Models of Influence in Online Social Networks

Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users' behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users' behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends' behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.     

Studies on dielectric relaxation and ac conductivity of cellulose acetate hydrogen phthalate–poly (vinyl pyrrolidone) blend

The dielectric constant, dielectric loss, and ac conductivity of polyblends of cellulose acetate hydrogen phthalate (CAP) and poly (vinyl pyrrolidone) (PVP) of different compositions were measured in the temperature range of 300–430 K and in the frequency range of 50 Hz–100 kHz. In the blends, the dielectric constant as well as the dielectric loss as a function of the temperature display a single peak corresponding to the glass transition temperature (T_g) in the region between the T_g values of the pure polymers. The T_g values observed agree well with those values obtained from DSC. Dielectric studies show that CAP forms a miscible blend with PVP. Ac conductivity values were calculated from the dielectric data and the conduction mechanism is discussed. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 1702–1708, 2002     

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

It is estimated that 70% or more of broadband bandwidth is consumed by transmitting music, games, video and other content through Peer-to-Peer (P2P) clients. In order to detect, identify, and manage P2P traffic, some port, payload and transport layer feature based methods were proposed. Most of them were applied to offline traffic classification mainly due to the performance reason. In this paper, a network processors (NPs) based online hybrid traffic classifier is proposed. The designed hardware classifier is able to classify P2P traffic based on the static characteristic namely on line speed, and the Flexible Neural Tree(FNT) based software classifier helps learning and selecting P2P traffic attributes from the statistical characteristics of the P2P traffic. Experiment results illustrate that the hybrid classifier performs well for online classification of P2P traffic from gigabit network. The proposed framework also depicts good expansion capabilities to add new P2P features and to adapt to new P2P applications online.     

A layered algorithm for quantifier elimination from linear modular constraints

Linear equalities, disequalities and inequalities on fixed-width bit-vectors, collectively called linear modular constraints, form an important fragment of the theory of fixed-width bit-vectors. We present a practically efficient and bit-precise algorithm for quantifier elimination from conjunctions of linear modular constraints. Our algorithm uses a layered approach, whereby sound but incomplete and cheaper layers are invoked first, and expensive but complete layers are called only when required. We then extend this algorithm to work with arbitrary Boolean combinations of linear modular constraints as well. Experiments on an extensive set of benchmarks demonstrate that our techniques significantly outperform alternative quantifier elimination techniques based on bit-blasting and linear integer arithmetic.     

Quasi-solid-state dye-sensitized solar cells utilizing TiO2/graphite composite counter electrode and TiO2/N719 sensitizer photoelectrode for low-cost power generation

Journal of Materials Science: Materials in Electronics - Dye-sensitized solar cells (DSCs) have evolved through the highest devotion of the research community as a low-cost alternative for...     

Bio-accumulation of metals on reef associated organisms of Lakshadweep Archipelago

Tissue samples of marine organisms from the coastal waters of Agatti Island were subjected to analysis of metals (cadmium, cobalt, copper, iron, magnesium, manganese, nickel, lead and zinc), using Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES) for the assessment of the present condition of the Island ecosystem and compiling the baseline data for future monitoring, with respect of metal accumulation of marine organisms. Tissue samples of fish, shellfish and seaweed revealed that the metals have different levels of accumulation viz. Cd = 0.08-0.14, Co = 0.01-0.02, Cu = 0.16-0.98, Fe = 3.19-5.3, Mg = 86.73-152.45, Mn = 0.17-0.55, Ni = 0.06-0.26, Pb = 0.11-0.46 and Zn = 3.26-14.2 μg g⁻¹ dry wt. Metal concentrations were more in shellfish and less in finfish. Concentrations of toxic metals such as Cd, Co, Ni and Pb were well below the permissible limits proposed by the World Health Organization.     

Microstructure and Mechanical Property Correlation of Mg-Si Alloys

Silicon - The present work aims at understanding the microstructure and mechanical property correlation of hypo (Mg-0.5, 0.7, 1.15 wt% Si) and hyper (Mg-2, 4, 6, 8 and 10 wt% Si) eutectic binary...     

DESIGN OPTIMIZATION OF INDUCTION MOTORS WITH DIFFERENTIAL EVOLUTION ALGORITHMS WITH AN APPLICATION IN TEXTILE SPINNING

This article deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machines in the textile industry, using three newly developed versions of differential evolution (DE) algorithms called modified DE versions (CMDE, GMDE, and LMDE). Efficiency, which decides the operating or running cost of the motor (industry), is considered as the objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance-related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries, and less costly to operate, certain constraints are modified in accordance with the demands of the spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed by the proposed algorithms consumes less power input.