Communication: Evidence of dynamic heterogeneity in glassy polymer monolayers from interface microrheology measurements

We have developed a novel nanoparticle tracking based interface microrheology technique to perform in situ studies on confined complex fluids. To demonstrate the power of this technique, we show, for the first time, how in situ glass formation in polymers confined at air-water interface can be directly probed by monitoring variation of the mean square displacement of embedded nanoparticles as a function of surface density. We have further quantified the appearance of dynamic heterogeneity and hence vitrification in polymethyl methacrylate monolayers above a certain surface density, through the variation of non-Gaussian parameter of the probes. (C) 2010 American Institute of Physics. [doi:10.1063/1.3471584].

An algorithm to detect linearly separable clusters of binary patterns

The clusters of binary patterns can be considered as Boolean functions of the (binary) features. Such a relationship between the linearly separable (LS) Boolean functions and LS clusters of binary patterns is examined. An algorithm is presented to answer the questions of the type: “Is the cluster formed by the subsets of the (binary) data set having certain features AND/NOT having certain other features, LS from the remaining set?” The algorithm uses the sequences of Numbered Binary Form (NBF) notation and some elementary (NPN) transformations of the binary data.

An algorithm to detect linearly separable clusters of binary patterns

The clusters of binary patterns can be considered as Boolean functions of the (binary) features. Such a relationship between the linearly separable (LS) Boolean functions and LS clusters of binary patterns is examined. An algorithm is presented to answer the questions of the type: “Is the cluster formed by the subsets of the (binary) data set having certain features AND/NOT having certain other features, LS from the remaining set?” The algorithm uses the sequences of Numbered Binary Form (NBF) notation and some elementary (NPN) transformations of the binary data.

Stability Analysis of Composite Skew Plates Using a Dynamic Method

Stability analysis is carried out considering free lateral vibrations of simply supported composite skew plates that are subjected to both direct and shear in-plane forces. An oblique stress component representation is used, consistent with the skew-geometry of the plate. A double series, expressed in Chebyshev polynomials, is used here as the assumed deflection surface and Ritz method of solution is employed. Numerical results for different combinations of side ratios, skew angle, and in-plane loadings that act individually or in combination are obtained. In this method, the in-plane load parameter is varied until the fundamental frequency goes to zero. The value of the in-plane load then corresponds to a critical buckling load. Plots of frequency parameter versus in-plane loading are given for a few typical cases. Details of crossings and quasi degeneracies of these curves are presented.

Static and dynamic properties of incommensurate smectic-AIC liquid crystals

We study the elasticity, topological defects, and hydrodynamics of the recently discovered incommensurate smectic (AIC) phase, characterized by two collinear mass density waves of incommensurate spatial frequency. The low-energy long-wavelength excitations of the system can be described by a displacement field u(x) and a ��phason�� field w(x) associated, respectively, with collective and relative motion of the two constituent density waves. We formulate the elastic free energy in terms of these two variables and find that when w=0, its functional dependence on u is identical to that of a conventional smectic liquid crystal, while when u=0, its functional dependence on w is the same as that for the angle variable in a slightly anisotropic XY model. An arbitrariness in the definition of u and w allows a choice that eliminates all relevant couplings between them in the long-wavelength elastic energy. The topological defects of the system are dislocations with nonzero u and w components. We introduce a two-dimensional Burgers lattice for these dislocations, and compute the interaction between them. This has two parts: one arising from the u field that is short ranged and identical to the interaction between dislocations in an ordinary smectic liquid crystal, and one arising from the w field that is long ranged and identical to the logarithmic interaction between vortices in an XY model. The hydrodynamic modes of the AIC include first- and second-sound modes whose direction-dependent velocities are identical to those in ordinary smectics. The sound attenuations have a different direction dependence, however. The breakdown of hydrodynamics found in conventional smectic liquid crystals, with three of the five viscosities diverging as 1/? at small frequencies ?, occurs in these systems as well and is identical in all its details. In addition, there is a diffusive phason mode, not found in ordinary smectic liquid crystals, that leads to anomalously slow mechanical response analogous to that predicted in quasicrystals, but on a far more experimentally accessible time scale.

Study Of The Hvdc-Torsional Interactions Through Digital Dynamic Simulation

This paper presents the detailed dynamic digital simulation for the study of phenomenon of torsional interaction between HVDC-Turbine generator shaft, dynamics using the novel converter model presented in [ 1 ] The system model includes detailed representation of the synchronous generator and the shaft dynamics, the ac and dc network transients. The results of a case study indicate the various factors that influence the torsional interaction.

Critical behavior of electrical resistivity in polar + nonpolar binary liquid systems

The behavior of electrical resistivity in the critical region of three polar + nonpolar binary liquid systems CS2 +(CH3CO)2O, C6H12+(CH3CO)2O, and n‐C7H16+(CH3CO)2O is studied. For the mixtures with critical composition, the two phase region shows a conductivity behavior with σ1−σ2∼ (−ϵ)β with β?0.35. In the one phase region dR/dT has a singularity ϵ−b with b?0.35. A possible theory of the impurity conduction is given, which broadly explains these results. The possibility of dR/dT being positive or negative is also discussed.

Dispersion of resistivity anomalies in critical binary liquid systems

The critical resistivity in the binary liquid systems n-C7H16 + CH3OH and CS2 + CH3NO2 is measured from 10 Hz to 100 kHz. There is no noticeable effect of the frequency on the resistivity singularities. Thus any contribution from dielectric dispersion is not appreciable.

Dynamic security assessment of power systems using structure-preserving energy functions

An application of direct methods to dynamic security assessment of power systems using structure-preserving energy functions (SPEF) is presented. The transient energy margin (TEM) is used as an index for checking the stability of the system as well as ranking the contigencies based on their severity. The computation of the TEM requires the evaluation of the critical energy and the energy at fault clearing. Usually this is done by simulating the faulted trajectory, which is time-consuming. In this paper, a new algorithm which eliminates the faulted trajectory estimation is presented to calculate the TEM. The system equations and the SPEF are developed using the centre-of-inertia (COI) formulation and the loads are modelled as arbitrary functions of the respective bus voltages. The critical energy is evaluated using the potential energy boundary surface (PEBS) method. The method is illustrated by considering two realistic power system examples.

Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

Effect of primary system damping on the optimum design of an untuned viscous dynamic vibration absorber

Explicit criteria for the optimum design of an untuned viscous dynamic vibration absorber are developed for the case of a viscously damped single degree of freedom springmass system. It is shown that for the particular case of an undamped main system, the results reduce to the classical ones obtained by using the concept of a fixed point on the response curve.

Enhanced Pair Hydrophobicity in the Water-Dimethylsulfoxide (DMSO) Binary Mixture at Low DMSO Concentrations

We observe a surprisingly sharp increase in the pair hydrophobicity in the water climethylsulfoxide (DMSO) binary mixture at small DMSO concentrations, with the mole fraction of DMSO (x(D)) in the range 0.12-0.16. The increase in pair hydrophobicity is measured by an increase in the depth of the first minimum in the potential of mean force (PMF) between two methane molecules. However, this enhanced hydrophobicity again weakens at higher DMSO concentrations. We find markedly unusual behavior of the pure binary mixture (in the same composition range) in the diffusion coefficient of DMSO and in the local composition fluctuation of water, We find that, in the said composition range, the average coordination number of the methyl groups (of distinct DMSO) varies between 2.4 and 2.6, indicating the onset of the formation of a chain-like extended connectivity in an otherwise stable tetrahedral network comprising of water and DMSO molecules. We propose that the enhanced pair hydrophobicity of the binary mixture at low DMSO concentrations is due to the participation of the two methane molecules in the local structural order and the emerging molecular associations in the water-DMSO mixture.

Interdiffusion study in binary gold-tin system

Solid state reactive diffusion in binary Au-Sn system has been studied using the diffusion couple consisting of pure elements Au and Sn annealed in the temperature range of 180-100 degrees C for 25 h Interdiffusion zone consists of four intermetallic phases Au5Sn, AuSn, AuSn2, and AuSn4 Activation energy for parabolic growth constant and integrated diffusivity for each phase has been calculated to indicate about the possible mechanism for diffusion controlled growth process Parabolic growth constant of individual phases has also been compared Kirkendall marker plane position has been indicated in the interdiffusion zone and furthermore the ratio of intrinsic diffusivities of species has also been determined. (C) 2010 Elsevier Ltd. All rights reserved.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.