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As an environment-friendly technique, the water-based solution process has attracted numerous attention. Due to the ability to fabricate oxide semiconductor films for transistors with acceptable electric properties at relatively low temperatures (~200 °C lower compared with organic solvent), it has great potential in future display technology, and various correlation technologies are keeping emerging. Here, an efficient and facile water route thermal-assisted brush printing is demonstrated, which can be applied to fabricate the semiconductor layer of indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) by the solution process. The synergistic effects of thermal-annealing and brush printing on the film formation process were comprehensively investigated, implying the synergistic effects induce the acceleration of the evaporation of the precursor solvent and promotes the lower surface roughness of as-prepared thin-film, resulting in improved transistor properties. Mechanically, the proposed film-forming model of thermal-assisted brush printing is also elaborated, which is expected to be a general guideline for the solution processing of metal-oxide semiconductor thin-films. • Thermal-assisted brush printed In-Ga-Zn oxide thin-film transistors are achieved. • Electrical properties of short-time annealing transistors are improved. • Synergistic effect of thermal-annealing and brush-printing was investigated. [ABSTRACT FROM AUTHOR]

Fermentation of food components by microbes occurs both during certain food production processes and in the gastro-intestinal tract. In these processes specific compounds are produced that originate from either biotransformation reactions or biosynthesis, and that can affect the health of the consumer. In this review, we summarize recent advances highlighting the potential to improve the nutritional status of a fermented food by rational choice of food-fermenting microbes. The vast numbers of microbes residing in the human gut, the gut microbiota, also give rise to a broad array of health-active molecules. Diet and functional foods are important modulators of the gut microbiota activity that can be applied to improve host health. A truly multidisciplinary approach is required to increase our understanding of the molecular mechanisms underlying health beneficial effects that arise from the interaction of diet, microbes and the human body. [Copyright &y& Elsevier]

Wind power, as a potential new energy generation technology, is gradually developing towards to the mainstream energy in the world. However, the inherent random volatility of wind brings severe challenges to the safe operation of the grid and the reliability of power supply, one of the effective ways to solve the problem is to improve the accuracy of wind speed prediction. However, most of wind speed prediction model cannot well mine the inherent regularity of wind speed data. Therefore, this paper introduces variational mode decomposition (VMD) algorithm. And the Short-term Wind Speed Prediction Model based on GA-ANN improved by VMD is proposed, which can effectively improve the accuracy of wind speed prediction. Firstly, hierarchical cluster method in this paper is employed to extract the historical data with high similarity to the predicted day. And then the appropriate number of decompositions K is selected by judging the value of sample entropy, so that the extracted historical data is decomposed into K subsequences by the variational mode decomposition. Next, with the global optimization ability of genetic algorithm, the artificial neural network is optimized to improve the forecasting performance. Finally, the short-term wind speed forecasting model based on GA-ANN improved by VMD is employed to predict the wind speed of each subsequence and superimposed them to obtain the final wind speed prediction sequence. The results in this paper show that (1) the model can find the periodic fluctuation of wind speed through historical data by hierarchical cluster method, so that significantly improving the accuracy of short-term wind speed prediction; (2) for the wind speed prediction, the error value of GA-ANN model is smaller than that of BP neural network; (3) in view of the inherent nature of the wind, the model proposed in this paper can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results. The research work can help the relevant departments of the power system to accurately assess the risk of power grid operation, make a reasonable generation plan, effectively reduce the cost of power operation, and then greatly promote the development of green energy. • Proposing a new windspeedprediction model named HC-VMD-GA-BPmodel. • Using hierarchical cluster methodtoselect the historical wind speed data. • VMD is introduced to decomposethe original wind speed sequence. • Artificial neural networkisoptimized by GA to improve the prediction performance. • The HC-VMD-GA-BP model is comparedwith other models to verify itseffectiveness and high prediction accuracy. [ABSTRACT FROM AUTHOR]