Eshet Lakew Tesfaye

Yeasts play a crucial role in converting sugars in flour into carbon dioxide (CO₂) and ethanol, enabling dough leavening. This study focused on identifying efficient wild yeast strains isolated from various fruits for leavening under controlled laboratory conditions. Of the 88 wild yeast isolates obtained, three (AAUGr5, AAUOr7, and AAUPi3) were selected based on their ability to produce CO₂ efficiently, using D-glucose (3 g/L) as a substrate and their non-hydrogen sulfide (H₂S) production character. These isolates were tentatively identified as members of the Saccharomyces genus through cultural, morphological, and biochemical analyses and optimal physicochemical profiling. Optimal growth conditions in Yeast Extract Peptone Dextrose (YPD) medium were a pH of 5 and a temperature of 30 °C. Maximum growth was observed with 2% (w/v) D-glucose and 0.5% (w/v) NaCl concentrations, achieving peak biomass after 96 hrs of incubation. Notably, isolate AAUGr5 displayed superior leavening performance compared to commercial yeast with 9.09% and 8.92% at 30 °C and room temperature, respectively. Furthermore, a co-culture of these H₂S-free wild yeast strains demonstrated enhanced leavening activity, underscoring their potential for baking applications to produce high-quality bread.

The rapid expansion of energy infrastructure in emerging economies, particularly in India and Africa, necessitates advanced control and computational strategies to ensure the seamless integration of green energy resources with conventional power systems. This study conducts a comprehensive analysis of state-of-the-art control mechanisms and optimization techniques for hybrid power networks, focusing on enhancing grid stability, frequency regulation, and resilience under dynamic loading and climatic variations. It explores advanced generation control strategies, including adaptive and predictive control frameworks, to mitigate the inherent intermittency of renewable energy sources. Furthermore, the paper examines multi-objective optimization methodologies for energy dispatch, frequency stabilization, and reliability enhancement in multi-entity power networks. By proposing a robust and computationally efficient framework for hybrid energy integration, this study contributes to the development of resilient, self-sustaining power systems crucial for ensuring long-term energy security, operational efficiency, and economic growth in rapidly developing regions.

Dye degradation mediated by oxidoreductive enzymes has been well explored in textile wastewater treatment. Ligninolytic enzymes (LEs) play key roles in the biodegradation of textile dyes. This paper aimed to provide a comprehensive review of the potentiality of LEs in textile dye biodegradation. The sources, biodegradation mechanisms, and dye removal efficiencies of LEs were discussed. Moreover, factors affecting dye biodegradation efficiency, and applications and limitations of LEs were highlighted. LEs are extracellular protein complexes that have a synergetic effect on dye biodegradation and can remove up to 99% of the dye using one or more LEs over wide pH and temperature ranges. Laccase is a highly explored and examined enzyme compared to lignin peroxidase (LiP) and manganese peroxidase (MnP) for dye biodegradation. Although extensive research has been conducted on fungal laccases, it is worth noting that bacterial laccases exhibit unique properties, such as stability at elevated temperatures and alkaline pH, which are not typically observed in fungal laccases, making them an ideal candidate for the treatment of textile wastewater. Further research on the optimization of experimental conditions is required to improve the dye biodegradation efficiency of these enzymes. It is also crucial for improving biodegradation capabilities using recombinant DNA technology.

Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/SIMULINK environment.