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.
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.
This study examines the effects of green supply chain management (GSCM) dimensions on economic performance and their indirect impact through supply chain resilience in Ethiopian manufacturing firms. Survey data from 306 companies were analyzed using structural equation modeling. Results indicate that green manufacturing and other GSCM practices improve economic performance, both directly and indirectly through supply chain resilience, whereas reverse logistics negatively affects economic performance. Supply chain resilience significantly mediates the effects of most GSCM dimensions on economic performance, highlighting its role in enabling firms to prepare for and recover from disruptions. The findings extend the resource-based view and dynamic capabilities theory by showing how GSCM practices act as strategic capabilities in emerging markets. Practically, Ethiopian manufacturers should integrate environmental practices into supply chains to enhance resilience and performance while managing the costs associated with reverse logistics.
The paper focused on optimizing vibration-induced robotic gas metal arc welding (GMAW) to enhance Rockwell hardness of the fusion zone (FZ), stainless steel heat affected zone (HAZ-SS) and mild steel heat affected zone (HAZ-MS) of the dissimilar AISI 304 stainless steel and AISI 1018 mild steel joint. Five input parameters each with three levels were employed for the execution of 32 experiments designed using response surface methodology central composite design. The experimental investigation revealed that maximum FZ hardness of 107.6 HRB was achieved at current 100 A, voltage 26 V, welding speed 7 mm sec-1, gas flow rate 6 L min-1, and amplitude 0.4 mm; and maximum HAZ-SS and HAZ-MS of 86.8 HRB, and 71.9 HRB respectively were obtained at current 85 A, voltage 22 V, welding speed 5 mm sec-1, gas flow rate 8 L min-1, and amplitude 0.6 mm. A hybrid artificial neural network-genetic algorithm (ANN-GA) method was utilized to predict and optimize the response variables and the related control parameters. Best prediction of the ANN model was attained at an MSE of 0.0011376 and coefficient of regression R of 0.9988 for normalized data and an MSE of 2.797784 on original data for a 5-10-3 configuration back-propagation network using the Bayesian Regularization algorithm. The genetic algorithm optimal results for FZ, HAZ-SS, and HAZ-MS were 119.2 HRB, 91.3 HRB, and 78.6 HRB respectively. From the confirmation test, an average response of FZ 116.4 HRB, HAZ-SS 90.3 HRB, and HAZ-MS 77.5 HRB were achieved with error percentages of 1.11%, 2.35% and 1.42% respectively.
