MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. (c) 2012 Elsevier Ltd. All rights reserved.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Monoclonal Antibodies against Hepatitis C Genotype 3a Virus Like Particle Inhibit Virus Entry in Cell Culture System

The envelope protein (E1-E2) of Hepatitis C virus (HCV) is a major component of the viral structure. The glycosylated envelope protein is considered to be important for initiation of infection by binding to cellular receptor(s) and also known as one of the major antigenic targets to host immune response. The present study was aimed at identifying mouse monoclonal antibodies which inhibit binding of virus like particles of HCV to target cells. The first step in this direction was to generate recombinant HCV-like particles (HCV-LPs) specific for genotypes 3a of HCV (prevalent in India) using the genes encoding core, E1 and E2 envelop proteins in a baculovirus expression system. The purified HCV-LPs were characterized by ELISA and electron microscopy and were used to generate monoclonal antibodies (mAbs) in mice. Two monoclonal antibodies (E8G9 and H1H10) specific for the E2 region of envelope protein of HCV genotype 3a, were found to reduce the virus binding to Huh7 cells. However, the mAbs generated against HCV genotype 1b (D2H3, G2C7, E1B11) were not so effective. More importantly, mAb E8G9 showed significant inhibition of the virus entry in HCV JFH1 cell culture system. Finally, the epitopic regions on E2 protein which bind to the mAbs have also been identified. Results suggest a new therapeutic strategy and provide the proof of concept that mAb against HCV-LP could be effective in preventing virus entry into liver cells to block HCV replication.

Variance-Reduced Particle Filters for Structural System Identification Problems

A few variance reduction schemes are proposed within the broad framework of a particle filter as applied to the problem of structural system identification. Whereas the first scheme uses a directional descent step, possibly of the Newton or quasi-Newton type, within the prediction stage of the filter, the second relies on replacing the more conventional Monte Carlo simulation involving pseudorandom sequence with one using quasi-random sequences along with a Brownian bridge discretization while representing the process noise terms. As evidenced through the derivations and subsequent numerical work on the identification of a shear frame, the combined effect of the proposed approaches in yielding variance-reduced estimates of the model parameters appears to be quite noticeable. DOI: 10.1061/(ASCE)EM.1943-7889.0000480. (C) 2013 American Society of Civil Engineers.

Motion segmentation using curve fitting on Lagrangian particle trajectories

In this paper we present a segmentation algorithm to extract foreground object motion in a moving camera scenario without any preprocessing step such as tracking selected features, video alignment, or foreground segmentation. By viewing it as a curve fitting problem on advected particle trajectories, we use RANSAC to find the polynomial that best fits the camera motion and identify all trajectories that correspond to the camera motion. The remaining trajectories are those due to the foreground motion. By using the superposition principle, we subtract the motion due to camera from foreground trajectories and obtain the true object-induced trajectories. We show that our method performs on par with state-of-the-art technique, with an execution time speed-up of 10x-40x. We compare the results on real-world datasets such as UCF-ARG, UCF Sports and Liris-HARL. We further show that it can be used toper-form video alignment.

Nucleation dynamics and pool boiling characteristics of high pressure refrigerant using thermochromic liquid crystals

This paper reports an experimental investigation of oscillating temperature field beneath a single isolated nucleation site using a non-invasive TLC (thermochromic liquid crystal) based thermography technique. Empirical correlations are presented to demonstrate the influence of system pressure and wall heat flux on different ebullition characteristics in the nucleate pool boiling regime of refrigerant R-134a. TLC transient response and two-phase flow structure are captured using synchronized, high resolution imaging. It is observed that the area of influence of nucleation site exhibits a two-part distinct transient behavior during the bubble growth period and broadens to a maximum of 1.57 times the bubble diameter at the instant of bubble departure. This is accompanied by a sharp fall of 2.5 degrees C in the local excess temperature at the nucleation site, which results in momentary augmentation (similar to 40%) in the local heat transfer coefficient at the nucleation origin. The enhanced heat transfer rate observed during the bubble peel-off event is primarily due to transient micro-convection in the wake of the retreating bubble. Further, the results indicate that a slight increase in system pressure from 813.6 to 882.5 kPa has no considerable effect on either the wall superheat or the overall heat transfer coefficient and ebullition frequency. In addition, correlations have been obtained for bubble Reynolds number, Jackob number and the dimensionless bubble generation frequency in terms of modified boiling number.

IGF-1 stimulated upregulation of cyclin D1 is mediated via STAT5 signaling pathway in neuronal cells

Signal Transducer and Activator of Transcription (STATs) regulate various target genes such as cyclin D1, MYC, and BCL2 in nonneuronal cells which contribute towards progression as well as prevention of apoptosis and are involved in differentiation and cell survival. However, in neuronal cells, the role of STATs in the activation and regulation of these target genes and their signaling pathways are still not well established. In this study, a robust cyclin D1 expression was observed following IGF-1 stimulation in SY5Y cells as well as neurospheres. JAK/STAT pathway was shown to be involved in this upregulation. A detailed promoter analysis revealed that the specific STAT involved was STAT5, which acted as a positive regulatory element for cyclin D1 expression. Overexpression studies confirmed increase in cyclin D1 expression in response to STAT5a and STAT5b constructs when compared to dominant-negative STAT5. siRNA targeting STAT5, diminished the cyclin D1 expression, further confirming that STAT5 specifically regulated cyclin D1 in neuronal cells. Together, these findings shed new light on the mechanism of IGF-1 mediated upregulation of cyclin D1 expression in neural cell lines as well as in neural stem cells via the JAK/STAT5 signaling cascade.

Free energy barriers for homogeneous crystal nucleation in a eutectic system of binary hard spheres

In this study, the free energy barriers for homogeneous crystal nucleation in a system that exhibits a eutectic point are computed using Monte Carlo simulations. The system studied is a binary hard sphere mixture with a diameter ratio of 0.85 between the smaller and larger hard spheres. The simulations of crystal nucleation are performed for the entire range of fluid compositions. The free energy barrier is found to be the highest near the eutectic point and is nearly five times that for the pure fluid, which slows down the nucleation rate by a factor of 10(-31). These free energy barriers are some of highest ever computed using simulations. For most of the conditions studied, the composition of the critical nucleus corresponds to either one of the two thermodynamically stable solid phases. However, near the eutectic point, the nucleation barrier is lowest for the formation of the metastable random hexagonal closed packed (rhcp) solid phase with composition lying in the two-phase region of the phase diagram. The fluid to solid phase transition is hypothesized to proceed via formation of a metastable rhcp phase followed by a phase separation into respective stable fcc solid phases.

Determination of favorable inter-particle interactions for formation of substitutionally ordered solid phases from a binary mixture of oppositely charged colloidal suspensions

The solid phase formed by a binary mixture of oppositely charged colloidal particles can be either substitutionally ordered or substitutionally disordered depending on the nature and strength of interactions among the particles. In this work, we use Monte Carlo molecular simulations along with the Gibbs-Duhem integration technique to map out the favorable inter-particle interactions for the formation of substitutionally ordered crystalline phases from a fluid phase. The inter-particle interactions are modeled using the hard core Yukawa potential but the method can be easily extended to other systems of interest. The study obtains a map of interactions depicting regions indicating the type of the crystalline aggregate that forms upon phase transition.

A peer-peer particle swarm optimizer

Particle Swarm Optimization is a parallel algorithm that spawns particles across a search space searching for an optimized solution. Though inherently parallel, they have distinct synchronizations points which stumbles attempts to create completely distributed versions of it. In this paper, we attempt to create a completely distributed peer-peer particle swarm optimization in a cluster of heterogeneous nodes. Since, the original algorithm requires explicit synchronization points we modified the algorithm in multiple ways to support a peer-peer system of nodes. We also modify certain aspect of the basic PSO algorithm and show how certain numerical problems can take advantage of the same thereby yielding fast convergence.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.

Loss and gain signals in broadband stimulated-Raman spectra: Theoretical analysis

Stimulated optical signals obtained by subjecting the system to a narrow band and a broadband pulse show both gain and loss Raman features at the red and blue side of the narrow beam, respectively. Recently observed temperature-dependent asymmetry in these features Mallick et al., J. Raman Spectrosc. 42, 1883 (2011); Dang et al., Phys. Rev. Lett. 107, 043001 (2011)] has been attributed to the Stokes and anti-Stokes components of the third-order susceptibility, chi((3)). By treating the setup as a steady state of an open system coupled to four quantum radiation field modes, we show that Stokes and anti-Stokes processes contribute to both the loss and gain resonances. chi((3)) predicts loss and gain signals with equal intensity for electronically off-resonant excitation. Some asymmetry may exist for resonant excitation. However, this is unrelated to the Stokes vs anti-Stokes processes. Any observed temperature-dependent asymmetry must thus originate from effects lying outside the chi((3)) regime.

Fermions in synthetic non-Abelian gauge potentials: rashbon condensates to novel Hamiltonians

Recent advances in the generation of synthetic gauge fields in cold atomic systems have stimulated interest in the physics of interacting bosons and fermions in them. In this paper, we discuss interacting two-component fermionic systems in uniform non-Abelian gauge fields that produce a spin-orbit interaction and uniform spin potentials. Two classes of gauge fields discussed include those that produce a Rashba spin-orbit interaction and the type of gauge fields (SM gauge fields) obtained in experiments by the Shanxi and MIT groups. For high symmetry Rashba gauge fields, a two-particle bound state exists even for a vanishingly small attractive interaction described by a scattering length. Upon increasing the strength of a Rashba gauge field, a finite density of weakly interacting fermions undergoes a crossover from a BCS like ground state to a BEC state of a new kind of boson called the rashbon whose properties are determined solely by the gauge field and not by the interaction between the fermions. The rashbon Bose-Einstein condensate (RBEC) is a quite intriguing state with the rashbon-rashbon interactions being independent of the fermion-fermion interactions (scattering length). Furthermore, we show that the RBEC has a transition temperature of the order of the Fermi temperature, suggesting routes to enhance the transition temperatures of weakly interacting superfluids by tuning the spin-orbit coupling. For the SM gauge fields, we show that in a regime of parameters, a pair of particles with finite centre-of-mass momentum is the most strongly bound. In other regimes of centre-of-mass momenta, there is no two-body bound state, but a resonance like feature appears in the scattering continuum. In the many-body setting, this results in flow enhanced pairing. Also, strongly interacting normal states utilizing the scattering resonance can be created opening the possibility of studying properties of helical Fermi liquids. This paper contains a general discussion of the physics of Feshbach resonance in a non-Abelian gauge field, where several novel features such as centre-of-mass-momentum-dependent effective interactions are shown. It is also shown that a uniform non-Abelian gauge field in conjunction with a spatial potential can be used to generate novel Hamiltonians; we discuss an explicit example of the generation of a monopole Hamiltonian.