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.

Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services

Abstract: With the exponential increase of the mobile devices and the fast development of cloud computing, a new computing paradigm called mobile cloud computing (MCC) is put forward to solve the limitation of the mobile device's storage, communication, and computation. Through mobile devices, users can enjoy various cloud computing services during their mobility. However, it is difficult to ensure security and protect privacy due to the openness of wireless communication in the new computing paradigm. Recently, Tsai and Lo proposed a privacy-aware authentication (PAA) scheme to solve the identification problem in MCC services and proved that their scheme was able to resist many kinds of existing attacks. Unfortunately, we found that Tsai and Lo's scheme cannot resist the service provider impersonation attack, i.e., an adversary can impersonate the service provider to the user. Also, the adversary can extract the user's real identity during executing the service provider impersonation attack. To address the above problems, in this paper, we construct a new PAA scheme for MCC services by using an identity-based signature scheme. Security analysis shows that the proposed PAA scheme is able to address the serious security problems existing in Tsai and Lo's scheme and can meet security requirements for MCC services. The performance evaluation shows that the proposed PAA scheme has less computation and communication costs compared with Tsai and Lo's PAA scheme.

Reliability of climate models for China through the IPCC Third to Fifth Assessment Reports

Abstract: Based on observation and reanalysis data, 77 coupled global climate models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports are evaluated in terms of their ability to simulate the mean state and year-to-year variability of surface air temperature at 2 m and precipitation over China and the climatological East Asian monsoon for the late decades of the 20th century. Results show that GCMs reliably reproduce the geographical distribution of the variables considered. Compared with observations, however, most GCMs have topography-related cold biases (although these are smaller than those found in previous studies), excessive precipitation, an underestimated southeast–northwest precipitation gradient, an overestimated magnitude and spatial variability of the interannual variability of temperature and precipitation, and an inadequate strength of the East Asian monsoon circulation. Pairwise comparison reveals that GCMs continue to improve from the TAR via the AR4 to the AR5 for temperature, but have little change for precipitation and the East Asian monsoon. The ability of GCMs varies with season and is affected to certain degree by their horizontal resolutions. Both the arithmetic mean and the median of multiple GCMs are little affected by filtering GCMs in terms of their ability, and the multi-model mean outperforms most of individual GCMs in every respect.

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

Abstract: In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy of 96.03%. On the Trento data, it achieves an overall accuracy of 99.12%. These results sufficiently certify the effectiveness of our proposed model.

The Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 (FGOALS-g3): Description and Evaluation

Abstract: This paper introduces the Flexible Global Ocean–Atmosphere–Land System Model: Grid-Point Version 3 (FGOALS-g3) and evaluates its basic performance based on some of its participation in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) experiments. Our results show that many significant improvements have been achieved by FGOALS-g3 in terms of climatological mean states, variabilities, and long-term trends. For example, FGOALS-g3 has a small (–0.015°C/100 yr) climate drift in 700-yr pre-industrial control (piControl) runs, and smaller biases in climatological mean variables, such as the land/sea surface temperatures (SST), seasonal soil moisture cycle , compared with its previous version FGOALS-g2 during the historical period. The characteristics of climate variabilities, e.g., MJO eastward/westward propagation ratios, spatial patterns of interannual variability of tropical SST anomalies, and relationship between the East Asian Summer Monsoon and El Niño-Southern Oscillation (ENSO), are well captured by FGOALS-g3. In particular, the cooling trend of globally averaged surface temperature during 1940–1970, which is a challenge for most CMIP3 and CMIP5 models, is well reproduced by FGOALS-g3 in historical runs. In addition to the external forcing factors recommended by CMIP6, anthropogenic groundwater forcing from 1965 to 2014 was incorporated into the FGOALS-g3 historical runs. Plain Language Summary The sixth phase of the Coupled Model Intercomparison Project (CMIP6) is a crucial support for the sixth Assessment Report of Intergovermental Panel on Climate Change (IPCC AR6), and will also provide important foundation for research in climate change in the next few years. This paper gives the description of FGOALS-g3 model, its experiment configures and the experiments conducted according to the experimental design of CMIP6, and evaluates the preliminary performance of model simulation. This work offers references to CMIP6 data users and provides enormous output datasets for assessing and understanding climate change.

Initiation, maintenance, and properties of convection in an extreme rainfall event during SCMREX: Observational analysis

Abstract: A long-lived mesoscale convective system (MCS) with extreme rainfall over the western coastal region of Guangdong on 10 May 2013 during the Southern China Monsoon Rainfall Experiment (SCMREX) is studied. The environmental conditions are characterized by little convective inhibition, low-lifting condensation level, moderate convective available potential energy and precipitable water, and lack of low-level jets from the tropical ocean. Repeated convective back building and subsequent northeastward “echo training” of convective cells are found during the MCS's development stages. However, the initiation/maintenance factors and organization of convection differ significantly during the earlier and later stages. From midnight to early morning, convection is continuously initiated as southeasterly flows near the surface impinge on the east side of mesoscale mountains near the coastal lines and then moves northeastward, leading to formation of two quasi-stationary rainbands. From early morning to early afternoon, new convection is repeatedly triggered along a mesoscale boundary between precipitation-induced cold outflows and warm air from South China Sea and Gulf of Tokin, resulting in the formation of “band training” of several parallel rainbands that move eastward in a later time, i.e., two scales of “training” of convective elements are found. As the MCS dissipates, a stronger squall line moves into the region from the west and passes over within about 3.5 h, contributing about 10%–15% to the total rainfall amount. It is concluded that terrain, near-surface winds, warm advection from the upstream ocean in the boundary layer, and precipitation-generated cold outflows play important roles in initiating and maintaining the extreme rain-producing MCS.