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.

Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate

Abstract: Lake evaporation is a sensitive indicator of the hydrological response to climate change. Variability in annual lake evaporation has been assumed to be controlled primarily by the incoming surface solar radiation. Here we report simulations with a numerical model of lake surface fluxes, with input data based on a high-emissions climate change scenario (Representative Concentration Pathway 8.5). In our simulations, the global annual lake evaporation increases by 16% by the end of the century, despite little change in incoming solar radiation at the surface. We attribute about half of this projected increase to two effects: periods of ice cover are shorter in a warmer climate and the ratio of sensible to latent heat flux decreases, thus channelling more energy into evaporation. At low latitudes, annual lake evaporation is further enhanced because the lake surface warms more slowly than the air, leading to more long-wave radiation energy available for evaporation. We suggest that an analogous change in the ratio of sensible to latent heat fluxes in the open ocean can help to explain some of the spread among climate models in terms of their sensitivity of precipitation to warming. We conclude that an accurate prediction of the energy balance at the Earth’s surface is crucial for evaluating the hydrological response to climate change.

Puzzling haze events in China during the coronavirus (COVID-19) shutdown.

Abstract: It is a puzzle as to why more severe haze formed during the New Year Holiday in 2020 (NYH-20), when China was in an unprecedented state of shutdown to contain the coronavirus (COVID-19) outbreak, than in 2019 (NYH-19). We performed a comprehensive measurement and modeling analysis of the aerosol chemistry and physics at multiple sites in China (mainly in Shanghai) before, during, and after NYH-19 and NYH-20. Much higher secondary aerosol fraction in PM2.5 were observed during NYH-20 (73%) than during NYH-19 (59%). During NYH-20, PM2.5 levels correlated significantly with the oxidation ratio of nitrogen (r 2 = 0.77, p < 0.01), and aged particles from northern China were found to impede atmospheric new particle formation and growth in Shanghai. A markedly enhanced efficiency of nitrate aerosol formation was observed along the transport pathways during NYH-20, despite the overall low atmospheric NO2 levels.

Minimum error bounded efficient ℓ 1 tracker with occlusion detection

Abstract: Recently, sparse representation has been applied to visual tracking to find the target with the minimum reconstruction error from the target template subspace. Though effective, these L1 trackers require high computational costs due to numerous calculations for l 1 minimization. In addition, the inherent occlusion insensitivity of the l 1 minimization has not been fully utilized. In this paper, we propose an efficient L1 tracker with minimum error bound and occlusion detection which we call Bounded Particle Resampling (BPR)-L1 tracker. First, the minimum error bound is quickly calculated from a linear least squares equation, and serves as a guide for particle resampling in a particle filter framework. Without loss of precision during resampling, most insignificant samples are removed before solving the computationally expensive l 1 minimization function. The BPR technique enables us to speed up the L1 tracker without sacrificing accuracy. Second, we perform occlusion detection by investigating the trivial coefficients in the l 1 minimization. These coefficients, by design, contain rich information about image corruptions including occlusion. Detected occlusions enhance the template updates to effectively reduce the drifting problem. The proposed method shows good performance as compared with several state-of-the-art trackers on challenging benchmark sequences.

Heterogeneous ad hoc networks

Abstract: The Heterogeneous Ad Hoc Networks (HANETs) are important components of the Internet of things, which become an inevitable trend in the future researches and applications. In recent years, the ad hoc networks have been widely employed in many fields, especially in environment monitoring, weapon control, intelligent transportation, smart city and other domains. HANETs consist of wireless sensor networks, smart ad hoc networks, wireless fidelity networks, telecommunication networks, vehicular ad hoc networks, etc. The digital information and physical objects are integrated through appropriate communication methods, thus new applications and services are created. Different applications use the independent network structures, which form a heterogeneous network platform and increase operational complexity of communication between each other. This paper presents a typical architecture of the large-scale HANETs, and investigates research advances of the current key technologies. To address existing issues, we suggest some potential solutions to deal with the current challenges, such as self-organization, big data transmission, privacy protection, data fusion and processing for large-scale HANETs.