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.
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.
In view of the diversification of pollutants in current sewage, further improving the application efficiency of water treatment agents and realizing multi-functionalization are important directions for the research of water treatment agents.
Regional innovation output is influenced by many factors such as macroeconomic environments, residents consumption, fixed asset investment, foreign trade, fiscal revenue and expenditure, education, and research and development (R&D) input. Correctly predicting regional innovation output is an important subject in the economic field. In this paper, we propose four regularized Poisson regressions to forecast regional innovation output for 31 provinces in China. Firstly, we screen out 20 important factors and combine with four penalties: ridge penalty , least absolute shrinkage and selection operator penalty (LASSO), smoothly clipped absolute deviation penalty (SCAD), and minimax concave penalty (MCP) to construct four regularized Poisson regressions. Secondly, we introduce the cyclic coordinate descent (CCD) algorithm and the training set to complete variable selection and obtain the least squares weighted iterative estimators and make model selection by introducing three criterions to compare goodness of fit to different models. Finally, we apply the testing set and the learned regressions to exhibit the prediction performances and found that SCAD/MCP regularized Poisson regression predicts better than /LASSO regularized Poisson regression in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In particular, MCP regularized Poisson regression outperforms the other three regularized Poisson regressions in predicting the number of granted patents in the three regions.
In recent years, organic carbon nanotubes (CNTs) have attracted wide attention because of their excellent and unique proper-ties in electrical, optical, mechanical, and other fields, as well as their potential application in the water treatment field. Metal composite photocatalysts generally have the problems of electron-hole recombination, which seriously affect their photocata-lytic performance. It was found that the surface modification of metal composite photocatalyst using organic carbon nano-tubes could effectively improve the photocatalytic activity and stability of metal composite photocatalyst materials. This paper aims to provide the present research progress of organic carbon nanotubes modified metal composite photocatalytic materials in water pollution control, including the preparation methods of organic carbon nanotubes and their modified metal compo-site photocatalysis materials, as well as the applications of organic carbon nanotubes modified metal composite photocatalytic materials in water pollution control field. Concluding remarks and future trends are also pointed out. This paper can provide guidance for the design of high performance carbon nanotube metal composite photocatalytic materials.
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