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: Population & Bombyx mori. 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: Population, Bombyx mori, Gene, Electrochemiluminescence, Biosensor
Papers published on a yearly basis
Papers
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TL;DR: Brain activation of "Aha" effects with event-related functional magnetic resonance imaging (fMRI) during solving Chinese logogriphs indicates that the precuneus might be involved in successful prototype events retrieval, and the left inferior frontal/middle frontal gyrus and the cerebellum might be involvement in re-arrangement of visual stimulus and deployment of attentional resources.
104 citations
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TL;DR: The results demonstrated that BFT could effectively reduce ammonia nitrogen, nitrite and nitrate concentration in C. auratus ponds when C/N ratio was greater than 15:1.
104 citations
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TL;DR: A new fuzzy evidential MCDM method under uncertain environments is proposed, where the rating of the criteria and the importance weight of the criterion are given by experts' judgments, represented by triangular fuzzy numbers.
Abstract: Research highlights? We proposed a method to deal with MCDM problem under the framework of Demspter-Shafer evidence theory. ? A new fuzzy evidential MCDM method under uncertain environments is proposed. ? The linguistic variables can be transformed into basic probability assignments. ? Data from different criteria can be combined based on the Demspter rule. Multiple-criteria decision-making (MCDM) is concerned with the ranking of decision alternatives based on preference judgements made on decision alternatives over a number of criteria. First, taking advantage of data fusion technology to comprehensively consider each criterion data is a reasonable idea to solve the MCDM problem. Second, in order to efficiently handle uncertain information in the process of decision making, some well developed mathematical tools, such as fuzzy sets theory and Dempster Shafer theory of evidence, are used to deal with MCDM. Based on the two main reasons above, a new fuzzy evidential MCDM method under uncertain environments is proposed. The rating of the criteria and the importance weight of the criteria are given by experts' judgments, represented by triangular fuzzy numbers. Then, the weights are transformed into discounting coefficients and the ratings are transformed into basic probability assignments. The final results can be obtained through the Dempster rule of combination in a simple and straight way. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.
104 citations
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TL;DR: A CSI amplitude fingerprinting-based localization algorithm in Narrowband Internet of Things system, in which a centroid algorithm based on CSI propagation model is optimized and this algorithm can effectively reduce positioning error.
Abstract: With the proliferation of mobile devices, indoor fingerprinting-based localization has caught considerable interest on account of its high precision. Meanwhile, channel state information (CSI), as a promising positioning characteristic, has been gradually adopted as an enhanced channel metric in indoor positioning schemes. In this paper, we propose a CSI amplitude fingerprinting-based localization algorithm in Narrowband Internet of Things system, in which we optimize a centroid algorithm based on CSI propagation model. In particular, in the fingerprint matching, we utilize the method of multidimensional scaling (MDS) analysis to calculate the Euclidean distance and time-reversal resonating strength between the target point and the reference points and then employ the ${K}$ -nearest neighbor (KNN) algorithm for location estimation. By conjugate gradient method, moreover, we optimize the localization error of triangular centroid algorithm and combine the positioning result with MDS and KNN’s estimated position to get the final estimated position. Experiment results show that compared to some existing localization methods, our proposed algorithm can effectively reduce positioning error.
104 citations
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TL;DR: The genetic map and QTL detected for fiber quality traits are promising for further breeding programs of upland cotton with improved fiber quality.
Abstract: Composite cross populations (CP) developed from three or more cultivars/lines are frequently used to improve agronomic and economic traits in crop cultivar development programs. Employing CP in linkage map construction and quantitative trait locus (QTL) mapping may increase the marker density of upland cotton (Gossypium hirsutum L.) genetic maps, exploit more adequate gene resources and facilitate marker-assisted selection (MAS). To construct a relatively high-density map and identify QTL associated with fiber quality traits in upland cotton, three elite upland cultivars/lines, Yumian 1, CRI 35 and 7,235, were used to obtain the segregating population, Yumian 1/CRI 35//Yumian 1/7,235. A genetic map containing 978 simple sequence repeat (SSR) loci and 69 linkage groups was constructed; the map spanned 4,184.4 cM, covering approximately 94.1% of the entire tetraploid cotton genome. A total of 63 QTL were detected, explaining 8.1–55.8% of the total phenotypic variance: 11 QTL for fiber elongation, 16 QTL for fiber length, 9 QTL for fiber micronaire reading, 10 QTL for fiber strength and 17 QTL for fiber length uniformity. The genetic map and QTL detected for fiber quality traits are promising for further breeding programs of upland cotton with improved fiber quality.
104 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 |