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

Erythrina brucei has been applied as a green manure to improve soil fertility in southern Ethiopia.It has been nodulated by indigenous rhizobia. The objectives of this study were to evaluate theeffects of E. brucei inoculation with microbial consortia consisted of Bradyrhizobium shewense,Acinetobacter soli and arbuscular mycorrhizal fungi (AMF)on E. brucei growth, soil nitrogen andphosphorous status after application as a green manure.A field experiment was conducted byinoculating E. Brucei with different microbial consortia. E. brucei inoculated with the microbialconsortia were grown for 150 days. Its shoot length was measured at 60, 90, 120 and 150 daysafter planting. Then, plants were uprooted and mulched as a green manure. The soil nitrogen,available phosphorous and soil organic matter analysis were done. The experimental design wascompletely randomized block design with eight treatments comprised of three replications.Inoculated treatments did not show a significant (p < 0.05) difference in shoot length in the first60 days. However, shoot length was increased between 19.1 and 41.3 %, 10.5–43.4 % and8.7–37.6 %, respectively at 90, 120 and 150 days. The soil organic matter was improved in bothinoculated and un-inoculated treatments. The improvements in the soil organic matter of un-inoculated treatments may be due to the decomposition of un-inoculated plants biomass in thesoil. The B. shewense inoculation improved the soil nitrogen by 17 %. The soil phosphorous wasimproved in 57 % of inoculated treatments. The inoculation of E. brucei with microbial consortiaenhanced its growth and improved soil fertility when applied as a green manure. Inoculating thegreen manure legumes with symbiotically effective rhizobia and plant-beneficial microbes canenhance the growth of E. brucei and its nutrient uptake.
Keywords: Legumes, Soil fertility, Shoot length, Microbial inputs, Organic matter

Political narrative is an instrument for political actors to construct a shared meaning, and it can be harnessed to harm political opponents. The Italian invaders, the Ethiopian Student Movement, the Tigray People’s Liberation Front, the Oromo Liberation Front, and the incumbent regime contributed in varying degrees to the Amhara domination narrative. TPLF, in its political manifesto and later in the 1995 FDRE constitution, institutionally crafted an anti- Amhara narrative, reaching a crescendo after Abiy Ahmed assumed office in 2018. Thus, this research article tried to scrutinise and weigh the discourse of institutionally crafted Amhara existential threats. The study employed a qualitative research tradition and an exploratory research design approach that involved a political-economic analysis. The study finds that the century-old Amhara domination narrative, coupled with institutionally supported recurrent mass killings and expulsion, especially in Oromia, Benishangul- Gumuz, and Amhara regional states, posed a real and perceived existential threat that gave birth to the Amhara Fano armed struggle.KEYWORDS: Narrative; institution; existential threat; identity;Fano; Amhara

Induction motor drives with variable speed applications that employ vector control are quitepopular nowadays because they provide strong dynamic performance and flexible speed control.By decoupling the torque-producing current components of stator current from the rotor flux,Indirect Field Oriented Control is recognized for generating excellent performance in inductionmotor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers’ performance in termsof load variation capabilities, motor parameter variation, and speed tracking. Consequently, acomparison of the three controllers is important to select which controller performs high in in-duction motor drive. Indirect Field Oriented Control of induction motor with Fractional Pro-portional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) controllers are all discussed in this work along with their designs and compar-ative analysis. The square of error was used as a fitness function to genetically optimize the FPIand IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS)controller uses a hybrid learning approach. It is trained by the FPI controller’s input-output data.Using the results of MATLAB simulations under various operating situations, the performance ofthe ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller in-cludes an extra parameter for adjustment, namely integration order, it performed better than IPIcontroller for speed control of the induction motor. According to the simulation findings, thepercentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %,12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot.Additionally, with the ANFIS controlled induction motor drive, the speed achieves the requiredset value at 0.14 s. For no load, constant, and changing loads, the induction motor drive’s per-formance has been examined.
Keywords: Induction motor, Indirect field oriented control, Fractional PI, ANFIS, Integer PI