The purpose of this study was to conduct a literature review on the motivational processes in a Sport Education curriculum model among high school-aged students using self-determination theory and achievement goal theory as theoretical frameworks. Literature for analysis was searched through electronic databases including Academic Research Complete, ERIC, PsycINFO, SPORTDiscus, and Web of Science by entering ‘Sport Education’, ‘physical education’, and ‘high school’ or ‘secondary school’ as keywords. Articles for review were then selected using the following criteria: (a) written in English; (b) published in a peer-reviewed journal; (c) a Sport Education curriculum model implemented in high school settings with three season phases; (d) providing empirical findings; and (e) investigating motivational variables as main outcomes. A total of 18 articles were identified of moderate and high quality based on a quality assessment. A systematic review of the articles resulted in three main findings: (a) self-determination theory and achievement goal theory strongly support the positive motivational influence of Sport Education; (b) Sport Education is relatively consistent in promoting motivational outcomes across genders, grade levels, sports, and motivational profiles; and (c) more research with long-term follow-up data and teacher participants in diverse school settings is needed to examine potential differences in the motivational impact of Sport Education programs.

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In the past two decades, researchers have made remarkable progress in accelerating flash calculation, which is very useful in a variety of engineering processes. In this paper, general phase splitting problem statements and flash calculation procedures using the Successive Substitution Method are reviewed, while the main shortages are pointed out. Two acceleration methods, Newton's method and the Sparse Grids Method are presented afterwards as a comparison with the deep learning model proposed in this paper. A detailed introduction from artificial neural networks to deep learning methods is provided here with the authors' own remarks. Factors in the deep learning model are investigated to show their effect on the final result. A selected model based on that has been used in a flash calculation predictor with comparison with other methods mentioned above. It is shown that results from the optimized deep learning model meet the experimental data well with the shortest CPU time. More comparison with experimental data has been conducted to show the robustness of our model.

Improving the energy efficiency is a fundamental way to ensure energy security and sustainable development, and is also the requirement of supply-side structural reform of China’s energy. This paper uses the DEA-BCC model to estimate China’s energy efficiency at the provincial level, analyzes its regional differences from 2006 to 2016, and applies a panel data model to analyze the influencing factors of energy efficiency. It selects labor, capital stock and total energy consumption as inputs and takes real GDP and comprehensive index of environmental pollution as desirable and undesirable outputs, respectively. The results show that (1) energy efficiency when undesirable output is included is generally lower than when undesirable output is excluded; (2) There is a considerable difference in energy efficiency among provinces, and China’s energy efficiency, by and large, shows a trend of declining. The energy efficiency of four major regions demonstrates obvious regional differences: coastal region>northeastern region> middle region >western region; (3) The economic development level, technological progress, energy price and urbanization level are positively associated with energy efficiency, while the proportion of secondary industry and the energy consumption structure dominated by coal and oil are negatively correlated with energy efficiency.