Biological application of multi-component nanowires in hybrid devices powered by F-1-ATPase motors

In this paper, construction of hybrid device by integrating nanowires with F1-ATPase motors is described. The nickel nanowires and multi-segment nanowires, including gold and nickel, were fabricated by electrochemical deposition in nanoporous templates. The nickel nanowires functionalized by biotinylated peptide can be assembled directly onto F1-ATPase motors to act as the propellers. If the multicomponent nanowires, including gold and nickel, were selectively functionalized by the thiol group modified ssDNA and the synthetic peptide, respectively, the biotinylated F1- ATPase motors can be attached to the biotinylated peptide on nickel segment of the nanowires. Then, the multi-component nanowires can also be used as the propellers, and one may observe the rotations of the multi-component nanowires driven by F1-ATPase motors. Therefore, introduction of multiple segments along the length of a nanowire can lead to a variety of multiple chemical functionalities, which can be selectively bound to cells and special biomolecules. This method provides an insight for the construction of other hybrid devices with its controlling arrangement of different biomolecule on designed nanometer scale structures.

The Uniaxial Tensile Deformation of Ni Nanowire: Atomic-Scale Computer Simulations

The mechanical deformations of nickel nanowire subjected to uniaxial tensile strain at 300 K are simulated by using molecular dynamics with the quantum corrected Sutten-Chen many-body force field. We have used common neighbor analysis method to investigate the structural evolution of Ni nanowire during the elongation process. For the strain rate of 0.1%/ps, the elastic limit is up to about 11% strain with the yield stress of 8.6 GPa. At the elastic stage, the deformation is carried mainly through the uniform elongation of the distances between the layers (perpendicular to the Z-axis) while the atomic structure remains basically unchanged. With further strain, the slips in the {111} planes start to take place in order to accommodate the applied strain to carry the deformation partially, and subsequently the neck forms. The atomic rearrangements in the neck region result in a zigzag change in the stress-strain curve; the atomic structures beyond the region, however, have no significant changes. With the strain close to the point of the breaking, we observe the formation of a one-atom thick necklace in Ni nanowire. The strain rates have no significant effect on the deformation mechanism, but have some influence on the yield stress, the elastic limit, and the fracture strain of the nanowire.

Numerical simulation of rock failure and earthquake process on mesoscopic scale

On the basis of the lattice model of MORA and PLACE, Discrete Element Method, and Molecular Dynamics approach, another kind of numerical model is developed. The model consists of a 2-D set of particles linked by three kinds of interactions and arranged into triangular lattice. After the fracture criterion and rules of changes between linking states are given, the particle positions, velocities and accelerations at every time step are calculated using a finite-difference scheme, and the configuration of particles can be gained step by step. Using this model, realistic fracture simulations of brittle solid (especially under pressure) and simulation of earthquake dynamics are made.

Teracluster LSSC-II - Its Designing Principles and Applications in Large Scale Numerical Simulations

The teracluster LSSC-II installed at the State Key Laboratory of Scientific and Engineering Computing, Chinese Academy of Sciences is one of the most powerful PC clusters in China. It has a peek performance of 2Tflops. With a Linpack performance of 1.04Tflops, it is ranked at the 43rd place in the 20th TOP500 List (November 2002), 51st place in the 21st TOP500 List (June 2003), and the 82nd place in the 22nd TOP500 List (November 2003) with a new Linpack performance of 1.3Tflops. In this paper, we present some design principles of this cluster, as well as its applications in some largescale numerical simulations.

Statistical analysis of synthetic earthquake catalogs generated by models with various levels of fault zone disorder

The stress release model, a stochastic version of the elastic rebound theory, is applied to the large events from four synthetic earthquake catalogs generated by models with various levels of disorder in distribution of fault zone strength (Ben-Zion, 1996) They include models with uniform properties (U), a Parkfield-type asperity (A), fractal brittle properties (F), and multi-size-scale heterogeneities (M). The results show that the degree of regularity or predictability in the assumed fault properties, based on both the Akaike information criterion and simulations, follows the order U, F, A, and M, which is in good agreement with that obtained by pattern recognition techniques applied to the full set of synthetic data. Data simulated from the best fitting stress release models reproduce, both visually and in distributional terms, the main features of the original catalogs. The differences in character and the quality of prediction between the four cases are shown to be dependent on two main aspects: the parameter controlling the sensitivity to departures from the mean stress level and the frequency-magnitude distribution, which differs substantially between the four cases. In particular, it is shown that the predictability of the data is strongly affected by the form of frequency-magnitude distribution, being greatly reduced if a pure Gutenburg-Richter form is assumed to hold out to high magnitudes.

Multi-pipe string electromagnetic detection tool and its applications

The MID-K, a new kind of multi-pipe string detection tool is introduced. This tool provides a means of evaluating the condition of in-place pipe string, such as tubing and casino. It is capable of discriminating the defects of the inside and outside, and estimating the thickness of tubing and casing. It is accomplished by means of a low frequency eddy current to detect flaws on the inner surface and a magnetic flux leakage to inspect the full thickness. The measurement principle, the technology and applications are presented in this paper.

Visual pattern recognition network: Its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition. (C) 1996 Optical Society of America

Morphological ordered gray-scale erosion for optical image processing

An ordered gray-scale erosion is suggested according to the definition of hit-miss transform. Instead of using three operations, two images, and two structuring elements, the developed operation requires only one operation and one structuring element, but with three gray-scale levels. Therefore, a union of the ordered gray-scale erosions with different structuring elements can constitute a simple image algebra to program any combined image processing function. An optical parallel ordered gray-scale erosion processor is developed based on the incoherent correlation in a single channel. Experimental results are also given for an edge detection and a pattern recognition. (C) 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)00306-7].

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

Determining of the delay time for a heating ventilating and air-conditioning plant using multi-weights neurons approach

This paper presents an multi weights neurons approach to determine the delay time for a Heating ventilating and air-conditioning (HVAC) plan to respond to control actions. The multi weights neurons is a fully connected four-layer network. An acceleration technique was used to improve the general delta rule for the learning process. Experimental data for heating and cooling modes were used with both the multi weights neurons and a traditional mathematical method to determine the delay time. The results show that multi weights neurons can be used effectively determining the delay time for HVAC systems.

A speaker-independent continuous speech recognition system using biomimetic pattern recognition

In speaker-independent speech recognition, the disadvantage of the most diffused technology (HMMs, or Hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of Biomimetic pattern recognition (BPR) in recognizing some mandarin continuous speech in a speaker-independent manner. A speech database was developed for the course of study. The vocabulary of the database consists of 15 Chinese dish's names, the length of each name is 4 Chinese words. Neural networks (NNs) based on Multi-weight neuron (MWN) model are used to train and recognize the speech sounds. The number of MWN was investigated to achieve the optimal performance of the NNs-based BPR. This system, which is based on BPR and can carry out real time recognition reaches a recognition rate of 98.14% for the first option and 99.81% for the first two options to the persons from different provinces of China speaking common Chinese speech. Experiments were also carried on to evaluate Continuous density hidden Markov models (CDHMM), Dynamic time warping (DTW) and BPR for speech recognition. The Experiment results show that BPR outperforms CDHMM and DTW especially in the cases of samples of a finite size.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Electro-mass olfactory multi-sensor (EMMS)

For an olfactory sensor or electronic nose, the task is not only to detect the object concentration, but also to recognize it. It is well known that all the elements can be identified by their charge to mass ratio e(+)/m. We tried to imitate this principle for molecular recognition. Two kinds of sensors are used simultaneously in testing. One is quartz crystal microbalance (QCM) for detecting the change in mass, the other is interdigital electrode (IE) for detecting the change in conduction, as an electro-mass multi-sensor (EMMS). in this paper, the principle and the feasibility of this method are discussed. The preliminary results on the recognition of alcohol by EMMS coated with lipids are presented. Meanwhile, the multi-sensor can also be used as an instrument for research on some physico-chemistry problems. The change in conduction of coated membrane caused by one absorbed molecule is reported. It is found that when a QCM is coated with membrane, it still obeys the relationship Delta F (frequency change of QCM) = K Delta m (mass change of absorbed substance) and the proportional coefficient, K, depends not only on quartz properties but also on membrane characteristics as well. (C) 2000 Elsevier Science S.A. All rights reserved.