Ethiopian Journal of Science and Sustainable Development

Quantification and Evaluation of Radon-222 in Groundwater of Flood-Affected Areas in Borno State, North-Eastern Nigeria: Implications for Lung Cancer Risk

Aliyu Adamu — 2025-12-30

Radon-222 (²²²Rn), a naturally occurring radioactive gas, which is soluble in water may damage internal organs if ingested or inhaled. This study investigated the concentration of ²²²Rn in groundwater of 28 locations across five flood-affected areas of Borno state, in September 2024. Groundwater samples were collected and analyzed using a Tri-Carb-LSA 1000 Liquid Scintillation Counter. Elevated levels of ²²²Rn were recorded in Jere (12.35 Bq/L), Konduga (11.33 Bq/L), and Magumeri (10.81 Bq/L). The concentrations in Konduga and Jere exceed the U.S. Environmental Protection Agency maximum contaminant level of 11.1 Bq/L. The total (ingestion and inhalation) annual effective doses varied by age and sex and ranged between 6.57 and 69.49 µSv/y for males, and between 2.01 and 64.52 µSv/y for females. Stomach received the highest absorbed doses (4.83–57.20 µSv/y), consistent with its role as the primary reservoir for ingested water. Lungs also received non-negligible doses of up to 12.11 µSv/y through systemic circulation. Over 95% of the total internal organ dose was attributable to alpha radiation, known for its high linear energy transfer and potential to cause cellular damage. This underscores a significant risk of gastrointestinal cancers and compounds the lung cancer risk. Adults had higher dose burdens than children due to larger water intake volumes. Males exhibited slightly elevated organ doses compared to females, likely due to physiological and metabolic differences. The findings emphasize the need for targeted public health interventions, including regular radon monitoring, awareness creation, and the introduction of point-of-use water treatment systems to mitigate exposur

The Dietary Impacts of Orange Peel Meal on Growth, Wound-Healing and Blood Components of Clarias gariepinus

Olumuyiwa Ayodeji Akanmu — 2025-12-30

A twelve-week feeding trial was conducted to examine the dietary impact of air-dried sweet orange (Citrus sinensis) peel meal (OPM) on growth, wound healing and blood components of Clarias gariepinus juveniles. The study involved five dietary treatments comprising OPM included at 0 (control treatment), 10, 20, 30 and 40g/kg respectively in the experimental diets labelled T1 to T5 and had three replicates constituting fifteen plastic aquaria. A total of 210 fish were randomly distributed into the aquaria at the rate of 14 fish per aquarium and were fed at 3% of their body weight twice daily (8.00-9.00am and 4.00-5.00pm). During the feeding trial, growth and feed utilization parameters were determined while, blood indices were measured at the end of the feeding trial. Wound healing was monitored after the feeding trial on the surviving fish by cutting 1cm² area into the flesh of the fish selected from each treatment at its lateral line and tail regions. The study revealed that air-dried orange peel meal at 10g/kg optimized fish growth while 30g/kg improved the packed cell volume, haemoglobin, red blood cell, white blood cells, total protein, albumin, globulin, aspartate amino transferase, and alanine transaminase of C. gariepinus. Higher ammonia content was detected as the OPM amount increased. Thus, air-dried orange peel meal has promising potentials in enhancing growth, blood components and wound healing in C. gariepinus juveniles. Fish farmers may consider incorporating processed orange peel meal into fish feed for the production of fast-growing and healthy fish by monitoring amount of ammonia.

Forecasting Vehicle Emissions Using an Integrated COPERT- Artificial Neural Network Modeling Framework

Amanuel Gebisa Aga — 2025-12-30

Urban vehicle emission modeling has traditionally relied on conventional regression methods that inadequately capture complex non-linear interactions among influencing variables. Moreover, the combined influence of fleet composition and local environmental conditions remains poorly understood. This study integrated COPERT-derived baseline passenger vehicle (PV) emission factors with an Artificial Neural Network (ANN) model to predict Addis Ababa’s city-specific PV emission levels. The framework also employed Polynomial Linear Regression (PLR) model to forecast PV fleet growth between 2005 and 2025 and to evaluate the associated environmental impacts from 2018 to 2025. The models utilized climate data, vehicle activity patterns, and PV registration records as key inputs. Results reveal that PV ownership in Addis Ababa has increased more than twentyfold over the past two decades. Baseline emission factors indicated substantial reductions in CO and NO_x emissions with higher Euro classification levels, although CO₂ emissions remain persistently high. The ANN-based predictions show a 25% increase in CO₂ emissions, while NOx emissions rose from 1.89 to 2.08 tons/year for gasoline and from 6.02 to 7.27 tons/year for diesel PVs. CO emissions peaked at 26.25 tons/year in 2021 before declining to 21.10 tons/year by 2025, following the ban on internal combustion engine PVs. The ANN model achieved high predictive accuracy, with R² values ranging from 0.96 to 0.99. Overall, the integrated COPERT–ANN framework offers a robust, data-driven approach for urban emission prediction, providing valuable insights to guide sustainable transport planning and emission mitigation in rapidly growing cities.

Geological and Geotechnical Assessment of the Bargun Dam Site of Somali Region, Ethiopia

Birhanu Ermias — 2025-12-30

The lack of detailed geological and geotechnical investigations at the proposed Bargun Dam site, which is located in Ethiopia’s Shebelle River Basin, poses significant risks to design integrity, safety, and long-term performance. This study presents a comprehensive geological and geotechnical evaluation aimed at ensuring a safe and sustainable dam design. Field investigations, including geological mapping, test pitting, and soil and rock sampling, were followed by laboratory analyses to determine the physical and mechanical properties of foundation materials. Rock mass quality was assessed using the Rock Mass Rating (RMR), Rock Quality Index (Q), Rock Mass Index (RMi), and Hoek–Brown criteria. Slope stability was analyzed kinematically, while bearing capacity (qu) and permeability (Ks) were estimated using empirical correlations applicable to data-scarce environments. The results indicated RMR, Q, RMi, qu, and Ks values of 46–59, 1.06, 0.48, 0.55–23.22 MPa, and 2.14×10⁻²–3.7×10⁻⁵ cm/s, respectively. The average rock mass deformation modulus ranged from 5.03 to 9.64 GPa, suggesting a potential risk of differential settlement. Based on the kinematic analysis the left abutment slope is susceptible to oblique toppling, requiring slope modification. However, the site’s low seismicity and manageable sediment conditions are favorable for dam construction. Overall, detailed subsurface exploration to identify concealed cavities and targeted grouting are recommended to mitigate leakage and stability risks, ensuring the dam’s long-term safety and functionality.

Enhanced Software Defect Prediction Using Ensemble Learning with Correlation-Based Feature Selection and SMOTE

Bahiru Shifaw Yimer — 2025-12-30

Numerous studies have explored software defect prediction using machine learning algorithms; however, their performance on publicly available defect datasets often remains limited due to high feature dimensionality and class imbalance. This study addresses these issues using five AEEEM project datasets; namely, Eclipse Equinox (EQ), JDT, Apache Lucene (LC), Mylyn (ML), and PDE UI. Seven ensemble learning algorithms (AdaBoost, Gradient Boosting, XGBoost, Random Forest, Extra Trees (ET), Bagging, and Stacking) were implemented. To reduce dimensionality, three feature selection techniques, namely, Correlation-Based Feature Selection (CFS), Sequential Forward Selection (SFS), and Correlation-based Filter (CO), were applied, while the Synthetic Minority Oversampling Technique (SMOTE) method was employed to handle class imbalance. Experiments were conducted using 10-fold and nested cross-validation, and model performance was evaluated using accuracy, recall, precision, F-measure, and Area under ROC curve (AUC) metrics. The combination of CO feature selection with the ET ensemble algorithm outperformed all other models across the five datasets. Using nested cross-validation with grid search optimization, accuracies of 92.1, 97.3, 99.1, 98.2, and 98.5 % were achieved for the EQ, JDT, LC, ML, and PDE datasets, respectively. These findings demonstrate that integrating effective feature selection and data balancing significantly enhances defect prediction performance compared to models using default hyper-parameters.

Determinants of Rural Women’s Entry into Income Earning Employment: Empirical Evidence from Sebeta Hawas District of Central Oromia, Ethiopia

Aynalem Megersa — 2025-12-30

Enhancing women’s labor force participation remains a central component of the gender equality and women’s empowerment agenda. This paper examines the major determinants of rural women’s entry into non-farm employment. A mixed-methods research design was employed, generating both quantitative and qualitative data through surveys, life history calendars, interviews, and focus group discussions. A multistage sampling technique was used to select 1,066 survey participants. Quantitative data were analyzed using descriptive statistics and event history analysis, while qualitative data were analyzed thematically to substantiate and enrich the quantitative findings. The study identified a range of factors that significantly influence women’s entry into non-farm employment, which were categorized as individual, family-related, and contextual factors. The results further indicate that certain variables, such as membership in associations, never-married status, attainment of secondary or higher education, and residence in households with high wealth status, exert a consistent influence across both wage employment and self-employment. In contrast, other factors, including primary education, birth cohort, migration status, previously married status, household size, policy period, and place of residence, exhibit varying effects depending on the type of employment. The findings suggest that promoting women’s entry into employment requires broadening current government efforts, which predominantly focus on job creation, by placing greater emphasis on ensuring women’s access to decent work, social services, and infrastructure, as well as on transforming gender relations within households and society at large.

Species Composition, Relative Abundance and Diversity of Forager Honey Bees in and around Gishwati-Mukura National Park, Rwanda

Kibogo Andrew — 2025-12-30

This study examined the influence of habitat type, seasonality, temperature, and humidity on forager bee abundance, species richness, and diversity in and around Gishwati–Mukura National Park (GMNP). Bees were sampled using pan traps across three habitat types, namely primary, restored, and disturbed forests, over a 13-month period covering both dry and rainy seasons. A total of 179 forager bee specimens were recorded, representing seven species from Apidae and Halictidae families. Significant differences in bee abundance were observed among habitat types. In disturbed forests, bee abundance was higher during the rainy season, whereas in restored and primary forests, abundance was greater during the dry season. These results indicate that seasonal variation significantly affects forager bee abundance in GMNP, although seasonal trends were generally consistent across habitat types. Overall, species richness was low. Temperature and humidity exhibited a significant negative effect on bee abundance, suggesting that forager bee populations increased under cooler and drier conditions and declined under warmer and more humid conditions. Although the findings suggest that bees are thriving within GMNP, the study was limited to a single year; therefore, future research should incorporate multi-year sampling to capture longer-term trends. The study contributes to existing knowledge on bee ecology and pollination services and underscores the potential of beekeeping as a sustainable livelihood option for communities surrounding GMNP, which may help reduce encroachment, poaching, and habitat degradation. The findings also provide valuable insights for park management and conservation policymakers.

Leveraging Genome Editing to Revive Multi-Ear Traits for Climate Resilient Maize: Systematic Review

Abenezer Abebe Tefera — 2025-12-30

Maize is central to global food security because of its high yield potential and broad adaptability. However, modern breeding has unintentionally narrowed its genetic base by favoring a single-ear ideotype, thereby reducing resilience to climate stress and limiting yield stability under adverse conditions. As climate variability intensifies, the buffering capacity of multi-ear phenotypes is becoming increasingly important. This review synthesizes evidence demonstrating that reintroducing prolificacy is both promising and challenging, as the trait is polygenic and constrained by physiological trade-offs involving source-sink balance, ear initiation, and developmental synchrony. Recent advances in genome-editing technologies, including CRISPR-Cas systems, base editing, and prime editing, now allow precise modification of key regulators of ear architecture, such as tb1, fea2, RA1/RA2, and hormonal pathways controlling axillary meristem activity and ear number. Emerging insights into synchrony-related genes, including GIF1, RA2, and gibberellin-deactivation loci, further support targeted editing strategies to coordinate ear development and minimize yield penalties. By integrating these molecular tools, breeders can design multi-ear ideotypes that enhance yield stability and climatic adaptation. To accelerate progress, future research should prioritize the systematic identification and validation of promoters and cis-regulatory elements that fine-tune prolificacy pathways, alongside the development of high-throughput phenotyping platforms capable of capturing subtle variation in ear number, developmental synchrony, and resource allocation.