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The purpose of this study was to verify a Chinese version of Community of Inquiry (CoI) instrument with learning presence and explore the causal relationships of the factors in the instrument. This study first examined the reliability and validity of the instrument. All four presences had acceptable levels of reliability (all Cronbach's α> .765 or higher). The confirmatory factor modeling approach was used to assess its validity. Then, the study used path analysis and regression analysis to explore the causal relationships of the presences. The key findings showed that teaching and social presences directly influenced the perceptions of learning presence. Learning presence was a partial mediating variable of interactional relationship within CoI constructs.

The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due to the scarcity of diversity; 2) ISTA does not incorporate the historical information into the updating rules, and the historical information has been proven helpful to speed up the convergence. To address these challenging issues, we propose a novel formulation of ISTA (named as adaptive ISTA) by introducing a novel \textit{adaptive momentum vector}. To efficiently solve the proposed adaptive ISTA, we recast it as a recurrent neural network unit and show its connection with the well-known long short term memory (LSTM) model. With a new proposed unit, we present a neural network (termed SC2Net) to achieve sparse codes in an end-to-end manner. To the best of our knowledge, this is one of the first works to bridge the $\ell_1$-solver and LSTM, and may provide novel insights in understanding model-based optimization and LSTM. Extensive experiments show the effectiveness of our method on both unsupervised and supervised tasks.

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MnO2 nanostructure was synthesized via a redox reaction of potassium permanganate in hydrochloric acid solution below 100°C at open environment. The effects of pH value in solution and reaction temperature on the crystal structure and morphology of MnO2 were investigated. It was revealed that layer folded δ-MnO2 microspheres were obtained at low reaction temperature and low HCl concentration, whereas α-MnO2 single-crystal nanorods were fabricated with increasing reaction temperature and HCl concentration. The possible formation mechanism of δ-MnO2 microspheres and α-MnO2 nanorods is suggested.