Improving maternal and perinatal outcomes can be achieved by identifying factors affecting successful vaginal delivery following induction of labor, particularly in developing countries with low rates of induction. Besides this, evidence regarding the magnitude of successful vaginal delivery following induction and associated factors is limited in Ethiopia. Therefore, this study aimed to assess the magnitude of vaginal delivery following induction and associated factors among laboring women at South Wollo Zone Public Hospitals of Ethiopia, 2023. An institutional-based cross-sectional study was conducted among 385 from April 1 to June 15, 2023. A systematic random sampling method was used to select each participant. A pretested structured interviewer-administered questionnaire and checklist were used to collect data. Epi-Data version 4.6 and SPSS version 26 software were used for data entry and analysis, respectively. Multivariable binary logistic regression was used to identify associated factors and an adjusted odds ratio with a 95% confidence interval was used to identify significant variables. Model fitness was checked using the Hosmer-Lemeshow goodness of fit test. The study reported that 75.6% of participants gave vaginal delivery following induction with a 95% CI (71.00-79.80). Mid-upper arm circumstance 23-28 cm (AOR = 2.55, 95% CI: 1.19-5.47), multiparty (AOR = 3.01, 95% CI: 1.430-6.33), favorable bishop (AOR = 3.79, 95% CI: 1.74-8.26), oxytocin with cervical ripening method (AOR = 3.74, 95% CI: 1.99-7.04), and birth weight less than 4000gram (AOR = 5.40, 95% CI: 1.54-18.91) were factors significantly associated with successful vaginal delivery following induction. Therefore, obstetric caregivers should consider pre-induction assessments such as bishop score of the cervix and fetal weight estimation and improving the nutritional status of pregnant women.

Keywords: Associated factors; Ethiopia; Induction of labor; Vaginal delivery.

In engineering applications, the bearing faults diagnosis is essential for maintaining reliability andextending the lifespan of rotating machinery, thereby preventing unexpected industrial productiondowntime. Prompt fault diagnosis using vibration signals is vital to ensure seamless operation ofindustrial system avert catastrophic breakdowns, reduce maintenance costs, and ensure continuousproductivity. As industries evolve and machines operate under diverse conditions, traditional faultdetection methods often fall short. In spite of significant research in recent years, there remains apressing need for improve existing methods of fault diagnosis. To fill this research gap, this researchwork aims to propose an efficient and robust system for diagnosing bearing faults, using deep andShallow features. Through the evaluated experiments, our proposed model Multi-Block Histogramsof Local Phase Quantization (MBH-LPQ) showed excellent performance in classification accuracy, andthe audio-trained VGGish model showed the best performance in all tasks. Contributions of this workinclude: Combine the proposed Shallow descriptor, derived from a novel hand-crafted discriminativefeatures MBH-LPQ, with deep features obtained from VGGish pre-trained of Convolutional NeuralNetwork (CNN) using audio spectrograms, by merging at the score level using Weighted Sum (WS).This approach is designed to take advantage of the complementary strengths of both feature models,thus enhancing overall bearing fault diagnostic performance. Furthermore, experiments conductedto verify the approach’s performance is assessed based on fault classification accuracy demonstrateda significant accuracy rate on two different noisy datasets, with an accuracy rate of 98.95% and 100%being reached on the CWRU and PU datasets benchmark, respectively.Keywords: Bearing fault diagnosis, Vibration signals, Transfer learning, Shallow descriptor, Deep features,MBH-LPQ, VGGish, CNN

Abstract The general objective of the study was to assess voting behaviour in Ethiopia, challenges and prospects, Amhara Regional State. The data for this study was obtained through mixed approach with sequential exploratory research design. Thematic approach has followed to analyze the data collected using key informant interview and focus groups discussions for qualitative data and descriptive analysis for quantitative date by using SPSS. The study explored the status of voting behaviour in the study area. Accordingly, the major challenges which affected voting behaviour of citizens are undemocratic legacy, oppressed government systems, weak democratic institutions, constraints of awareness about political participation and lack of trust on electoral election process and result. Finally, if all stakeholders have striving in collaboration, we could build strong democratic government and electoral process. Hence, researchers recommended that, each section of the society attained adult suffrage should be responsible for democratization process, free and fair election and to positively encourage the behaviour of citizens towards voting. Key words: Voting, voting behavior, unfair electoral system

Weather information (WI) related reliability problems and constraints are commonly reported factors forfarmers’ adaptation decisions to climate-induced impacts in Ethiopia. However, the level of reliability of theWI, kinds of the WI constraints, and how these constraints impede farmers’ adaptation decisions have notsystematically been studied. The present study investigated the reliability and constraints of WI from farmers’perspective in East Gojjam Zone. Three hundred fifty-eight farm households were selected from three woredasthrough random sampling. Interview surveys, focus group discussions, and key informant interviews wereapplied to generate quantitative and qualitative datasets. Frequency, mean score, and correlation wereemployed to analyze the quantitative dataset, while descriptive-interpretative method was used to analyzethe qualitative ones. The findings disclosed that farmers in the study area have diverse WI sources thoughonly farmers’ own experiences and development agents were perceived to be reliable sources for them.The correlation coefficient results confirmed that the number of WI sources had significant positive relationswith family size, and farmland size, while it had a non-significant negative correlation with the farm experiencesof the household heads. The study also identified a range of socioeconomic, demographic and institutional factors, and programming mechanisms and information contents of which the four top that negatively affect farmers’ adaptation decisions were spatial ambiguity, lack of trust, lack of information about the seasonal onset, itsend, and its distribution within the season. To guide farmers’ effective adaptation decisions, these constraints inthe information contents of the WI have to be resolved.KeywordsWeather information, reliability, constraints, East Gojjam, Ethiopia

Introduction: Currently, mortality by non-communicable diseases is increasing alarmingly. They account for approximately 35 million deaths each year, of which 14% are due to cardiovascular disease and 9.2% occur in Africa. Patients do not have access to healthcare services outside the healthcare setting, resulting in missed follow-ups and appointments and adverse outcomes. This study aimed to assess the willingness to use remote monitoring among cardiovascular patients in a resource-limited setting in Ethiopia.
Method: An institution-based cross-sectional study was conducted from April to June 2021 among cardiovascular patients at referral hospitals in Ethiopia. A structured interview questionnaire was used to collect the data. A systematic random sampling technique was used to select 397 study participants. Binary and multivariable logistic regression analyses were employed and a 95% confidence level with a p-value

Dry evergreen Afromontane forests are severely threatened due to the expansion of agriculture and overgrazing by livestock. The objective of this study was to investigate the composition of woody species, structure, regeneration status and plant communities in Seqela forest, as well as the relationship between plant community types and environmental variables. Systematic sampling was used to collect vegetation and environmental data from 52 (20 m x 20 m) (400 m2) plots. Density, Diameter at Breast Height (DBH), basal area, frequency, and importance value index (IVI) of woody species were computed to characterize the vegetation structure of the forest. Agglomerative hierarchical cluster analysis and Canonical Correspondence Analysis (CCA) with R software were used to identify plant communities and analyse the relationship between plant community types and environmental variables, respectively. A total of 68 woody plant species belonging to 63 genera and 44 families were identified. The Shannon diversity index and evenness values of the study area were 2.12 and 0.92, respectively. The total basal area and density of woody species were 27.4 m2 ha-1 and 1079.3 individual ha-1, respectively. The most frequent woody species in the Seqela forest included Albizia gummifera (51.92%), Croton macrostachyus (44.23%), Olinia rochetiana and Teclea nobilis (36.54%). Additionally, the most dominant species, as indicated by their importance value index (IVI), were Erythrina brucei (IVI = 11.24), Prunus africana (IVI=8.68), and Croton macrostachyus (IVI=7.38). Four plant community types were identified: Albizia gummifera - Ekebergia capensis, Prunus africana - Croton macrostachyus, Vachellia abyssinica - Dombeya torrida and Schefflera abyssinica - Teclea nobilis. The CCA results showed that the variation of species distribution and plant community formation were significantly (P < 0.05) related to altitude, organic matter, aspect, slope and soil available phosphorus. The regeneration status assessment of the forest revealed a good regeneration status, which was linked to diverse and abundant seed bank in the soil can ensure a continuous supply of seeds for regeneration; therefore, it is recommended to implement periodic soil seed bank assessments to monitor seed diversity and abundance and inform targeted conservation actions.

Smallholders cultivated sugarcane for household consumption, immediate cash, and feedinglivestock in Ethiopia. However, its production and productivity are constrained by a lack ofimproved varieties. Sugarcane germplasm was collected from Districts of West Gojjam,Ethiopia, to evaluate yield and its contributing characters and quantify the phenotypic diversityat Mankusa, Jabitehnan, Ethiopia. The experiment was conducted using a randomised completeblock design with three replications for two crop cycles, from April 2020 to March 2023. Datawere recorded for the number of tillers, internode length, sellable stalks, plant height, stalkdiameter, cane yield and biomass yield from twelve genotypes collected. The data recorded weresubjected to analysis of variance, and means were separated using the Duncan Multiple RangeTest at a 5% significance level. Variance and multivariate analysis indicated the existence ofhigh phenotypic diversity between genotypes in all quantitative traits studied. Acc 7/20 and Acc5/20 genotypes produced the highest number of tillers and longest internodes, while Acc 6/20produced the highest number of sellable stalks. The genotype Acc 4/20 was the longest, and thethickest genotype, which could be elite donors for it respected quality traits through crossing.The highest cane yield was recorded from Acc 4/20 (178.04 t/ha), Acc 7/20 (151.41 t/ha), Acc8/20 (134.1 t/ha) and Acc 12/20 (132 t/ha), producing 58, 34, 19 and 17% advantageous fromthe overall genotypes mean, respectively. Cluster analysis grouped the twelve sugarcanegenotypes into four clusters, indicating the possibility of broadening the genetic basis bycrossing genotypes in the different clusters. It also indicated that a cross between cluster II andIII genotypes could create the thickest canes with many sellable stalks. Therefore, genotypes Acc04/20, Acc 07/20, Acc 8/20 and Acc 12/20 have been recommended and must be evaluated in other sugarcane growing areas to exploit their potential.Keywords: Cluster Analysis, PCA, Sugarcane, Variability, Yield