The purpose of the study was to examine the association between depression and substance abuse and to assess theirprevalence and associated factors. A cross-sectional design was employed. To collect data for the present study, 2 scales (Beckdepression inventory and Alcohol, Smoking, and Substance Involvement screening Test [ASSIST]) was administered to students.Two hundred fifty seven Addis Ababa Institute of Technology final year regular undergraduate students were participated. Theresults revealed that a high correlation was found between depression and substance abuse. The prevalence of depression is27.2%. Similarly, the prevalence of alcohol abuse, khat abuse, cigarette abuse and cannabis abuse are 25.5%, 17.7%, 9.5%, and3.3% respectively. Hence, the overall substance abuse prevalence is 14%. Alcohol is most abused drug followed by khat abuse.Cigarette and cannabis abuse take the 3rd and 4th rank respectively. Multivariate test of significance reveals that gender, religionand the interaction of gender with religion, residence, and ethnicity had an effect on the two combined dependent measures.Females are more depressed than males. In turn, males are more substance abusers than females. The researchers suggestedthat the university to establish its own substance abuse prevention and treatment working center which is open for psychologists,therapist and other health workers.Abbreviations: AAIT = Addis Ababa Institute of Technology, ASSIST = Alcohol, Smoking, and Substance Involvement ScreeningTest, BDI = Beck Depression Inventory, MANOVA = Multiple Analysis of Variance, WHO = World Health Organization.Keywords: alcohol, depression, khat, substance abuse, university students

Background Microcephaly and macrocephaly, which are abnormal congenital markers, are associated withdevelopmental and neurologic deficits. Hence, there is a medically imperative need to conduct ultrasound imagingearly on. However, resource-limited countries such as Ethiopia are confronted with inadequacies such that access totrained personnel and diagnostic machines inhibits the exact and continuous diagnosis from being met.Objective This study aims to develop a fetal head abnormality detection model from ultrasound images via deeplearning.Methods Data were collected from three Ethiopian healthcare facilities to increase model generalizability.The recruitment period for this study started on November 9, 2024, and ended on November 30, 2024. Severalpreprocessing techniques have been performed, such as augmentation, noise reduction, and normalization.SegNet, UNet, FCN, MobileNetV2, and EfficientNet-B0 were applied to segment and measure fetal head structuresusing ultrasound images. The measurements were classified as microcephaly, macrocephaly, or normal using WHOguidelines for gestational age, and then the model performance was compared with that of existing industry experts.The metrics used for evaluation included accuracy, precision, recall, the F1 score, and the Dice coefficient.Results This study was able to demonstrate the feasibility of using SegNet for automatic segmentation,measurement of abnormalities of the fetal head, and classification of macrocephaly and microcephaly, with anaccuracy of 98% and a Dice coefficient of 0.97. Compared with industry experts, the model achieved accuracies of92.5% and 91.2% for the BPD and HC measurements, respectively.Conclusion Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrainedsettings. Future work needs to be done on optimizing model performance, trying complex models, and expandingdatasets to improve generalizability. If these technologies are adopted, they can be used in prenatal care delivery.Clinical trial number Not applicable.Keywords Microcephaly, Macrocephaly, Congenital abnormality, HC, BPD

Background Diarrheagenic Escherichia coli (E. coli) is a zoonotic pathogen that contaminates abattoir workers,slaughter environments, slaughter equipment, and carcasses during abattoir processing. Infection with E. coliis associated with the consumption of contaminated food and water, and it is a potential threat to the healthand welfare of both humans and animals. Hence, this study aimed to detect diarrheagenic E. coli and assess itsantibiogram profile in two abattoir settings, in one health lens.Methods A cross-sectional study in one health approach was conducted from December 2020 to June 2021. Atotal of 384 samples from abattoir workers’ hands, carcasses, knives, cattle feces, abattoir water and effluents werecollected. Bacterial culture and biochemical tests were conducted to isolate E. coli, while conventional polymerasechain reaction was performed to identify virulence genes. The antibiogram of diarrheagenic E. coli was tested againstnine antimicrobials using the Kirby Bauer disk diffusion method.Results A total of 115 (29.95%) E. coli were isolated from the 384 samples, and from these isolates, about 17 (14.8%)were confirmed to be diarrheagenic E. coli (DEC). Among the DEC pathotypes, nine (52.94%), five (29.4%), and three(17.65%) were Shiga toxin-producing, enterohemorrhagic, and enterotoxigenic E. coli, respectively. While 14 (82.35%)DEC isolates harbored the stx2 gene, five (29.41%) the eae gene, five (29.41%) the hlyA gene and three (17.65%)harbored the st gene. All the DEC isolates were resistant to erythromycin and vancomycin; whereas, they weresusceptible to ampicillin, nalidixic acid and norfloxacin. Furthermore, 64.7% of DEC isolates showed resistance to bothceftazidime and kanamycin and 88.24% of the isolates showed multidrug resistance.Conclusion This study detected DEC isolates having different virulence genes, which showed single and multipleantimicrobial resistance. Given the existing poor hygienic and sanitary practices along the abattoir-to-table food
chain, coupled with the habit of raw meat consumption, this result indicates a potential public and animal health riskfrom the pathogen and antimicrobial resistance.Keywords Abattoir setting, Antibiogram profile, Carcasses, Diarrheagenic E. Coli, Virulence genes

The study is to examine the status and identify factors that affect the participation of dairy cooperatives and its impact on household income. The study employed both quantitative and qualitative data. The two-stage stratified sampling method was employed purposive sampling technique was used to select dairy cooperative for the study. A total of 582 households were sampled using proportion to the sample size methods and the study unites were selected through systematic sample random sampling technique. Primary and secondary data sources were consulted to collect the necessary data. Focus group discussion, Key informant interview, and interview schedule survey questionnaire were the primary data collection methods used in the study. Descriptive and econometric analyses were employed to analyze the collected data. Binary logit model was employed to assess variables affecting the participation of dairy cooperative and its impact on household income by using propensity score much.The household Age, Family size, fair price of milk, sex, TLU, number of milking cow, credit access and distance from the home to diary cooperative office significantly influenced the decision to participate dairy cooperative. Age, fair price of milk, credit access, number of milking cows, sex and credit access positively influenced the participation whereas family size and distance from the home to dairy cooperative office negatively influenced the participate of in dairy cooperative . The study found that membership of dairy cooperative has significantly increased on income of households. Thus, farmers should be encouraged to participate dairy cooperative. Therefore, the government and other concerned bodies should focus encourage households to promote the membership of dairy cooperative.

ABSTRACT While it is dependent on erratic rainfall and subject to drought and low productivity, agriculture is still the dominant economic activity in Ethiopia. Climate-Smart Agriculture (CSA) technology adoption enhances productivity, resilience, and climate mitigation, yet its adoption by smallholder farmers in Ethiopia’s West Gojjam Zone remains limited despite government promotion. However, research on the factors behind the determinants of the low adoption rate is limited in the study area. The main objectives of this study were to investigate the factors influencing the adoption of five CSA-aligned practices: crop diversification, livestock diversification, irrigation, agroforestry, and the application of chemical fertilizers across selected woredas. Data were generated using structured questionnaires, key informant interviews, and focus groups from a total of 236 households selected using a multistage sampling technique. The analysis made by a multivariate probit model revealed that significant determinants included the gender of the household head, education, farm size, income, irrigation access, credit availability, extension services, and farming experience. Findings suggest that enhancing crop and livestock diversification requires expanding irrigation access, strengthening extension services, and integrating climate-smart advisory programs to equip farmers with essential skills and resources. Additionally, improving credit access, providing climate information through mobile phone SMS for free, and promoting farmer training can boost irrigation adoption, while targeted awareness campaigns can encourage experienced farmers to adopt modern irrigation technologies. Keywords: Adoption; Climate-Smart Agricultural Practices; Multivariate probit, West Gojjam

Investing in agricultural water management by improving irrigation schemes helps to establish climate-resilientand sustainable agri-food systems, thus contributing to sustainable poverty reduction. The purpose of this studywas to identify the determinants of farmers’ willingness to pay (WTP) for irrigation water improvements inNorthcentral Ethiopia. Primary data collected from a random sample of 132 households were analyzed using thedouble-bounded contingent valuation method and binary logit regression model to identify what determinesfarmers’ WTP for irrigation improvement. The findings show that farmers’ mean WTP for irrigation water im-provements is 141.60 Birr/ha/year (2.50 USD/ha/year). The results of bivariate Probit model revealed that croptype, education, relative location to the irrigation scheme, irrigated plot size, and perceived drought risk sta-tistically and positively determine farmers’ WTP for irrigation water improvement. These findings offer valuablepolicy implications on how best to guide practical agricaltural water management. Policy interventions aimed atenhancing farmers’ behavior, awareness, and perception of drought-related issues, while also promoting cashcrop production, are likely to drive more farmers towards a positive WTP for irrigation water improvement.
Keywords: Improved irrigation water; Double-bounded contingent valuation; Willingness to pay; Bivariate probit model

AbstractPurpose – This study aims to identify determinants of single-family residential property values and fill the gap by analyzing respondents’ willingness to pay/receive data alongside real transaction data. Ordinal logistic regression and ordinal least square regression were used.Design/methodology/approach – Ordinal logistic regression effectively analyzes willingness-to-pay/receive data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantifies the impact of continuous and categorical predictors on real transaction data.
Findings – Findings revealed strong associations between property values and several variables. Analysis of willingness-to-pay/accept data from 232 respondents showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district (p < 0.05). Similarly, ordinal least square regression analysis of transaction data confirmed the significance of most of these factors, except for property orientation, which indicates the difference of preference in the local market or reporting inconsistencies, demand further investigation. Variables such as views, proximity to wetlands, roads, green areas, religious institutions and schools were statistically insignificant across both data sets (p > 0.05).
Practical implications – It provides a robust basis for housing and urban development strategies. The stakeholders such as real estate developers, urban planners and policymakers are encouraged to incorporate these findings into housing policies, land value capture initiatives and urban planning frameworks to enhance residential property value and align with sustainable urban development goals.
Originality/value – This study contributes original insights into single-family residential property valuation by integrating willingness-to-pay and transaction data, substantiating the determinants of property value.
Keywords Appraisal, Ethiopia, Housing, Ordinal least square regression, Residential property, Value

Introduction: Stillbirth is still a major public health problem in middle- and low-income countries. However, there has been limited research conducted to identify determinants of stillbirth in Ethiopia. Therefore, this study aimed to identify the determinants of stillbirth among women who gave birth in public hospitals in the West Gojjam Zone, Northwest Ethiopia.
Methods: An unmatched case-control study was conducted among 418 mothers who gave birth from March 1-30, 2022. Consecutive and systematic sampling techniques were used to select the cases and controls, respectively. The collected data were entered into Epidata and exported into SPSS version 16 for analysis. Numerical descriptive statistics were expressed by using the mean with standard deviation (SD) and/or median with interquartile range (IQR), whereas categorical variables were expressed by proportions. Bivariable and multivariable binary logistic regression analyses were used to identify determinants of stillbirth. The model goodness of fit test was checked using the Hosmer-Lemeshow test. Variables having a P-value ≤ 0.25 in the bivariable analysis were entered into the multivariable analysis model. Adjusted odds ratio with 95% confidence intervals (CIs) was used to report the strength of association, and variables with a P-value < 0.05 were considered statistically significant.
Results: A total of 105 cases and 313 controls were included in this study. The odds of having stillbirth were higher among women who were illiterate (AOR: 1.6, 95% CI: 1.34, 7.55), had first ANC visit in the second trimester (AOR: 11.4, 95% CI: 2.99, 43.71), had an induced mode of delivery (AOR: 8.7, 95% CI: 2.10, 36.03), history of stillbirth (AOR: 1.5, 95% CI: 1.45, 4.90), bad obstetric history (AOR: 4.8, 95% CI: 1.44, 15.89), history of preterm (AOR: 7.6, 95% CI: 1.57, 37.21), not vaccinated for TT (AOR: 8.8, 95% CI: 2.23, 35.17), labor not followed by using partograph (AOR: 3.1, 95% CI: 1.10, 8.42), and history of abortion (AOR: 11, 95% CI: 2.91, 41.31).
Conclusion: The determinants of stillbirth included women who were illiterate, started ANC visits in the second trimester, had an induced mode of delivery, history of stillbirth, bad obstetric history, history of preterm, history of abortion, not vaccinated for TT, and not followed by partograph. It is better to improve partograph utilization during intrapartum care and screen mothers who had a higher risk of adverse birth outcomes during their pregnancy to avert the problem.

Keywords: Cases; Controls; Determinants; Ethiopia; Stillbirth.

Abstract Over the years, agriculture in the Amhara region has faced challenges due to climate variability and change. In this regard, this paper aims to develop and adapt a crop selection dataset model for resilience to climate change. Wheat, maize, and teff data were investigated from the Central Statistics Agency of Ethiopia (CSA). Climate parameter data were taken from reanalysis models, satellites, and in situ sources. An Artificial Neural Network (ANN) model was applied to predict El Niño and La Niña events. The results reveal that the presence of El Niño leads to a reduction in wind speed and an increase in cloud base height (CBH), resulting in a precipitation deficiency. El Niño is weakening the trend winds and enhancing the cloud base height, leading to a deficiency of summer Precipitation. The crop selection model is developed to select a climate change resilience crop. The occurrences of El Niño enhance maize production in the midland farm areas. The average value of the crop selection dataset model parameters (slope ‘n’, intercept ‘a’, and the magnitude of the error ‘ ’) is found to be -1.937, 28.498, and 0.168, respectively, that satisfy the developed model requirements. The average error between the actual crop data and the model value is found to be -0.156 quintals per hectare. In general, the impact of El Niño on teff in middlealtitude areas is affected by 25.36%, while on wheat production in lowland areas is affected by 18.44%. However, maize production in the midland region is enhanced by 21.90%. In this sense, farmers in the middle-altitude areas use vast farmlands for maize during the El Niño phase. In the future, researchers made improvements on the draft crop selection dataset model and tailored it for use across the country via various scenarios. Keywords: Altitude, Artificial intelligence, Climate change, Crop model, El Niño-La Niña

Abstract Over the years, agriculture in the Amhara region has faced challenges due to climate variability and change. In this regard, this paper aims to develop and adapt a crop selection dataset model for resilience to climate change. Wheat, maize, and teff data were investigated from the Central Statistics Agency of Ethiopia (CSA). Climate parameter data were taken from reanalysis models, satellites, and in situ sources. An Artificial Neural Network (ANN) model was applied to predict El Niño and La Niña events. The results reveal that the presence of El Niño leads to a reduction in wind speed and an increase in cloud base height (CBH), resulting in a precipitation deficiency. El Niño is weakening the trend winds and enhancing the cloud base height, leading to a deficiency of summer Precipitation. The crop selection model is developed to select a climate change resilience crop. The occurrences of El Niño enhance maize production in the midland farm areas. The average value of the crop selection dataset model parameters (slope ‘n’, intercept ‘a’, and the magnitude of the error ‘ ’) is found to be -1.937, 28.498, and 0.168, respectively, that satisfy the developed model requirements. The average error between the actual crop data and the model value is found to be -0.156 quintals per hectare. In general, the impact of El Niño on teff in middlealtitude areas is affected by 25.36%, while on wheat production in lowland areas is affected by 18.44%. However, maize production in the midland region is enhanced by 21.90%. In this sense, farmers in the middle-altitude areas use vast farmlands for maize during the El Niño phase. In the future, researchers made improvements on the draft crop selection dataset model and tailored it for use across the country via various scenarios. Keywords: Altitude, Artificial intelligence, Climate change, Crop model, El Niño-La Niña