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Institution

Xuzhou Institute of Technology

EducationXuzhou, China
About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the relationship between the interaction of language and law, and also considered the processes of their influence on each other, and tried to trace the process of language juridisation and standardization of the expression of law.
Abstract: The purpose of the article is to clarify the factors of the mutual influence of law and language. The author analyzes juridical material of Chinese and English. As the result of the analysis the correlation of law and language is determined. In the analysis the author establishes the relationship between the interaction of language and law, and also considers the processes of their influence on each other. The author attempts to trace the process of language juridisation and standardization of the expression of law. The author managed to update the problems of jurislingistics at the intersection of law and language.

3 citations

Posted ContentDOI
22 Jun 2018-bioRxiv
TL;DR: Strain QX1 could have applications as an alkaline-tolerant, broad-spectrum temperature-toleranceant, efficient denitrifying bacterium in the field of denitrification repair, and the effects of nitrogen source, carbon source, pH value, and temperature on the growth of the strain, in addition to itsDenitrification characteristics were evaluated.
Abstract: An efficient aerobic denitrifying bacterium, QX1, identified as Arthrobacter sp. by morphological observations, physiological-biochemical tests, and 16s rDNA sequence analysis, was isolated from burdock fields. A phylogenetic tree for this strain was built based on the 16s rDNA sequence, and the effects of nitrogen source, carbon source, pH value, and temperature on the growth of the strain, in addition to its denitrification characteristics, were evaluated. The results showed that under conditions of a gas bath with shaking at 160 rpm, suitable growth conditions for this strain were LB medium plus 1% acetic acid sodium, 30°C, and pH 8. With these conditions, the strain grew to the logarithmic phase after about 4 h, the logarithmic phase was maintained for 16 h, steady growth was observed for 24 h, and growth began to decline thereafter. QX1 exhibited alkali resistance and could tolerate low (15°C) and high temperatures (40°C). At 30°C, pH 8, and shaking at 160 rpm, strains could degrade nitrate, with a removal rate of 89.92% within 48 h in synthetic sewage. Thus, strain QX1 could have applications as an alkaline-tolerant, broad-spectrum temperature-tolerant, efficient denitrifying bacterium in the field of denitrification repair.

3 citations

Proceedings ArticleDOI
01 Nov 2019
TL;DR: By comparing K-means clustering, L RR clustering and the improved LRR clustering method of self-adapting graph regularization low rank representation, the experiment proves that the latter has better effect in clustering image data collected from different angles.
Abstract: At present, the scale and types of data collected by people have shown explosive growth. It is very difficult to obtain specific and effective classification labels for high-dimensional data. By using subspace clustering method with low rank representation, the linear representation matrix of the data with the lowest rank is found, and the global structure of the original data is preserved to achieve the purpose of optimizing clustering. By comparing K-means clustering, LRR clustering and the improved LRR clustering method of self-adapting graph regularization low rank representation, the experiment proves that the latter has better effect in clustering image data collected from different angles.

3 citations

Proceedings ArticleDOI
15 Oct 2015
TL;DR: In this article, a new application of high resolution continuum source flame atomic absorption spectrometry has been developed for the determination of Fe and Zn in garbanzo, which provided scientific basis for determination of metal elements in food.
Abstract: A new application of high resolution continuum source flame atomic absorption spectrometry has been developed for the determination of Fe and Zn in garbanzo. The selected fuel flows of Fe and Zn were 80 L/h and 90 L/h respectively, and the appropriate burner heights of Fe and Zn were 5 mm and 6 mm respectively by single factor experiments. Under the optimum working conditions, the proposed methods are fast, inexpensive and show good performances: the relative standard deviations were less than 2.6%, good correlation coefficients and the recoveries of F e and Zn were 96.7%±2.5% and 94.3%±3.1%, respectively. The results showed that the contents of Fe and Zn in garbanzo were 68.6±1.5 mg/kg and 18.1±0.6 mg/kg, respectively. Therefore, the proposed method was accurate and stable with a high practical value. It provided scientific basis for determination of metal elements in food.

3 citations

Proceedings ArticleDOI
06 Sep 2012
TL;DR: In this article, two thermolysis routes (that is, solvothermal treatment in toluene at 180 °C for 24 hours and direct heating in air at 200 °C and 5 h) were proposed to prepare ThO2 nanoparticles, by using thorium nitrate (Th(NO3)4·5H2O) as the precursor.
Abstract: Two thermolysis routes (that is, solvothermal treatment in toluene at 180 °C for 24 h and direct heating in air at 200 °C for 5 h) were proposed to prepare ThO2 nanoparticles, by using thorium nitrate (Th(NO3)4·5H2O) as the precursor. The as-prepared products were characterized by X-ray diffraction and transmission electron microscopy. The characterization results suggested that both of the proposed thermolysis routes can be used to prepare ultrafine ThO2 nanoparticles from thorium nitrate.

3 citations


Authors

Showing all 1711 results

NameH-indexPapersCitations
Peng Wang108167254529
Qiong Wu5131612933
Wenping Cao341764093
Bin Hu302133121
Syed Abdul Rehman Khan291312733
Jingui Duan29933807
Vivian C.H. Wu251052566
Lei Chen16991062
Chao Wang1674741
Wenbin Gong1627953
Jing Li16401025
Chao Liu1543737
Qinglin Wang1472595
Yaocheng Zhang1454566
Chao Wang1325774
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20237
202228
2021328
2020181
2019121
201873