Research on time series prediction of multi-process based on deep learning.

Aiming at the problem of data fluctuation in multi-process production, a Soft Update Dueling Double Deep Q-learning (SU-D3QN) network combined with soft update strategy is proposed. Based on this, a time series combination forecasting model SU-D3QN-G is proposed. Firstly, based on production data, Gate Recurrent Unit (GRU) is used for prediction. Secondly, based on the model, SU-D3QN algorithm is used to learn and add bias to it, and the prediction results of GRU are corrected, so that the prediction value of each time node fits in the direction of reducing the absolute error. Thirdly, experiments were carried out on the dataset of a company. The data sets of four indicators, namely, the outlet temperature of drying silk, the loose moisture return water, the outlet temperature of feeding leaves and the inlet water of leaf silk warming and humidification, are selected, and more than 1000 real production data are divided into training set, inspection set and test set according to the ratio of 6:2:2. The experimental results show that the SU-D3QN-G combined time series prediction model has a great improvement compared with GRU, LSTM and ARIMA, and the MSE index is reduced by 0.846-23.930%, 5.132-36.920% and 10.606-70.714%, respectively. The RMSE index is reduced by 0.605-10.118%, 2.484-14.542% and 5.314-30.659%. The MAE index is reduced by 3.078-15.678%, 7.94-15.974% and 6.860-49.820%. The MAPE index is reduced by 3.098-15.700%, 7.98-16.395% and 7.143-50.000%.

Degradation of algae promotes the release of arsenic from sediments under high-sulfate conditions.

Sulfate concentrations in eutrophic waters continue to increase; however, the transformations of arsenic (As) in sediments under these conditions are unclear. In this study, we constructed a series of microcosms to investigate the effect of algal degradation on As transformations in sediments with high sulfate concentrations. The results showed that both the elevated sulfate levels and algal degradation enhanced the release of As from sediments to the overlying water, and degradation of algal in the presence of elevated sulfate levels could further contribute to As release. Sulfate competed with arsenate for adsorption in the sediments, leading to As desorption, while algal degradation created a strongly anaerobic environment, leading to the loss of the redox layer in the surface sediments. With high sulfate, algal degradation enhanced sulfate reduction, and sulfur caused the formation of thioarsenates, which may cause re-dissolution of the arsenides, enhancing As mobility by changing the As speciation. The results of sedimentary As speciation analysis indicated that elevated sulfur levels and algal degradation led to a shift of As from Fe2O3/oxyhydroxide-bound state to specifically adsorbed state at the sediment water interface. This study indicated that algal degradation increases the risk of As pollution in sulfate-enriched eutrophic waters.

Advances in application of single-cell RNA sequencing in cardiovascular research.

Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and identifying targets for disease treatment. In recent years, emerging single-cell RNA sequencing technologies have been used to make breakthroughs regarding decoding developmental trajectories, phenotypic transitions, and cellular interactions in the cardiovascular system, providing new insights into cardiovascular disease. This paper reviews the technical processes of single-cell RNA sequencing and the latest progress based on single-cell RNA sequencing in the field of cardiovascular system research, compares single-cell RNA sequencing with other single-cell technologies, and summarizes the extended applications and advantages and disadvantages of single-cell RNA sequencing. Finally, the prospects for applying single-cell RNA sequencing in the field of cardiovascular research are discussed.