Narrow Results

Kinesin is a stepping motor that moves along microtubules in discrete steps of 8 nm and there may exist three events in kinesin movement, i.e. the forward step, the backward step and the detachment. On the basis of the data obtained in the advanced experiments and the assumption that the rates of the three stochastic reaction events are Boltzmann type, the mechanical kinetic and thermodynamic properties of the kinesin motor are investigated through a statistical method. The mean dwell time, velocity, run length, power and efficiency of the motor are calculated theoretically. The effects of the temperature and load on the performance of the motor are discussed. It is found that the temperature dependent relations of both the mean run length and the efficiency η_M are not simply linear. The results are in good agreement with those obtained in previous experimental researches.

We extend the statistical mechanical theory of cosmological many-body problem to a system consisting of two kinds of galaxies with different masses. The general partition function for such a two-component system, in the grand canonical ensemble, is developed. From this partition function various thermodynamical quantities and distribution functions are evaluated. A quantity b_m, called the clustering parameter for the two-component system, which inherently takes into account the ratio of the number of particles (galaxies) of each kind and their masses, emerges directly from the calculations. Various clustering phenomena can be explained on the basis of this parameter. We find these results in agreement with the available N-body simulation results as well as the observational distribution function determined from the 2MASS survey. Besides, our distribution function, for the two-component system, makes these comparisons more objective and easily comprehensible.

In this work, the surface of AISI 430 stainless steel was coated with a hyper-eutectic FeCrC and pure B₄C powders using the plasma transferred arc (PTA) welding method. Further, the microstructure, microhardness, and abrasive wear resistance of the coated layer were evaluated in this work. Hard coating layers were formed between 750HV and 1000HV from the PTA melting coating process with high energy input (due to PTA). Due to the addition of FeCrC and B₄C, the martensite, austenite, M₇(C-B)₃, M${}_{23}$(C-B)₆, Cr(C-B), FeCr, Fe₂B, Fe₃C, Fe₂C, SiC, and Fe₃(C-B) phases were formed in the coating layer which makes the layer very hard. The abrasive wear resistance of the coated layer was evaluated using the Taguchi method with L${}_{16}$ mixed-level orthogonal index. Samples, wear distance (m), applied load (N), and abrasive grain size (grid) were chosen as input parameters. The mass loss due to wearing was evaluated based on “the smaller the better”. All input parameters significantly influence the wear, but the highest effect was found with the wear (slip) distance. Also, a linear regression equation was obtained to estimate the mass loss between the parameters and the levels used.

The paper gives an intelligent mining sequential pattern method as a way to solve the problem of simulating Olympic temporary pots. Mining sequential patterns serves an essential role to the mining domain and offer useful resources to large number of data users. Although most mining method offer complete mining results, they simply collect and statistic all data and do not provide an effectively analytical method. This study aims to develop an intelligent mining sequential pattern method that unlike traditional statistical method uses a number of intelligent methods, including the distributed mining of sequential pattern discovery algorithm based on the prefix-projected technique and clustering algorithm, and provides more meaningful and accurate results, and at the same time improves the efficiency.