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.

Modeling study of PM 2.5 pollutant transport across cities in China's Jing-Jin-Ji region during a severe haze episode in December 2013

Abstract: . To study the influence of particulate matter (PM) transported from surrounding regions on the high PM2.5 pollution levels in Beijing, the GRAPES-CUACE model was used to simulate a serious haze episode that occurred on 6–7~December 2013. The results demonstrate the model's suitability for describing haze episodes throughout China, especially in the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region. A very close positive correlation was found between the southerly wind speed over the plain to the south of Beijing and changes in PM2.5 in Beijing, both reaching maximum values at about 900 hPa, suggesting that the lower atmosphere was the principal layer for pollutant PM transport from its southern neighbouring region to Beijing. During haze episodes, and dependent upon the period, Beijing was either a pollution source or sink for its surrounding area. PM input from Beijing's environs was much higher than the output from the city, resulting in the most serious pollution episode, with the highest PM2.5 values occurring from 00:00 to 10:00 UTC (08:00 to 18:00 LT), 7 December 2013. PM pollutants from the environs of the city accounted for over 50 % of the maximum PM2.5 values reached in Beijing. At other times, the Beijing area was a net contributor to pollution in its environs.

A seasonal discrete grey forecasting model for fashion retailing

Abstract: In the fashion retail industry, level of forecasting accuracy plays a crucial role in retailers' profit. In order to avoid stock-out and maintain a high inventory fill rate, fashion retailers require specific and accurate sales forecasting systems. One of the key factors of an effective forecasting system is the availability of long and comprehensive historical data. However, in the fashion retail industry, the sales data stored in the point-of-sales (POS) systems are always not comprehensive and scattered due to various reasons. This paper presents a new seasonal discrete grey forecasting model based on cycle truncation accumulation with amendable items to improve sales forecasting accuracy. The proposed forecasting model is to overcome two important problems: seasonality and limited data. Although there are several works suitable with one of them, there is no previous research effort that overcome both problems in the context of grey models. The proposed algorithms are validated using real POS data of three fashion retailers selling high-ended, medium and basic fashion items. It was found that the proposed model is practical for fashion retail sales forecasting with short historical data and outperforms other state-of-art forecasting techniques.

Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks

Abstract: The Internet of Vehicles (IoV) is employed to gather real-time traffic information for drivers, and base stations in 5G systems are used to assist in traffic data transmission. For rapid implementation, the applications in vehicles are available to be offloaded to edge nodes (ENs) which are enhanced from micro base stations. Despite the benefits of IoV and ENs, the explosive growth of offloaded vehicle applications exceeds the capacity of ENs, causing the overload of fractional ENs. Therefore, it is necessary to offload the computing applications in overloaded ENs to other idle ENs, while it is a challenge to select appropriate offloading destination ENs. In this paper, we first consider edge computing framework for computation offloading in IoV under the architecture of 5G networks. We then formulate a multi-objective optimization problem to select suitable destination ENs, which aims to minimize the vehicle application offloading delay and offloading cost as well as realizing the load balance of ENs. Moreover, a computation offloading method for IoV, named COV, is designed to solve the multi-objective optimization problem. Finally, various simulation analyses demonstrate the effectiveness and efficiency of COV.