Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Catalysis. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Hierarchical Chemical Bonds Contributing to the Intrinsically Low Thermal Conductivity in α-MgAgSb Thermoelectric Materials

Abstract: Understanding the lattice dynamics and phonon transport from the perspective of chemical bonds is essential for improving and finding high-efficiency thermoelectric materials and for many applications Here, the coexistence of global and local weak chemical bonds is elucidated as the origin of the intrinsically low lattice thermal conductivity of non-caged structure Nowotny–Juza compound, α-MgAgSb, which is identified as a new type of promising thermoelectric material in the temperature range of 300–550 K The global weak bonds of the compound lead to a low sound velocity The unique three-centered MgAgSb bonds in α-MgAgSb vibrate locally and induce low-frequency optical phonons, resulting in “rattling-like” thermal damping to further reduce the lattice thermal conductivity The hierarchical chemical bonds originate from the low valence electron count of α-MgAgSb, with the feature shared by Nowotny–Juza compounds Low lattice thermal conductivities are therefore highly possible in this series of compounds, which is verified by phonon and bulk modulus calculations on some of the compositions

APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

Abstract: Background: It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. Results: In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individualfeature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/ myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. Conclusion: We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Abstract: Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

Improved NO x Reduction in the Presence of SO 2 by Using Fe 2 O 3 -Promoted Halloysite-Supported CeO 2 -WO 3 Catalysts

Abstract: Currently, selective catalytic reduction (SCR) of NO x with NH3 in the presence of SO2 by using vanadium-free catalysts is still an important issue for the removal of NO x for stationary sources. Developing high-performance catalysts for NO x reduction in the presence of SO2 is a significant challenge. In this work, a series of Fe2O3-promoted halloysite-supported CeO2-WO3 catalysts were synthesized by a molten salt treatment followed by the impregnation method and demonstrated improved NO x reduction in the presence of SO2. The obtained catalyst exhibits superior catalytic activity, high N2 selectivity over a wide temperature range from 270 to 420 °C, and excellent sulfur-poisoning resistance. It has been demonstrated that the Fe2O3-promoted halloysite-supported CeO2-WO3 catalyst increased the ratio of Ce3+ and the amount of surface oxygen vacancies and enhanced the interaction between active components. Moreover, the SCR reaction mechanism of the obtained catalyst was studied using in situ diffuse reflectance infrared Fourier transform spectroscopy. It can be inferred that the number of Bronsted acid sites is significantly increased, and more active species could be produced by Fe2O3 promotion. Furthermore, in the presence of SO2, the Fe2O3-promoted halloysite-supported CeO2-WO3 catalyst can effectively prevent the irreversible bonding of SO2 with the active components, making the catalyst exhibit desirable sulfur resistance. The work paves the way for the development of high-performance SCR catalysts with improved NO x reduction in the presence of SO2.