A dual-LIF (dLIF) setup combined with CE for microRNA (miRNA) detection is proposed in this study. An argon ion laser (488 nm) and a solid state laser (640 nm) were chosen to excite the fluorescent dye-labeled DNA probe after splinted ligation of miRNA. The crosstalk of emission spectrum of Alex Fluor 488 and Alex Fluor 647 is minimized with a zero crosstalk matrix for Alex Fluor 647 to 488 channels. The linear ranges of the device for the fluorescent dye-labeled DNA probe were both from 1.0 nM to 0.1 pM. The limits of detection for Alexa Fluor 488-labeled DNA and Alex Fluor 647-labeled DNA were 9.3 and 31 fM, respectively. The detection of specific miRNA has been accomplished by combining splinted ligation with the fluorescent dye-labeled oligonucleotides. The linear range for the synthetic miRNA is from 1.0 nM to 1.0 pM. Without PCR amplification, CE-dLIF was applied to discriminate a pre-miR-10b*-transfected cells (contains precursor miR-10b*) from hepatocellular carcinoma cell (control cells). Therefore, this result indicates CE-dLIF has great potential to provide a rapid comparative assay for miRNAs detection.
Usually, a typical wind turbine system cannot generate electricity power consistently, because the power outputs are heavily depended on wind speed. However, terrain, temperature, humidity and other factors can also affect wind speed. Therefore, wind power forecasting is a complex, multi-dimensional, and highly non-linear system. Neural network is able to learn the relationship between system inputs and outputs without mathematical conversion, and perform complex non-linear mapping, data classification, knowledge processing, and so forth. In addition, neural network also has the ability of parallel processing to reduce computing time, so it is suitable for wind power forecasting. The purpose of this paper is to use neural network technology to design a wind power forecasting system. Moreover, the efficiency analysis of the proposed wind power forecasting system in Kinmen farm is described. Finally, we use MATLAB to implement the proposed wind power forecasting system in Kinmen farm, which is capable of forecast within 48hours ahead.
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