Structural Evolution and Property Changes in Nd_{60}Al_{10}Fe_{20}Co_{10} Bulk Metallic Glass During Crystallization

The structural evolution and property changes in Nd60Al10Fe20Co10 bulk metallic glass (BMG) upon crystallization are investigated by the ultrasonic method, x-ray diffraction, density measurement, and differential scanning calorimetry. The elastic constants and Debye temperature of the BMG are obtained as a function of annealing temperature. Anomalous changes in ultrasonic velocities, elastic constants, and density are observed between 600–750 K, corresponding to the formation of metastable phases as an intermediate product in the crystallization process. The changes in acoustic velocities, elastic constants, density, and Debye temperature of the BMG relative to its fully crystallized state are much smaller, compared with those of other known BMGs, the differences being attributed to the microstructural feature of the BMG.

Effects of the orientation of microgravity on the concentration distribution in crystallization from solution

A quasi-steady state growth and dissolution in a 2-D rectangular enclosure is numerically investigated. This paper is an extension to indicate the effects of the orientation of gravity on the concentration field in crystallization from solution under microgravity, especially on the lateral non-uniformity of concentration distribution at the growth surface. The thermal and solute convection are included in this model.

Glass transition and crystallization process of hard magnetic bulk Nd60Al10Fe20Co10 metallic glass

Glass transition and crystallization process of bulk Nd60Al10Fe20Co10 metallic glass were investigated by means of dynamic mechanical thermal analysis (DMTA), differential scanning calorimetry (DSC), X-ray diffraction (XRD) and scanning electronic microscopy (SEM). It is shown that the glass transition and onset crystallization temperature determined by DMTA at a heating rate of 0.167 K/s are 480 and 588 K respectively. The crystallization process of the metallic glass is concluded as follows: amorphous alpha-->alpha' + metastable FeNdAl novel phase -->alpha' + primary delta phase-->primary delta phase + eutectic delta phase Nd3Al phase + Nd3Co phase. The appearance of hard magnetism in this alloy is ascribed to the presence of amorphous phase with highly relaxed structure. The hard magnetism disappeared after the eutectic crystallization of amorphous phase.

Kinetics of Glass Transition and Crystallization in Carbon Nanotube Reinforced Mg-Cu-Gd Bulk Metallic Glass

Mg65 Cu25 Gdlo bulk metallic glass and its carbon nanotube reinforced composite were prepared. Differential scanning calorimeter (DSC) was used to investigate the kinetics of glass transition and crystallization processes. The influence of CNTs addition to the glass matrix on the glass transition and crystallization kinetics was studied. It is shown that the kinetic effect on glass transition and crystallization are preserved for both the monothetic glass and its glass composite. Adding CNTs in to the glass matrix reduces the influence of the heating rate on the crystallization process. In addition, the CNTs increase the energetic barrier for the glass transition. This results in the decrease of GFA . The mechanism of the GFA decrease was also discussed.

Study of Growth Mechanism of Lysozyme Crystal by Batch Crystallization Method

The lysozyme crystals were made by batch crystallization method and the distribution of aggregate in solution were measured by dynamic light scattering. The results showed that the dimension of aggregate increased with the increase of the concentration of lysozyme and NaCl, lysozyme molecules aggregated gradually in solution and finally arrived at balance each other. The higher the concentrations of lysozyme and NaCl were, the faster the growth rate of (I 10) face was. The growth rates of lysozyme crystal were obtained by a Zeiss microscope, and the effective surface energy (a) of growing steps were calculated about 4.01 X 10(-8) J.cm(-2) according to the model of multiple two-dimensional nucleation mechanism.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Melting and Crystallization of Nd60Al10Fe20Co10 Bulk Metallic Glass under High Pressure

Crystallization, melting and structural evolution upon crystallization in Nd60Al10Fe20Co10 bulk metallic glass (BMG) are in situ investigated by x-ray diffraction with synchrotron radiation under high pressure. It is found that the crystallization is pressure promoted, while themelting is inhibited. The crystallization and melting process are also changed under high pressure. The features of the crystallization and melting under high pressure are discussed.

The Fluid Phenomena In the Crystallization Of the Protein Crystal

This paper reports that an optical diagnostic system consisting of Mach-Zehnder interferometer with a phase shift device and image processor has been used for study of the kinetics of protein crystal growing process. The crystallization process of protein crystal by vapour diffusion is investigated. The interference fringes are observed in real time. The present experiment demonstrates that the diffusion and the sedimentation influence the crystallization of protein crystal which grows in solution, and the concentration capillary convection associated with surface tension occurs at the vicinity of free surface of the protein mother liquor, and directly affects on the outcome of protein crystallization. So far the detailed analysis and the important role of the fluid phenomena in protein crystallization have been discussed a little in both space- and ground-based crystal growth experiments. It is also found that these fluid phenomena affect the outcome of protein crystallization, regular growth, and crystal quality. This may explain the fact that many results of space-based investigation do not show overall improvement.

Forward Smoothing Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Essentially, it is an on-line or "forward only" implementation of a forward filtering backward smoothing SMC algorithm proposed by Doucet, Godsill and Andrieu (2000). Compared to the standard \emph{path space} SMC estimator whose asymptotic variance increases quadratically with time even under favorable mixing assumptions, the non asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.