Bahir Dar University

The production of activated carbon from agricultural waste, such as corn cob, has gained significant attention due to its cost-effectiveness and environmental benefits. This study investigates the influence of activation conditions, specifically the percentage of activating agent (Zink Chloride) and the activation temperature on the physicochemical properties of activated carbon derived from corn cob, an abundant agricultural by-product. A series of experiments were conducted using different Zncl 2 concentrations (0, 25, 50 and 75 %) and activation temperatures (500, 600, and 700°C). Results indicated that both activation parameters significantly influenced the physicochemical properties of the activated carbon. Higher activation temperatures and moderate Zncl 2 concentrations enhanced microspore development, leading to improve adsorption performance. The optimal conditions were identified as 50 % Zncl 2 and an activation temperature of 600°C, yielding a high-quality activated carbon with a well-developed pore structure and excellent adsorption properties. This study provides valuable insights into optimizing the production of activated carbon from corn cob for environmental and industrial applications and also the potential of corn cob as a sustainable precursor for activated carbon production and provides insights into optimizing activation parameters for improved material performance.

Iron-Folic Acid (IFA) is a crucial process indicator, that helps to halt the burden of iron deficiency anemia worldwide, especially in low and middle income countries like Ethiopia. Iron-folic acid supplementation is a crucial method in Ethiopia to reduce maternal deaths from anemia and poor birth outcomes. Despite these initiatives, there is still extremely little adherence to IFA consumption for the 90-day or more WHO-recommended timeframe. Therefore, the aim of this study was to use the DHS 2016 information to create a predictive model for IFA consumption and figure out the key factors influencing it among Ethiopian pregnant women. A secondary data analysis of Ethiopian DHS from 2016 was performed. A total weight of sample of 3109 women (15–49) was included in this study. Data were extracted using STATA version 17 software, and imported to a Jupyter notebook for further analysis. Machin learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, xgboost, and shap packages. Random Forest demonstrated the best performance with accuracy of 92.93%, precision of 91.32%, recall of 94.77%, F1-score of 93.99% and AUC 94%. Region, distance to health facility, age at first marriage, household head, number of ANC visit, media exposure, community literacy, husband occupation, gravidity, family size were top predicting factors of IFA utilization among women in Ethiopia. Random Forest were best predictive models with improved performance. Highly predicting factors of IFA utilization were identified using machine learning algorithms particularly random forest. These findings can guide policymakers and program managers to target interventions more effectively, promote ANC attendance, enhance community literacy and address household-level determinants to improve IFA supplementation rates. However, as the analysis is based on cross-sectional survey data, causal inferences cannot be made.

Abstract Background: Despite the availability of an effective vaccine, measles remains a major public health problem in Ethiopia, with recurrent outbreaks reported in several areas in Amhara Regional State. Understanding measles vaccination coverage and real-world protective effectiveness is critical for assessing immunization program performance and guiding elimination strategies. Therefore, this study was conducted to determine the regional measles vaccine coverage and the vaccine protective effectiveness level in the region. Methods: A retrospective test-negative case-control analysis of measles surveillance data and retrospective analysis of district health information system 2 (DHIS2) data from 2019-2024 was employed to determine vaccine effectiveness and vaccine coverage respectively. Measles vaccine coverage was determined by dividing the number of children vaccinated as numerator to the expected number of children legible for vaccination as denominator. Measles vaccine effectiveness level was determined using field evaluation approaches of vaccine effectiveness. The adjusted vaccine effectiveness was calculated using the multivariable logistic regression after adjustment of potential confounders, including age, sex, residence, and outbreak year. Results: Measles vaccination coverage for both vaccine doses showed variation across geographic locations, time periods with clear seasonal and monthly differences. A total of 3417 participants comprising 2528 cases and 889 controls were enrolled in the analysis of measles vaccine effectiveness. A cumulative vaccine effectiveness estimates was 67% in the region with a clear variation across different years. The subgroup analysis showed comparable level of measles vaccine effectiveness (66.6% vs 66.9%) between males and females. The higher level of measles vaccine effectiveness estimates, 76.2%, among children aged 9 months-2 years while lower vaccine effectiveness estimates, 41.1%, was demonstrated among individuals aged ≥15 years. Conclusion: It was identified a high and increasing administrative measles vaccine coverage with geographical disparities. The estimated overall vaccine effectiveness level was low far apart from the expected level of protection against the vaccine coverage. Two vaccine doses administered individuals had a higher protection level than a single dose received individuals. There was a better vaccine protection level during the recent periods of vaccination compared to earlier periods. Therefore, it is important to increase the access of vaccines to the most unreachable areas maintaining vaccine integrity during delivery. Vaccine coverage should be monitored based on the head count of legible children in each district in the region. It is also important to strengthen the routine measles vaccination practices in any situations and booster dose should also be administered for those older age children.

Access to improved water and sanitation (WaTSAN) is essential for public health and sustainable development. This study examines trends, determinants, and socio-economic inequalities in WaTSAN access in Ethiopia from 2005 to 2019, and provides projections for 2025–2030 to inform Sustainable Development Goal 6 (SDG 6). A cross-sectional study was conducted using Ethiopian Demographic and Health Survey (EDHS) data from 55,736 households, with a stratified two-stage cluster sampling Technique. Multivariate decomposition and Wagstaff analysis were used to examine socioeconomic disparities in WaTSAN access, accounting for income, education, and geographic variation. Future projections employed both linear and logistic growth models with sensitivity analyses to account for methodological uncertainty. The proportion of households using improved WaTSAN sources increased from 4.66% in 2005 to 16.62% in 2019. The decomposition results show that 57.75% of the overall increase in the proportion of households using improved WaTSAN sources was due to Endowment (differences in the compositional characteristics of respondents), while 42.25% was due to Coefficient (the effect of independent variables). The Wagstaff index revealed pronounced inequalities, favoring wealthier households (0.335 [95% CI 0.2898, 0.381]) and those headed by individuals with higher education (0.1387 [95% CI 0.1047, 0.1727]). Regional and rural–urban disparities remain significant, particularly among communities with limited media exposure. Projections indicate that improved water coverage may reach 69–73% by 2030, though with substantial uncertainty (95% CIs 20–126% for the linear model, 35–100% for the logistic model). While Ethiopia has made progress in expanding WaTSAN access, significant socioeconomic and geographic disparities persist. Targeted policies, poverty reduction strategies, and increased media exposure are crucial for achieving equitable access. Projections suggest accelerated interventions are needed to achieve SDG 6 targets by 2030. The considerable uncertainty in long-term forecasts highlights the need for frequent monitoring and adaptive policy responses.