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

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

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

Background: Managing chronic illness requires navigating a complex trajectory from diagnosis to end-of-life, with each phase necessitating specific nursing approaches. Effective management throughout these phases is vital for improving patient outcomes and quality of life.
Objective: This study aims to explore nursing approaches in managing chronic illness across its trajectory, from diagnosis to end-of-life care, focusing on phase-specific care, emotional support, education, interdisciplinary collaboration, and the challenges faced by nurses.
Methods: A qualitative research design using a grounded theory approach was employed to construct a theoretical framework grounded with the insights and experience of nurses' approaches across the chronic illness trajectory within Western Amhara, Ethiopia. The study comprised 24 nurses who were selected through the process of purposeful and theoretical sampling methods. Data was collected via in-depth interviews. Data analysis followed a constant comparative method, involving open, axial, and selective coding to identify key strategies and challenges across the illness trajectory.
Results: The primary finding of this study emphasizes the evolving and adaptive role of nurses in chronic illness management, highlighting their ability to provide personalized care, emotional support, and education throughout the illness trajectory. Central to the investigation is the theory of nurses' evolving and adaptive role in chronic illness management, where they adjust their strategies to address the physical, emotional, and psychological needs of patients and families, from pre-diagnosis to end-of-life care. The study identifies key adaptive strategies, including fostering resilience, facilitating interdisciplinary collaboration, and managing fluctuating symptoms. Despite challenges such as heavy workloads and emotional strain, nurses require training for continuous professional development, technological integration, and collaborative platforms to reinforce their critical role in optimizing patient outcomes in chronic illness management.
Conclusion: This study highlights nurses' adaptive role in chronic illness care, focusing on phase-specific interventions, emotional support, interdisciplinary collaboration, and education across entire illness trajectory to meet diverse needs of patients and their families. Despite challenges such as heavy workloads and emotional strain, the study recommends ongoing professional development and technological integration to optimize patient outcomes.

Keywords: chronic illness trajectory; diagnosis; end-of-life; grounded theory; nursing approaches.

This paper presents a novel approach to modeling and controlling a solar photovoltaic conversionsystem(SPCS) that operates under real-time weather conditions. The primary contribution is theintroduction of an uncertain model, which has not been published before, simulating the SPCS’sactual functioning. The proposed robust control strategy involves two stages: first, modifying thestandard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage usingreal-time measurements of temperature, solar irradiance, and wind speed. This modification leadsto determining and linearizing the nonlinear current-voltage (I-V) characteristics of the photovoltaic(PV) array near standard test conditions (STC), resulting in an uncertain equivalent resistance used tosynthesize an overall model. In the second stage, a robust fixed-order H∞ controller is designed basedon this uncertain model, with frequency-domain specifications framed as a weighted-mixed sensitivityproblem. The optimal solution provides the controller parameters, ensuring good reference trackingdynamics, noise suppression, and attenuation of model uncertainties. Performance assessments atSTC compare the standard and robust P&O-MPPT strategies, demonstrating the proposed method’ssuperiority in performance and robustness, especially under sudden meteorological changes andvarying loads. Experiment results confirm the new control strategy’s effectiveness over the standardapproach.

Background: Diagnosing pulmonary tuberculosis (pTB) in children is challenging due to the difficulties in acquiring respiratory specimens, which unspecific and paucibacillary disease presentation, and the lack of sensitive diagnostic assays with non-invasive sample collection methods. As a result, millions of children around the world get tuberculosis (TB) each year, which is a leading cause of morbidity and mortality.
Objective: The aim of this study was to assess the diagnostic accuracy of Stool and Respiratory Sample-based Genexpert MTB/RIF assay from presumptive TB among children in Northwest, Ethiopia.
Methods and Materials: Hospital based cross-sectional with diagnostic accuracy study was conducted on consecutively recruited presumptive TB children. Data were collected by sem-structured questionnaires. Single respiratory (5ml) and 3g stool specimen were collected Lowenstein Jensen (LJ) and Xpert assay. Laboratory SOPs were strictly followed to assure the quality of whole procedures. The diagnostic accuracy of stool Xpert was evaluated against respiratory specimen Xpert, culture and composite reference standards (CRS). Sensitivity, specificity, and predictive values for the stool Xpert assay were calculated with a 95% confidence interval (95% CI) with MedCal statistical software. Data were entered in EPIData V4.2 and exported to SPSS 25 for further analysis.
Results: A total of 557 children were recruited; 510 of whom had complete microbiological results. Overall, pTB was diagnosed in 52/510 (10.2%) of the children with presumptive TB. Of these, only four had microbiologically unconfirmed pTB, were clinically diagnosed with positive response to anti-TB and the remaining 48 were microbiologically confirmed (Positive Xeprt and LJ culture). Stool specimen Xpert had sensitivity of 93.8 %( 95%CI: 82.8-98.6) and specificity of 99.8% (95%CI: 98.7–100) compared to culture; however, the sensitivity of stool was 88.5% (72-95.6) and specificity 100% (99.2-100) when compared to CRS. The Xpert on respiratory specimen had sensitivity and specificity of 95.8 % (85.8– 99.5) and 99.8% (98.7–100) to culture and 92.3 %( 81.4-97.9) and 100% (99.2-100) compared to CRS.
Conclusion: The sensitivity and specificity of Xpert assay for stool specimen is almost similar to that of respiratory specimen. Stool specimen is a highly promising alternative specimen in the diagnosis of pTB in children when respiratory specimen is impossible.


Key words: Diagnostic accuracy, pulmonary tuberculosis, Xpert MTB/RIF, Stool, Children