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Surface sediment samples were collected from four areas (the Jingdezhen Industrialized Area (JDZ), Upstream (UP), the Dexing Mining Area (DX), and Downstream (DM)) to investigate the concentration and chemical composition of heavy metals. The sediments were analysed for Cu, Zn, Pb, Cd, Cr, As, and Ni using a sequential extraction scheme according to the improved BCR (European Community Bureau of Reference) method. The obtained results show that the maximum values of Cu (793.52 μg·g−1), Zn (72.09 μg·g−1), Pb (222.19 μg·g−1), and Cd (1.60 μg·g−1) were collected from the DX sampling area, while the JDZ area had the highest concentrations of Cr (97.09 μg·g−1), As (318.05 μg·g−1), and Ni (66.35 μg·g−1). The majority of metal values far exceeded their corresponding background values. The risk analysis of geo-accumulation index (Igeo) indicated that the heavy metals Cu and As were the main pollution factors and each element of the pollution degree followed the order of: Cu > As > Pb > Cd > Cr > Zn. Metal partitioning characteristics were also considered and more than 80% of metals show potential bioavailability and toxic effects.

Detecting communities or clusters in a real-world, networked system is of considerable interest in various fields such as sociology, biology, physics, engineering science, and interdisciplinary subjects, with significant efforts devoted in recent years. Many existing algorithms are only designed to identify the composition of communities, but not the structures. Whereas we believe that the local structures of communities can also shed important light on their detection. In this work, we develop a simple yet effective approach that simultaneously uncovers communities and their centers. The idea is based on the premise that organization of a community generally can be viewed as a high-density node surrounded by neighbors with lower densities, and community centers reside far apart from each other. We propose so-called “community centrality” to quantify likelihood of a node being the community centers in such a landscape, and then propagate multiple, significant center likelihood throughout the network via a diffusion process. Our approach is an efficient linear algorithm, and has demonstrated superior performance on a wide spectrum of synthetic and real world networks especially those with sparse connections amongst the community centers.

Numerous drought indices have been developed and applied to monitor the severity of drought. It has been demonstrated that the evaluation of the indices is very important for further utilization of remotely sensed and meteorological information. The objective of this article is to investigate and compare the different methods derived from satellite/meteorological data for drought monitoring during the typical dry year (2006) in mid-eastern China. The compared six drought indices include the vegetation condition index (VCI), percent of average seasonal greenness (PASG), temperature condition index (TCI), vegetation supply water index (VSWI), percentage of precipitation anomalies (PPA) and standardized precipitation index (SPI). These indices are calculated based on different data sources including reflective data, thermal data, the combination of reflective and thermal data and meteorological data. The correlation matrix and regression relationships among the integrals under all drought indices, the integral under the relative air humidity (RAH) curve and cumulative rainfall at the location of 11 agro-meteorological stations for 2006 were calculated. Spatial comparison analysis among the drought indices reveals that all the indices have certain coincidence in the detected regional-scale distribution of drought especially those derived from the same data set, while obviously local-scale distribution differences were found among the different groups of indices. Compared to curves of the reflective and thermal indices, the overall trend of VSWI series has better consistence with the PPA curve. Based on correlation and regression analysis, it is demonstrated that VSWI can better reflect both the amount of precipitation and the severity of drought due to lack of rainfall. Furthermore, land surface temperature (LST) contributes more to the result of hybrid index (VSWI) than reflective information. There is logarithmic relationship between integral of VSWI and cumulative precipitation, while obvious linear correlations were found between integral under VSWI curve and integral under the RAH/TCI/PASG curves. According to the filed observation of droughts from agro-meteorological stations in the study area, it can be concluded that any single index is not sufficient to precisely depicting