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AND Gate - Springer Nature

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Last Updated: 03 August 2022

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Mid-long term forecasting of reservoir inflow using the coupling of time-varying filter-based empirical mode decomposition and gated recurrent unit

The water resources system's accurate and reliable runoff forecast, as well as watershed planning and management, as well as scientific operation. Firstly, several intrinsic mode functions were obtained by decomposing the original runoff series by time-varying filter-based empirical mode decomposition. After merging, Then, the gated recurrent unit model based on particle swarm optimization, was used to forecast each IMF. Complete ensemble empirical mode decomposition with adaptive noise coupled gated recurrent unit and particle swarm optimization, compares to three models including TVF-EMD-PSO-GRU, extreme-point symmetric mode decomposition coupled gated swarm optimization, and particle swarm optimization, as well as particle swarm optimization, particle swarm optimization. The results of the model were tested with examples. Tangnai hydrological station in the upper reaches of the Yangtze River's upstream and annual runoff forecasting were taken as examples.

Source link: https://doi.org/10.1007/s11356-022-21634-8


A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network

Wind energy has emerged as one of the most renewable energy sources. Given the shortcomings of traditional wind power point forecasts, a new hybrid model with three key modules used for data processing, deterministic point estimation, and interval estimation has been suggested to forecast the wind speed interval. Point prediction on the wind speed series is performed by the second module, the deterministic point prediction module. The third module, the interval forecast module, uses the nonparametric kernel density estimation technique to determine the upper and lower bounds of the wind speed interval. In addition, the final wind speed prediction interval can be calculated by incorporating the prediction results of multiple interval prediction studies to increase the stability and generalization of the wind speed interval prediction.

Source link: https://doi.org/10.1007/s11356-022-21904-5


Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately

The shift of stock price patterns in the capital market is highly fluid. This paper introduces eight new architectural models for stock price forecasting by identifying joint movement patterns in the stock market. The goal is to combine the LSTM and GRU models with four neural network block architectures. Root Mean Squared Perception Error, Root Mean Squared Perception Error, and Rooted Mean Dimensional Error were all tested on the computer.

Source link: https://doi.org/10.1186/s40537-022-00597-0


Structures and gating mechanisms of human bestrophin anion channels

Bestrophin-1 and bestrophin-2 are two members of the bestrophin family of calcium-activated chloride channels with critical roles in ocular physiology and direct pathological relevance. The partial open conformations at the two Ca2+-dependent gates of the channels' display partial open conformations in comparison to the partially transparent conformations in Ca2+-bound Best1's Ca2+-bound Best1 structures, which are in accordance with the significantly less recent work by Best1 in electrophysiological recordings. Comparison of the closed and open states shows a C-terminal auto-inhibitory segment, which restricts the channel concentrically by wrapping around the channel periphery in an inter-protomer manner and must be approved to allow channel opening. Our results show that removing the AS from Best1 and Best2 results in truncation mutants with similar habits, as well as swapping the AS between Best1 and Best2 results in chimeric mutants with swapped procedures, undermining a key role of the AS in determining characterization bestrophin specificity in bestrophins.

Source link: https://doi.org/10.1038/s41467-022-31437-7

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions