Institution
Southwest University
Education•Chongqing, China•
About: Southwest University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Gene & Population. The organization has 29772 authors who have published 27755 publications receiving 409441 citations. The organization is also known as: Southwest University in Chongqing & SWU.
Topics: Gene, Population, Catalysis, Bombyx mori, Adsorption
Papers published on a yearly basis
Papers
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TL;DR: HCDs used as a delivery system for doxorubicin (DOX) drug delivery system exhibits pH-controlled release, and is rapidly taken up by cells.
392 citations
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TL;DR: In this paper, a parametrization PC-PK1 for the nuclear covariant energy density functional with nonlinear point-coupling interaction is proposed by fitting to observables of 60 selected spherical nuclei, including the binding energies, charge radii, and empirical pairing gaps.
Abstract: A new parametrization PC-PK1 for the nuclear covariant energy density functional with nonlinear point-coupling interaction is proposed by fitting to observables of 60 selected spherical nuclei, including the binding energies, charge radii, and empirical pairing gaps. The success of PC-PK1 is illustrated in the description of infinite nuclear matter and finite nuclei including the ground-state and low-lying excited states. In particular, PC-PK1 provides a good description for the isospin dependence of binding energy along either the isotopic or the isotonic chain, which makes it reliable for application in exotic nuclei. The predictive power of PC-PK1 is also illustrated for the nuclear low-lying excitation states in a five-dimensional collective Hamiltonian in which the parameters are determined by constrained calculations for triaxial shapes.
385 citations
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TL;DR: In this article, a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time is presented.
384 citations
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TL;DR: It is found that the VR technologies adopted for CEET evolve over time, from desktop-based VR, immersive VR, 3D game- based VR, to Building Information Modelling (BIM)-enabled VR.
Abstract: Virtual Reality (VR) has been rapidly recognized and implemented in construction engineering education and training (CEET) in recent years due to its benefits of providing an engaging and immersive environment. The objective of this review is to critically collect and analyze the VR applications in CEET, aiming at all VR-related journal papers published from 1997 to 2017. The review follows a three-stage analysis on VR technologies, applications and future directions through a systematic analysis. It is found that the VR technologies adopted for CEET evolve over time, from desktop-based VR, immersive VR, 3D game-based VR, to Building Information Modelling (BIM)-enabled VR. A sibling technology, Augmented Reality (AR), for CEET adoptions has also emerged in recent years. These technologies have been applied in architecture and design visualization, construction health and safety training, equipment and operational task training, as well as structural analysis. Future research directions, including the integration of VR with emerging education paradigms and visualization technologies, have also been provided. The findings are useful for both researchers and educators to usefully integrate VR in their education and training programs to improve the training performance.
375 citations
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Chinese Academy of Sciences1, New York University2, Anhui Medical University3, Capital Medical University4, Chongqing Medical University5, Jinan University6, Zhejiang University7, Central South University8, Kunming Medical University9, China Medical University (PRC)10, Shanghai Jiao Tong University11, Sichuan University12, Southeast University13, Soochow University (Suzhou)14, Southwest University15, Hangzhou Normal University16, Peking University17, Xi'an Jiaotong University18, Shanxi Medical University19
TL;DR: It is found that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments, and reduced rather than increased FC within the DMN is found.
Abstract: Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naive MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.
375 citations
Authors
Showing all 29978 results
Name | H-index | Papers | Citations |
---|---|---|---|
Frank B. Hu | 250 | 1675 | 253464 |
Hongjie Dai | 197 | 570 | 182579 |
Jing Wang | 184 | 4046 | 202769 |
Chao Zhang | 127 | 3119 | 84711 |
Jianjun Liu | 112 | 1040 | 71032 |
Miao Liu | 111 | 993 | 59811 |
Jun Yang | 107 | 2090 | 55257 |
Eric Westhof | 98 | 472 | 34825 |
En-Tang Kang | 97 | 763 | 38498 |
Chang Ming Li | 97 | 896 | 42888 |
Wei Zhou | 93 | 1640 | 39772 |
Li Zhang | 92 | 918 | 35648 |
Heinz Rennenberg | 87 | 527 | 26359 |
Tao Chen | 86 | 820 | 27714 |
Xun Wang | 84 | 606 | 32187 |