Multi-step interval prediction of ultra-short-term wind power based on CEEMDAN-FIG and CNN-BiLSTM.

Aiming at the uncertainty of wind power and the low accuracy of multi-step interval prediction, an ultra-short-term wind power multi-step interval prediction method based on complete ensemble empirical mode decomposition with adaptive noise-fuzzy information granulation (CEEMDAN-FIG) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed. Firstly, the CEEMDAN is used to decompose the wind power time series into several sub-components to reduce the non-stationary characteristics of the wind power time series. Then, different components are selected for FIG, and the maximum value sequence, average value sequence, minimum value sequence gotten from FIG, and the remaining components without FIG are combined with the wind speed data, wind direction data, and the temperature data. They all are input into the CNN-BiLSTM combined prediction model to obtain the initial wind power prediction interval. The prediction results of the maximum value sequence, the average value sequence, and the minimum value sequence are respectively superimposed on the prediction results of the remaining components to obtain the upper limit, point prediction, and lower limit of the initial prediction interval. Finally, the improved coverage width criterion is used as the objective function to optimize the interval, and the forecast interval of wind power under a given confidence level is generated. Taking the actual operating data of a certain unit of a wind farm as an example, the validity of the proposed model is verified.

Transcriptomic Profiling Reveals Complex Molecular Regulation in Cotton Genic Male Sterile Mutant Yu98-8A.

Although cotton genic male sterility (GMS) plays an important role in the utilization of hybrid vigor, its precise molecular mechanism remains unclear. To characterize the molecular events of pollen abortion, transcriptome analysis, combined with histological observations, was conducted in the cotton GMS line, Yu98-8A. A total of 2,412 genes were identified as significant differentially expressed genes (DEGs) before and during the critical pollen abortion stages. Bioinformatics and biochemical analysis showed that the DEGs mainly associated with sugars and starch metabolism, oxidative phosphorylation, and plant endogenous hormones play a critical and complicated role in pollen abortion. These findings extend a better understanding of the molecular events involved in the regulation of pollen abortion in genic male sterile cotton, which may provide a foundation for further research studies on cotton heterosis breeding.

A highly sensitive plasmonic DNA assay based on triangular silver nanoprism etching.

Specific nucleic acid detection by using simple and low-cost assays is important in clinical diagnostics, mutation detection, and biodefense applications. Most current methods for the quantification of low concentrations of DNA require costly and sophisticated instruments. Here, we have developed a facile DNA detection platform based on a plasmonic triangular silver nanoprism etching process, in which the shape and size of the nanoprisms were altered accompanied by a substantial surface plasmon resonance shift. Through the combination of enzyme-linked hybridization chain reaction amplification and inherent sensitivity of plasmonic silver nanoprims, this assay could detect as low as 6.0 fM target DNA. Considering the high sensitivity and selectivity of this plasmonic DNA assay, it is expected to be of great interest in clinical diagnostics.