Enhance Student Learning Motivation through Literature in English Teaching

This paper proposes that literature should be used as a means to improve students’ motivation in learning English instead of as an end for literature appreciation class only. The benefits of using literature in English teaching are discussed and some strategies of using literature in classroom teaching are presented. The requirements of teachers in utilizing literature in class are also expounded

Voxel selection in fMRI data analysis based on sparse representation

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method

Does mandatory CSR disclosure affect enterprise total factor productivity?

Corporate social responsibility (CSR) reports are important carriers of enterprises non-financial information disclosure, which are inextricably related to the production efficiency and performance of enterprises. The objective of this paper is discovering the causal effect of the CSR mandatory disclosure policy and the total factor productivity (TFP) of enterprises. This paper uses the sharp regression discontinuity design based on the micro data of the enterprises to study the impact by taking China’s mandatory disclosure policy in 2008 as a quasi-natural experiment. This paper makes some contribution to the impact of mandatory CSR disclosure on enterprise TFP and the mechanism and heterogeneity of this impact. The research draws the following conclusions: First, the CSR mandatory disclosure can significantly improve the TFP of enterprises on the whole, and this effect has the characteristics of long-term and dynamic decline. Second, the mechanism of mandatory disclosure of CSR on TFP is through the mediating effect of R&D and innovation expenditures. Third, the heterogeneity of the impact of CSR mandatory disclosure on TFP is reflected in two aspects: industry and equity nature differences. These conclusions are strongly correlated with the contingent decision-making behaviour of enterprises and give some ideas to the policy makers

Decoding hand movement velocity from electroencephalogram signals during a drawing task

<p>Abstract</p> <p>Background</p> <p>Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.</p> <p>Methods</p> <p>Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.</p> <p>Results</p> <p>The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.</p> <p>Conclusions</p> <p>These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.</p

Does geopolitics have an impact on energy trade? Empirical research on emerging countries

The energy trade is an important pillar of each country’s development, making up for the imbalance in the production and consumption of fossil fuels. Geopolitical risks affect the energy trade of various countries to a certain extent, but the causes of geopolitical risks are complex, and energy trade also involves many aspects, so the impact of geopolitics on energy trade is also complex. Based on the monthly data from 2000 to 2020 of 17 emerging economies, this paper employs the fixed-effect model and the regression-discontinuity (RD) model to verify the negative impact of geopolitics on energy trade first and then analyze the mechanism and heterogeneity of the impact. The following conclusions are drawn: First, geopolitics has a significant negative impact on the import and export of the energy trade, and the inhibition on the export is greater than that on the import. Second, the impact mechanism of geopolitics on the energy trade is reflected in the lagging effect and mediating effect on the imports and exports; that is, the negative impact of geopolitics on energy trade continued to be significant 10 months later. Coal and crude oil prices, as mediating variables, decreased to reduce the imports and exports, whereas natural gas prices showed an increase. Third, the impact of geopolitics on energy trade is heterogeneous in terms of national attribute characteristics and geo-event types

Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts’ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance