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

Studies indicate that students who have access to highly qualified teachers tendto achieve at a higher rate, regardless of other factors. However, the essenceof quality teaching and teacher quality has not been adequately establishedin these studies. Nonetheless, recent developments favoring integrationshave led to three lines of teaching quality research: professional standards,value-added measures, and student evaluations. This study explores howthe quality of mathematics and English language teachers is associated withstudents’ achievement using a professional standard observation tool for studentevaluation. A representative multistage sample of students and teachers selectedfrom high schools in the East Gojjam Administrative Zone participated in thestudy. By using the domains in the Framework for Teaching (FfT) as indicatorsof teaching quality, the study identified the indicators that are associated withthe academic achievement of students in mathematics and English subjects. Amultiple linear regression analysis was used to study the relationships betweenthe independent variables (teachers’ quality indicators) and the dependentvariable (students’ grade 10 exam scores). Of the four domains of FfT, the deliveryof instruction revealed a positive and significant association (sig = 016) withstudents’ scores in the English language. The delivery of instruction encompassescommunicating with students, using questioning and discussion techniques,and demonstrating flexibility and responsiveness, which are positively associatedwith students’ scores in the English language. Conversely, managing classroomprocedures was the only subdomain associated (sig = 014) with an increasein students’ mathematics scores. Accordingly, suggestions are made for furtherresearch and practice.KEYWORDS: teacher quality, FFT, achievement, domains, effectiveness

Background Despite global efforts to mitigate COVID-19 infection through vaccination and therapeutic interven-tions, morbidity and mortality rates continued at variable rates. Although mortality risk and clinical features of COVID-19 are well-documented, recovery patterns and prognostic factors post-admission remain inconclusive, particu-larly in resource-limited settings like Ethiopia. This systematic review and meta-analysis (SRM) aimed to estimatethe pooled incidence rate of recovery and predictors among hospitalized COVID-19 patients in Ethiopia.Methods We searched (N = 1,191) articles using Preferred Reporting Items for Systematic Reviews and Meta-Anal-yses (PRISMA) guideline from PubMed/MEDLINE (N = 755), Scopus (N = 137), Web of Science (N = 84), Science Direct(N = 148), Cochran (N = 25), and Google Scholar searching (N = 42) from December 2019 to February 2024. The datawere extracted using a Microsoft Excel spreadsheet and exported to Stata TM version 17.0 for further analysis. The Arti-cle quality was assessed using the Joanna Briggs Institute checklist. The pooled incidence rate of recovery was esti-mated using a weighted inverse variance random-effects meta-regression. Heterogeneity among studies was evalu-ated using the I2 statistic. Subgroup analyses and sensitivity tests were also conducted to explore publication bias. Thisfile is registered in international Prospero with ID (CRD42024518569).Result Sixteen (N = 16) published studies with 7,676 hospitalized COVID-19 patients were included in the finalreport. The mean age of participants ranged from 29 (± 17) to 57.5 (± 3) years, with male patients constitutingthe largest proportion of participants, 4,491(58.5%). During recovery screening, 6,304(82.21%) cases were dischargedas improved, 159 (2.1%) attriters, and 818 (10.6%) died during inpatient treatment. The pooled incidence of recovery,mortality, and attrition rates were found to be 82.32% (95% CI: 78.81–85.83; I 2 = 94.8%), 14.3% (I2 = 98.45%), and 2.7%(I 2 = 81.34%), respectively. Incidence of recovery rate varied across regions and epidemic phases, with the highest rateobserved in Addis Ababa (89.94%, I 2 = 78.33%) and the lowest reported in the Tigray region (59.7%, I2 = 0.0%). Acrossepidemic phases, the recovery rate was 88.05% (I 2 = 29.56%) in Phase II, 84.09% (I2 = 97.57%) in Phase I, and 78.92%(I 2 = 96.9%) in Phase III, respectively. Factors included being aged 15–30 years (pooled OR = 2.01), male sex (pooledOR = 1.46), no dyspnea (pooled OR = 2.4; I 2 = 79%), and no baseline comorbidities (pooled OR = 1.15; I2 = 89.3%) werepredictors for recovery.Conclusion and recommendation In Ethiopia, more than eight out of ten hospitalized COVID-19 patientsrecovered after inpatient treatment. However, the incidence of recovery rates varied significantly across epidemicphases, study settings, and regions. Factors including younger age, male sex, no dyspnea (shortness of breathing), and no underlying comorbidity heightened recovery. It is highly recommended those inpatients cares should focuson high-risk groups (older adults) and implement standardized treatment protocols in each study setting. Regionswith lower recovery rates need aid in logistical support and training for healthcare providers.Keywords Admitted patients, COVID-19 infection, Ethiopia, SARS-CoV- 2 cases