Nanjing University of Information Science and Technology

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.

Learning ordinal discriminative features for age estimation

Abstract: In this paper, we present a new method for facial age estimation based on ordinal discriminative feature learning. Considering the temporally ordinal and continuous characteristic of aging process, the proposed method not only aims at preserving the local manifold structure of facial images, but also it wants to keep the ordinal information among aging faces. Moreover, we try to remove redundant information from both the locality information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation. Finally, we formulate these two issues into a unified optimization problem of feature selection and present an efficient solution. The experiments are conducted on the public available Images of Groups dataset and the FG-NET dataset, and the experimental results demonstrate the power of the proposed method against the state-of-the-art methods.

The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities

Abstract: . A large change in albedo has a significant effect on glacier ablation. Atmospheric aerosols – e.g. black carbon (BC) and dust – can reduce the albedo of glaciers and thus contribute to their melting. In this study, two main themes were explored: (1) the decrease in albedo of the Zhadang glacier on Mt. Nyainqentanglha between 2001 and 2012, as observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Terra satellite, and the correlation of this albedo with mass balance; and (2) the concentrations of BC and dust in the glacier measured during 2012, and the associated impacts of these impurities on albedo and radiative forcings (RF). The average albedo of the Zhadang glacier from the MODIS increased with the altitude and fluctuated but had a decreasing trend (−0.003 a−1) during the period 2001–2012, with the highest (0.722) in 2003 and the lowest (0.597) in 2009 and 2010. The mass balance of the glacier has a positively significant correlation with its surface albedo derived from MODIS. Snow samples were collected on the Zhadang glacier to measure the BC and dust in the summer of 2012. The impacts of BC and dust on albedo reduction in different melting conditions were identified with the SNow ICe Aerosol Radiative (SNICAR) model initiated by in situ observation data. The sensitivity analysis showed that BC was a major factor in albedo reduction when the glacier was covered by newly fallen snow. Nevertheless, the contribution of dust to albedo reduction can reach as high as 56%, much exceeding that of BC (28%), when the glacier experiences strong surficial melting and its surface is almost bare ice. The average RF caused by dust could increase from 1.1 to 8.6 W m−2, exceeding the RF caused by BC after snow was deposited and surface melting occurred in the Zhadang glacier. This implies that it may be dust that primarily dominates the melting of some glaciers in the inner Tibetan Plateau during melting seasons, rather than BC.

Probabilistic dual hesitant fuzzy set and its application in risk evaluation

Abstract: Propose the concept of the probabilistic dual hesitant fuzzy set (PDHFS).Define the basic operational laws and some aggregation operators of the PDHFS for the information fusion.Propose the concept of entropy for the PDHFS and develop a visual analysis method for the aggregated results.Apply the proposed theory and methods to the Arctic geopolitical risk evaluation. The concurrence of randomness and imprecision widely exists in real-world problems. To describe the aleatory and epistemic uncertainty in a single framework and take more information into account, in this paper, we propose the concept of probabilistic dual hesitant fuzzy set (PDHFS) and define the basic operation laws of PDHFSs. For the purpose of applications, we also develop the basic aggregation operator for PDHFSs and give the general procedures for information fusion. Next, we propose a visualization method based on the entropy of PDHFSs so as to analyze the aggregated information and improve the final evaluation results. The proposed method is then applied to the risk evaluations. A case study of the Arctic geopolitical risk evaluation is presented to illustrate the validity and effectiveness. Finally, we discuss the advantages and the limitations of the PDHFS in detail.

Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics

Abstract: Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15-300-m depth) from the study area. The results show that NO3-, then SO42-, and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na+ has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.