Synthesis and characterization of porous flowerlike alpha-Fe2O3 nanostructures for supercapacitor application

Porous flower-like alpha-Fe2O3 nanostructures synthesized by an ethylene glycol mediated self-assembly process are crystalline and porous with BET surface area of 64.6 m(2) g(-1). The discharge capacitance is 127 F g(-1) when the electrodes are cycled in 0.5 M Na2SO3 at a current density of 1 A g(-1). Capacitance retention after 1000 cycles is about 80% of the initial capacitance. The high discharge capacitance and its retention are attributed to high surface area and porosity of the iron oxide. As the iron oxides are inexpensive, the nano alpha-Fe2O3 is expected to be of potential use for supercapacitor application.

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.

Microstructure and texture evolution during sub-transus thermomechanical processing of Ti-6Al-4V-0.1B alloy: part I. hot rolling in (alpha plus beta) phase field

In the current study, the evolution of microstructure and texture has been studied for Ti-6Al-4V-0.1B alloy during sub-transus thermomechanical processing. This part of the work deals with the deformation response of the alloy by rolling in the (alpha + beta) phase field. The (alpha + beta) annealing behavior of the rolled specimen is communicated in part II. Rolled microstructures of the alloys exhibit either kinked or straight alpha colonies depending on their orientations with respect to the principal rolling directions. The Ti-6Al-4V-0.1B alloy shows an improved rolling response compared with the alloy Ti-6Al-4V because of smaller alpha lamellae size, coherency of alpha/beta interfaces, and multiple slip due to orientation factors. Accelerated dynamic globularization for this alloy is similarly caused by the intralamellar transverse boundary formation via multiple slip and strain accumulation at TiB particles. The (0002)(alpha) pole figures of rolled Ti-6Al-4V alloy shows ``TD splitting'' at lower rolling temperatures because of strong initial texture. Substantial beta phase mitigates the effect of starting texture at higher temperature so that ``RD splitting'' characterizes the basal pole figure. Weak starting texture and easy slip transfer for Ti-6Al-4V-0.1B alloy produce simultaneous TD and RD splittings in basal pole figures at all rolling temperatures.

Microstructure and texture evolution during sub-transus thermo-mechanical processing of Ti-6Al-4V-0.1B alloy: part II. static annealing in (alpha plus beta) regime

The first part of this study describes the evolution of microstructure and texture in Ti-6Al-4V-0.1B alloy during sub-transus rolling vis-A -vis the control alloy Ti-6Al-4V. In the second part, the static annealing response of the two alloys at self-same conditions is compared and the principal micromechanisms are analyzed. Faster globularization kinetics has been observed in the Ti-6Al-4V-0.1B alloy for equivalent annealing conditions. This is primarily attributed to the alpha colonies, which leads to easy boundary splitting via multiple slip activation in this alloy. The other mechanisms facilitating lamellar to equiaxed morphological transformations, e.g., termination migration and cylinderization, also start early in the boron-modified alloy due to small alpha colony size, small aspect ratio of the alpha lamellae, and the presence of TiB particles in the microstructure. Both the alloys exhibit weakening of basal fiber (ND||aOE (c) 0001 >) and strengthening of prism fiber (RD||aOE (c) aOE(a)) upon annealing. A close proximity between the orientations of fully globularized primary alpha and secondary alpha phases during alpha -> beta -> alpha transformation has accounted for such a texture modification.

Fluctuations in the alpha-effect and grand solar minima

The parameters of a special type of alpha-effect known in dynamo theory as the Babcock-Leighton mechanism are estimated using the data of sunspot catalogs. The estimates support the presence of the Babcock-Leighton alpha-effect on the Sun. Fluctuations of the alpha-effect are also estimated. The fluctuation amplitude appreciably exceeds themean value, and the characteristic time for the fluctuations is comparable to the period of the solar rotation. Fluctuations with the parameters found are included in a numericalmodel for the solar dynamo. Computations show irregular changes in the amplitudes of the magnetic cycles on time scales of centuries and millennia. The calculated statistical characteristics of the grand solar minima and maxima agree with the data on solar activity over the Holocene.

Odd-even effect in the elastic modulii of alpha,omega-Alkanedicarboxylic acids

Nanoindentation studies on alpha,omega-alkanedicarboxylic acids reveal that the elastic modulus, E, shows an odd-even alternation in exactly the same manner as the melting temperature, T-m. The results are consistent with the hypothesis that the strained molecular conformations in the odd diacids are the reasons for these alternations in T-m. The same packing features that lower T-m in the odd acids lead to easy accommodation of the deformation during nanoindentation and hence their low E.

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.

Stall flutter of NACA 0012 airfoil at low Reynolds numbers

In the present work, we experimentally study and demarcate the stall flutter boundaries of a NACA 0012 airfoil at low Reynolds numbers (Re similar to 10(4)) by measuring the forces and flow fields around the airfoil when it is forced to oscillate. The airfoil is placed at large mean angle of attack (alpha(m)), and is forced to undergo small amplitude pitch oscillations, the amplitude (Delta alpha) and frequency (f) of which are systematically varied. The unsteady loads on the oscillating airfoil are directly measured, and are used to calculate the energy transfer to the airfoil from the flow. These measurements indicate that for large mean angles of attack of the airfoil (alpha(m)), there is positive energy transfer to the airfoil over a range of reduced frequencies (k=pi fc/U), indicating that there is a possibility of airfoil excitation or stall flutter even at these low Re (c=chord length). Outside this range of reduced frequencies, the energy transfer is negative and under these conditions the oscillations would be damped. Particle Image Velocimetry (PIV) measurements of the flow around the oscillating airfoil show that the shear layer separates from the leading edge and forms a leading edge vortex, although it is not very clear and distinct due to the low oscillation amplitudes. On the other hand, the shear layer formed after separation is found to clearly move periodically away from the airfoil suction surface and towards it with a phase lag to the airfoil oscillations. The phase of the shear layer motion with respect to the airfoil motions shows a clear difference between the exciting and the damping case.

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.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Cooperative Particle Swarm Optimization Based Receiver for Large-Dimension MIMO-ZPSC Systems

In this paper, we propose a cooperative particle swarm optimization (CPSO) based channel estimation/equalization scheme for multiple-input multiple-output zero-padded single-carrier (MIMO-ZPSC) systems with large dimensions in frequency selective channels. We estimate the channel state information at the receiver in time domain using a PSO based algorithm during training phase. Using the estimated channel, we perform information symbol detection in the frequency domain using FFT based processing. For this detection, we use a low complexity OLA (OverLap Add) likelihood ascent search equalizer which uses minimum mean square (MMSE) equalizer solution as the initial solution. Multiple iterations between channel estimation and data detection are carried out which significantly improves the mean square error and bit error rate performance of the receiver.