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Now showing items 1 - 16 of 89

  • Topological phase transition in the trirutile-type MgBi2O6

    Shang, Fanfan   Wang, Zhenwei   Wang, Guangtao  

    Based on the first-principle calculations and k.p effective model analysis, we predicted a new topological semimetal (TSM) MgBi2O6. Without spin-orbit-coupling (SOC) and under the generalized-gradient-approximation (GGA), MgBi2O6 is a nodal-line semimetal. When the exchange-correlation energy was changed to HSE06, MgBi2O6 was trivial insulator in the equilibrium volume, but it became TSM under 7% hydrostatic tensile strain. MgBi2O6 might be an important platform to study the topological properties because of the two following advantages for measurements: (1) The nodal line, drumhead-liked surface state and Fermi Arc are very closely to the Fermi level; (2) The band structure is very "clean" (no other bulk bands except the related inverted conduction and valence bands around the Fermi level), which avoids the surface states been embedded into the bulk states. (C) 2020 Elsevier B.V. All rights reserved.
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  • Hyperuricemia is associated with impaired intestinal permeability in mice

    Xu, Daxing   Lv, Qiulan   Wang, Xiaofeng   Cui, Xuena   Zhao, Peng   Yang, Xiaomin   Liu, Xiu   Yang, Wan   Yang, Guanpin   Wang, Guangtao   Wang, Pengjun   Wang, Zenglan   Li, Zhiyuan   Xing, Shichao  

    Hyperuricemia is associated with many metabolic diseases. However. the underlying mechanism remains unknown. The gut microbiota has been demonstrated to play significant roles in the immunity and metabolism of the host. In the present study, we constructed a hyperuricemic mouse model to investigate whether the metabolic disorder caused by hyperuricemia is related to intestinal dysbiosis. A significantly increased intestinal permeability was detected in hyperuricemic mice. The difference in microflora between wild-type and hyperuricemic mice accompanies the translocation of gut microbiota to the extraintestinal tissues. Such a process is followed by an increase in innate immune system activation. We observed increased LPS and TNF-alpha levels in the hyperuricemic mice, indicating that hyperuricemic mice were in a state of low-grade systemic inflammation. In addition, hyperuricemic mice presented early injury of parenteral tissue and disordered lipid metabolism. These findings suggest that intestinal dysbiosis due to an impaired intestinal barrier may be the key cause of metabolic disorders in hyperuricemic mice. Our findings should aid in paving a new way of preventing and treating hyperuricemia and its complications. NEW & NOTEWORTHY Hyperuricemia is associated with many metabolic diseases. However, the underlying mechanism remains unknown. We constructed a hyperuricemic mouse model to explore the relationship between intestinal dysbiosis and metabolic disorder caused by hyperuricemia.
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  • High thermoelectric performance in low-cost SnS0.91Se0.09 crystals

    He, Wenke   Wang, Dongyang   Wu, Haijun   Xiao, Yu   Zhang, Yang   He, Dongsheng   Feng, Yue   Hao, Yu-Jie   Dong, Jin-Feng   Chetty, Raju   Hao, Lijie   Chen, Dongfeng   Qin, Jianfei   Yang, Qiang   Li, Xin   Song, Jian-Ming   Zhu, Yingcai   Xu, Wei   Niu, Changlei   Li, Xin   Wang, Guangtao   Liu, Chang   Ohta, Michibiro   Pennycook, Stephen J.   He, Jiaqing   Li, Jing-Feng   Zhao, Li-Dong  

    Thermoelectric technology allows conversion between heat and electricity. Many good thermoelectric materials contain rare or toxic elements, so developing low-cost and high-performance thermoelectric materials is warranted. Here, we report the temperature-dependent interplay of three separate electronic bands in hole-doped tin sulfide (SnS) crystals. This behavior leads to synergistic optimization between effective mass (m*) and carrier mobility (mu) and can be boosted through introducing selenium (Se). This enhanced the power factor from similar to 30 to similar to 53 microwatts per centimeter per square kelvin (mu W cm(-1) K-2 at 300 K). while lowering the thermal conductivity after Se alloying. As a result, we obtained a maximum figure of merit ZT (ZT(max)) of similar to 1.6 at 873 K and an average ZT (ZI(ave)) of similar to 125 at 300 to 873 K in SnS0.91Se0.09 crystals. Our strategy for band manipulation offers a different route for optimizing thermoelectric performance. The high-performance SnS crystals represent an important step toward low-cost, Earth-abundant, and environmentally friendly thermoelectrics.
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  • Realizing n-type BiCuSeO through halogens doping

    Zhang, Xiaoxuan   Wang, Dongyang   Wang, Guangtao   Zhao, Li-Dong  

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  • Dirac fermions in the layered titanium-based oxypnictide superconductor

    Shi, Xianbiao   Chen, Li   He, Peng   Wang, Guangtao   Zheng, Gongping   Liu, Xin   Zhao, Weiwei  

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  • The power frequency voltage divider calibration device and its uncertainty

    Xu, Dongdong   Zhang, Weiwei   Wang, Nan   Wang, Guangtao   Xu, Guangke  

    The power frequency high voltage divider is widely used to measure the power frequency high voltage and it is an important measuring instrument for high voltage test. Whether it is accurate or not directly affects the insulation monitoring level of power equipment closely related to the safety of power production, and it is of vital importance for power enterprises to ensure the measurement accuracy of power frequency high-voltage divider. The newly implemented JJG496-2016 recommended three measurement methods: difference comparison, equal power bridge method and voltage ratio method. At present, only some literatures have introduced the measurement method using voltage ratio method and the measurement uncertainty evaluation procedure. This paper introduces a calibration device and measuring method of power frequency high voltage divider using the difference comparison method, and gives the uncertainty of the ratio difference and phase difference of power frequency high voltage divider obtained under the standard test environment. (C) 2019 Published by Elsevier Ltd.
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  • Electronic and optical properties of bilayer PbI2:a first-principles study

    Shen, Chenhai   Wang, Guangtao  

    By employing first-principles methods, we investigate the effects of stacking patterns and interlayer coupling on the electronic structures and optical properties of bilayer (BL) PbI2. For optical properties, excitonic effects are considered. The results show that crystal-type BL PbI2 stacking pattern is the most stable bilayer structures with the equilibrium interlayer distance of 3.27 A and a direct band structure. Moreover, for all considered patterns, the interlayer coupling can induce the band structures to transform from indirect to direct and also the band gap values to vary from 2.56 eV to 2.62 eV. In addition, our calculations show that the exciton binding energy of the most stable pattern is 0.81 eV, and excitonic effects have obvious influences on optical responses of BL PbI2. These results may be useful to future experimental studies on optoelectronic properties of two-dimensional BL PbI2 nanosheets.
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  • Electronic structure and magnetism of RbGd2Fe4As4O2

    Wang, Zhenwei   Wang, Guangtao   Tian, Xin  

    The electronic structure, magnetism, and Fermi surface (FS) nesting of new superconductor RbGd2Fe4As4O2 (12442) were studied by the first-principes calculation. Its calculated ground state is the stripe antiferromagnetic state. The hole-like FSs will overlap with the electron-like FS sheets by the electron doping. Such significant FS nesting induces a peak of the bare susceptibility chi(0)((q) over right arrow) at the X point, suggesting the parent compound RbGd2Fe4As4O2 is a self hole-doping induced superconductor. (C) 2017 Elsevier B.V. All rights reserved.
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  • A New Dirac Semimetal in Hexagonal BaGaSnH

    Wang, Zhenwei   Wang, Guangtao  

    By first-principles calculations, we find that BaGaSnH is a topological Dirac semimetal with a pair of Dirac points at (0, 0; +/- 0.45 2 pi/c) when spin -orbit coupling (SOC) is considered. The Dirac points are exactly at the Fermi energy, which makes the experimental measurement easy. The Dirac semimetal state as well as the topological properties are sensitive to the strain, which makes the compound enter the trivial semiconductor state. The finding of a Dirac semimetal phase in BaGaSnH may stimulate further research on the topological properties of hexagonal materials and promote new practical applications.
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  • Pnictide-height dependent ferromagnetism in CuFeAs and CuFeSb

    Wang, Guangtao   Shi, Xianbiao   Wang, Dongyang  

    Electronic structures and magnetism properties of CuFeAs and CuFeSb are investigated by using first-principles calculations. We found that CuFeAs and CuFeSb share similar electronic structures and magnetic properties. Unlike the antiferromagnetic isostructure LiFeAs, the ground state of both compounds is ferromagnetic state driven by the Stoner ferromagnetic instability. Their ground state is very sensitive to the height of anion (As or Sb), translating from the ferromagnetic state to the stripe antiferromagnetic ordering when the anion height is smaller than a critical value. Such magnetic phase transition can be understood by the J(1)-J(2) Heisenberg model. Reducing the anion height will decrease the nearest-neighbor interaction J(1) but increase the next-nearest-neighbor interaction J(2). The competing between the anion height dependent antiferromagnetic superexchange mediated by As(Sb) and the ferromagnetic direct exchange between Fe results the variations of magnetic structure with anion height. (C) 2016 Elsevier B.V. All rights reserved.
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  • A machine learning based software process model recommendation method

    Song, Qinbao   Zhu, Xiaoyan   Wang, Guangtao   Sun, Heli   Jiang, He   Xue, Chenhao   Xu, Baowen   Song, Wei  

    Among many factors that influence the success of a software project, the software process model employed is an essential one. An improper process model will be time consuming, error-prone and cost expensive, and further lower the quality of software. Therefore, how to choose an appropriate software process model is a very important problem for software development. Current works focus on the selection criteria and often lead to subjective results. In this paper, we propose a software process model recommendation method, to help project managers choose the most appropriate software process model for a new project at an early stage of development process according to historical software engineering data. The proposed method casts the process model recommendation into a classification problem. It first evaluates the different combinations of the alternative classification and attribute selection algorithms, and the best one is used to build the recommendation model with historical software engineering data; then, the constructed recommendation model is used to predict process models for a new software project with only a few data. We also analyze the mutual impacts between process models and different types of project factors, to further help managers locate the most suitable process model. We found process models are also responsible for defect count, defect severity and software change. Experiments on the data sets from 37 different development teams of different countries show that the average recommendation accuracy of our method reaches up to 82.5%, which makes it potentially useful in practice. (C) 2016 Elsevier Inc. All rights reserved.
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  • Automatic Clustering via Outward Statistical Testing on Density Metrics

    Wang, Guangtao   Song, Qinbao  

    Clustering is one of the research hotspots in the field of data mining and has extensive applications in practice. Recently, Rodriguez and Laio [1] published a clustering algorithm on Science that identifies the clustering centers in an intuitive way and clusters objects efficiently and effectively. However, the algorithm is sensitive to a preassigned parameter and suffers from the identification of the "ideal" number of clusters. To overcome these shortages, this paper proposes a new clustering algorithm that can detect the clustering centers automatically via statistical testing. Specifically, the proposed algorithm first defines a new metric to measure the density of an object that is more robust to the preassigned parameter, further generates a metric to evaluate the centrality of each object. Afterwards, it identifies the objects with extremely large centrality metrics as the clustering centers via an outward statistical testing method. Finally, it groups the remaining objects into clusters containing their nearest neighbors with higher density. Extensive experiments are conducted over different kinds of clustering data sets to evaluate the performance of the proposed algorithm and compare with the algorithm in Science. The results show the effectiveness and robustness of the proposed algorithm.
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  • Pnictide-height dependent ferromagnetism in CuFeAs and CuFeSb

    Wang, Guangtao   Shi, Xianbiao   Wang, Dongyang  

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  • Electronic structure and magnetism of ThFeAsN

    Wang, Guangtao   Shi, Xianbiao  

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  • Automatic Clustering via Outward Statistical Testing on Density Metrics

    Wang, Guangtao   Song, Qinbao  

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  • A dissimilarity-based imbalance data classification algorithm

    Zhang, Xueying   Song, Qinbao   Wang, Guangtao   Zhang, Kaiyuan   He, Liang   Jia, Xiaolin  

    Class imbalances have been reported to compromise the performance of most standard classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this problem, various solutions have been explored mainly via balancing the skewed class distribution or improving the existing classification algorithms. However, these methods pay more attention on the imbalance distribution, ignoring the discriminative ability of features in the context of class imbalance data. In this perspective, a dissimilarity-based method is proposed to deal with the classification of imbalanced data. Our proposed method first removes the useless and redundant features by feature selection from the given data set; and then, extracts representative instances from the reduced data as prototypes; finally, projects the reduced data into a dissimilarity space by constructing new features, and builds the classification model with data in the dissimilarity space. Extensive experiments over 24 benchmark class imbalance data sets show that, compared with seven other imbalance data tackling solutions, our proposed method greatly improves the performance of imbalance learning, and outperforms the other solutions with all given classification algorithms.
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