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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]

Abstract: Dendritic cells (DC) excel at presenting antigen to T cells and thus make a key contribution to the induction of primary and secondary immune responses. Sinomenine has been used for centuries in the treatment of patients with autoimmune diseases as it possesses immunosuppressive and anti-inflammatory activities. However, the effect of sinomenine on the differentiation, maturation, and functionality of DC derived from monocytes has not been studied. We show here that DC differentiation is promoted when monocytes are treated with GM-CSF and IL-4 (IL-4) in the presence of sinomenine (200 μg/ml), as evidenced by the upregulation of CD1a while CD14 was decreased. In addition, incubation of immature DC with sinomenine significantly blunted lipopolysaccharide (LPS)-induced DC maturation, as shown by the reduction of expression of the maturation marker CD83 and co-stimulatory molecules, including CD86, B7-H1, and CD40. Moreover, sinomenine also prevented decreases in antigen (FITC-Dextran or Lucifer Yellow) uptake by LPS-treated DC. Mixed lymphocyte reactions (MLRs) revealed that sinomenine-treated DC impede the secretion of the cytokines IL-2 and IFN-γ by co-cultured CD4+ T cells. Therefore, modulation of DC differentiation, maturation, and functionality by sinomenine is of potential relevance to its immunomodulatory effects in controlling specific immune responses in autoimmune diseases, transplantation, and other immune-mediated conditions. [Copyright &y& Elsevier]

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]

Currently, there are few studies on acid-soluble pectin from okra, especially in biological activity for antioxidant and anti-inflammatory. In this study, the antioxidant properties of acid-soluble okra pectin components and their anti-inflammatory were explored. Firstly, two acid-soluble okra pectic fractions, namely crude acid-soluble okra pectin (CAOP) and acid-soluble okra pectin (AOP), were obtained and exhibited structural and compositional variation. The two pectic fractions contained a low degree of esterification (42.0–46.5%) and a relatively high uronic acid content (31.6–37.3%). AOP was composed of galacturonic acid (79.1 mol/%), galactose (4.3 mol/%), rhamnose (14.5 mol/%) and xylose (2.1 mol/%), and the molecular weight was 92.8 kDa. Morphological and thermal properties of acid-soluble okra pectin components were also investigated. Compared to CAOP, AOP expressed better antioxidant activity, and suppressed the NO production in LPS-induced RAW 264.7 macrophages. All the above results indicated that AOP had the potential to act as a natural antioxidant or a functional anti-inflammatory food, which would broaden the development and utilization of okra resources. • The physicochemical properties of acid-soluble pectin from okra were studied. • The structure of acid-soluble pectin from okra was analyzed. • The pectin exhibited a protective effect against LPS-induced cell toxicity. • The pectin possessed antioxidant properties and anti-inflammatory potential. [ABSTRACT FROM AUTHOR]

Abstract: The co-inhibitory molecule B7-H1 has been broadly detectable on human inflammatory renal tubular epithelial cells (TECs) and is proposed to limit tubular damage through down-regulation of tubulointerstitial infiltration T cell activation. Nevertheless, factors that initiate B7-H1 expression on TECs remain unclarified. The terminal complement complex C5b-9, which deposits diffusely on tubules and glomerules of diseased kidneys, is now recognized as a mediator that triggers cellular activation rather than inducing cell death. Whether the up-regulation of B7-H1 on tubules is also induced by C5b-9 is uncertain. Here, after assembling functional sublytic C5b-9 on the membranes of TECs based on purified complement components, we found that B7-H1 gene transcription and protein synthesis was enhanced by C5b-9. Promoter constructs in a luciferase assay, site-directed mutagenesis and laser scanning confocal microscopy assay (LSCM) revealed that the transcription factor NF-κB is primarily responsible for C5b-9-mediated B7-H1 expression. To further detect the physiologic function of B7-H1, triggering B7-H1 with its agonist mAb (clone 5H1) profoundly enhanced Fas expression on C5b-9-treated TECs and thus induced TEC apoptosis. Interestingly, pretreatment of TECs with Fas blocking antibodies prevented this effect. Our results propose that C5b-9 regulates tubular pathogenesis in glomerulonephritis or other renal autoimmune diseases, possibly through enhances cell apoptosis mediated by B7-H1 signals, in addition to it directly promotes tubular damage. [Copyright &y& Elsevier]