scispace - formally typeset
Search or ask a question
Institution

Nanjing University of Information Science and Technology

EducationNanjing, China
About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the long-term changes in temperature and precipitation in the Hindu Kush Himalayan (HKH) region based on climate datasets LSAT-V1.1 and CGP1.0.

126 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors collected PM2.5 samples during a haze-fog event in winter, as well as in spring, summer, and fall in 2013 within an urban area (Xianlin) and city center area (Gulou) of Nanjing, a megacity of SE China.

125 citations

Journal ArticleDOI
TL;DR: In this paper, the power of the Mann-Kendall (MK) statistical test has been widely applied in the trend detection of the hydrometeorological time series, and the results indicate that in addition to the significance level and the sample length, the MK test power has a close relationship with the sample variance and the magnitude of the trend.
Abstract: The Mann-Kendall (MK) statistical test has been widely applied in the trend detection of the hydrometeorological time series. Previous studies have mainly focused on the null hypothesis of “no trend” or the “Type I Error”. However, few studies address the capability of the MK test to successfully recognize the trends. In some cases, especially when the trend test is jointly applied with hydropower station design, flood risk assessment, and water quality evaluation, the “Type II error” is equally important and should not be neglected. To cope with this problem, we carry out Monte Carlo simulations and the results indicate that in addition to the significance level and the sample length, the MK test power has a close relationship with the sample variance and the magnitude of the trend. For a given time series with fixed length, the power of the MK test increases as the slope increases and declines with increasing sample variance. A deterministic relationship between the slope and the standard deviation of the white noise that can be used for evaluating the power of the MK test has also been detected. Furthermore, we find that a positive autocorrelation contained in the time series will increase both the Type I and the Type II errors due to the enlargement of the variance in the MK statistics. Finally, we recommend that researchers slightly increase the significance level and lengthen the time series sample to improve the power of the MK test in future studies.

125 citations

Journal ArticleDOI
TL;DR: The traditional LSH technique is extended to incorporate the time factor and a novel time-aware and privacy-preserving service recommendation approach based on LSH is proposed that achieves a good tradeoff between recommendation accuracy and efficiency while guaranteeing privacy- Preservation.

125 citations

Journal ArticleDOI
TL;DR: Comprehensive security analysis is conducted to show that the proposed protocol fixes these flaws of Amin et al.

125 citations


Authors

Showing all 14448 results

NameH-indexPapersCitations
Ashok Kumar1515654164086
Lei Zhang135224099365
Bin Wang126222674364
Shuicheng Yan12381066192
Zeshui Xu11375248543
Xiaoming Li113193272445
Qiang Yang112111771540
Yan Zhang107241057758
Fei Wang107182453587
Yongfa Zhu10535533765
James C. McWilliams10453547577
Zhi-Hua Zhou10262652850
Tao Li102248360947
Lei Liu98204151163
Jian Feng Ma9730532310
Network Information
Related Institutions (5)
Chinese Academy of Sciences
634.8K papers, 14.8M citations

90% related

University of Science and Technology of China
101K papers, 2.4M citations

88% related

City University of Hong Kong
60.1K papers, 1.7M citations

88% related

Harbin Institute of Technology
109.2K papers, 1.6M citations

88% related

Nanjing University
105.5K papers, 2.2M citations

87% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023173
2022552
20213,001
20202,492
20192,221
20181,822