Phase-precipitation studies of Fe-B-Si metallic glasses using Mössbauer spectroscopy

Phase-precipitation studies have been performed on samples of the metallic glasses Fe₇₉B₁₆Si₅ and Fe₇₈B₁₃Si₉, heated in the range 300–475 °C for various times (1–16 h) using ⁵⁷Fe Mössbauer transmission spectroscopy and X-ray diffraction methods. These measurements have helped in identifying the temperature ranges and annealing durations in which the amorphous structure of these metallic glasses is retained. The results revealed that the thermal stability increases as boron is replaced by silicon in the Fe-B-Si metallic glasses and that these alloys remain amorphous below 450 °C. The various phases precipitated above this temperature were identified as -Fe, -(Fe, Si), Fe₃B, and Fe₂B. The direction of magnetization in the two metallic glasses appears to change upon annealing.     

Common-input models for multiple neural spike-train data

Recent developments in multi-electrode recordings enable the simultaneous measurement of the spiking activity of many neurons. Analysis of such multineuronal data is one of the key challenge in computational neuroscience today. In this work, we develop a multivariate point-process model in which the observed activity of a network of neurons depends on three terms: (1) the experimentally-controlled stimulus; (2) the spiking history of the observed neurons; and (3) a hidden term that corresponds, for example, to common input from an unobserved population of neurons that is presynaptic to two or more cells in the observed population. We consider two models for the network firing-rates, one of which is computationally and analytically tractable but can lead to unrealistically high firing-rates, while the other with reasonable firing-rates imposes a greater computational burden. We develop an expectation-maximization algorithm for fitting the parameters of both the models. For the analytically tractable model the expectation step is based on a continuous-time implementation of the extended Kalman smoother, and the maximization step involves two concave maximization problems which may be solved in parallel. The other model that we consider necessitates the use of Monte Carlo methods for the expectation as well as maximization step. We discuss the trade-off involved in choosing between the two models and the associated methods. The techniques developed allow us to solve a variety of inference problems in a straightforward, computationally efficient fashion; for example, we may use the model to predict network activity given an arbitrary stimulus, infer a neuron's ring rate given the stimulus and the activity of the other observed neurons, and perform optimal stimulus decoding and prediction. We present several detailed simulation studies which explore the strengths and limitations of our approach.     

Nanoindentation measurement of core–skin interphase viscoelastic properties in a sandwich glass composite

Mechanics of Time-Dependent Materials - Debonding at the core–skin interphase region is one of the primary failure modes in core sandwich composites under shear loads. As a result, the...     

Source codes as random number generators

A random number generator generates fair coin flips by processing deterministically an arbitrary source of nonideal randomness. An optimal random number generator generates asymptotically fair coin flips from a stationary ergodic source at a rate of bits per source symbol equal to the entropy rate of the source. Since optimal noiseless data compression codes produce incompressible outputs, it is natural to investigate their capabilities as optimal random number generators. We show under general conditions that optimal variable-length source codes asymptotically achieve optimal variable-length random bit generation in a rather strong sense. In particular, we show in what sense the Lempel-Ziv (1978) algorithm can be considered an optimal universal random bit generator from arbitrary stationary ergodic random sources with unknown distributions     

Design optimization of a large scale rooftop photovoltaic system

This paper presents the optimization process of a grid connected photovoltaic (PV) system, which is intended to replace a large-scale thermal solar system on the rooftop of a Federal office building. A PV energy conversion model is described. Based on this model, array surface tilt angle and array size are optimized. The optimization method is based on maximizing the utilization of the array output energy, and, at the same time, minimizing the electricity power sold to grid. An effectiveness factor is introduced that takes into account both of these parameters. The array configuration and the output parameters are determined by comparing several PV modules. A 43.2 kW PV system is designed and operational problems such as harmonic effects and anti-islanding are discussed. Finally, the system performance is simulated and through economic analysis it has shown that the cost of PV system can be recouped in 13 years under the current renewable energy incentive program by the state of Illinois.     

A dynamic model for predicting the motion of solder droplets during assembly

The dynamic motion of a solder droplet during assembly is a complex, unsteady, free surface problem involving surface tension and viscous effects. The motion of the droplet is coupled with the motion of the component or chip to be assembled and involves dynamic contact lines. A methodology based on a non-uniform rational b-spline (NURBS) discretization has been developed for the dynamic analysis of the droplet motion. A surface energy based formulation has been developed to incorporate the surface tension effects. The developed methodology leads to an updated Lagrangian scheme with a Galerkin in space and Least square in time formulation. The NURBS representation used for the spatial discretization enables the method to handle problems involving complex droplet geometries. The ability of the NURBS representation to provide both global and local control, along with the least square method used in this methodology, enables us to develop an unconditionally stable time integration scheme which can be optimized to achieve desired accuracy and numerical dissipation efficiently. A sample problem of droplet shape evolution has been solved to demonstrate the path prediction capability of the proposed methodology. In future, the method can be applied to solve various real world dynamic motion problems associated with droplets.     

Principal curves with bounded turn

Principal curves, like principal components, are a tool used in multivariate analysis for ends like feature extraction. Defined in their original form, principal curves need not exist for general distributions. The existence of principal curves with bounded length for any distribution that satisfies some minimal regularity conditions has been shown. We define principal curves with bounded turn, show that they exist, and present a learning algorithm for them. Principal components are a special case of such curves when the turn is zero.     

Effect of puffing on oil characteristics ofAmaranth (Rajgeera) seeds

Puffing or popping is a common method of processingAmaranthus cruentus (Syn.Amaranthus paniculatus L. or Rajgeera) grain. Investigations into the effect of this processing treatment have shown the percent unsaturation in the oil to decrease from 75.5% to 62.3%. The maximum effect is on linoleic acid, the quantity of which decreased sharply from 46.8% to 27.0%. Squalene also increased by 15.5%, due to puffing of amaranth seeds.     

Algorithmic ADC operating with a single reference voltage for PLL-based chemical sensing applications

This paper deals with the design of an algorithmic switched-capacitor analog-to-digital converter (ADC), operating with a single reference voltage, a single-ended amplifier, a single-ended comparator, and presenting a small input capacitance. The ADC requires two clock phases per conversion bit and N clock cycles to resolve the N-bits. The ADC achieves a measured peak signal-to-noise-ratio (SNR) of 49.9 dB and a peak signal-to-noise-and-distortion-ratio (SNDR) of 46.7 dB at P_in = ?6dBFS with a sampling rate of 0.25 MS/s. The measured differential-non-linearity and integral-non-linearity are within +0.6/?0.5 and +0.2/?0.5 LSB, respectively. The ADC power consumption is 300 μW and it is implemented in 90 nm CMOS technology with a single power supply of 1.2 V. The ADC saves power at system-level by requiring only a single reference voltage. At system level, this solution is therefore not only robust but competitive as well.     

Intelligent Biohybrid Materials for Therapeutic and Imaging Agent Delivery

Many molecular imaging techniques rely on tracer methods to visualize specific physiological processes in cells, animals, and humans. A new family of "smart" delivery systems for biomolecules has opened new opportunities for the molecular imaging field. One class of polymeric carriers reversibly become membrane destabilizing in response to sharp pH changes and were designed for delivering proteins and nucleic acids to intracellular compartments. These carriers could enable the use of imaging agents and intracellular reporters whose site of action made them previously inaccessible. A second class of stimuli-responsive polymer-biomolecule conjugates can be reversibly formed into particles of closely defined sizes. The ability to control when and where the protein or DNA species is in the free versus particle form may allow imaging applications that exploit their differential size and diffusion properties.