff, a versatile crop, serves both as a food source and a cash crop in ethiopia. it is recognizedfor its potential to enhance the income of smallholder farmers, improve food security, andcontribute to sustainable development goals. This study aims to assess the impact of Teffcommercialization by smallholder farmers on food security. Both primary and secondary datawere used using the 2020/2021 cropping season. a three-stage sampling procedure was usedto draw 352 sample households. Food security was assessed using proxy indicators: householddietary diversity and food consumption score. The descriptive statistical results showed that182 (51.7%) and 170 (48.3%) sample households were subsistence, and commercializedhousehold heads respectively. notably, commercial farmers exhibited better household dietarydiversity (91.2%), whereas subsistence farmers scored lower in terms of food consumption(29.1%). Male household headship reduced hddS for commercializing farmers (−1.6); creditusage boosted hddS for commercialized groups (1.1), and livestock ownership improvedhddS for subsistence groups (0.21) in the second-stage endogenous switching regression.The model result also showed that, Teff commercialization positively impacted hddS and FcS,with average treatment effects of 3.81 and 4.46, respectively. Transitional heterogeneity resultsshowed that commercialized farmers had lower household dietary diversity (−0.47) and lowerfood consumption score (−14.19) than subsistence households. in light of these findings,encouraging smallholder farmers to transition from subsistence production to commercializationis crucial for supplementing their overall production. additionally, government efforts shouldfocus on raising awareness about nutrition-sensitive agricultural practices.
KEYWORDS: commercialization; endogenous Switching; Regression Model; Food Security; Smallholder; Teff