Adama Science and Technology University

The escalating pressure on land and water resources necessitates precise tools for identifying suitable irrigation areas, especially in rain-fed agricultural systems vulnerable to climate variability. This study presents a comprehensive, basin-wide land suitability assessment for surface irrigation in Ethiopia’s Genale Dawa River Basin (GDRB), the nation’s third-largest basin with significant untapped potential. We employed a GIS-based Analytic Hierarchy Process (AHP) to integrate 11 biophysical and infrastructural criteria: soil depth, texture, pH, organic matter, cation exchange capacity, water-holding capacity, rockiness, slope, land use/land cover, and proximity to rivers and roads. Factor weights were derived via expert-driven pairwise comparisons (Consistency Ratio = 0.079), and a weighted overlay analysis generated a final suitability map classified into four classes: highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (N). The analysis reveals that only 8.32% of the GDRB is highly suitable (S1) for surface irrigation, while a vast 73.29% is moderately suitable (S2). The highly suitable zones are primarily located in the central plains, characterized by deep Vertisols, gentle slopes, and better access to water, whereas moderately suitable lands require targeted interventions to overcome constraints related to soil texture, rockiness, or infrastructure access. The findings advocate for a two-phase development strategy: immediate investment in highly suitable areas for rapid gains, coupled with a long-term program of land improvement and infrastructure development to unlock the potential of moderately suitable lands. This study provides a critical, evidence-based geospatial tool for policymakers to optimize land and water resources, enhance climate resilience, and achieve sustainable agricultural intensification in Ethiopia and similar data-scarce basins.

ABSTRACT This study integrates the SWAT and WEAP models to address water allocation challenges in Ethiopia’s Abaya Chamo sub-basin. SWAT simulated water availability, while WEAP allocated water to the domestic, irrigation, industrial, and environmental sectors. Baseline (2020) water demands were analysed, and four scenarios were explored: reference (2021–2035), high population growth, irrigation expansion, and irrigation efficiency improvement. The SWAT model estimated the annual water potential at 4.38 × 10 9 m 3 . In 2020, total water demand was 1.22 × 10 9 m 3 , with an unmet demand of 0.17 × 10 9 m 3 . Projections to 2035 revealed persistent unmet demand across all scenarios. The irrigation expansion scenario resulted in a critical water deficit of 0.65 × 10 9 m 3 . In contrast, improving irrigation efficiency from 50% to 60% alleviated water shortages. The findings underscore the importance of developing water storage infrastructure and promoting efficient water-use practices. This study emphasizes the role of advanced modelling tools in guiding sustainable water management in the region.

The advent of Software-Defined Networking (SDN) has transformed the landscape of cloud network management by decoupling the control and data planes, enabling centralized programmability and dynamic policy enforcement. However, this paradigm shift introduces new challenges for real-time anomaly detection and DDoS mitigation. This paper proposes an intelligent anomaly mitigation framework that leverages deep learning techniques—specifically Sparse Autoencoders (SAE) for feature extraction and a hybrid CNN-LSTM model for capturing both spatial and temporal traffic anomalies. The system is implemented and evaluated using Python 3.9 with TensorFlow 2.x on a Ryu SDN controller integrated with OpenFlow-based switches in Mininet. Experiments are conducted across various SDN topologies including Star, Mesh, Tree, and Random configurations. Benchmark datasets CIC-DDoS2019 and UNSW-NB15 are used for model training and validation. The proposed framework achieves 99.1% detection accuracy, a 30% improvement in false positive rate over traditional methods, and reduces mitigation time by up to 40%, confirming its suitability for real-time deployment in dynamic SDN environments.

Solar desalination presents a promising solution for freshwater production, and this study evaluates its effectiveness by analyzing experimental data to estimate the performance of a solar still. The study shows that the performance of a solar still is affected by several key factors, such as solar radiation, feed flow rate, ambient temperature, relative humidity, and wind speed. To better understand and predict these influences, researchers used a back-propagation artificial neural network (ANN) and compared its results to those of a traditional stepwise regression model. The results revealed that the ANN offered significantly higher accuracy in predicting the freshwater output, outperforming the regression-based approach. This underscores the potential of machine learning in optimizing solar desalination systems under dynamic environmental conditions.

Sustainable groundwater management in regions with limited data remains a significant challenge. Identifying potential groundwater zones by combining geological and hydrological information is crucial for the effective use and protection of this resource. In the Southwest Shewa Zone of Ethiopia, groundwater is the main source for household, agricultural, and industrial needs. However, limited hydrogeological data often leads to drilling ineffective wells, emphasizing the need for precise, data-driven mapping methods. This study uses a stacked ensemble machine learning approach to identify groundwater potential zones. The framework combines Adaptive Boosting, Random Forest, Histogram-Based Gradient Boosting, and Extreme Gradient Boosting using a meta-learner to improve prediction accuracy and minimize bias. The area was classified into five groundwater potential levels: very low, low, moderate, high, and very high. Results indicate that 55.8% of the area falls within high to very high groundwater potential zones, while 29.8% corresponds to very low to low potential. Model evaluation using recall, precision, F1-score, and Receiver Operating Characteristic (ROC) demonstrates strong predictive capability and reliable class discrimination across all groundwater potential zones.These findings demonstrate the strong predictive performance of the stacked ensemble learning model and provide a scientific basis for identifying groundwater prospecting zones and guiding well siting in the study area.

Biochar, a low-cost, and carbon-rich product of the thermal decomposition of biomass under oxygen-limited conditions and at relatively low temperatures, has recently been identified as a promising porous material with a wide range of industrial applications. In the present study, a comprehensive analysis of proximate, ultimate, nutrient profile, structural, and textural properties of a biochar derived from two Ethiopian indigenous Brassica carinata cultivars was conducted. The characterization of the biochar was achieved by employing a variety of well-established methods, including proximate analysis (moisture, volatile matter, ash content, and fixed carbon), ultimate analysis (C, S, and O content), atomic oxygen to carbon ratio (O/C), morphological and elemental composition analysis through scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Furthermore, a combination of mercury intrusion porosimetry (MIP), dynamic vapor sorption (DVS), and gas adsorption methods such as nitrogen and krypton gas adsorption, were used for an in-depth study of the porous structure. SEM morphological characterization showed that the biochar surfaces showed multiple pores of diverse sizes and shapes. EDS elemental composition analysis revealed that sodium, aluminium, and silicon were not detected, but potassium, calcium, magnesium, and iron were all present in noticeable amounts. Furthermore, ultimate analysis showed that the most prevalent elements were carbon (86 wt.%) and oxygen (10.41‒9.77 wt.%), while sulphur was present in negligible concentrations. MIP analysis demonstrated that the porosities of the biochars varied from 62.68 to 69.99 wt.%, with the Holetta-1 biochar showing the highest porosity. The superior porosity of Holetta-1, as confirmed via MIP analysis, yielded higher values for bulk volume (2.36 mL g− 1), skeletal volume (1.65 mL g− 1), and total intrusion volume (1.65 mL g− 1) compared to the Yellow Dodolla. The most frequent pore diameters were 172.46 μm for Yellow Dodolla and 111.42 μm for Holetta-1. The MIP log differential pore diameter distributions were observed to vary from 18 to 411 μm and 10 to 411 μm, respectively, for Yellow Dodolla and Holetta-1. Despite the biochars’ low specific surface areas (0.17–0.21 m² g⁻¹), krypton sorption was a suitable technique for its characterization compared to DVS and nitrogen sorption methods. In conclusion, the characterization studies confirmed that this carbon-rich porous material possesses unique and valuable properties, with these attributes position it as a promising alternative for diverse industrial applications, contributing to the development of a bio-based circular economy.

People living with Type 2 Diabetes Mellitus (T2DM) face a heightened risk of experiencing severe complications from COVID-19, underscoring the importance of vaccination. Nonetheless, the impact of COVID-19 vaccines-especially the Johnson & Johnson (Ad26.COV2.S) vaccine-on glucose regulation has not been fully elucidated. This study evaluates the impact of vaccination on glycemic parameters, including Random Blood Sugar (RBS) and Hemoglobin A1c (HbA1c), and identifies factors influencing glycemic variability. Between May 2023 and June 2024, a prospective cohort study was carried out at Adama Hospital Medical College in Ethiopia. Adults diagnosed with Type 2 Diabetes Mellitus were divided into two cohorts based on vaccination status: those who received the vaccine and those who did not. Glycemic parameters were recorded at baseline and subsequently at three-month intervals-specifically at 3, 6, 9, and 12 months following vaccination. To evaluate trends over time and identify influencing factors, including demographic and clinical variables, longitudinal data were analyzed using Generalized Estimating Equations (GEE). Vaccinated individuals exhibited transient elevations in RBS, peaking at three months post-vaccination before stabilizing. In contrast, HbA1c levels demonstrated a gradual increase over time. Greater glycemic variability was observed in younger individuals and females. The primary determinants of variations in glycemic levels were vaccination status, duration following immunization, and demographic characteristics. In contrast, diabetes treatments and lifestyle-related factors showed only a limited influence. The Johnson & Johnson COVID-19 vaccine was associated with short-term RBS fluctuations and a sustained increase in HbA1c levels in T2DM patients. These findings highlight the need for personalized glycemic monitoring post-vaccination. Despite these metabolic variations, the vaccine's protective role against severe COVID-19 outweighs transient glycemic disturbances. Incorporating vaccination efforts into comprehensive diabetes management is crucial, and additional studies are warranted to investigate the underlying biological mechanisms.