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The article sets off from a speech given by Benedict XVI in the German Bundestag in 2011, where he requested an ecological learning process to aim at sustainable human development. Appreciating the Ecological Movement, he asked to learn to listen to Nature’s language and act accordingly, which he applies analogically to “human ecology”. The article bridges elements of “Listening to Nature” and the Natural Moral Law Tradition in Benedict’s speech and in Francis’ Encyclical Letter Laudatu Si’ inview of serving human flourishing in an ecological civilization. Keywords: Natural Moral Law; Nature, Laudatu Si’, Benedict XVI, Francis; Ecological Civilization/生態文明, Sustainable Development, Ecology of Man.
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This commentary reviews recent research in terms of tourist’s mobilities in terms practices of walking, cycling and driving. It concludes by reflecting on the contemporary lock down of travel in terms of the global pandemic and its consequences for waiting, stillness and immobility – particularly in terms of flying.
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Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
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This study examines the psychometric properties of a Chinese version of the Engaged Teacher Scale (C-ETS). A translated questionnaire with 16 items was administered to a sample of 341 primary and secondary school teachers in Hong Kong. A series of confirmatory factor analyses were performed to assess the construct, convergent, and discriminant validity of the scale in alternative models. Results provide support for a second-order model with teacher engagement as an overarching construct with four hypothesized dimensions: emotional engagement, cognitive engagement, social engagement (students), and social engagement (colleagues). The C-ETS provides a useful measure for teacher engagement in Chinese societies. Contributions and limitations of the study are discussed.
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