Opto‐Mechanical Effect in Photoactive Nematic Side‐Chain Liquid‐Crystalline Elastomers

The mechanical behaviour of monodomain nematic side‐chain liquid‐crystalline elastomers containing azoderivatives as pendant groups or crosslinkers has been studied under UV irradiation and in the darkness at different temperatures. From the evaluation of the opto‐mechanical experiments, the mechanical efficiency, kinetic rates, activation energies and the isomerization mechanism of the azocompounds in the liquid‐crystalline matrix could be determined, as well as the effect of the chemical constitution of the azobenzene derivatives and their role in the elastomeric network.

     

Assessment of maximum likelihood PCA missing data imputation

Maximum likelihood principal component analysis (MLPCA) was originally proposed to incorporate measurement error variance information in principal component analysis (PCA) models. MLPCA can be used to fit PCA models in the presence of missing data, simply by assigning very large variances to the non‐measured values. An assessment of maximum likelihood missing data imputation is performed in this paper, analysing the algorithm of MLPCA and adapting several methods for PCA model building with missing data to its maximum likelihood version. In this way, known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR) methods are implemented within the MLPCA method to work as different imputation steps. Six data sets are analysed using several percentages of missing data, comparing the performance of the original algorithm, and its adapted regression‐based methods, with other state‐of‐the‐art methods. Copyright © 2016 John Wiley & Sons, Ltd.