Optical amplification and stability of spiroquaterphenyl compounds and blends

In this contribution, we present a systematic investigation on a series of spiroquaterphenyl compounds optimised for solid state lasing in the near ultraviolet (UV). Amplified spontaneous emission (ASE) thresholds in the order of 1 μJ/cm2 are obtained in neat (undiluted) films and blends, with emission peaks at 390 1 nm for unsubstituted and meta-substituted quaterphenyls and 400 4 nm for para-ether substituted quaterphenyls. Mixing with a transparent matrix retains a low threshold, shifts the emission to lower wavelengths and allows a better access to modes having their intensity maximum deeper in the film. Chemical design and blending allow an independent tuning of optical and processing properties such as the glass transition.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.