968 resultados para Topological entropy


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Three new compounds of bismuth, C4N2H10]center dotBi(C7H4NO4)(C7H3NO4)]center dot H2O, I, Bi(C5H3N2O4) (C5H2N2O4)], II, and Bi(mu(2)-OH)(C7H3NO4)], III, have been prepared by the reaction between bismuth nitrate and heterocyclic aromatic dicarboxylic acids, 2,6-pyridinedicarboxylic acid, 4,5-imidazoledicarboxylic acid, and 3,4-pyridinedicarboxylic acid, respectively, under hydrothermal conditions. The structures of all the compounds have linkages between Bi2O2 and the corresponding dicarboxylate forming a simple molecular unit in I, a bilayer arrangement in II, and a three-dimensional extended structure in III. The topological arrangement of the nodal building units in the structures indicates that a brucite-related layer (II) and fluorite-related arrangement (III) can be realized in these structures. By utilizing the secondary interactions, one can correlate the structure of III to a Kagome-related one. The observation of such classical inorganic related structures in the bismuth carboxylates is noteworthy. Lewis acid catalytic studies on the formation of ketal suggest the possible participatory role of the lone pair of electrons. All the compounds are characterized employing elemental analysis, IR, UV-vis, and thermal studies.

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The main theme of this paper is to study the flammability suppression of hydrocarbons by blending with carbon dioxide, and to evaluate these mixtures as possible working fluids in organic Rankine cycle for medium temperature concentrated solar power applications. The analysis takes into account inevitable irreversibilities in the turbine, the pump, and heat exchangers. While the isopentane + CO2 mixture suffers from high irreversibility mainly in the regenerator owing to a large temperature glide, the propane + CO2 mixture performs more or less the same as pure propane albeit with high cycle pressures. In general, large temperature glides at condensing pressures extend the heat recovery into the two-phase dome, which is an advantage. However, at the same time, the shift of the pinch point towards the warm end of the regenerator is found to be a major cause of irreversibility. In fact, as the number of carbon atoms in alkanes decreases, their blend with CO2 moves the pinch point to the colder end of the regenerator. This results in lower entropy generation in the regenerator and improved cycle efficiency of propane + CO2 mixtures. With this mixture, real cycle efficiencies of 15-18% are achievable at a moderate source temperature of 573 K. Applicability for a wide range of source temperatures is found to be an added advantage of this mixture.

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String theory and gauge/gravity duality suggest the lower bound of shear viscosity (eta) to entropy density (s) for any matter to be mu h/4 pi k(B), when h and k(B) are reduced Planck and Boltzmann constants respectively and mu <= 1. Motivated by this, we explore eta/s in black hole accretion flows, in order to understand if such exotic flows could be a natural site for the lowest eta/s. Accretion flow plays an important role in black hole physics in identifying the existence of the underlying black hole. This is a rotating shear flow with insignificant molecular viscosity, which could however have a significant turbulent viscosity, generating transport, heat and hence entropy in the flow. However, in presence of strong magnetic field, magnetic stresses can help in transporting matter independent of viscosity, via celebrated Blandford-Payne mechanism. In such cases, energy and then entropy produces via Ohmic dissipation. In,addition, certain optically thin, hot, accretion flows, of temperature greater than or similar to 10(9) K, may be favourable for nuclear burning which could generate/absorb huge energy, much higher than that in a star. We find that eta/s in accretion flows appears to be close to the lower bound suggested by theory, if they are embedded by strong magnetic field or producing nuclear energy, when the source of energy is not viscous effects. A lower bound on eta/s also leads to an upper bound on the Reynolds number of the flow.

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Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

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Thermodynamic properties of Ca7V4O17 are measured for the first time using a solid-state electrochemical cell incorporating single crystal of CaF2 as the electrolyte over the temperature range from (900 to 1175) K. An equimolar mixture of CaO and CaF2 is used as the reference electrode and a mixture of Ca3V2O8, Ca7V4O17 and CaF2 as the measuring electrode. Both the electrodes are placed under flowing oxygen gas at ambient pressure. The standard Gibbs energy change for the reaction: 2Ca(3)V(2)O(8) + CaO -> Ca7V4O17; which is related to the chemical potential of CaO in the two-phase region (Ca3V2O8 + Ca7V4O17) of the pseudo-binary system CaO + V2O5, is obtained from the electromotive force of the cell as: Delta(r)G(o) +/- 127/(J . mol(-1)) = Delta mu(CaO) = -11453 + 8.273(T/K). The derived standard enthalpy of formation of Ca7V4O17 from elements in their normal standard states is ( 8208.97 +/- 8) kJ . mol (1) and its standard entropy is (560.05 +/- 7.5) J . K (1) . mol (1), both at T = 298.15 K. The results indicate that Ca7V4O17 decomposes into Ca3V2O8 and CaO at T = (1384 +/- 3) K.

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Two multicriterion decision-making methods, namely `compromise programming' and the `technique for order preference by similarity to an ideal solution' are employed to prioritise 22 micro-catchments (A1 to A22) of Kherthal catchment, Rajasthan, India and comparative analysis is performed using the compound parameter approach. Seven criteria - drainage density, bifurcation ratio, stream frequency, form factor, elongation ratio, circulatory ratio and texture ratio - are chosen for the evaluation. The entropy method is employed to estimate weights or relative importance of the criterion which ultimately affects the ranking pattern or prioritisation of micro-catchments. Spearman rank correlation coefficients are estimated to measure the extent to which the ranks obtained are correlated. Based on the average ranking approach supported by sensitivity analysis, micro-catchments A6, A10, A3 are preferred (owing to their low ranking) for further improvements with suitable conservation and management practices, and other micro-catchments can be processed accordingly at a later phase on a priority basis. It is concluded that the present approach can be explored for other similar situations with appropriate modifications.

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State estimation is one of the most important functions in an energy control centre. An computationally efficient state estimator which is free from numerical instability/ill-conditioning is essential for security assessment of electric power grid. Whereas approaches to successfully overcome the numerical ill-conditioning issues have been proposed, an efficient algorithm for addressing the convergence issues in the presence of topological errors is yet to be evolved. Trust region (TR) methods have been successfully employed to overcome the divergence problem to certain extent. In this study, case studies are presented where the conventional algorithms including the existing TR methods would fail to converge. A linearised model-based TR method for successfully overcoming the convergence issues is proposed. On the computational front, unlike the existing TR methods for state estimation which employ quadratic models, the proposed linear model-based estimator is computationally efficient because the model minimiser can be computed in a single step. The model minimiser at each step is computed by minimising the linearised model in the presence of TR and measurement mismatch constraints. The infinity norm is used to define the geometry of the TR. Measurement mismatch constraints are employed to improve the accuracy. The proposed algorithm is compared with the quadratic model-based TR algorithm with case studies on the IEEE 30-bus system, 205-bus and 514-bus equivalent systems of part of Indian grid.

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Sub-solidus phase relations in the ternary systems CaO-RuO2-SiO2 and CaO-RuO2-V2O5 have been refined using thermodynamic data on calcium ruthenates, silicates and vanadates. Tie lines are established by considering Gibbs energy change for exchange reactions. Quaternary oxides have not been detected in these systems. Because of the relatively large entropy associated with phase transition of Ca2SiO4 from olivine to alpha' structure at 1120 K, reversal of one tie line is seen in the system CaO-RuO2-SiO2 between 950 and 1230 K. There is no change in sub-solidus phase relation as a function of temperature in the system CaO-RuO2-V2O5. Since vanadium can exist in several lower oxidation states, the computed sub-solidus phase relations are valid only at high oxygen partial pressures. There is fair agreement between the computed phase diagram and the limited experimental information available for CaO-deficient compositions in the literature. (C) 2013 Elsevier Ltd. All rights reserved.

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The contour tree is a topological abstraction of a scalar field that captures evolution in level set connectivity. It is an effective representation for visual exploration and analysis of scientific data. We describe a work-efficient, output sensitive, and scalable parallel algorithm for computing the contour tree of a scalar field defined on a domain that is represented using either an unstructured mesh or a structured grid. A hybrid implementation of the algorithm using the GPU and multi-core CPU can compute the contour tree of an input containing 16 million vertices in less than ten seconds with a speedup factor of upto 13. Experiments based on an implementation in a multi-core CPU environment show near-linear speedup for large data sets.

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This paper reports the first observations of transition from a pre-vortex breakdown (Pre-VB) flowreversal to a fully developed central toroidal recirculation zone in a non-reacting, double-concentric swirling jet configuration and its response to longitudinal acoustic excitation. This transition proceeds with the formation of two intermediate, critical flow regimes. First, a partially penetrated vortex breakdown bubble (VBB) is formed that indicates the first occurrence of an enclosed structure as the centre jet penetration is suppressed by the growing outer roll-up eddy; resulting in an opposed flow stagnation region. Second, a metastable transition structure is formed that marks the collapse of inner mixing vortices. In this study, the time-averaged topological changes in the coherent recirculation structures are discussed based on the non-dimensional modified Rossby number (Ro(m)) which appears to describe the spreading of the zone of swirl influence in different flow regimes. Further, the time-mean global acoustic response of pre-VB and VBB is measured as a function of pulsing frequency using the relative aerodynamic blockage factor (i.e., maximum radial width of the inner recirculation zone). It is observed that all flow modes except VBB are structurally unstable as they exhibit severe transverse radial shrinkage (similar to 20%) at the burner Helmholtz resonant modes (100-110 Hz). In contrast, all flow regimes show positional instability as seen by the large-scale, asymmetric spatial shifting of the vortex core centres. Finally, the mixing transfer function M (f) and magnitude squared coherence lambda(2)(f) analysis is presented to determine the natural couplingmodes of the system dynamic parameters (u', p'), i.e., local acoustic response. It is seen that the pre-VB flow mode exhibits a narrow-band, low pass filter behavior with a linear response window of 100-105 Hz. However, in the VBB structure, presence of critical regions such as the opposed flow stagnation region alters the linearity range with the structure showing a response even at higher pulsing frequencies (100-300 Hz). (C) 2013 AIP Publishing LLC.

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We study black hole solutions in Chern-Simons higher spin supergravity based on the superalgebra sl(3 vertical bar 2). These black hole solutions have a U(1) gauge field and a spin 2 hair in addition to the spin 3 hair. These additional fields correspond to the R-symmetry charges of the supergroup sl(3 vertical bar 2). Using the relation between the bulk field equations and the Ward identities of a CFT with N = 2 super-W-3 symmetry, we identify the bulk charges and chemical potentials with those of the boundary CFT. From these identifications we see that a suitable set of variables to study this black hole is in terms of the charges present in three decoupled bosonic sub-algebras of the N = 2 super-W-3 algebra. The entropy and the partition function of these R-charged black holes are then evaluated in terms of the charges of the bulk theory as well as in terms of its chemical potentials. We then compute the partition function in the dual CFT and find exact agreement with the bulk partition function.

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We present a comprehensive study of two of the most experimentally relevant extensions of Kitaev's spinless model of a one-dimensional p-wave superconductor: those involving (i) longer-range hopping and superconductivity and (ii) inhomogeneous potentials. We commence with a pedagogical review of the spinless model and, as a means of characterizing topological phases exhibited by the systems studied here, we introduce bulk topological invariants as well as those derived from an explicit consideration of boundary modes. In time-reversal symmetric systems, we find that the longer range hopping leads to topological phases characterized by multiple Majorana modes. In particular, we investigate a spin model that respects a duality and maps to a fermionic model with multiple Majorana modes; we highlight the connection between these topological phases and the broken symmetry phases in the original spin model. In the presence of time-reversal symmetry breaking terms, we show that the topological phase diagram is characterized by an extended gapless regime. For the case of inhomogeneous potentials, we explore phase diagrams of periodic, quasiperiodic, and disordered systems. We present a detailed mapping between normal state localization properties of such systems and the topological phases of the corresponding superconducting systems. This powerful tool allows us to leverage the analyses of Hofstadter's butterfly and the vast literature on Anderson localization to the question of Majorana modes in superconducting quasiperiodic and disordered systems, respectively. We briefly touch upon the synergistic effects that can be expected in cases where long-range hopping and disorder are both present.

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Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of 2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

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Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

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Four-dimensional fluorescence microscopy-which records 3D image information as a function of time-provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations. Unfortunately, most deconvolution methods perform very poorly at low signal-to-noise ratios, thereby requiring moderate photon doses to obtain acceptable resolution. We present a unique deconvolution method that combines an entropy-based regularization function with kernels that can exploit general spatial characteristics of the fluorescence image to push the required dose to extreme low levels, resulting in an enabling technology for high-resolution in vivo biological imaging.