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General deep learning framework for emissivity engineering

Chinese scientists developed a deep learning framework to optimize wavelength-selective thermal emitters (WS-TEs) for applications like radiative cooling and ga ....

Natural Science Foundation Of Hubei Province , Knowledge Innovation Shuguang Program , School Of Energy , Bilateral Joint Research , Program Of Wuhan National Laboratory For Optoelectronics , National Natural Science Foundation Of China , Huazhong University Of Science , Technology Program , Key Research , Light Science , Power Engineering , Huazhong University , National Natural Science Foundation , Natural Science Foundation , Hubei Province , Open Project Program , Wuhan National Laboratory , Wuhan City Science , Joint Research Projects , Eneral Deep Learning Engineering Ws Tes , All Journal News , Materials Science , Chinese Academy Of Sciences ,

Frontiers | A rapid and reliable method for the determination of Lactiplantibacillus plantarum during wine fermentation based on PMA-CELL-qPCR

Real-time monitoring of microbial dynamics during fermentation is essential for wine quality control. This study developed a method that combines the fluorescent dye propidium monoazide (PMA) with CELL-qPCR, which can distinguish between dead and live microbes for Lactiplantibacillus plantarum. This method could detect the quantity of microbes efficiently and rapidly without DNA extraction during wine fermentation. The results showed that (1) the PMA-CELL-qPCR enumeration method developed for L. plantarum was optimized for PMA treatment concentration, PMA detection sensitivity and multiple conditions of sample pretreatment in wine environment, and the optimized method can accurately quantify 10 4 -10 8 CFU/mL of the target strain (L. plantarum) in multiple matrices; (2) when the concentration of dead bacteria in the system is 10 4 times higher than the concentration of live bacteria, there is an error of 0.5-1 lg CFU/mL in the detection results. The optimized sample pretreatment method ....

United States , New Jersey , La Banda , San Diego , Propidium Monoazide , Bio Rad , Program Of The College Student Innovation , Co Ltd , Health Part , Extraction Kit Beijing Tiangen Biochemical Technology Co , Lab Net International Inc , Lactic Acid Bacteria , Guangxi Academy Of Agricultural Sciences Fundamental Research , China Center , Forestry University , Entrepreneurship Training Program , Acid Bacteria , Industrial Culture Collection , Beijing Tiangen Biochemical Technology , Faststart Essential , Solis Bio Dyne , Thermal Cycler , Real Time System , Manager Software , Cabernet Sauvignon , Malic Acid Assay Kit ,

Transition-metal composite provides cost-effective boost for lackluster zinc-air battery performance

Transition-metal composite provides cost-effective boost for lackluster zinc-air battery performance
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Ding Ren , Juan Wang , Qiang Zhang , Xinzhi Ma , Chang Xin Zhao , Bo Quan Li , Jia Qi Huang , Institute Of Technology , Tsinghua University Initiative Scientific Research Program , National Natural Science Foundation Of China , Harbin Normal University , Program Of Key Laboratory For Photonic , Tsinghua University , Ministry Of Education , Beijing Institute , National Natural Science Foundation , Technology Research Fund Program , Young Scholars , Open Project Program , Key Laboratory , Electric Bandgap Materials , Jia Ning Liu , Harbin Normal ,

Frontiers | Predicting Spike Features of Hodgkin-Huxley-Type Neurons With Simple Artificial Neural Network

Hodgkin-Huxley (HH) -type model is the most famous computational model for simulating neural activity. It shows the highest accuracy in capturing neuronal spikes, and its model parameters have definite physiological meanings. However, HH-type models are computationally expensive. To address this problem, a previous study proposed a spike prediction module (SPM) to predict whether a spike will take place 1 ms later based on three voltage values with intervals of 1ms. Although SPM does well, it fails to evaluate the informative features of the spike. In this study, the feature prediction module (FPM) based on simple ANN was proposed to predict spike features including maximum voltage, minimum voltage, and dropping interval. Nine different HH-type models were adopted whose firing patterns cover most of the firing behaviors observed in the brain. Voltage and spike feature samples under constant external input current were collected for training and testing. Experiment results illustrated t ....

United Kingdom , City Of , Noadaptation Fohlmeister , Noadaptaton Wang , Cerebellumorphic Yang , Cambridge University , Cambridge University Press , National Natural Science Foundation Of China , National Key Research , Neuronal Networks Of The Hippocampus , Development Program Of China , Atypical Features , Adaptation Ermentrout , Adaptation Gouwens , Spike Features , Voltage Samples , Firing Patterns , National Natural Science Foundation , Development Program , Open Project Program , State Key Laboratory , Mathematical Engineering , Advanced Computing , Supplementary Material , Med Abstract , Crossref Full Text ,