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Approximately 172 million rural households in China in 2022, will still rely on traditional biofuels instead of electricity. This study explores the feasibility of achieving rural electrification in China utilization various Energy origins and assesses its potential impact on climate change mitigation. To do so, we employ the Regional Energy Model (REM) to formulate hypotheses for rural power distribution. Covering the era from 2020 until 2050. We evaluate consequences in terms of greenhouse gas discharges, principal energy consumption, associated expenses by comparing these possibilities with a Business- as -Usual (BU) scenario. Our findings reveal that diesel-based arrangements tend to produce 'the maximum Carbon Dioxide (CO2) emissions, trailed by network-based solutions. Electrification focused on renewable energy-driven applications has the potential to reduce total CO2 emissions by up to 97 % and primary energy consumption by 43 % in 2050 compared to the BU scenario. We also find that electrification through decentralized diesel systems tends to be the priciest choice. Rural electrification utilizing renewable energy emerges as a cost-effective choice when the majority of end-use applications rely on renewable energy sources. However, it becomes less economical than network expansions when electric end-user appliances are predominant. In summary, this study investigates the viability of renewable energy for rural power distribution and its role in climate change abatement in rural China, offering policy suggestions. • Analyzes six energy-based scenarios for China's rural electrification until 2050. • Evaluates GHG emissions, energy use, and costs impacts with regional energy model. • Provides policy insights and uncertainties for China's sustainability goals. • An oil price and distance-based analysis also is done with extra 6 scenarios. [ABSTRACT FROM AUTHOR]

As sustainable urban communities continue to seek alternative energy sources, this study explores the inclusion of renewable resources in the energy mix. With a focus on liquefied natural gas (LNG) regasification, parabolic trough solar collectors, dual-loop power cycles, proton exchange membrane electrolysis, and hydrogen liquefaction cycle, this research conducts a comprehensive examination of an integrated system. The primary objective is to provide a diverse range of valuable outputs, including electricity, liquefied hydrogen, desalinated water, and cooling for coastal areas. Through careful analysis of regional geographic features, San Francisco emerges as a suitable location due to its favorable solar radiation intensity and existing LNG transportation infrastructure. The findings reveal that the integrated system has the potential to deliver approximately 5750.4 MWh of cooling, 14988.85 MWh of electricity, and 1,491,084 m3 of fresh water, thereby significantly contributing to the city's utility demands. In terms of data-driven optimization, artificial neural networks serve as intermediary mechanisms to establish correlations between the developed code and the optimization algorithm. The results demonstrate that the optimized system exhibits notable improvements in production capacity. Moreover, significant reductions in the levelized cost of electricity (0.19 Cent/kWh), fresh water (1.22 Cent/m3), and hydrogen (0.08 $/kg) are observed. Although the optimized case entails a 16.11% higher cost rate compared to the base system, it is projected to generate a 25.59% higher profit over its operational lifespan. Additionally, the payback period of the optimized system is shortened by 6.31%, making it an attractive long-term investment for sustainable urban development. [Display omitted] • Designing a solar and LNG-based multigeneration system for coastal areas. • Incorporating a hydrogen liquefaction unit for efficient storage of hydrogen. • Applying dynamic techno-economic and environmental appraisals to analyze the system. • Performing multi-criteria and machine learning approaches to optimize the system. • The payback period of the optimized system has been shortened by 5.31%. [ABSTRACT FROM AUTHOR]

This research introduces a stochastic scheduling approach that incorporates risk constraints for an energy hub (EH), considering uncertainties related to renewable generation and load demands. The proposed method utilizes the Conditional Value at Risk (CVaR) technique to assess and quantify risks. By striking a balance between reducing operational and emissions costs and increasing risk aversion, the approach presents a trade-off. The EH comprises various components such as a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP), and plug-in electric vehicles (PEVs). Uncertain variables encompass factors such as wind speed, solar irradiation, different demands, and market prices. To optimize profits and enhance the consumption curve, demand response programs (DRPs) for electrical, thermal, and cooling demands are implemented. To address the uncertainties associated with input random variables, the efficient k-means data clustering method is employed. A new slime mold algorithm, based on coughing and chaos theory, has been proposed to enhance the problem's solution. The algorithm incorporates innovative operators to improve its capabilities. By utilizing the coughing mechanism and chaos theory, the algorithm explores the solution space more effectively, resulting in improved outcomes for the problem at hand. The results demonstrate significant flexibility in EH management and are extensively discussed. Simulation results indicate that integrating PEVs, FCPP, and DRPs can lead to reductions of 2 %, 7 %, and 11 % in the EH's operating costs, respectively. Furthermore, considering PEVs, FCPP, and DRPs can improve the EH's risk cost by 1.98 %, 6.7 %, and 10.5 %, respectively. Based on the numerical results, in Case 4 led to a remarkable 12.65 % reduction in operational costs while simultaneously achieving a 15.43 % decrease in emission costs, showcasing the effectiveness of the proposed approach in optimizing energy management in an energy hub system. • Risk-Constrained Stochastic Scheduling: Introduces CVaR for EH risk management. • Balance Costs and Risk Aversion: Optimize cost, emissions, and risk. • Diverse EH Energy Sources: Integrates wind, solar, fuel cell, CHP, and EVs. • Demand Response Programs: Adjust consumption for efficiency and savings. • Innovative Solution Algorithm: Slime mold algorithm enhances EH management. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) and depression are leading causes of mortality and morbidity globally, and these conditions are increasing recognized as being fundamentally interconnected. The recently recognized gut-heart-brain axis offers insights into depression following MI, but effective treatments for this comorbidity remain lacking. To address this medical need, we employed an animal model of MI to investigate the potential repurposing of sotagliflozin (SOTA), an approved sodium-glucose cotransporter 1 and 2 (SGLT1/2) inhibitor for diabetes, for managing depression following MI and identifying potential SOTA-associated microbial mechanisms. SOTA treatment improved cardiac dysfunction and alleviated depression-like behaviors induced by MI, accompanied by alterations in gut microbiota composition, such as changes in the Prevotellaceae NK3B31 group , Alloprevotella , and Prevotellaceae UCG-001. Moreover, fecal microbiota transplantation (FMT) using fecal samples from SOTA-treated MI mice demonstrated that gut microbiota contributed to the beneficial effects of SOTA on cardiac dysfunction and depression-like behaviors in MI mice. Intriguingly, FMT-based intervention and concordance analysis of gut microbiota before and after FMT suggested that Prevotellaceae NK3B31 group , Alloprevotella , and Prevotellaceae UCG-001 were associated with the beneficial effects of SOTA. Furthermore, functional prediction of gut microbiota and correlation analysis support the significance of these dynamic microbial communities. In conclusion, these findings suggest that SOTA could serve as a potential drug to ameliorate cardiac dysfunction and depressive symptoms in MI patients via through the gut-heart-brain axis. • Sotagliflozin ameliorated cardiac function and depression-like behaviors induced by myocardial infarction (MI). • Sotagliflozin restored the altered composition of the gut microbiota in an animal model of MI. • Gut microbiota contributed to the beneficial effects of sotagliflozin on MI mice. • Sotagliflozin improved the condition of mice with MI through the gut-heart-brain axis. [ABSTRACT FROM AUTHOR]

In recent years, the escalating demand for electric energy has underscored the need for robust prediction models capable of accurately anticipating consumption patterns. The imperative lies in enabling utilities and policymakers to optimize resource allocation, strategically plan infrastructure development, and ensure the stability and efficiency of the power grid. This study undertakes a comprehensive comparative analysis of machine learning techniques employed in predicting net electricity consumption in Turkey. The primary goal is to augment the accuracy and performance of electricity load forecasting, thereby contributing to effective energy management and fostering sustainable development within the power sector. Two machine learning models, including CatBoost and Extreme Gradient Boosting (XGBoost), are strategically integrated with optimization algorithms such as Sparrow Search Algorithm (SSA), Phasor Particle Swarm Optimization (PPSO), and Hybrid Grey Wolf Optimization (GWO). The core analysis centers on evaluating the performance of these integrated models based on key accuracy metrics and runtime efficiency. Notably, the results underscore that the XGBoost-SSA model emerges as the superior performer, exhibiting heightened accuracy and superior performance in predicting electricity consumption. This model showcases the highest coefficient of determination (R2) value and demonstrates lower errors during the testing phase, thereby presenting a promising and effective approach for electricity consumption prediction in the specific context of Turkey. • This study presents an advanced framework for electricity consumption prediction. • CatBoost and XGBoost hybridized with optimization algorithms for load forecasting. • Statistical criteria are used to rigorously evaluate model performance. • XGBoost-SSA model outperforms others, with higher accuracy in predicting electricity consumption. [ABSTRACT FROM AUTHOR]