Purification and characterization of two cellobiohydrolases from Chaetomium thermophile var. coprophile

Cellobiohydrolases I and II were purified to homogeneity from culture filtrates of a thermophilic fungus, Chaetomium thermophile var. coprophile, by using a combination of ion-exchange and gel filtration chromatographic procedures. The molecular weights of cellobiohydrolase I and II were estimated to be 60000 and 40000 and the enzymes were found to be glycoproteins containing 17 and 22.8% carbohydrate, respectively. The two forms differed in their amino-acid composition mainly with respect to threonine, alanine, methionine and arginine. Antibodies produced against either form of cellobiohydrolases failed to cross-react with the other. The tryptic maps of the two enzymes were found to be different. The temperature optima for cellobiohydrolase I and II were 75 and 70°C, and they were optimally active at pH 5.8 and 6.4, respectively. Both enzymes were stable at higher temperatures and were able to degrade crystalline cellulosic materals.

Robust/optimal temperature profile control using neural networks

An approximate dynamic programming (ADP) based neurocontroller is developed for a heat transfer application. Heat transfer problem for a fin in a car's electronic module is modeled as a nonlinear distributed parameter (infinite-dimensional) system by taking into account heat loss and generation due to conduction, convection and radiation. A low-order, finite-dimensional lumped parameter model for this problem is obtained by using Galerkin projection and basis functions designed through the 'Proper Orthogonal Decomposition' technique (POD) and the 'snap-shot' solutions. A suboptimal neurocontroller is obtained with a single-network-adaptive-critic (SNAC). Further contribution of this paper is to develop an online robust controller to account for unmodeled dynamics and parametric uncertainties. A weight update rule is presented that guarantees boundedness of the weights and eliminates the need for persistence of excitation (PE) condition to be satisfied. Since, the ADP and neural network based controllers are of fairly general structure, they appear to have the potential to be controller synthesis tools for nonlinear distributed parameter systems especially where it is difficult to obtain an accurate model.

Transient signal detection using wigner-ville distribution and wavelet denoising

The problem of detecting an unknown transient signal in noise is considered. The SNR of the observed data is first enhanced using wavelet domain filter The output of the wavelet domain filter is then transformed using a Wigner-Ville transform,which separates the spectrum of the observed signal into narrow frequency bands. Each subband signal at the output of the Wigner-ville block is subjected kto wavelet based level dependent denoising (WBLDD)to supress colored noise A weighted sum of the absolute value of outputs of WBLDD is passed through an energy detector, whose output is used as test statistic to take the final decision. By assigning weights proportional to the energy of the corresponding subband signals, the proposed detector approximates a frequency domain matched filter Simulation results are presented to show that the performance of the proposed detector is better than that of the wavelet packet transform based detector.

Parallelization of an unstructured data based cell centre finite volume code, HIFUN-3D

This work describes the parallelization of High Resolution flow solver on unstructured meshes, HIFUN-3D, an unstructured data based finite volume solver for 3-D Euler equations. For mesh partitioning, we use METIS, a software based on multilevel graph partitioning. The unstructured graph used for partitioning is associated with weights both on its vertices and edges. The data residing on every processor is split into four layers. Such a novel procedure of handling data helps in maintaining the effectiveness of the serial code. The communication of data across the processors is achieved by explicit message passing using the standard blocking mode feature of Message Passing Interface (MPI). The parallel code is tested on PACE++128 available in CFD Center

Max-Coloring Paths: Tight Bounds and Extensions

The max-coloring problem is to compute a legal coloring of the vertices of a graph G = (V, E) with a non-negative weight function w on V such that Sigma(k)(i=1) max(v epsilon Ci) w(v(i)) is minimized, where C-1, ... , C-k are the various color classes. Max-coloring general graphs is as hard as the classical vertex coloring problem, a special case where vertices have unit weight. In fact, in some cases it can even be harder: for example, no polynomial time algorithm is known for max-coloring trees. In this paper we consider the problem of max-coloring paths and its generalization, max-coloring abroad class of trees and show it can be solved in time O(vertical bar V vertical bar+time for sorting the vertex weights). When vertex weights belong to R, we show a matching lower bound of Omega(vertical bar V vertical bar log vertical bar V vertical bar) in the algebraic computation tree model.

Modified linear prediction method for directions of arrival estimation of narrow-band plane waves

A modified linear prediction (MLP) method is proposed in which the reference sensor is optimally located on the extended line of the array. The criterion of optimality is the minimization of the prediction error power, where the prediction error is defined as the difference between the reference sensor and the weighted array outputs. It is shown that the L2-norm of the least-squares array weights attains a minimum value for the optimum spacing of the reference sensor, subject to some soft constraint on signal-to-noise ratio (SNR). How this minimum norm property can be used for finding the optimum spacing of the reference sensor is described. The performance of the MLP method is studied and compared with that of the linear prediction (LP) method using resolution, detection bias, and variance as the performance measures. The study reveals that the MLP method performs much better than the LP technique.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Faster Algorithms for All-pairs Approximate Shortest Paths in Undirected Graphs

Let G - (V, E) be a weighted undirected graph having nonnegative edge weights. An estimate (delta) over cap (u, v) of the actual distance d( u, v) between u, v is an element of V is said to be of stretch t if and only if delta(u, v) <= (delta) over cap (u, v) <= t . delta(u, v). Computing all-pairs small stretch distances efficiently ( both in terms of time and space) is a well-studied problem in graph algorithms. We present a simple, novel, and generic scheme for all-pairs approximate shortest paths. Using this scheme and some new ideas and tools, we design faster algorithms for all-pairs t-stretch distances for a whole range of stretch t, and we also answer an open question posed by Thorup and Zwick in their seminal paper [J. ACM, 52 (2005), pp. 1-24].

Optimal Weighted Antenna Selection For Imperfect Channel Knowledge From Training

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the ``best'' antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

Flame retardant polyphosphate esters: 1. Condensation polymers of bisphenols with aryl phosphorodichloridates: synthesis, characterization and thermal studies

Polyphosphate esters were synthesized by the solution polycondensation of bisphenols with aryl phosphorodichloridates. The polymers were characterized by i.r. and 1H, 13C and 31P n.m.r. spectroscopy. The molecular weights were determined by end group analysis using 1H and 31P n.m.r. spectral data. The thermal stability of the polymers was analysed by thermogravimetry.

Flame retardant polyphosphate esters: 2. Condensation polymers of bisphenol A with alkyl phosphorodichloridates: synthesis, characterization and thermal studies

Polyphosphate esters based on bisphenol A and alkyl phosphorodichloridates have been synthesized and characterized by i.r. and n.m.r. spectroscopy. The molecular weights were calculated from 31P n.m.r. The thermal stability of the polymers were analysed by thermogravimetry.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Optimal Receive Antenna Selection in Time-Varying Fading Channels with Practical Training Constraints

Hardware constraints, which motivate receive antenna selection, also require that various antenna elements at the receiver be sounded sequentially to obtain estimates required for selecting the `best' antenna and for coherently demodulating data thereafter. Consequently, the channel state information at different antennas is outdated by different amounts and corrupted by noise. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, a preferable strategy is to linearly weight the channel estimates of different antennas differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then characterize explicitly the optimal selection weights that minimize the SEP. We also consider packet reception, in which multiple symbols of a packet are received by the same antenna. New suboptimal, but computationally efficient weighted selection schemes are proposed for reducing the packet error rate. The benefits of weighted selection are also demonstrated using a practical channel code used in third generation cellular systems. Our results show that optimal weighted selection yields a significant performance gain over conventional unweighted selection.

Thermal gap conductance at low contact pressures (<1 MPa): Effect of gold plating and plating thickness

Thermal contact conductance (TCC) measurements are made on bare and gold plated (<= 0.5 mu m) oxygen free high conductivity (OFHC) Cu and brass contacts in vacuum, nitrogen, and argon environments. It is observed that the TCC in gaseous environment is significantly higher than that in vacuum due to the enhanced thermal gap conductance. It is found that for a given contact load and gas pressure, the thermal gap conductance for bare OFHC Cu contacts is higher than that for gold plated contacts. It is due to the difference in the molecular weights of copper and gold, which influences the exchange of kinetic energy between the gas molecules and contact surfaces. Furthermore, the gap conductance is found to increase with increasing thickness of gold plating. Topography measurements and scanning electron microscopy (SEM) analysis of contact surfaces revealed that surfaces become smoother with increasing gold plating thickness, thus resulting in smaller gaps and consequently higher gap conductance. (C) 2010 Elsevier Ltd. All rights reserved.

Purification and characterization of three toxins and two agglutinins from Abrus precatorius seed by using lactamyl-Sepharose affinity chromatography

Three toxins, abrin-I, -II, and -III, and two agglutinins, APA-I and -II, were purified from the seeds of Abrus precatorius by lactamyl-Sepharose affinity chromatography followed by gel filtration and DEAE-Sephacel column chromatography. abrin-I did not bind on DEAE-Sephacel column chromatography and the bound abrin-II, abrin-III, APA-I, and APA-II were eluted with a sodium acetate gradient. The identity of each protein was established by sodium dodecylsulfate-polyacrylamide gel electrophoresis and isoelectric focusing. The relative molecular weights are abrin-I, 64,000; abrin-II and abrin-III, 63,000 each: APA-I, 130,000; and APA-II, 128,000. Isoelectric focusing revealed microheterogeneity due to the presence of isoforms in each protein. Toxicity and binding studies further confirmed the differences among the lectins. The time course of inhibition of protein synthesis in thymocytes by the toxins showed lag times of 78, 61, and 72 min with Ki's of 0.55, 0.99, and 0.74 ms−1 at a 0.63 nImage concentration of each of abrin-I, -II, and -III, respectively. A Scatchard plot obtained from the equilibrium measurement for the lectins binding to lactamyl-Sepharose beads showed nonlinearity, indicating a cooperative mode of binding which was not observed for APA-I binding to Sepharose 4B beads. Further, by the criterion of the isoelectric focusing profile, it was shown that the least toxic abrin-I and the highly toxic abrin-II isolated by lactamyl-Sepharose chromatography were not retained on a low-affinity Sepharose 4B matrix, which signifies the necessity of using a high-affinity matrix for the purification of the lectins.