Hot deformation and microstructural evolution in an alpha(2)/O titanium aluminide alloy Ti-25Al-15Nb

Deformation processing and microstructural development of an alpha(2)/O aluminide alloy Ti-25Al-15Nb (at.%) was studied in the temperature range of 950 to 1200 degrees C and strain rate range of 10(-3) to 100 s(-1). Regions of processing and instability were identified using dynamic materials model. Dynamic recrystallization (DRX) of alpha(2)/O phase and p phase were seen to occur in the region of 950 to 1050 degrees C/0.001 to 0.05 s(-1) and 1125 to 1175 degrees C/0.001 to 0.1 s(-1), respectively. Unstable flow was seen to occur in the region of 1050 to 1190 degrees C/10 to 100 s(-1). Thermal activation analysis showed that DRX of alpha(2)/O and beta was controlled by cross-slip.

Processing map for the hot working of near-alpha titanium alloy 685

Using a dynamic materials model, processing and instability maps have been developed for near-alpha titanium alloy 685 in the temperature range 775-1025 degrees C and strain-rate range of 0.001-10 s(-1) to optimise its hot workability. The alloy's beta-transus temperature lies at about 1020 degrees C. The material undergoes superplasticity with a peak efficiency of 80% at 975 degrees C and 0.001 s(-1), which are the optimum parameters for alpha-beta working. The occurrence of superplasticity is attributed to two-phase microduplex structure, higher strain-rate sensitivity, low flow stress and sigmoidal variation between log flow stress and log strain rate. The material also exhibits how localisation due to adiabatic shear-band formation up to its beta-transus temperature with strain rates greater than 0.02 s(-1) and thus cracking along these regions. (C) 1997 Published by Elsevier Science S.A.

Role of feedback in mammalian vision: a new hypothesis and a computational model

This paper presents a novel hypothesis on the function of massive feedback pathways in mammalian visual systems. We propose that the cortical feature detectors compete not for the right to represent the output at a point, but for exclusive rights to abstract and represent part of the underlying input. Feedback can do this very naturally. A computational model that implements the above idea for the problem of line detection is presented and based on that we suggest a functional role for the thalamo-cortical loop during perception of lines. We show that the model successfully tackles the so called Cross problem. Based on some recent experimental results, we discuss the biological plausibility of our model. We also comment on the relevance of our hypothesis (on the role of feedback) to general sensory information processing and recognition. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.

On the diversity and complexity of Zero Forcing receivers for MIMO Zero Padded systems

Zero padded systems with linear receivers are shown to be robust and amenable to fast implementations in single antenna scenarios. In this paper, properties of such systems are investigated when multiple antennas are present at both ends of the communication link. In particular, their diversity and complexity are evaluated for precoded transmissions. The linear receivers are shown to exploit multipath and receive diversities, even in the absence of any coding at the transmitter. Use of additional redundancy to improve performance is considered and the effect of transmission rate on diversity order is analyzed. Low complexity implementations of Zero Forcing receivers are devised to further enhance their applicability.

Jointly Optimal MMSE design for non-redundant FIR precoding and equalization

We provide a filterbank precoding framework (FBP) for frequency selective channels using the minimum mean squared error (MMSE) criterion. The design obviates the need for introducing a guard interval between successive blocks, and hence can achieve the maximum possible bandwidth efficiency. This is especially useful in cases where the channel is of a high order. We treat both the presence and the absence of channel knowledge at the transmitter. In the former case, we obtain the jointly optimal precoder-equalizer pair of the specified order. In the latter case, we use a zero padding precoder, and obtain the MMSE equalizer. No restriction on the dimension or nature of the channel matrix is imposed. Simulation results indicate that the filterbank approach outperforms block based methods like OFDM and eigenmode precoding.

Interplay between NS3 protease and human La protein regulates translation-replication switch of Hepatitis C virus

HCV NS3 protein plays a central role in viral polyprotein processing and RNA replication. We demonstrate that the NS3 protease (NS3(pro)) domain alone can specifically bind to HCV-IRES RNA, predominantly in the SLIV region. The cleavage activity of the NS3 protease domain is reduced upon HCV-RNA binding. More importantly, NS3(pro) binding to the SLIV hinders the interaction of La protein, a cellular IRES-trans acting factor required for HCV IRES-mediated translation, resulting in inhibition of HCV-IRES activity. Although overexpression of both NS3(pro) as well as the full length NS3 protein decreased the level of HCV IRES mediated translation, replication of HCV replicon RNA was enhanced significantly. These observations suggest that the NS3(pro) binding to HCV IRES reduces translation in favor of RNA replication. The competition between the host factor (La) and the viral protein (NS3) for binding to HCV IRES might regulate the molecular switch from translation to replication of HCV.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

Functional regulation of PVBV Nuclear Inclusion protein-a protease activity upon interaction with Viral Protein genome-linked and phosphorylation

Regulation of NIa-Pro is crucial for polyprotein processing and hence, for successful infection of potyviruses. We have examined two novel mechanisms that could regulate NIa-Pro activity. Firstly, the influence of VPg domain on the proteolytic activity of NIa-Pro was investigated. It was shown that the turnover number of the protease increases when these two domains interact (as: two-fold; trans: seven-fold) with each other. Secondly, the protease activity of NIa-Pro could also be modulated by phosphorylation at Ser129. A mutation of this residue either to aspartate (phosphorylation-mimic) or alanine (phosphorylation-deficient) drastically reduces the protease activity. Based on these observations and molecular modeling studies, we propose that interaction with VPg as well as phosphorylation of Ser129 could relay a signal through Trp143 present at the protein surface to the active site pocket by subtle conformational changes, thus modulating protease activity of NIa-Pro. (C) 2011 Elsevier Inc. All rights reserved.

Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

Structure and properties of zinc alloy based metal matrix composites

Zinc-aluminium cast alloys (ZA alloys) exhibit good castability and mechanical properties but these alloys lack creep resistance and high temperature stability. One solution to improve these properties is to reinforce with ceramic particles or fibres, to result in MMCs. MMCs can be produced using casting technique involving infiltration. A systematic investigation was taken and this paper discusses the salient findings of the study on the ZA-27 alloy based MMCs produced through squeeze casting. (Reinforcing fibers: SAFFIL (chopped alumina) or mullite.)

Nanostructured titanium-nickel alloys: a comparison of processing routes

Rapid solidification, mechanical alloying and devitrificaiton of precursor metallic glasses are all possible routes for the synthesis of nanocrystals and nanocomposites, though their efficacy is system dependent. In a comprehensive study of alloys across the Ti-Ni phase diagram, nanocrystals of Ti and Ni and nanocomposites of alpha -Ti and Ti sub 2 Ni, Ti sub 2 Ni and TiNi and beta -Ti and glass have been produced. By the addition of Al, devitrification of metallic glasses created by mechanical alloying led to nanocrystalline intermetallic compounds. The evolution of these nanocrystalline microstructures has been rationalized on the basis of thermodynamic and kinetic considerations involving the metastable phase diagram for this system.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses. © 2012 American Physical Society.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.