Solid-state diffusion in amorphous zirconolite

his research utilised Queen Mary's MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1. We are grateful to E. Maddrell for discussions and to CSC for support

Applying the Fuzzy Analytic Network Process to Establish the Relative Importance of Knowledge Sharing Barriers

Knowledge sharing (KS) is the key to creativity and innovation in any organizations. Overcoming the KS barriers has created new challenges for designing in dynamic and complex environment. There may be interrelations and interdependences among the barriers. The purpose of this paper is to present a review of literature of KS barriers and impute the relative importance of them through the fuzzy analytic network process that is a generalization of the analytical hierarchy process (AHP). It helps to prioritize the barriers to find ways to remove them to facilitate KS. The study begins with a brief description of KS barriers and the most critical ones. The FANP and its role in identifying the relative importance of KS barriers are explained. The paper, then, proposes the model for research and expected outcomes. The study suggests that the use of the FANP is appropriate to impute the relative importance of KS barriers which are intertwined and interdependent. Implications and future research are also propose

Genotoxic Effect of Atrazine, Arsenic, Cadmium and Nitrate, Individually and in Mixtures at Maximum Contaminant Levels on mammalian Breast Cell Lines

There is strong evidence that hormonally active agents (HAAs) such as Atrazine (ATZ), Cadmium (Cd), Arsenic (As) and Nitrate (NO3) have both estrogenic activity and carcinogenic potential. Atrazine has clastogenic effects and may also act as tumor promoter as it induces the aromatase enzyme. Arsenic and Cadmium have been implicated in the etiology of skin, lung, prostate and liver cancers. Nitrate in drinking water has been found to increase the risk of bladder cancer.This study examined the genotoxicity of the aforementioned HAAs alone and in mixtures using mammalian breast cell lines, MCF-7 and MCF-10A, which are estrogen receptorpositive (ER+) and estrogen receptor-negative (ER-), respectively. To study the clastogenic potential by whole cell and flow karyotype damage, cells were exposed to environmentally relevant concentrations of ATZ, Cd, As and NO3 for 4 and 7 days.Results indicated that all treatments induced whole cell clastogenicity in MCF-7 cells; except Cd and NO3 after 4 and 7 days as well as the 10% quaternary As mixture after 1 week. In MCF-10A cells, all treatments except the 10% mixture induced whole cell clastogenicity after 4 days, where flow karyotype damage was detected in all treatments except for the 10% mixture after 1 week. Estrogen caused whole cell damage but not flow karyotype damage in MCF-7. On the other hand, estrogen caused flow karyotype damage and not whole cell damage in MCF-10A cells, suggesting that estrogen receptor modulated the genotoxicity of estrogen. Cd caused flow karyotype damage but not whole cell damage in MCF-7 indicating that Cd’s gentoxicity is not related to its estrogenic activity.Keywords: HAAs, clastogenicity, flow-karyotype, genotoxicity, MCF-7, MCF-10

Damage and repair classification in reinforced concrete beams using frequency domain data

This research aims at developing a new vibration-based damage classification technique that can efficiently be applied to a real-time large data. Statistical pattern recognition paradigm is relevant to perform a reliable site-location damage diagnosis system. By adopting such paradigm, the finite element and other inverse models with their intensive computations, corrections and inherent inaccuracies can be avoided. In this research, a two-stage combination between principal component analysis and Karhunen-Loéve transformation (also known as canonical correlation analysis) was proposed as a statistical-based damage classification technique. Vibration measurements from frequency domain were tested as possible damage-sensitive features. The performance of the proposed system was tested and verified on real vibration measurements collected from five laboratory-scale reinforced concrete beams modelled with various ranges of defects. The results of the system helped in distinguishing between normal and damaged patterns in structural vibration data. Most importantly, the system further dissected reasonably each main damage group into subgroups according to their severity of damage. Its efficiency was conclusively proved on data from both frequency response functions and response-only functions. The outcomes of this two-stage system showed a realistic detection and classification and outperform results from the principal component analysis-only. The success of this classification model is substantially tenable because the observed clusters come from well-controlled and known state conditions

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods

Sparsest factor analysis for clustering variables: a matrix decomposition approach

We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA