986 resultados para driven potential


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Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

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A simple method using a combination of conformal mapping and vortex panel method to simulate potential flow in cascades is presented. The cascade is first transformed to a single body using a conformal mapping, and the potential flow over this body is solved using a simple higher order vortex panel method. The advantage of this method over existing methodologies is that it enables the use of higher order panel methods, as are used to solve flow past an isolated airfoil, to solve the cascade problem without the need for any numerical integrations or iterations. The fluid loading on the blades, such as the normal force and pitching moment, may be easily calculated from the resultant velocity field. The coefficient of pressure on cascade blades calculated with this methodology shows good agreement with previous numerical and experimental results.

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The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio, state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (sigma'(v0)), soil type, shear wave velocity (V-s) and earthquake parameters such as peak horizontal acceleration (a(max)) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are sigma'(v0), soil type. V-s, a(max) and M. In the other model based on shear wave velocity alone uses V-s, a(max) and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. (C) 2010 Elsevier B.V. All rights reserved.

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The design and preparation of novel M3L2 trigonal cages via the coordination-driven self-assembly of preorganized metalloligands containing octahedral aluminum(III), gallium(III), or ruthenium(II) centers is described. When tritopic or dinuclear linear metalloligands and appropriate complementary subunits are employed, M3L2 trigonal-bipyramidal and trigonal-prismatic cages are self-assembled under mild conditions. These three-dimensional cages were characterized with multinuclear NMR spectroscopy (H-1 and P-31) and high-resolution electrospray ionization mass spectrometry. The structure of one such trigonal-prismatic cage, self-assembled from an arene ruthenium metalloligand, was confirmed via single-crystal X-ray crystallography. The fluorescent nature of these prisms, due to the presence of their electron-rich ethynyl functionalities, prompted photophysical studies, which revealed that electron-deficient nitroaromatics are effective quenchers of the cages' emission. Excited-state charge transfer from the prisms to the nitroaromatic substrates can be used as the basis for the development of selective and discriminatory turn-off fluorescent sensors for nitroaromatics.

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An attempt has been made to forecast the potential of thermophilic fungi to grow in soil in the laboratory and in the field in the presence of a predominantly mesophilic fungal flora at usual temperature. The respiratory rate of thermophilic fungi was markedly responsive to changes in temperature, but that of mesophilic fungi was relatively independent of such changes. This suggested that in a thermally fluctuating environment, thermophilic fungi may be at a physiological disadvantage compared to mesophilic fungi. In mixed cultures in soil plates, thermophilic fungi outgrew mesophilic fungi under a fluctuating temperature regime only when the amplitude of the fluctuating temperatures was small and approached their temperature optima for growth. An antibody probe was used to detect the activity of native or an introduced strain of a thermophilic fungus, Thermomyces lanuginosus, under field conditions. The results suggest that although widespread, thermophilic fungi are ordinarily not an active component of soil microflora. Their presence in soil most likely may be the result of the aerial dissemination of propagules from composting plant material.

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This article discusses the potential of bio-dimethyl ether (DME) as a promising fuel for India in the transportation sector where a majority of imported petroleum in the form of diesel is used. Specifically, the suitability of DME in terms of its properties vis-a-vis those of diesel, ability to liquefy DME at low pressures similar to liquefied petroleum gas (LPG), and ease of production from renewable feedstock (biomass), and most importantly, very low emissions including near-zero soot levels are some of the features that make it an attractive option. A detailed review presents the state-of-the-art on various aspects such as estimates of potential bio-DME production, methods of synthesis of bio-DME, important physicochemical properties, fuel-injection system-related concerns (both conventional and common-rail system), fuel spray characteristics which have a direct bearing on the engine performance, and finally, exhaust emissions. Future research directions covering all aspects from production to utilization are summarized (C) 2010 American Institute of Physics. doi:10.1063/1.3489529]

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The structure and organization of dodecyl sulfate (DDS) surfactant chains intercalated in an Mg-Al layered double hydroxide (LDH), Mg(1-x)Alx(OH)(2), with differing Al/Mg ratios has been investigated. The Mg-Al LDHs can be prepared over a range of compositions with x varying from 0.167 to 0.37 and therefore provides a simple system to study how the organization of the alkyl chains of the intercalated DDS anions change with packing density; the Al/Mg ratio or x providing a convenient handle to do so. Powder X-ray diffraction measurements showed that at high packing densities (x >= 0.3) the alkyl chains of the intercalated dodecyl sulfate ions are anchored on opposing LDH sheets and arranged as bilayers with an interlayer spacing of similar to 27 angstrom. At lower packing densities (x < 0.2) the surfactant chains form a monolayer with the alkyl chains oriented flat in the galleries with an interlayer spacing of similar to 8 angstrom. For the in between compositions, 0.2 <= x < 0.3, the material is biphasic. MD simulations were performed to understand how the anchoring density of the intercalated surfactant chains in the Mg-Al LDH-DDS affects the organization of the chains and the interlayer spacing. The simulations are able to reproduce the composition driven monolayer to bilayer transformation in the arrangement of the intercalated surfactant chains and in addition provide insights into the factors that decide the arrangement of the surfactant chains in the two situations. In the bilayer arrangement, it is the dispersive van der Waals interactions between chains in opposing layers of the anchored bilayer that is responsible for the cohesive energy of the solid whereas at lower packing densities, where a monolayer arrangement is favored, Coulomb interactions between the positively charged Mg-Al LDH sheets and the negatively charged headgroup of the DDS anion dominate.

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Payment systems all over the world have grown into a complicated web of solutions. This is more challenging in the case of mobile based payment systems. Mobile based payment systems are many and consist of different technologies providing different services. The diffusion of these various technologies in a market is uncertain. Diffusion theorists, for example Rogers, and Davis suggest how innovation is accepted in markets. In the case of electronic payment systems, the tale of Mondex vs Octopus throws interesting insights on diffusion. Our paper attempts to understand the success potential of various mobile payment technologies. We illustrate what we describe as technology breadth in mobile payment systems using data from payment systems all over the world (n=62). Our data shows an unexpected superiority of SMS technology, over other technologies like NFC, WAP and others. We also used a Delphi based survey (n=5) with experts to address the possibility that SMS will gain superiority in market diffusion. The economic conditions of a country, particularly in developing countries, the services availed and characteristics of the user (for example number of un-banked users in large populated countries) may put SMS in the forefront. This may be true more for micro payments using the mobile.

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A computerized non-linear-least-squares regression procedure to analyse the galvanostatic current-potential data for kinetically hindered reactions on porous gas-diffusion electrodes is reported. The simulated data fit well with the corresponding measured values. The analytical estimates of electrode-kinetic parameters and uncompensated resistance are found to be in good agreement with their respective values obtained from Tafel plots and the current-interrupter method. The procedure circumvents the need to collect the data in the limiting-current region where the polarization values are usually prone to errors. The polarization data for two typical cases, namely, methanol oxidation on a carbon-supported platinum-tin electrode and oxygen reduction on a Nafion-coated platinized carbon electrode, are successfully analysed.

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Potassium disilicate glass and melt have been investigated by using a new partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygenrich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

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Potassium disilicate glass and melt have been investigated by using anew partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygen-rich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

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Time evolution of mean-squared displacement based on molecular dynamics for a variety of adsorbate-zeolite systems is reported. Transition from ballistic to diffusive behavior is observed for all the systems. The transition times are found to be system dependent and show different types of dependence on temperature. Model calculations on a one-dimensional system are carried out which show that the characteristic length and transition times are dependent on the distance between the barriers, their heights, and temperature. In light of these findings, it is shown that it is possible to obtain valuable information about the average potential energy surface sampled under specific external conditions.

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We describe the use of poly(alpha-methylstyrene peroxide) (P alpha MSP), an alternating copolymer of alpha-methylstyrene and oxygen, as initiator for the radical polymerization of vinyl monomers. Thermal decomposition of P alpha MSP in 1,4-dioxane follows first-order kinetics with an activation energy (E(a)) of 34.6 kcal/mol. Polymerization of methyl methacrylate (MMA) and styrene using P alpha MSP as an initiator was carried out in the temperature range 60-90 degrees C. The kinetic order with respect to the initiator and the monomer was close to 0.5 and 1.0, respectively, for both monomers. The E(a) for the polymerization was 20.6 and 22.9 kcal/mol for MMA and styrene, respectively. The efficiency of P alpha MSP was found to be in the range 0.02-0.04. The low efficiency of P alpha MSP was explained in terms of the unimolecular decomposition of the alkoxy radicals which competes with primary radical initiation. The presence of peroxy segments in the main chain of PMMA and polystyrene was confirmed from spectroscopic and DSC studies. R(i)'/2I values for P alpha MSP compared to that of BPO at 80 degrees C indicate that P alpha MSP can be used as an effective high-temperature initiator.

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The basis set dependence of the topographical structure of the molecular electrostatic potential (MESP), as well as the effect of substituents on the MESP distribution, has been investigated with substituted benzenes as test cases. The molecules are studied at HF-SCF 3�21G and 6�31G** levels, with a further MESP topographical investigation at the 3�21G, double-zeta, 6�31G*, 6�31G**, double-zeta polarized and triple-zeta polarized levels. The MESP critical points for a 3�21G optimized/6�31G** basis are similar to the corresponding 6�31G** optimized/6�31G** ones. More generally, the qualitative features of the MESP topography computed at the polarized level are independent of the level at which optimization is carried out. For a proper representation of oxygen lone pairs, however, optimization using a polarized basis set is required. The nature of the substituent drastically changes the MESP distribution over the phenyl ring. The values and positions of MESP minima indicate the most active site for electrophilic attack. This point is strengthened by a study of disubstituted benzenes.