Understanding the mechanism of heat stress tolerance caused by high trehalose accumulation in Saccharomyces cerevisiae using DNA microarray

DNA microarray analysis was performed to examine the stress tolerance mechanism of a Saccharomyces cerevisiae recombinant strain exhibiting high trehalose accumulation and heat stress tolerance. Results suggest that the upregulation of sugar transporter genes is one of the key events for heat stress tolerance of the recombinant strain.     

Evolving an emotion recognition module for an intelligent agent using genetic programming and a genetic algorithm

Most studies use the facial expression to recognize a user’s emotion; however, gestures, such as nodding, shaking the head, or stillness can also be indicators of the user’s emotion. In our research, we use the facial expression and gestures to detect and recognize a user’s emotion. The pervasive Microsoft Kinect sensor captures video data, from which several features representing facial expressions and gestures are extracted. An in-house extensible markup language-based genetic programming engine (XGP) evolves the emotion recognition module of our system. To improve the computational performance of the recognition module, we implemented and compared several approaches, including directed evolution, collaborative filtering via canonical voting, and a genetic algorithm, for an automated voting system. The experimental results indicate that XGP is feasible for evolving emotion classifiers. In addition, the obtained results verify that collaborative filtering improves the generality of recognition. From a psychological viewpoint, the results prove that different people might express their emotions differently, as the emotion classifiers that are evolved for particular users might not be applied successfully to other user(s).     

Edge effect on electronic transport properties of graphene nanoribbons and presence of perfectly conducting channel

Numerical calculations have been performed to elucidate unconventional electronic transport properties in disordered nanographene ribbons with zigzag edges (zigzag ribbons). The energy band structure of zigzag ribbons has two valleys that are well separated in momentum space, related to the two Dirac points of the graphene spectrum. The partial flat bands due to edge states make the imbalance between left- and right-going modes in each valley, i.e. appearance of a single chiral mode. This feature gives rise to a perfectly conducting channel in the disordered system, i.e. the average of conductance 〈g〉 converges exponentially to 1 conductance quantum per spin with increasing system length, provided impurity scattering does not connect the two valleys, as is the case for long-range impurity potentials. Ribbons with short-range impurity potentials, however, through inter-valley scattering, display ordinary localization behavior. Symmetry considerations lead to the classification of disordered zigzag ribbons into the unitary class for long-range impurities, and the orthogonal class for short-range impurities. The electronic states of graphene nanoribbons with general edge structures are also discussed, and it is demonstrated that chiral channels due to the edge states are realized even in more general edge structures except for armchair edges.     

气干与湿润状态下多孔隙岩石的黏弹性特性与力学模型

为研究多孔隙岩石的变形特性,提出一个适用于多孔隙岩石的非线形流变模型（本构方程）.本模型是由弹簧与阻尼器串连组成,弹簧部分采用作者曾经提出的本构方程;代表黏性部分的阻尼器设定加载前黏度很小,随着荷载的增加而逐渐加大.在低应力水平下的蠕变试验中,由于弹性系数没有明显的下降,因而设定阻尼器的应变与蠕变应变相同.为验证模型的准确性,对泥质砂岩、大谷凝灰岩、田下凝灰岩与河津凝灰岩4类岩石分别进行理论计算与试验测试.对于各类岩石,计算得到的应力-应变曲线与实测曲线基本一致,而且用此流变模型,发现气干与湿润状态下杨氏模量具有明显的差异.研究结果表明,在这2种状态下,杨氏模量的差异是由于非弹性应变（即阻尼器的应变）不同而引起的.在湿润状态下,阻尼器的应变增加越大,杨氏模量则变得越小.即使在较低的应力条件下,阻尼器的应变比以往研究中所认为的应变要大很多.     

基于图像清晰度无波前传感器自适应光学系统的研究

自适应光学技术能够校正大气湍流造成的光束畸变,但常规自适应光学系统需要波前传感器测量波前信息.在本文中系统不使用波前传感器,而是以图像清晰度作为优化指标,使用无模型的随机并行梯度优化算法,对大气湍流引起的波前误差进行时实补偿,结果表明此系统算法经过多次迭代后达到很好的校正效果.     

Extended SPRT for structural change detection of time series based on a multiple regression model

It is important to detect a structural change in a time series quickly as a trigger to remodeling the forecasting model. The well-known Chow test has been used as the standard method for detecting change, especially in economics. However, we have proposed the application of the sequential probability ratio test (SPRT) for detecting the change in single-regression modeled time-series data. In this article, we show experimental results using SPRT and the Chow test when applied to time-series data that are based on multiple regression models. We also clarify the effectiveness of SPRT compared with the Chow test in its ability to detect change early and correctly, and its computational complexity. Moreover, we extend the definition of the point at which structural change is detected with the SPRT method, and show an improvement in the accuracy of change detection.