Predicting Nodal Influence via Local Iterative Metrics

Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively is shown to better predict nodal influence than a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derives a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order $1$ to $K$ that are produced in an iterative process, encoding gradually more global information as $K$ increases. Three iterative metrics are considered, which converge to three classical node centrality metrics respectively. Our results show that for each of the three iterative metrics, the prediction quality is close to optimal when the metric of relatively low orders ($K\sim4$) are included and increases only marginally when further increasing $K$. The best performing iterative metric set shows comparable prediction quality to the benchmark that combines seven centrality metrics, in both real-world networks and synthetic networks with community structures. Our findings are further explained via the correlation between an iterative metric and nodal influence, the convergence of iterative metrics and network properties

The long-term impact of ranking algorithms in growing networks

When users search online for content, they are constantly exposed to rankings. For example, web search results are presented as a ranking of relevant websites, and online bookstores often show us lists of best-selling books. While popularity-based ranking algorithms (like Google’s PageRank) have been extensively studied in previous works, we still lack a clear understanding of their potential systemic consequences. In this work, we fill this gap by introducing a new model of network growth that allows us to compare the properties of networks generated under the influence of different ranking algorithms. We show that by correcting for the omnipresent age bias of popularity-based ranking algorithms, the resulting networks exhibit a significantly larger agreement between the nodes’ inherent quality and their long-term popularity, and a less concentrated popularity distribution. To further promote popularity diversity, we introduce and validate a perturbation of the original rankings where a small number of randomly-selected nodes are promoted to the top of the ranking. Our findings move the first steps toward a model-based understanding of the long-term impact of popularity-based ranking algorithms, and our novel framework could be used to design improved information filtering tools

Automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging of rat brain

AimsTo construct an automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain with high accuracy, which could preserve the inherent voxel intensity and Regions of interest (ROI) morphological characteristics simultaneously.Methods and resultsThe transformation relationship from standardized space to individual space was obtained by firstly normalizing individual image to the Paxinos space and then inversely transformed. On the other hand, all the regions defined in the atlas image were separated and resaved as binary mask images. Then, transforming the mask images into individual space via the inverse transformations and reslicing using the 4th B-spline interpolation algorithm. The boundary of these transformed regions was further refined by image erosion and expansion operator, and finally combined together to generate the individual parcellations. Moreover, two groups of MEMRI images were used for evaluation. We found that the individual parcellations were satisfied, and the inherent image intensity was preserved. The statistical significance of case-control comparisons was further optimized.ConclusionsWe have constructed a new automatic method for individual parcellation of rat brain MEMRI images, which could preserve the inherent voxel intensity and further be beneficial in case-control statistical analyses. This method could also be extended to other imaging modalities, even other experiments species. It would facilitate the accuracy and significance of ROI-based imaging analyses

An Oil Spill Spatial Data Model for Qinzhou Bay Based on the KML

Qinzhou Bay is an important channel of Chinese southwest goes to sea. The Qinzhou Bay was chosen as the study area in this paper. Using the international advanced model Oilmap, Analysis of oil spill on the initial oil membrane formation factors, exclude some of initial oil film effects are not important factors, Find suitable for oil spill earlier an oil spill force model. Combined with the natural condition of Qinzhou Bay, The transport process of oil membrane in the condition of different wind and current was observed in Qinzhou Bay by means of experiments. This paper analyzes the main factors, such as the shape of oil membrane and migration directions, which leads to the oil spill, and got a suitable model of oil spill to Qinzhou Bay. In order to achieve its visual in the software of geographic information system, the model of oil spill was defined through the KML

Sediment resuspension and transport in the offshore subaqueous Yangtze Delta during winter storms

Storm-induced episodic sediment redistribution in coastal systems can reshape geomorphic bodies, disrupt ecosystems, and cause economic damage. However, cold-wave-storm-induced hydrodynamic changes and residual sediment transport in large, exposed subaqueous deltas, such as the Yangtze Delta, are poorly understood because it is typically expensive and difficult to obtain systematic field data in open coast settings during storm events. We conducted a successful field survey of waves, currents, changes in water depth, and turbidity at a station (time-averaged water depth of 20 m) in the offshore subaqueous Yangtze Delta over 10 days during winter, covering two storms and two fair-weather periods. During the storm events, strong northerly winds drove southward longshore currents (~0.2 m/s) and resulted in increased wave height and sediment resuspension, thereby leading to massive southward sediment transport. In contrast, both southward and northward transports were limited during the fair-weather periods. A better understanding of the storm-induced sediment transport can be obtained by using an approximately half-day lag in sediment transport behind wind force, given the time needed to form waves and longshore drift, the inertia of water motion, and the slow settling velocity of fine-grained sediment. Our results directly support previous findings of southward sediment transport from the Yangtze Delta during winter, which is deposited in the Zhejiang–Fujian mud belt in the inner shelf of the East China Sea. In addition, the southward sediment transport from the Yangtze Delta occurs mainly during episodic storm events, rather than during the winter monsoon, and winter storms dominate over typhoons in driving southward sediment transport from the delta. This study highlights the importance of storms, especially during winter storms, in coastal sediment redistribution, which is of particular significance when considering the projected increase in storm intensity with global warming

Targeted synthesis of an electroactive organic framework

A new strategy for targeted design and synthesis of an electroactive microporous organic molecular sieve (JUC-Z2) is described. Experiment demonstrated that such a targeted synthesis approach to achieve phenyl-phenyl coupling was a controllable process and predominately generated two-dimensional polymer sheets, significantly different from the traditional chemical or electrochemical oxidation methods to prepare conducting polymers. Successive self-assembly leads to a lamellar organic framework comprised of stacked polymer sheets with an hcb topology. JUC-Z2 was found to have a well-defined uniform micropore distribution (similar to 1.2 nm), a large surface area (BET = 2081 m(2) g(-1)) and high physicochemical stability (> 440 degrees C). After doping with I(2), JUC-Z2 exhibits typical p-type semiconductive properties. As the first example of an electroactive organic framework, JUC-Z2 possesses a unique ability of electrochemical ion recognition, arising from the synergistic function of the uniform micropores and the N-atom redox site.State Basic Research Project[2011CB808703]; NSFC[91022030, 20771041, 20773101, 20833005]; "111'' project[B07016]; Ministry of Science and Technology[2006DFA41190]; Jilin Science and Technology Department[20106021

Predicting nodal influence via local iterative metrics

Abstract Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders ( $$K\sim 4$$ K ∼ 4 ) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with $$K=4$$ K = 4 is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range $$[91\%,106\%]$$ [ 91 % , 106 % ] across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark

Impacts of Climate and Land-Use Changes on the Hydrological Processes in the Amur River Basin

Under the joint effects resulted from different changes of climate and land-use regimes, spatial-temporal variations of hydrological processes took place in certain principles. Identifying the impact of changes in individual land-use types/climatic factors on hydrological processes is significant for water management and sustainability of watersheds. In this study, seven simulation scenarios were developed using the soil and water assessment tool (SWAT) model to distinguish the impacts of climate and land-use changes on the hydrological processes in the Amur River Basin (ARB) for four periods of 1980–1990, 1991–1999, 2000–2006, and 2007–2013, respectively. Based on the multi-period simulation scenario data, partial least squares regression and ridge regression analyses were performed to further evaluate the effects of changes in individual land-use types/climatic factors on hydrologic components. The results suggested that summer precipitation and summer average temperature were the dominant climatic factors, and crops and wetlands were the principal land-use types contributing to the hydrological responses. In addition, the drastic changes in crop and wetland areas and a clear decline in summer precipitation between the periods of 1991–1999 and 2000–2006 may account for the highest-intensity impacts of climate and land-use changes on the runoff at the outlet (−31.38% and 16.17%, respectively) during the four periods