Madda Walabu University, one of the public universities in Ethiopia, was established in 2006. The university is located in Bale Zone, in the town of Robe, about 430 km (270 mi) from the capital city, Addis Ababa. The university has 46 undergraduate and 28 postgraduate programs.
Demand for sustainable energy systems has resulted in building interest for converting agricultural residues such as coffee husk and sugarcane bagasse into usable syngas through the biomass gasification. This is done by burning the biomass in limited air or other gas supply and converting it into a mixture of carbon monoxide, hydrogen, and methane. Hence, in this paper, performance of a throated updraft gasifier is analyzed by utilizing coffee husk and bagasse biomass feedstocks. As such, a detailed study on the thermodynamic equilibrium model and experimental analysis is performed. Syngas composition, lower heating value (LHV), gas yield, and cold gas efficiency (CGE), were predicted using a stoichiometric equilibrium model. Moreover, experimental analysis is performed for the analysis of syngas composition. Thus, for both feedstocks when equivalence ratio (ER) is increased the value of CO2, N2, and gas yield increases. Nevertheless, the value of CH4, CO, H2, and LHV, and CGE has decreases. Similarly, with increased moisture content (MC), the value of CO, N2, CH4, LHV, and CGE have fallen, however, the value of CO2, H2, and gas yield have increased. At optimum operating conditions corresponding to an ER of 0.30 and a MC of 12
A microbial fuel cell (MFC) is a modern, environmentally friendly, and cost-effective energy conversion technology that utilizes renewable organic waste as fuel, converting stored chemical energy into usable bioelectricity in the presence of a biocatalyst. Despite advancements in MFC technology, several challenges remain in optimizing power production efficiency, particularly regarding anode materials and modifications. In this study, low-cost biosynthesized iron oxide nanoparticles (Fe3O4 NPs) were coated with a polyaniline (PANI) conducting matrix to synthesize hybrid Fe3O4/PANI binary nanocomposites (NCs) as modified MFC anodes via an in-situ polymerization process. Characterization techniques, including UV–Vis, XRD, SEM, and FT-IR, revealed the successful synthesis of green-routed nano-scaled materials with altered optical properties after matrix coating, high crystallinity in the iron oxide phase, rougher surface morphology, and characteristic Fe–O peaks at 594 cm⁻1. Additionally, the electrochemical behavior of the prepared nano-materials was characterized by cyclic voltammetry (CV), where low ΔEp values (0.473 V) for Fe3O4/PANI NCs indicated the presence of reversible charge transfer mechanisms at the electrode surface, reflecting a high rate of electron transfer. The synthesized nanocomposite was used to modify pencil graphite anodes to construct four single-chamber MFCs: bare pencil graphite anodes, pencil graphite anodes modified with Fe3O4, PANI, and Fe3O4/PANI nanocomposites. The maximum open circuit voltage (OCV) value was 645 ± 24.50 mV, with a high power output of 424.51 ± 6.86 mW/m2 and current density of 2475.01 ± 1.23 mA m−2 produced by the Fe3O4/PANI NCs modified pencil graphite electrode, which is more than six times the efficiency in terms of power density compared to the unmodified pencil graphite electrode (PGE). These results demonstrate that the synthesized nanocomposite plays an effective and value-added role in modifying traditional carbon anode electrodes within an MFC energy conversion device system.
Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search-Appraisal-Synthesis-Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.
This study evaluates the performance of seven Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and their Ensemble mean in reproducing historical maximum (Tmax) and minimum (Tmin) temperatures across five agro-ecological zones (AEZs) of Ethiopia during 1995–2014. The assessment covers daily to annual time scales using observational and GCM datasets. Model performance was evaluated using Percent Bias (PBIAS), Root Mean Square Error (RMSE), and correlation coefficient (r), while the Comprehensive Rating Index (CRI) was applied for ranking. Results show substantial spatial and temporal variability in model performance. For Tmax, the Ensemble mean and EC-Earth3-veg performed best at the daily scale, while EC-Earth3-veg, MPI-ESM1-2-LR and BCC-CSM2-MR excelled at the monthly scale across most AEZs. Seasonally, top-performing models included Ensemble mean, MPI-ESM1-2-LR and MRI-ESM2-0 during Bega (October-January), EC-Earth3-veg, CNRM-CM6-1 and Ensemble mean during Belg (February-May) and CNRM-CM6-1 and MPI-ESM1-2-LR during Kiremt (June-September). For Tmin, CNRM-CM6-1, BCC-CSM2-MR and Ensemble mean ranked highest at both daily and monthly scales in most AEZs. At the annual scale, MRI-ESM2-0, Ensemble mean, MPI-ESM1-2-LR and CNRM-CM6-1 excel for Tmax, while EC-Earth3-veg, BCC-CSM2-MR and Ensemble mean lead for Tmin across most AEZs. MIROC6 consistently exhibited the weakest performance for both Tmax and Tmin across most AEZs and time periods. Thus, the Ensemble mean of all evaluated models does not consistently rank among the top three performers across all AEZs and time scales. The identified best-performing CMIP6 models provide valuable tools for assessing climate change impacts and developing region-and time-specific adaptation strategies in Ethiopia. Given the variability in GCM performance across AEZs and time scales, national or large-scale studies would benefit from using the Ensemble mean derived from the best-performing models. This study provides crucial insights into the strengths and weaknesses of different GCMs, supporting evidence-based decision-making and enhancing efforts to build climate resilience and adaptation strategies in the region.
In the sphere of science and technology, nanotechnology stands out as a rapidly advancing field, characterized by the manipulation of metal and metal oxide materials at scales of ≤ 100 nanometres. In the present study, copper nanoparticles (CuNPs) were synthesized through a green method using the aqueous extract of Vernonia amygdalina leaves as a natural reducing and capping agent, after which the catalytic degradation of toxic organic dyes, namely, Safranin O commonly known as basic red 2 (SO-BR2) and Rhodamine B (RhB), in the presence of NaBH4 was evaluated. The synthesized CuO NPs were characterized using UV‒Vis, FT-IR, XRD, and SEM techniques, and their results confirmed the successful synthesis of the nanomaterial a via the plant-assisted method with characteristic peak of Cu-O bond interaction at 809 cm⁻¹ a band gap of 3.71 eV, a mean crystalline size of 2.406 nm and varied morphologies with medium agglomeration. The catalytic degradation of SO-BR2 and RhB by the CuNPs in the presence of NaBH4 exhibited maximum efficiencies of 97.74 ± 0.0410
