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.

Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology

Abstract: In this paper, a Stacked Sparse Autoencoder (SSAE) based framework is presented for nuclei classification on breast cancer histopathology. SSAE works very well in learning useful high-level feature for better representation of input raw data. To show the effectiveness of proposed framework, SSAE+Softmax is compared with conventional Softmax classifier, PCA+Softmax, and single layer Sparse Autoencoder (SAE)+Softmax in classifying the nuclei and non-nuclei patches extracted from breast cancer histopathology. The SSAE+Softmax for nuclei patch classification yields an accuracy of 83.7%, F1 score of 82%, and AUC of 0.8992, which outperform Softmax classifier, PCA+Softmax, and SAE+Softmax.

Changes in climate extreme events in China associated with warming

Abstract: The science that humans are the cause of global warming, and that the associated climate change would lead to serious changes in climate extreme events, food production, freshwater resources, biodiversity, human mortality, etc. is unequivocal. After several political negotiations, a 2 °C warming has been considered to be the benchmark for such damaging changes. However, an increasing amount of scientific research indicates that higher levels of warming are increasingly likely. What would the world be like if such higher levels of warming occurred? This study aims to provide information for better politically driven mitigation through an investigation of the changes in temperature- and precipitation-based extreme indices using CMIP5 (coupled model intercomparison project phase 5) simulations of a warming of 1, 2, and 3 °C in China. Warming simulations show more dramatic effects in China compared with the global average. In general, the results show relatively small change signals in climate extreme events in China at 1 °C, larger anomalies at 2 °C, and stronger and more extended anomalies at 3 °C. Changes in the studied temperature indices indicate that warm events would be more frequent and stronger in the future, and that cold events would be reduced and weakened. For changes in the precipitation indices, extreme precipitation generally increases faster than total wet-day precipitation, and China will experience more intensified extreme precipitation events. Furthermore, the risk of flooding is projected to increase, and the dry conditions over northern China are projected to be mitigated. In certain regions, particularly Southwest China, the risks of both drought and flood events would likely increase despite the decreased total precipitation in the future. Uncertainties mainly derived from inter-model and scenario variabilities are attached to these projections, but a high model agreement can be generally observed in the likelihood of these extreme changes.

Change-points in climate extremes in the Zhujiang River Basin, South China, 1961–2007

Abstract: In this paper, change-points in time series of annual extremes in temperature and precipitation in the Zhujiang River Basin are analyzed with the CUSUM test. The data cover the period 1961–2007 for 192 meteorological stations. Annual indicators are analyzed: mean temperature, maximum temperature, warm days, total precipitation, 5-day maximum precipitation, and dry days. Significant change-points (1986/87, 1997/98, 1968/69, and 2003/04) are detected in the time series of most of the indicators. The change-point in 1986/87 is investigated in more detail. Most stations with this change-point in temperature indicators are located in the eastern and coastal areas of the basin. Stations with this change-point in dry days are located in the western area. The means and trends of the temperature indicators increase in the entire basin after 1986/87. The highest magnitudes can be found at the coast and delta. Decreasing (increasing) tendencies in total and 5-day maximum precipitation (dry days) are mostly observed in the western and central regions. The detected change-points can be explained by changes in the indices of the Western Pacific subtropical high and the East Asian summer monsoon as well as by change-points in wind directions. In years when the indices simultaneously increase and decrease (indices taking reverse directions to negative and positive) higher annual temperatures and lower annual precipitation occur in the Zhujiang River Basin. The high station density and data quality are very useful for spatially assessing change-points of climatic extreme events. The relation of the change points to large-scale oscillation can provide valuable data for planning adaptation measures against climate risks, e.g. for flood control, disaster preparedness, and water resource management.

Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion

Abstract: The cement industry is a significant contributor to anthropogenic CO2. For China, the cement industry is crucial for development, considering the surging urbanization. CO2 emissions from the industry are detrimental to the planet, its ecosystem, and inhabitants. Forecasting of the emissions is a critical step in the emissions' mitigation strategies, and to achieve sustainable development. However, the level of uncertainty accompanying CO2 estimates leads to discrepancies in predictions. The current work aims to study the estimation of cement industry CO2 emissions from an uncertainty-driven technical perspective, and present a forecast for the Chinese cement industry emissions using a novel grey prediction model. By modeling the framework of China's cement industry and the CO2 emissions estimation techniques as grey systems with partially known information, this study develops an interval grey number-based approach to calculate the relative uncertainty. A grey sequence is generated from the whitenization of the interval grey numbers to represent annual emissions from different sources. The proposed approach is more flexible than the conventional midpoint estimate-based approach recommended by JCGM. The proposed model, V-GM(1,N), is found to give the highest accuracy of 97.29% in simulating the actual cement industry CO2 emissions data from 2005 to 2018. Comparative analysis of the proposed model with other forecasting models revealed the superiority of the model. The proposed framework, involving the forecasting model and uncertainty analysis approach, is likely to facilitate the decision-makers in making realistic and reliable forecasts at reasonable computational costs.