255 resultados para predictions
Resumo:
Thermal diffusivity and conductivity of hot pressed ZrB2 with different amounts of B4C (0-5 wt%) and ZrB2-SiC composites (10-30 vol% SiC) were investigated experimentally over a wide range of temperature (25-1500 degrees C). Both thermal diffusivity and thermal conductivity were found to decrease with increase in temperature for all the hot pressed ZrB2 and ZrB2-SiC composites. At around 200 degrees C, thermal conductivity of ZrB2-SiC composites was found to be composition independent. Thermal conductivity of ZrB2-SiC composites was also correlated with theoretical predictions of the Maxwell Eucken relation. The dominated mechanisms of heat transport for all hot pressed ZrB2 and ZrB2-SiC composites at room temperature were confirmed by Wiedemann Franz analysis by using measured electrical conductivity of these materials at room temperature. It was found that electronic thermal conductivity dominated for all monolithic ZrB2 whereas the phonon contribution to thermal conductivity increased with SiC contents for ZrB2-SiC composites.
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An analysis of the retrospective predictions by seven coupled ocean atmosphere models from major forecasting centres of Europe and USA, aimed at assessing their ability in predicting the interannual variation of the Indian summer monsoon rainfall (ISMR), particularly the extremes (i.e. droughts and excess rainfall seasons) is presented in this article. On the whole, the skill in prediction of extremes is not bad since most of the models are able to predict the sign of the ISMR anomaly for a majority of the extremes. There is a remarkable coherence between the models in successes and failures of the predictions, with all the models generating loud false alarms for the normal monsoon season of 1997 and the excess monsoon season of 1983. It is well known that the El Nino and Southern Oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in the interannual variation of ISMR and particularly the extremes. The prediction of the phases of these modes and their link with the monsoon has also been assessed. It is found that models are able to simulate ENSO-monsoon link realistically, whereas the EQUINOO-ISMR link is simulated realistically by only one model the ECMWF model. Furthermore, it is found that in most models this link is opposite to the observed, with the predicted ISMR being negatively (instead of positively) correlated with the rainfall over the western equatorial Indian Ocean and positively (instead of negatively) correlated with the rainfall over the eastern equatorial Indian Ocean. Analysis of the seasons for which the predictions of almost all the models have large errors has suggested the facets of ENSO and EQUINOO and the links with the monsoon that need to be improved for improving monsoon predictions by these models.
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Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an ``adaptive threshold,'' i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
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We describe here the rheological response of dense, slowly deforming granular materials to shear in a cylindrical Couette cell. All components of the stress on the outer cylinder are measured pointwise as a function of the depth, for different methods of construction of the bed that presumably lead to distinct fabrics. The static stress profiles for the different construction protocols are different, but a stress profile that is independent of construction history emerges when the granular column is sheared for sufficient time, in accord with the predictions of plasticity theories. However the qualitative features of the the stress profile under shear differs radically from the predictions of plasticity theories and data reported in earlier studies. We discuss a hypothesis for the anomalous stress profiles that was proposed recently by us, and the ways in which further experiments may to conducted to verify it.
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We extend our analysis of transverse single spin asymmetry in electroproduction of J/psi to include the effect of the scale evolution of the transverse momentum dependent (TMD) parton distribution functions and gluon Sivers function. We estimate single spin asymmetry for JLab, HERMES, COMPASS, and eRHIC energies using the color evaporation model of charmonium production, using an analytically obtained approximate solution of TMD evolution equations discussed in the literature. We find that there is a reduction in the asymmetry compared with our predictions for the earlier case considered by us, wherein the Q(2) dependence came only from DGLAP evolution of the unpolarized gluon densities and a different parametrization of the TMD Sivers function was used.
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We consider a recently proposed four-level quantum heat engine (QHE) model to analyze the role of quantum coherences in determining the thermodynamic properties of the engine, such as flux, output power, and efficiency. A quantitative analysis of the relative effects of the coherences induced by the two thermal baths is brought out. By taking account of the dissipation in the cavity mode, we define useful work obtained from the QHE and present some analytical results for the optimal values of relative coherences that maximizes flux (hence output power) through the engine. We also analyze the role of quantum effects in inducing population inversion (lasing) between the states coupled to the cavity mode. The universal behavior of the efficiency at maximum power (EMP) is examined. In accordance with earlier theoretical predictions, to leading order, we find that EMP similar to eta(c)/2, where eta(c) is Carnot efficiency. However, the next higher order coefficient is system dependent and hence nonuniversal.
Resumo:
The cylindrical Couette device is commonly employed to study the rheology of fluids, but seldom used for dense granular materials. Plasticity theories used for granular flows predict a stress field that is independent of the shear rate, but otherwise similar to that in fluids. In this paper we report detailed measurements of the stress as a function of depth, and show that the stress profile differs fundamentally from that of fluids, from the predictions of plasticity theories, and from intuitive expectation. In the static state, a part of the weight of the material is transferred to the walls by a downward vertical shear stress, bringing about the well-known Janssen saturation of the stress in vertical columns. When the material is sheared, the vertical shear stress changes sign, and the magnitudes of all components of the stress rise rapidly with depth. These qualitative features are preserved over a range of the Couette gap and shear rate, for smooth and rough walls and two model granular materials. To explain the anomalous rheological response, we consider some hypotheses that seem plausibleapriori, but showthat none survive after careful analysis of the experimental observations. We argue that the anomalous stress is due to an anisotropic fabric caused by the combined actions of gravity, shear, and frictional walls, for which we present indirect evidence from our experiments. A general theoretical framework for anisotropic plasticity is then presented. The detailed mechanics of how an anisotropic fabric is brought about by the above-mentioned factors is not clear, and promises to be a challenging problem for future investigations. (C) 2013 AIP Publishing LLC.
Resumo:
We report a nuclear magnetic resonance experiment, which simulates the quantum transverse Ising spin system in a triangular configuration, and further demonstrate that multipartite quantum correlations can be used to distinguish between the frustrated and the nonfrustrated regimes in the ground state of this system. Adiabatic state preparation methods are used to prepare the ground states of the spin system. We employ two different multipartite quantum correlation measures to analyze the experimental ground state of the system in both the frustrated and the nonfrustrated regimes. As expected from theoretical predictions, the experimental data confirm that the nonfrustrated regime shows higher multipartite quantum correlations compared to the frustrated one.
Resumo:
A one-dimensional coupled multi-physics based model has been developed to accurately compute the effects of electrostatic, mechanical, and thermal field interactions on the electronic energy band structure in group III-nitrides thin film heterostructures. Earlier models reported in published literature assumes electro-mechanical field with uniform temperature thus neglecting self-heating. Also, the effects of diffused interface on the energy band structure were not studied. We include these effects in a self-consistent manner wherein the transport equation is introduced along with the electro-mechanical models, and the lattice structural variation as observed in experiments are introduced at the interface. Due to these effects, the electrostatic potential distribution in the heterostructure is altered. The electron and hole ground state energies decrease by 5% and 9%, respectively, at a relative temperature of 700 K, when compared with the results obtained from the previously reported electro-mechanical model assuming constant and uniform temperature distribution. A diffused interface decreases the ground state energy of electrons and holes by about 11% and 9%, respectively, at a relative temperature of 700 K when compared with the predictions based on uniform temperature based electro-mechanical model. (C) 2013 AIP Publishing LLC.
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Residue depth accurately measures burial and parameterizes local protein environment. Depth is the distance of any atom/residue to the closest bulk water. We consider the non-bulk waters to occupy cavities, whose volumes are determined using a Voronoi procedure. Our estimation of cavity sizes is statistically superior to estimates made by CASTp and VOIDOO, and on par with McVol over a data set of 40 cavities. Our calculated cavity volumes correlated best with the experimentally determined destabilization of 34 mutants from five proteins. Some of the cavities identified are capable of binding small molecule ligands. In this study, we have enhanced our depth-based predictions of binding sites by including evolutionary information. We have demonstrated that on a database (LigASite) of similar to 200 proteins, we perform on par with ConCavity and better than MetaPocket 2.0. Our predictions, while less sensitive, are more specific and precise. Finally, we use depth (and other features) to predict pK(a)s of GLU, ASP, LYS and HIS residues. Our results produce an average error of just <1 pH unit over 60 predictions. Our simple empirical method is statistically on par with two and superior to three other methods while inferior to only one. The DEPTH server (http://mspc.bii.a-star.edu.sg/depth/) is an ideal tool for rapid yet accurate structural analyses of protein structures.
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This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.
Resumo:
Seven double cysteine mutants of maltose binding protein (MBP) were generated with one each in the active cleft at position 298 and the second cysteine distributed over both domains of the protein. These cysteines were spin labeled and distances between the labels in biradical pairs determined by pulsed double electron-electron resonance (DEER) measurements. The values were compared with theoretical predictions of distances between the labels in biradicals constructed by molecular modeling from the crystal structure of MBP without maltose and were found to be in excellent agreement. MBP is in a molten globule state at pH 3.3 and is known to still bind its substrate maltose. The nitroxide spin label was sufficiently stable under these conditions. In preliminary experiments, DEER measurements were carried out with one of the mutants yielding a broad distance distribution as was to be expected if there is no explicit tertiary structure and the individual helices pointing into all possible directions.
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First principles calculations were done to evaluate the lattice parameter, cohesive energy and stacking fault energies of ordered gamma' (Ll(2)) precipitates in superalloys as a function of composition. It was found that addition of Ti and Ta lead to an increase in lattice parameter and decrease in cohesive energy, while Ni antisites had the opposite effect. Ta and Ti addition to stoichiometric Ni3Al resulted in an initial increase in the energies of APB((111)), CSF(111), APB((001)) and SISF(111). However, at higher concentrations, the fault energies decreased. Addition of Ni antisites decreased the energy of all four faults monotonically. A model based on nearest neighbor bonding was used for Ni-3(Al, Ta), Ni-3(Al, Ti) and Ni-3(Al, Ni) pseudo-binary systems and extended to pseudo- ternary Ni-3(Al, Ta, Ni) and Ni-3(Al, Ti, Ni) systems. Recipes were developed for predicting lattice parameters, cohesive energies and fault energies in pseudo- ternary systems on the basis of coefficients derived from simpler pseudobinary systems. The model predictions were found to be in good agreement with first principles calculations for lattice parameters, cohesive energies, and energies of APB((111)) and CSF(111).
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Entropy is a fundamental thermodynamic property that has attracted a wide attention across domains, including chemistry. Inference of entropy of chemical compounds using various approaches has been a widely studied topic. However, many aspects of entropy in chemical compounds remain unexplained. In the present work, we propose two new information-theoretical molecular descriptors for the prediction of gas phase thermal entropy of organic compounds. The descriptors reflect the bulk and size of the compounds as well as the gross topological symmetry in their structures, all of which are believed to determine entropy. A high correlation () between the entropy values and our information-theoretical indices have been found and the predicted entropy values, obtained from the corresponding statistically significant regression model, have been found to be within acceptable approximation. We provide additional mathematical result in the form of a theorem and proof that might further help in assessing changes in gas phase thermal entropy values with the changes in molecular structures. The proposed information-theoretical molecular descriptors, regression model and the mathematical result are expected to augment predictions of gas phase thermal entropy for a large number of chemical compounds.
Resumo:
The presence of software bloat in large flexible software systems can hurt energy efficiency. However, identifying and mitigating bloat is fairly effort intensive. To enable such efforts to be directed where there is a substantial potential for energy savings, we investigate the impact of bloat on power consumption under different situations. We conduct the first systematic experimental study of the joint power-performance implications of bloat across a range of hardware and software configurations on modern server platforms. The study employs controlled experiments to expose different effects of a common type of Java runtime bloat, excess temporary objects, in the context of the SPECPower_ssj2008 workload. We introduce the notion of equi-performance power reduction to characterize the impact, in addition to peak power comparisons. The results show a wide variation in energy savings from bloat reduction across these configurations. Energy efficiency benefits at peak performance tend to be most pronounced when bloat affects a performance bottleneck and non-bloated resources have low energy-proportionality. Equi-performance power savings are highest when bloated resources have a high degree of energy proportionality. We develop an analytical model that establishes a general relation between resource pressure caused by bloat and its energy efficiency impact under different conditions of resource bottlenecks and energy proportionality. Applying the model to different "what-if" scenarios, we predict the impact of bloat reduction and corroborate these predictions with empirical observations. Our work shows that the prevalent software-only view of bloat is inadequate for assessing its power-performance impact and instead provides a full systems approach for reasoning about its implications.