University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Chinese university students' acceptance of MOOCs

Abstract: At the start of a teaching revolution, Massive Open Online Courses (MOOCs) represent the latest stage in distance education, and offer open educational resources to students around the globe. With their growing popularity, this study examines the factors that influence students' decisions to use MOOCs. To integrate the theory of planned behavior (TPB) and the self-determination theory (SDT) as a research framework, 475 university students in China participated in a survey on the five constructs hypothesized to explain their intention to use MOOCs for learning. Data were analyzed using structural equation modeling. The results showed that attitude toward MOOCs and perceived behavioral control (PBC) were significant determinants of intention to use them. Autonomous motivation was an antecedent for all three core constructs of the TPB, while controlled motivation acted as an antecedent only for subjective norms. Implications of the findings are discussed. Attitude toward MOOCs significantly predicts the intention to use them.Perceived behavioral control (significantly predicts the intention to use MOOCs.Autonomous motivation was an antecedent for all three core constructs of TPB.Controlled motivation acted as an antecedent only for subjective norms.

Measuring Chinese entrepreneurial motivation: Personality and environmental influences

Abstract: Purpose – Debates in the literature on entrepreneurship concentrate on whether the focus should be on psychological variables or the external environment. Despite most studies being on the former, many others argue that the external environment is more useful in understanding business start‐ups. This paper seeks to examine the relative influence of both types of variables.Design/methodology/approach – Data were collected from 337 Chinese respondents in three different groups: first, people who do not want to start a business; second, people planning to set up a business; and finally, entrepreneurs who had started successful businesses. Respondents were assessed on three psychological/behavior variables (achievement striving, social networking/Guanxi, and optimism), and one external environment variable (perceived importance of a favorable business environment).Findings – Group comparisons revealed that psychological characteristics as well as the business environment were both significant predictors. Psyc...

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

Abstract: In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Phase Pure 2D Perovskite for High-Performance 2D–3D Heterostructured Perovskite Solar Cells

Abstract: Three-dimensional (3D) metal-halide perovskite solar cells (PSCs) have demonstrated exceptional high efficiency. However, instability of the 3D perovskite is the main challenge for industrialization. Incorporation of some long organic cations into perovskite crystal to terminate the lattice, and function as moisture and oxygen passivation layer and ion migration blocking layer, is proven to be an effective method to enhance the perovskite stability. Unfortunately, this method typically sacrifices charge-carrier extraction efficiency of the perovskites. Even in 2D-3D vertically aligned heterostructures, a spread of bandgaps in the 2D due to varying degrees of quantum confinement also results in charge-carrier localization and carrier mobility reduction. A trade-off between the power conversion efficiency and stability is made. Here, by introducing 2D C6 H18 N2 O2 PbI4 (EDBEPbI4 ) microcrystals into the precursor solution, the grain boundaries of the deposited 3D perovskite film are vertically passivated with phase pure 2D perovskite. The phases pure (inorganic layer number n = 1) 2D perovskite can minimize photogenerated charge-carrier localization in the low-dimensional perovskite. The dominant vertical alignment does not affect charge-carrier extraction. Therefore, high-efficiency (21.06%) and ultrastable (retain 90% of the initial efficiency after 3000 h in air) planar PSCs are demonstrated with these 2D-3D mixtures.