Computational representations for multi state design tasks and enumeration of mechanical device behaviour

The role of a computer emerged from modeling and analyzing concepts (ideas) to generate concepts. Research into methods for supporting conceptual design using automated synthesis had attracted much attention in the past decades. To find out how designers synthesize solution concepts for multi-state mechanical devices, ten experimental studies were conducted. Observations from these empirical studies would be used as the basis to develop knowledge involved in the multi-state design synthesis process. In this paper, we propose a computational representation for expressing the multi-state design task and for enumerating multi-state behaviors of kinematic pairs and mechanisms. This computational representation would be used to formulate computational methods for the synthesis process to develop a system for supporting design synthesis of multiple state mechanical devices by generating a comprehensive variety of solution alternatives.

Evidence of novel quasiparticles in a strongly interacting two-dimensional electron system: giant thermopower and metallic behaviour

We report thermopower (S) and electrical resistivity (rho (2DES) ) measurements in low-density (10(14) m(-2)), mesoscopic two-dimensional electron systems (2DESs) in GaAs/AlGaAs heterostructures at sub-Kelvin temperatures. We observe at temperatures a parts per thousand(2)0.7 K a linearly growing S as a function of temperature indicating metal-like behaviour. Interestingly this metallicity is not Drude-like, showing several unusual characteristics: (i) the magnitude of S exceeds the Mott prediction valid for non-interacting metallic 2DESs at similar carrier densities by over two orders of magnitude; and (ii) rho (2DES) in this regime is two orders of magnitude greater than the quantum of resistance h/e (2) and shows very little temperature-dependence. We provide evidence suggesting that these observations arise due to the formation of novel quasiparticles in the 2DES that are not electron-like. Finally, rho (2DES) and S show an intriguing decoupling in their density-dependence, the latter showing striking oscillations and even sign changes that are completely absent in the resistivity.

The precision with which single cells of Dictyostelium discoideum can locate a source of cyclic AMP

When stimulated by a point source of cyclic AMP, a starved amoeba of Dictyostelium discoideum responds by putting out a hollow balloon-like membrane extension followed by a pseudopod. The effect of the stimulus is to influence the position where either of these protrusions is made on the cell rather than to cause them to be made. Because the pseudopod forms perpendicular to the cell surface, its location is a measure of the precision with which the cell can locate the cAMP source. Cells beyond 1 h of starvation respond non-randomly with a precision that improves steadily thereafter. A cell that is starved for 1-2 h can locate the source accurately 43% of the time; and if starved for 6-7 h, 87% of the time. The response always has a high scatter; population-level heterogeneity reflects stochasticity in single cell behaviour. From the angular distribution of the response its maximum information content is estimated to be 2-3 bits. In summary, we quantitatively demonstrate the stochastic nature of the directional response and the increase in its accuracy over time.

Assessing future rainfall projections using multiple GCMs and a multi-site stochastic downscaling model

Impact of global warming on daily rainfall is examined using atmospheric variables from five General Circulation Models (GCMs) and a stochastic downscaling model. Daily rainfall at eleven raingauges over Malaprabha catchment of India and National Center for Environmental Prediction (NCEP) reanalysis data at grid points over the catchment for a continuous time period 1971-2000 (current climate) are used to calibrate the downscaling model. The downscaled rainfall simulations obtained using GCM atmospheric variables corresponding to the IPCC-SRES (Intergovernmental Panel for Climate Change - Special Report on Emission Scenarios) A2 emission scenario for the same period are used to validate the results. Following this, future downscaled rainfall projections are constructed and examined for two 20 year time slices viz. 2055 (i.e. 2046-2065) and 2090 (i.e. 2081-2100). The model results show reasonable skill in simulating the rainfall over the study region for the current climate. The downscaled rainfall projections indicate no significant changes in the rainfall regime in this catchment in the future. More specifically, 2% decrease by 2055 and 5% decrease by 2090 in monsoon (HAS) rainfall compared to the current climate (1971-2000) under global warming conditions are noticed. Also, pre-monsoon (JFMAM) and post-monsoon (OND) rainfall is projected to increase respectively, by 2% in 2055 and 6% in 2090 and, 2% in 2055 and 12% in 2090, over the region. On annual basis slight decreases of 1% and 2% are noted for 2055 and 2090, respectively.

Superparamagnetic behaviour and T-1, T-2 relaxivity of ZnFe2O4 nanoparticles for magnetic resonance imaging

In the present study, ZnFe2O4 nanoparticles were synthesized by the chemical co-precipitation followed by calcinations at 473 and 673K for 4h. Particle sizes obtained were 4 and 6nm for the calcination temperatures of 473 and 673K, respectively. To study the origin of system's low temperature spin dynamic behaviour, temperature dependence of susceptibility was investigated as a function of particle size and frequency. Slight increase in the grain size from 4nm at 473K to 6nm at 673K has led to a peak shift of temperature dependence of susceptibility measured at a constant frequency of 400Hz. Temperature dependence of at different frequencies also resulted in peak shift. Relaxation time dependence of peak temperature obeys a power law, which provides the fitting parameters within the range of superparamagnetic nature of the particles. Further, dependence of relaxation time and peak temperature obeys VogelFulcher law rather than NeelBrown equation demonstrating that the particles follow the behaviour of superparamagnetism of slightly interacting system. Spinlattice, T-1 and spinspin, T-2 relaxivity of proton of the water molecule in the presence of chitosan-coated superparamagnetic ZnFe2O4 nanoparticle yields the values of 0.002 and 0.360s(1)perppm.

Hardening and corrosion behaviour of copper added SUS 304H austenitic steels subjected to thermal cycling

This research was aimed at determining optimum Cu content for the alloy design of SUS 30411 austenitic steels having enhanced heat and corrosion resistance. Samples of the steel containing 1, 3, and 5 wt.% Cu were subjected to repeated heating and cooling to a temperature of 760 degrees C and to a maximum of 15 cycles. Hardness measurement and the corrosion behaviour in 1M NaCl solution were evaluated. The hardness increases with an increase in the number of heating cycles for the three compositions. The hardening response to the thermal cycles is however higher for the 1 wt.% Cu composition and decreases with an increase in the Cu wt.%. The SUS 30411 steel containing 3 wt.% Cu exhibited the least susceptibility to corrosion in the 1M NaCl solution irrespective of the number of heating cycles. The SUS 30411 steel containing 1 wt.% Cu was found to exhibit the highest susceptibility to corrosion for all heating cycles compared.

Deformation behaviour at macro- and nano-length scales: The development of orientation gradients

The deformation behaviour of macrocrystalline and nanocrystalline nickel shows a striking similarity in terms of higher intragranular misorientation and a texture with dominant Brass component on rolling. This is in contrast to microcrystalline nickel, with lower intragranular misorientation and typical Copper type texture. This has been attributed to the free surfaces in macrocrystalline sample and grain boundaries in nanocrystalline sample. Experimental evidence of `Grain Boundary Affected Zone' (GBAZ) showing multi-slip in contrast to limited slip in the grain interiors has been provided. The similarity in evolution of texture and intragranular misorientation is explained on the basis of reduced contribution from the GBAZ at the two extreme length scales.

Stochastic analysis of epidemics on adaptive time varying networks

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.

Application of the random eigenvalue problem in forced response analysis of a linear stochastic structure

The random eigenvalue problem arises in frequency and mode shape determination for a linear system with uncertainties in structural properties. Among several methods of characterizing this random eigenvalue problem, one computationally fast method that gives good accuracy is a weak formulation using polynomial chaos expansion (PCE). In this method, the eigenvalues and eigenvectors are expanded in PCE, and the residual is minimized by a Galerkin projection. The goals of the current work are (i) to implement this PCE-characterized random eigenvalue problem in the dynamic response calculation under random loading and (ii) to explore the computational advantages and challenges. In the proposed method, the response quantities are also expressed in PCE followed by a Galerkin projection. A numerical comparison with a perturbation method and the Monte Carlo simulation shows that when the loading has a random amplitude but deterministic frequency content, the proposed method gives more accurate results than a first-order perturbation method and a comparable accuracy as the Monte Carlo simulation in a lower computational time. However, as the frequency content of the loading becomes random, or for general random process loadings, the method loses its accuracy and computational efficiency. Issues in implementation, limitations, and further challenges are also addressed.

Fretting wear behaviour of hydroxyapatite-titanium composites in simulated body fluid, supplemented with 5 g l(-1) bovine serum albumin

Damaged articulating joints can be repaired or replaced with synthetic biomaterials, which can release wear debris due to articulation, leading to the osteolysis. In a recent work, it has been shown that it is possible to achieve a better combination of flexural strength/fracture toughness as well as in vitro bioactivity and cytocompatibility properties in spark plasma sintered hydroxyapatite-titanium (HA-Ti) composites. Although hydroxyapatite and titanium are well documented for their good biocompatibility, nanosized hydroxyapatite (HA) and titanium (Ti) particles can cause severe toxicity to cells. In order to address this issue, fretting wear study of HA-Ti composites under dry and wet (1x SBF, supplemented with 5 g l(-1) bovine serum albumin (BSA)) condition was performed to assess the wear resistance as well as wear debris formation, in vitro. The experimental results reveal one order of magnitude lower wear rate for HA-10 wt% Ti (7.5 x 10(-5) mm(3) N-1 m(-1)) composite than monolithic HA (3.9 x 10(-4) mm(3) N-1 m(-1)) in simulated body fluid. The difference in the tribological properties has been analyzed in the light of phase assemblages and mechanical properties. Overall, the results suggest the potential use of HA-Ti composites over existing HA-based biocomposites in orthopedic as well as dental applications.

Confinement induced stochastic sensing of charged coronene and perylene aggregates in alpha-hemolysin nanochannels

Biological nanopores provide optimum dimensions and an optimal environment to study early aggregation kinetics of charged polyaromatic molecules in the nano-confined regime. It is expected that probing early stages of nucleation will enable us to design a strategy for supramolecular assembly and biocrystallization processes. Specifically, we have studied translocation dynamics of coronene and perylene based salts, through the alpha-hemolysin (alpha-HL) protein nanopore. The characteristic blocking events in the time-series signal are a function of concentration and bias voltage. We argue that different blocking events arise due to different aggregation processes as captured by all atomistic molecular dynamics (MD) simulations. These confinement induced aggregations of polyaromatic chromophores during the different stages of translocation are correlated with the spatial symmetry and charge distribution of the molecules.

Differentially private stochastic gradient descent algorithm for multiparty classification

We consider the problem of developing privacy-preserving machine learning algorithms in a dis-tributed multiparty setting. Here different parties own different parts of a data set, and the goal is to learn a classifier from the entire data set with-out any party revealing any information about the individual data points it owns. Pathak et al [7]recently proposed a solution to this problem in which each party learns a local classifier from its own data, and a third party then aggregates these classifiers in a privacy-preserving manner using a cryptographic scheme. The generaliza-tion performance of their algorithm is sensitive to the number of parties and the relative frac-tions of data owned by the different parties. In this paper, we describe a new differentially pri-vate algorithm for the multiparty setting that uses a stochastic gradient descent based procedure to directly optimize the overall multiparty ob-jective rather than combining classifiers learned from optimizing local objectives. The algorithm achieves a slightly weaker form of differential privacy than that of [7], but provides improved generalization guarantees that do not depend on the number of parties or the relative sizes of the individual data sets. Experimental results corrob-orate our theoretical findings.

Sr2+ and Cd2+ coordination polymers: the effect of the different coordinating behaviour of a newly designed tricarboxylic acid

This article presents the investigation of the coordination behavior of a newly synthesized tricarboxylate ligand, obtained by joining imidazole dicarboxylic acid and 4-carboxybenzyl moieties cbimdaH(3), 1-(4-carboxybenzyl)-1H-imidazole-4,5-dicarboxylic acid]. Two novel coordination polymers were obtained through solvothermal reactions under similar conditions namely Sr(cbimdaH)(H2O)](n) (1) and Cd-2(cbimdaH)(2)(H2O)(6)](n)center dot(DMF)(3n)(H2O)(3n) (2), with the ligand behaving as a dianionic tricarboxylate linker. The single crystal X-ray structures show that while 1 forms a 3D coordination polymer, 2 forms a 1D polymer which is further assembled in three dimensions through supramolecular interactions (H-bonding). Complex 1 consists of Sr2+ ions in a distorted dodecahedral coordination geometry, while 2 consists of Cd2+ ions in distorted pentagonal bipyramidal geometries. A topology study reveals that 1 has a new topology based on the 5,6-coordinated 3D net architecture. The luminescence properties of the complexes in the solid state and their thermal stabilities were studied.

Constraining complex aquifer geometry with geophysics (2-D ERT and MRS measurements) for stochastic modelling of groundwater flow

Stochastic modelling is a useful way of simulating complex hard-rock aquifers as hydrological properties (permeability, porosity etc.) can be described using random variables with known statistics. However, very few studies have assessed the influence of topological uncertainty (i.e. the variability of thickness of conductive zones in the aquifer), probably because it is not easy to retrieve accurate statistics of the aquifer geometry, especially in hard rock context. In this paper, we assessed the potential of using geophysical surveys to describe the geometry of a hard rock-aquifer in a stochastic modelling framework. The study site was a small experimental watershed in South India, where the aquifer consisted of a clayey to loamy-sandy zone (regolith) underlain by a conductive fissured rock layer (protolith) and the unweathered gneiss (bedrock) at the bottom. The spatial variability of the thickness of the regolith and fissured layers was estimated by electrical resistivity tomography (ERT) profiles, which were performed along a few cross sections in the watershed. For stochastic analysis using Monte Carlo simulation, the generated random layer thickness was made conditional to the available data from the geophysics. In order to simulate steady state flow in the irregular domain with variable geometry, we used an isoparametric finite element method to discretize the flow equation over an unstructured grid with irregular hexahedral elements. The results indicated that the spatial variability of the layer thickness had a significant effect on reducing the simulated effective steady seepage flux and that using the conditional simulations reduced the uncertainty of the simulated seepage flux. As a conclusion, combining information on the aquifer geometry obtained from geophysical surveys with stochastic modelling is a promising methodology to improve the simulation of groundwater flow in complex hard-rock aquifers. (C) 2013 Elsevier B.V. All rights reserved.

Reaction dynamics under confinement: an exact path integral treatment of a two-stage model of stochastic gene expression

Gene expression in living systems is inherently stochastic, and tends to produce varying numbers of proteins over repeated cycles of transcription and translation. In this paper, an expression is derived for the steady-state protein number distribution starting from a two-stage kinetic model of the gene expression process involving p proteins and r mRNAs. The derivation is based on an exact path integral evaluation of the joint distribution, P(p, r, t), of p and r at time t, which can be expressed in terms of the coupled Langevin equations for p and r that represent the two-stage model in continuum form. The steady-state distribution of p alone, P(p), is obtained from P(p, r, t) (a bivariate Gaussian) by integrating out the r degrees of freedom and taking the limit t -> infinity. P(p) is found to be proportional to the product of a Gaussian and a complementary error function. It provides a generally satisfactory fit to simulation data on the same two-stage process when the translational efficiency (a measure of intrinsic noise levels in the system) is relatively low; it is less successful as a model of the data when the translational efficiency (and noise levels) are high.