Bahir Dar University (Amharic: ባሕር ዳር ዩኒቨርስቲ) is a university in the city of Bahir Dar, the capital of the Amhara Region in Ethiopia. The University is a combination of two smaller institutes formed earlier, after the departments were gradually raised to a degree level starting from 1996. The official slogan of the university is "Wisdom at the source of the Blue Nile" The University is composed of five colleges, four institutes, seven faculties, two academies and two schools.
Despite the rapid proliferation of generative AI, empirical understanding of how hotel managers integrate ChatGPT into their professional workflows remains limited. Adopting a constructivist grounded theory approach, this study develops a hotel manager-centered framework of ChatGPT usage benefits and challenges, explaining hotel managers’ utilization levels and continued use of ChatGPT. We identify five benefit domains—content creation and support, efficiency and productivity, well-being, enhanced communication, and knowledge enhancement—and three challenge domains: dependency, drawbacks, and capability requirements. By examining the interplay between these drivers and barriers, this study provides one of the first qualitative characterizations of professional ChatGPT engagement in hospitality. Theoretically, it extends technology adoption models by proposing a grounded framework explaining how benefits and challenges influence continuance use and utilization level. Practically, it offers a roadmap for hotel organizations to develop policies—and training that mitigate dependency risks while maximizing productivity gains from large language models.
Shear failure in reinforced concrete beams is brittle and sudden, posing significant risks to structural safety, and the incorporation of steel fibers has emerged as an effective way to improve shear resistance in beams lacking transverse reinforcement. Predicting the shear strength of such steel fiber-reinforced concrete (SFRC) beams remains challenging because of the nonlinear interactions among fiber, matrix, and loading parameters that traditional empirical design equations, developed from limited datasets, cannot fully capture. Machine learning models can analyze large datasets and uncover these complex relationships, but their lack of interpretability limits engineering trust and adoption. To address this, we develop an explainable machine learning framework using SHAP, partial dependence plots (PDP), LIME, and feature importance analysis on a database of 488 SFRC beam tests spanning eight design parameters. Among several models evaluated, XGBoost achieved the best predictive performance (5-fold cross-validated R² = 0.890 ± 0.086; testing R² = 0.977 on an 80/20 holdout split), outperforming a multivariate regression baseline and four widely used empirical formulas. SHAP analysis, verified through bootstrap stability testing, identified beam depth and width as the most influential parameters, followed by the shear-span-to-depth ratio and concrete compressive strength, with the longitudinal reinforcement ratio, steel fiber factor, and fiber volume fraction contributing more modestly. These findings can help structural engineers prioritize key design parameters when assessing SFRC beam shear capacity and support the future development of interpretable, data-driven design tools to complement existing empirical code equations.
Accurate simulation and projection of precipitation extremes are critical for climate adaptation planning. This study aims to evaluate the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) in simulating extreme precipitation and to project future changes in the Jemma subbasin. Daily precipitation data from seven meteorological stations in subtropical and temperate agroecological zones (AEZs) were used to evaluate CMIP6 GCM performance based on ten precipitation extreme indices. The top-performing GCMs were selected based on their skill assessed through the Comprehensive Rating Index (CRI). The GCMs were then used to project changes under three greenhouse gas emission scenarios (SSP24.5: medium emissions, SSP3-7.0: high emissions, and SSP5-8.5: very high emissions) for near (2030-2049), mid (2060-2079), and far future (2080-2099) periods relative to 1995-2014. The selected 8 GCMs demonstrated moderate skill, with CRI values ranging from 0.29 to 0.78 in the subtropical AEZ and 0.44 to 0.80 in the temperate AEZ. Indices such as Rx5day (multi-day maximum rainfall) and R10mm (heavy rainfall frequency) were captured more reliably than dry-spell metrics like consecutive dry days (CDD). Projections reveal notable variability in extreme precipitation across scenarios, periods, and AEZs. In the subtropical AEZ, R10mm and R20mm are projected to rise by 1.57 and 0.28 days/year, respectively, under SSP3-7.0 (near-term). Rx5day is expected to increase by 1.5 mm (SSP2-4.5, far-term) and 1.54 mm (SSP3-7.0, mid-term), while R95p (very wet day precipitation) rises by 7.51 mm (SSP3-7.0, near-term). In the temperate AEZ, mixed trends are observed: Rx1day increases by 0.88 mm (SSP5-8.5, far-term), Rx5day rises by 1.5 mm (SSP3-7.0, mid-term), but drops by 1.29 mm (SSP2-4.5, far-term). R95p generally trends upward, although a 5.36 mm decline occurs under SSP3-7.0 (near-term). Annual total wet-day rainfall (PRCPTOT) and SDII (simple daily intensity index) mostly increase, though occasional SDII declines suggest heightened uncertainty. The projected changes in precipitation extremes suggest increasing challenges for agricultural systems and water resource management, underscoring the need for climate-resilient planning in the Jemma sub-basin.
Magnesium (Mg)- based alloys and their composites have recently gained significant interest in the biomedical field as potential biodegradable materials due to their unique mechanical properties, bioresorbability, biocompatibility, and biological activity. Nevertheless, the widespread biomedical applications of Mg-based alloys and composites are still limited mostly by their superior corrosion rates and subsequent loss in mechanical integrity. In recent years, numerous research studies have been conducted to develop biodegradable Mg-based alloys and magnesium-based metal matrix composites (Mg/MMCs) with enhanced corrosion resistance and mechanical properties. In this paper, an effort has been made to discuss the various methodologies for processing biodegradable Mg/MMCs for important clinical applications. The major processing technologies for biodegradable Mg/MMCs, including liquid-state processing (e.g., stir casting), solid-state processing (e.g., powder metallurgy (PM)), in-situ processing, and modern additive manufacturing (e.g., powder bed fusion (PBF), selective laser melting (SLM), and wire arc additive manufacturing (WAAM)), are first briefly introduced. Subsequently, biodegradable Mg-based alloying designs and the current trend in biodegradable Mg-based alloys, including Mg/Ca, Mg/Zn, Mg/Cu, Mg/Sr, and Mg/RE, essential for biomedical applications, are reviewed in detail. This review article also comprehensively discusses the design of reinforcement materials for producing biodegradable Mg/MMCs for clinical applications. The current trends on biodegradable Mg/MMCs, including calcium phosphate (CaP)-based bioceramics, Si-containing bioceramics, biodegradable magnesium oxide (MgO), and carbon materials reinforced Mg/MMCs, are discussed. Special emphasis has been placed on the production techniques and various behaviors (e.g., mechanical properties, microstructure, biocompatibility, and corrosion behaviors) displayed by micro/nano-sized particles reinforced Mg/MMCs. The potential engineering applications of Mg/MMCs are also introduced. Ultimately, this review highlights the prospects of biodegradable Mg-based alloys and Mg/MMCs biomaterials in various biomedical applications. This review will serve as a valuable reference for young researchers and industry personnel with a comprehensive understanding of biodegradable Mg/MMCs utilized in biomedical applications.
Biohydrogen has emerged as a promising renewable energy carrier with significant potential to support global decarbonization and reduce dependence on fossil-derived hydrogen. This review provides a comprehensive and integrated assessment of biohydrogen production pathways, including thermochemical, biochemical, and electrochemical technologies. Major feedstocks such as lignocellulosic biomass, agricultural residues, food waste, industrial wastewater, sewage sludge, algae, and municipal solid waste are critically examined in relation to their availability, composition, pretreatment requirements, and hydrogen production potential. Key conversion pathways, including gasification, pyrolysis, hydrothermal gasification, biogas reforming, dark fermentation, photofermentation, biophotolysis, and microbial electrolysis, are systematically evaluated with respect to reactor design, hydrogen yield, process efficiency, scalability, and technological readiness. Particular emphasis is placed on pretreatment technologies and the trade-off between pretreatment severity and net energy balance, which strongly influence the sustainability and economic viability of lignocellulosic biohydrogen systems. In addition, recent advances in computational modeling, kinetic analysis, metabolic engineering, process simulation, and artificial intelligence are reviewed for their role in improving predictive capability, process optimization, and real-time control. The review further highlights the growing importance of hybrid systems, integrated biorefineries, engineered microbial consortia, advanced materials, and circular bioeconomy strategies in enhancing hydrogen recovery and reducing environmental impacts. Despite notable progress, challenges related to feedstock logistics, low hydrogen yield, process instability, high purification cost, and limited large-scale commercialization remain significant barriers. Overall, this review provides a forward-looking roadmap for the development of sustainable, efficient, and commercially viable biohydrogen production systems.
