PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Combining Plant Pathogenic Fungi and the Leaf-Mining Fly, Hydrellia pakistanae, Increases Damage to Hydrilla

Four fungal species, F71PJ Acremonium sp., F531 Cylindrocarpon sp., F542, Botrytis sp., and F964 Fusarium culmorum [Wm. G. Sm.] Sacc. were recovered from hydrilla [ Hydrilla verticillata (L. f.) Royle] shoots or from soil and water surrounding hydrilla growing in ponds and lakes in Florida and shown to be capable of killing hydrilla in a bioassay. The isolates were tested singly and in combination with the leaf-mining fly, Hydrellia pakistanae (Diptera: Ephydridae), for their capability to kill or severely damage hydrilla in a bioassay.

Mining Biodiversity Progress Report

Progress report from the Mining Biodiversity, Digging into Data Challenge round 3, project.

Measurements Of In Situ Stress And Mining-Induced Stress In Beiminghe Iron Mine Of China

In order to obtain the distribution rules of in situ stress and mining-induced stress of Beiminghe Iron Mine, the stress relief method by overcoring was used to measure the in situ stress, and the MC type bore-hole stress gauge was adopted to measure the mining-induced stress. In the in situ stress measuring, the technique of improved hollow inclusion cells was adopted, which can realize complete temperature compensation. Based on the measuring results, the distribution model of in situ stress was established and analyzed. The in situ stress measuring result shows that the maximum horizontal stress is 1.75-2.45 times of vertical stress and almost 1.83 times of the minimum horizontal stress in this mineral field. And the mining-induced stress measuring result shows that, according to the magnitude of front abutment pressure the stress region can be separated into stress-relaxed area, stress-concentrated area and initial stress area. At the -50 m mining level of this mine, the range of stress-relaxed area is 0-3 m before mining face; the range of stress-concentrated area is 3-55 m before mining face, and the maximum mining-induced stress is 16.5-17.5 MPa, which is 15-20 m from the mining face. The coefficient of stress concentration is 1.85.