A Community Detection Algorithm Based on Topology Potential and Spectral Clustering

Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods

A Novel Local Maximum Potential Point Search Algorithm for Topology Potential Field

Topology potential field is a novel model to describe interaction and association of network nodes, which has attracted plenty of attention in community detection, node importance evaluation and network hot topics detection. The local maximum potential point search is a critical step for this research. Hill-climbing is a traditional algorithm for local maximum point search, which may leave out some local maximum potential points, and search performance is greatly influenced by initial node sequence. Based on the detailed analysis of local maximum potential points' characteristics, this paper presents a novel local maximum potential point search algorithm. The results of simulation experiments showed that the new algorithm has better performance than the traditional hill-climbing method. It can find all local maximum potential points with high search efficiency

An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments

Geochemical Characteristics and Organic Matter Accumulation of Wufeng-Longmaxi Shales in the Southeast of the Sichuan Basin of South China

The Upper Ordovician Wufeng Formation and Lower Silurian Longmaxi Formation black shales are the critical targets for shale gas exploration in the Sichuan Basin of South China. The enrichment of organic matter (OM) in shale is the basis for the generation of large-scale shale gas; however, its controlling factors in Wufeng-Longmaxi shales are still under debate, and few studies have focused on the edge of the Sichuan Basin. Based on the mineral composition, total organic carbon (TOC), and systematic inorganic geochemistry analysis of 72 core samples from Wufeng and Longmaxi formations in Well Xike 1, southeastern Sichuan Basin, the sedimentary conditions (palaeoclimate, palaeoredox, and palaeoproductivity) were reconstructed, and the controlling factors of OM enrichment were identified. The mineral compositions are dominated by quartz, clay minerals, calcite, and feldspar, associated with minor dolomite, pyrite, and anhydrite. The TOC contents (0.31%-6.84%, avg. 2.22%) show an upward decreasing trend from the Wufeng Formation to Longmaxi Formation. The chemical index of alteration (CIA) ranges from 65 to 71 (avg. 69), indicating warm and humid climate with moderate weathering. The diagrams of Al2O3-TiO2, TiO2-Zr, Zr/Sc-Th/Sc, La/Th-Hf, and La-Th-Sc jointly indicate the contribution from felsic and intermediate rock weathering. The P/Al, Cu/Al, and Ni/Al ratios suggest that marine paleoproductivity was relatively high in the Wufeng Formation and relatively low to moderate in the Longmaxi Formation. The V/Cr, V/Sc, U/Th, MoEF/UEF, and Corg/P ratios indicate that the bottom water was anoxic during the Wufeng Formation deposition and then fluctuating dysoxic and/or oxic in the overlying Longmaxi Formation. The TOC content was positively correlated with productivity proxies (P/Al, Cu/Al, and Ni/Al) as well as redox proxies (U/Th, V/Cr, MoEF/UEF, and Corg/P), indicating that the OM accumulation in Wufeng-Longmaxi shales is mainly controlled by high productivity and anoxic bottom water conditions

Insights into the Sintering Resistance of Sphere-like Mn2O3 in Catalytic Toluene Oxidation: Effect of Manganese Salt Precursor and Crucial Role of Residual Trace Sulfur

Manganese oxides, as a highly active oxidation catalyst, are expected to have great potential for replacing platinum group material (PGM) in volatile organic compound (VOC) degradation. Manganese sulfate and manganese chloride are usually adopted as raw materials for catalyst preparation, and Cl- and SO42- adsorbed on the catalyst might affect the catalytic activities. In this study, sphere-like Mn(2)O(3 )was prepared from different manganese sources with a simple carbonate precipitation method, which was further used to systematically study the potential poisoning effect of residual trace species on the catalytic activity of toluene oxidation. The fully washed samples all show excellent toluene oxidation activity at the beginning; however, both chlorideand sulfate-derived samples exhibited unexpected severe inactivation after the thermal aging treatment, and the light-off temperature (T-90) of toluene oxidation increased by more than 116 and 252 degrees C, while this value is only 65 and 40 degrees C over nitrate- and acetatederived Mn2O3, respectively. The characterization results of ICP-OES and XPS demonstrated that the incorporated traced sulfur and chloride species existing in fresh samples were difficult to wash away completely, which has little influence on the catalytic performance of fresh samples but will greatly affect their anti-sintering behavior. During the thermal aging process, doped sulfur gradually forms uniformly dispersed sulfate that will cover on the catalyst surface, where the transports of reactants and the supplementary lattice oxygen would be restrained significantly

The Effect of Particle Size on the Interpretation of Pore Structure of Shale by N2 Adsorption

Gas adsorption experiments are becoming one of the most common methods to quantify and analyze the pore structures of shale samples in the petroleum industry. In this regard, particle size of the specimen plays an important role in the results that could ultimately affect the pore structure interpretation. Hence, in this study, five shale samples at different thermal maturity levels are picked, and all are crushed into different groups of particle sizes: less than 40 mesh (<375 μm), less than 60 mesh (<250 μm), less than 80 mesh (<187.5 μm), and less than 100 mesh (<150 μm). Next, N2 adsorption is used to characterize the pore structures of the samples within different particle sizes. Furthermore, to interpret the data, several attributes such as the pore volume, surface area, fractal dimension (from the fractal analysis), and heterogeneity index (from the multifractal analysis), are studied and compared between the samples and particle size intervals to provide us with the effect that particle size could have on the pore structure analysis. The results showed that as the particle size varies, the pore structures of the shale samples could get affected. Based on the comparison of the results, it is recommended that a suitable particle size for the shale pore structure characterization in N2 adsorption experiments should be less than 60 mesh (<250 μm)