Indentation-induced ductile behavior of glass–ceramics involving layered aluminosilicates

Contact damage in materials is critical in engineering applications because it influences mechanical resistance, such as wear, erosion, and impact failure. Indentation tests were performed using a tungsten carbide ball indenter (Hertzian contact) on the surfaces of glass–ceramics containing hexagonal CaAl₂Si₂O₈ or mica crystals (fluorophlogopite), both of which have a layered structure. The stress–strain relation and the permanent deformation on the surface, as well as the observation of the microcrack zone by X-ray computed tomography using synchrotron radiation, revealed that the glass–ceramic with hexagonal CaAl₂Si₂O₈ showed ductility similar to the quasi-plastic behavior previously observed in the mica glass–ceramic. The yield stresses of the glass–ceramics were estimated from the stress deviating from the stress–strain relation assuming complete elastic response between the ball and the sample. The ratio of the yield stress to Young modulus (Y/E) of the glass–ceramic with hexagonal CaAl₂Si₂O₈ was determined to be higher than that of the mica glass–ceramic.     

Experimental Risk Estimation for Offshore Software Outsourcing

It is necessary to estimate the risk for strategic decision of offshore software outsourcing projects. In this paper we propose an experimental risk estimation method based on questionnaire for externalizing the know‐how of experienced project managers. Introducing three parts of questionnaire contents, this paper describes the estimation method and presents the implementation procedure. Focusing on five client companies, this paper reports five types of risk estimation methods considering the bias of respondents with different backgrounds, and discusses the Recall, Precision together with Accuracy of these estimation methods. This paper also applies the t‐test and vector analysis to study the bias in vendor countries, software types, as well as client companies. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Estimator for generalization performance of machine learning model trained by biased data collected from multiple references

The data acquired in civil engineering tasks often involve high acquisition costs, and the available datasets tend to have a limited number of samples and are highly biased. To estimate the performance of machine learning models, k-fold cross-validation (k-CV) is widely used. However, if only limited data are available and the data distribution is biased, k-CV tends to overestimate the performance for practical applications. This study proposed a new estimator, leave one reference out and k-CV (LORO-k-CV), to determine the practical performance of machine learning models, that is, the generalization performance for population data in the target task, in case data are collected by multiple references resulting in biased data. LORO-k-CV is a combination of a new concept, LORO-CV, that estimates the performance in the extrapolation region of the training data without human intervention and k-CV, considering the ratio of the interpolation and extrapolation regions. The efficacy of LORO-k-CV was validated with its application to the regression task for the chloride-ion concentration of concrete structures. To more specifically demonstrate the advantages of LORO-k-CV in model construction, the feature selections were conducted using both k-CV and LORO-k-CV methods. These results revealed that LORO-k-CV can effectively construct a model with improved generalization performance even from the same data in cases where data are collected by multiple references, resulting in biased data.     

Effect of Al2O3 addition on the thermal expansion of sodium alkaline-earth silicate glasses: A molecular dynamics study

Coefficient of thermal expansion (CTE) is an important property to consider when utilizing oxide glasses in thermal treatment processes to avoid thermal damage at the interfaces of the glasses with heterogeneous materials. It is thus important to know the effect of additives on CTE for designing glasses. The use of alumina efficiently improves chemical and mechanical durability of oxide glasses while maintaining the functionality and productivity; however, alumina-doping often induces nonlinear variation of CTE. In this work, we therefore tried to investigate the relationship between CTE and the microstructure of sodium alkaline-earth aluminosilicate glasses using classical molecular dynamics (MD) simulations. To accurately model the glasses, we extended a force-matching potential by optimizing the parameter sets for Ca–O, Mg–O, and Na–O pair interactions using Bayesian optimization. The MD simulations reproduced the nonlinear variation of CTE as a function of alumina content, and detailed structural analyses identified inhomogeneous expansion in the glasses. It was found that the nonuniform CTE change at the nanoscale was related to the formation of an alumina-rich region, in which more fivefold-coordinated aluminum exist, when alumina content exceeded Na₂O content. Accordingly, the microstructural change by alumina-doping was identified as the origin of the nonlinear variation in the CTE of the glasses.     

Characterization of 2-Fluoro-2’-deoxyadenosine in Duplex,G-Quadruplex and i-Motif

Among various kinds of fluorine-substituted biomolecules, 2-fluoroadenine (2FA) and its derivatives have been actively investigated as therapeutic reagents, radio-sensitizers, and ¹⁹F NMR probes. In spite of their excellent properties, DNA containing 2FA has not been studied well. For fundamental understanding and future applications to the development of functional nucleic acids, we characterized 2FA-containing oligonucleotides for canonical right-handed DNA duplex, G-quadruplex, and i-motif structures. Properties of 2FA were similar to native adenine due to the small size of the fluorine atom, but it showed unique features caused by high electronegativity. This work provides useful information for future application of 2FA-modified DNA.     

Evolutionary selection of interesting class association rules using genetic relation algorithm

During the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.