马氏体相变研究的进展和瞻望SCIEI

叙述马氏体相变研究的一些进展和瞻望,包括马氏体相变的定义,马氏体相变热力学,奥氏体状态对马氏体相变的影响,动力学,形核和长大,以及马氏体相变晶体学。     

Single- and dual-ion-beam sputter deposition of titanium oxide films

The optical properties and the surface morphologies of single-ion-beam sputtering (SIBS) and dual-ion-beam sputtering (DIBS) depositions of titanium oxide films are investigated and compared. In the DIBS process, the ion-assisted deposition by the voltage of a low ion beam ranged from 50 to 300 V at a 0% and 44% oxygen percentage. Cosputtering with materials of Si, SiO(2) (fused silica), and Al is also utilized in SIBS to improve amorphous-structure film. For the low-absorption and surface-roughness film, the optimum deposition condition of DIBS and postdeposition baking temperature for SIBS and DIBS are essential to the process.     

Computational and mutagenesis studies of the streptavidin native dimer interface

Wt streptavidin forms a domain swapped tetramer consisting of two native dimers. The role of tetramerization has been studied previously and is known to contribute to biotin binding by allowing the exchange of W120 between adjacent subunits. However, the role of dimer formation in streptavidin folding and function has been largely overlooked to date, although native dimers are necessary for tetramer formation and thus for high affinity biotin binding. To understand how the side chain interactions at the dimer interface stabilize the subunit association, we studied the structural and functional consequences of introducing interfacial mutations by a combination of molecular dynamics (MD) simulation and biochemical characterization. In particular, we introduced rational mutations at the dimer interface to engineer new side chain interactions and measured the stability and function of the resulting mutants. We focused on two residues that form a "knob" and a "hole" pair, G74 and T76, since steric complementarity plays an important role at these positions. We introduced mutations that would change the polarity and side chain packing to test if the interface can be rationally redesigned. Both energy calculation and geometric parameterization were used to interpret the simulated structures and predict how the mutations affect the dimer stability. In this regard, obtaining precise energy estimates was difficult because the simulated structures have large stochastic variations and some mutants did not reach an equilibrium by the end of the simulation. In contrast, comparing the wt and mutants to one another and parameterizing the simulation using a geometric parameter, i.e. the degree of solvation of the buried interface, resulted in a testable prediction regarding which mutations would result in a stable dimer. We present experimental data (denaturation and binding measurements) to show that an intuitive parameter based on physical reasoning can be useful for characterizing simulations that are difficult to analyze quantitatively.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

Intelligent maintenance prediction system for LED wafer testing machine

Achieving high quality production of light-emitting diode (LED) wafers requires robust monitoring and the use of a stable test machine. In many factories, production continues 24 h a day. Stopping the manufacturing process at a factory is often difficult. Therefore, reducing inspection time and ensuring the stability of test machines are important. Traditionally, LED wafer factories examine their test machines during periodic maintenance. Standard lamp adjustments are performed to ensure their accuracy. This process interrupts the manufacturing process and requires extra manpower. It reduces productivity and increases production cost. Additionally, the accurate assessment of the aging of the components of the machine requires an experienced engineer. Correctly timing the maintenance and replacing the aging components of the LED wafer test machine are important. This work performed feature extraction to identify the working attributes of an LED wafer test machine. The intelligent maintenance prediction system then uses the radial basis function neural network and variability of the working attributes to predict the maintenance times and aging of the LED wafer test machines. Experimental results reveal that the accuracy of proposed system in predicting maintenance times exceeds 98 %.     

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process.     

Image Retrieval With Relevance Feedback Based on Graph-Theoretic Region Correspondence Estimation

This paper presents a graph-theoretic approach for interactive region-based image retrieval. When dealing with image matching problems, we use graphs to represent images, transform the region correspondence estimation problem into an inexact graph matching problem, and propose an optimization technique to derive the solution. We then define the image distance in terms of the estimated region correspondence. In the relevance feedback steps, with the estimated region correspondence, we propose to use a maximum likelihood method to re-estimate the ideal query and the image distance measurement. Experimental results show that the proposed graph-theoretic image matching criterion outperforms the other methods incorporating no spatially adjacent relationship within images. Furthermore, our maximum likelihood method combined with the estimated region correspondence improves the retrieval performance in feedback steps.     

Flow patterns in gas-assisted injection molding process in a channel

The flow field of a long bubble steadily expelling a viscous fluid confined by two closely located parallel plates is examined. In order to investigate the influence of bubble size on the flow field, a theoretical bubble profile is used to replace the complicated procedure for computing simultaneously the interface between the gas surface and fluid flows. The present study showed the two typical flow patterns and also a third flow pattern of the stagnation point moving in the region of the bubble tip front during transformation of the two typical flow patterns. The vorticity patterns are also drawn for various bubble profiles and are examined for their effect on the flow. The velocity field is also presented from two different viewpoints and the phenomena is examined. The stagnation point located on the center line between the bubble tip to the upstream is only found in the small range of in a channel, where λ is ratio of the bubble width to the distance between two parallel plates.