Urban air pollutant concentration prediction is dealing with a surge of massive environmental monitoring data and complex changes in air pollutants. This requires effective prediction methods to improve prediction accuracy and to prevent serious pollution incidents, thereby enhancing environmental management decision-making capacity. In this paper, a new pollutant concentration prediction method is proposed based on the vast amounts of environmental data and deep learning techniques. The proposed method integrates big data by using two kinds of deep networks. This method is based on the design that uses a convolutional neural network as the base layer, automatically extracting features of input data. A long shortterm memory network is used for the output layer to consider the time dependence of pollutants. Our model consists of these two deep networks. With performance optimization, the model can predict future particulate matter (PM 2.5) concentrations as a time series. Finally, the prediction results are compared with the results of numerical models. The applicability and advantages of the model are also analyzed. The experimental results show that it improves prediction performance compared with classic models. INDEX TERMS Air pollution, machine learning, neural network, numerical analysis, prediction method.
Goenka's 10-day Vipassana course is a widespread mindfulness course rooted in traditional Buddhism. Awareness and equanimity are two abilities cultivated in this course that are not featured in modern mindfulness-based psychotherapies and thereby not adequately measured by current mindfulness scales. The present article analyzed the Philadelphia Mindfulness Scale (PHLMS; Cardaciotto et al. in Assessment 15(2):204-223, 2008) and revised it into a short version to avoid confusion when measuring awareness and equanimity. Empirical data obtained using Chinese university students and Chinese Buddhists showed that the psychometric properties of the original version of the PHLMS had low factor loading on some items and that the short version had improved psychometric properties, especially for Buddhists. The short PHLMS also exhibited reasonable relationships with emotional outcomes and meditation practices among Buddhists. Implications for the future application of the PHLMS among Buddhists were also discussed.
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