A stochastical model for periodic domain structuring in ferroelectric crystals

A stochastical description is applied in order to understand how ferroelectric structures can be formed. The predictions are compared with experimental data of the so-called electrical fixing: Domains are patterned in photorefractive lithium niobate crystals by the combination of light-induced space-charge fields with externally applied electrical fields. In terms of our stochastical model the probability for domain nucleation is modulated according to the sum of external and internal fields. The model describes the shape of the domain pattern as well as the effective degree of modulation

Holographic grating formation in silver nanoparticle suspensions

Thermal gratings are recorded in silver nanoparticle suspensions by nanosecond pulsed holography. Initial transients in diffraction efficiency demonstrate competing effects in grating formation. The grating's final decay is consistent with the suspension's thermal conductivity

Holographic grating formation in a colloidal suspension of silver nanoparticles

Holographic gratings are recorded in colloidal suspensions of silver nanoparticles by utilizing interfering nanosecond pulses. The diffraction efficiency is measured with continuous-wave light. An instantaneous response together with a transient grating are observed: the nanoparticles absorb the pump light and heat up. Heat is transferred to the solvent, and a delayed thermal grating appears. The final decay time constant of this grating depends quadratically on the period length and has a typical value of 1 µs for grating spacings of several micrometers

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

Development and Validation of a Prognostic Model to Predict Overall Survival in Multiple System Atrophy

BackgroundMultiple system atrophy (MSA) is a devastating disease characterized by a variable combination of motor and autonomic symptoms. Previous studies identified numerous clinical factors to be associated with shorter survival. ObjectiveTo enable personalized patient counseling, we aimed at developing a risk model of survival based on baseline clinical symptoms. MethodsMSA patients referred to the Movement Disorders Unit in Innsbruck, Austria, between 1999 and 2016 were retrospectively analyzed. Kaplan-Meier curves and multivariate Cox regression analysis with least absolute shrinkage and selection operator penalty for variable selection were performed to identify prognostic factors. A nomogram was developed to estimate the 7 years overall survival probability. The performance of the predictive model was validated and calibrated internally using bootstrap resampling and externally using data from the prospective European MSA Study Group Natural History Study. ResultsA total of 210 MSA patients were included in this analysis, of which 124 patients died. The median survival was 7 years. The following clinical variables were found to significantly affect overall survival and were included in the nomogram: age at symptom onset, falls within 3 years of onset, early autonomic failure including orthostatic hypotension and urogenital failure, and lacking levodopa response. The time-dependent area under curve for internal and external validation was >0.7 within the first 7 years of the disease course. The model was well calibrated showing good overlap between predicted and actual survival probability at 7 years. ConclusionThe nomogram is a simple tool to predict survival on an individual basis and may help to improve counseling and treatment of MSA patients