With the increasing shortage of water resources and the improvement of people's awareness of environmental protection, the traditional water pollution control technology cannot meet the needs of the development of environmental protection. In recent years, the rapid development of nanotechnology and nanomaterials has provided a good opportunity for the innovation of water treatment technology, and has attracted extensive attention of many environmental researchers. In particular, new functional magnetic nanomaterials with good adsorption properties, good chemical stability, easy regeneration and easy solid-liquid separation have become hot topics in the field of water pollution control. This paper aims to provide the present research progress of magnetic nanomaterials in water pollution control, including the striking characteristics and preparation methods of the most well-known magnetic nanomaterials, as well as their applications in water pollution control field. Concluding remarks and future trends are also pointed out.
Nowadys, indoor air pollution has seriously harmed human health and has become a worldwide problem. Therefore, research on indoor air pollution is necessary. This paper systematically reviews the research progress of indoor air pollution in recent years, mainly including indoor pollutant types and sources, indoor pollutant detection methods and equipment, pollutant release simulation models and quality standards, indoor air treatment technologies, and points out the problems that exist in current researches. Furthermore, it proposes the direction of future research work.
Correctly predicting the stock price movement direction is of immense importance in the financial market. In recent years, with the expansion of dimension and volume in data, the nonstationary and nonlinear characters in finance data make it difficult to predict stock movement accurately. In this article, we propose a methodology that combines technical analysis and sentiment analysis to construct predictor variables and then apply the improved LASSO-LASSO to forecast stock direction. First, the financial textual content and stock historical transaction data are crawled from websites. Then transfer learning Finbert is used to recognize the emotion of textual data and the TTR package is taken to calculate the technical indicators based on historical price data. To eliminate the multi-collinearity of predictor variables after combination, we improve the long short-term memory neural network (LSTM) model with the Absolute Shrinkage and Selection Operator (LASSO). In predict phase, we apply the variables screened as the input vector to train the LASSO-LSTM model. To evaluate the model performance, we compare the LASSO-LSTM and baseline models on accuracy and robustness metrics. In addition, we introduce the Wilcoxon signed rank test to evaluate the difference in results. The experiment result proves that the LASSO-LSTM with technical and sentiment indicators has an average 8.53% accuracy improvement than standard LSTM. Consequently, this study proves that utilizing historical transactions and financial sentiment data can capture critical information affecting stock movement. Also, effective variable selection can retain the key variables and improve the model prediction performance.
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