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Green technology innovation is a key tool for fostering green technology innovation, which is a fundamental element of superior economic development. This paper establishes a threshold model and a robustness test model based on balanced data from 31 Chinese provinces from 2017 to 2021 to analyze the "cost of compliance effect", "Porter's hypothesis effect," and the existence of non-linear relationships between environmental regulations at different intensities on green technology innovation in the construction industry in each province of China. The goal is to increase the efficiency of green technological innovation in the construction industry and help it thrive in the sector. The results of the study show that there is a double threshold and a significant "V" shape of environmental regulation on green technology innovation in the construction industry in each province of China. Labour force and industry size have a facilitating effect on green technology innovation in the construction industry, capital investment has a positive but insignificant effect on green technology innovation in the construction industry. The degree of foreign trade development has an inhibiting effect on green technology innovation in the construction industry. Therefore, the government should flexibly adjust the threshold value of environmental regulation according to the actual situation of each province and region, so that it can be located in the optimal range to promote green technology innovation in the construction industry, and ultimately achieve high-quality economic development. [ABSTRACT FROM AUTHOR]

Lung cancer has become one of the most dangerous cancers to human health and the mortality rate is the highest among all the causes of cancer death. Non-small cell lung cancer (NSCLC) accounts for about 80%-85% of lung cancer. Chemotherapy is the main treatment for advanced NSCLC, but the 5-year survival rate is low. Epidermal growth factor receptor (EGFR) mutations are the most common driver mutations in lung cancer, but EGFR exon 20 insertions (EGFR ex20ins) mutation belongs to one of the rare mutations, accounting for about 4%-10% of overall EGFR mutations, thus around 1.8% of advanced NSCLC patients. In recent years, targeted therapies represented by EGFR tyrosine kinase inhibitors (TKIs) have become an important treatment option for patients with advanced NSCLC, however, NSCLC patients with EGFR ex20ins mutation are not sensitive to most of EGFR-TKIs treatments. Currently, some of the targeted drugs for EGFR ex20ins mutation have achieved significant efficacy, while some of them are still under clinical investigation. In this article, we will describe various treatment methods for EGFR ex20ins mutation and their efficacy. [ABSTRACT FROM AUTHOR]

Ensemble feature selection is one of the ensemble learning methods, where each classifier is trained or built by feature selection result. Ensemble feature selection is an effective way for dealing with high dimension and small sample data, such as microarray data. However, ensemble feature selection should achieve more accurate and stable classification performance. In this paper, we present a novel diversity measure based on information theory called Sum of Minimal Information Distance (SMID), which maximizes the relevance between feature subsets and class label as well as the diversity between feature subsets. Moreover, a novel ensemble feature selection framework satisfying this criterion is proposed. In this framework, features that have more mutual information with class label and more diversity between each other are retained. Different feature subsets are used to train base classifiers after being obtained by incremental search method, and then these classifiers are aggregated into a consensus classifier by majority voting. Comparing with three representative feature selection methods and five ensemble learning methods on ten microarray datasets, the experiment results show that the proposed method achieves better performance than the other methods in terms of the classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we apply both supervised and unsupervised machine learning techniques to predict the trend of financial time series based on trading rules. These techniques are K-means for clustering the similar group of data and support vector machine for training and testing historical data to perform a one-day-ahead trend prediction. To evaluate the method, we compare the proposed method with traditional back-propagation neural network and a standalone support vector machine. In addition, to implement this combination method, we use the financial time series data obtained from Yahoo Finance website and the experimental results also validate the effectiveness of the method. [ABSTRACT FROM AUTHOR]