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The activation, by residual stress, of the fast portion of rotational motion of single fluorescent probe molecules inside a polymer thin film near its glass transition temperature is studied at a single molecular level. Spin-casted poly n-butyl methacrylate thin films without thermal annealing are chosen as the model system and single molecule fluorescence defocused microscopy is adopted as the method. The rotational motion of the probes under residual stress is found to be more activated than that under mere thermal activation, and the kinetic energy exhibits a monotonic increase with the stress strength. A rough linear dependence of rotational kinetic energy at low stress is found, yielding the value of characteristic volume for the residual stress to activate the motion of the probes. The values of the volume are close to the van der Waals volume of the probes, indicating that the activation of the fast dynamics by residual stress is localized. The activation effect is weakened and vanishes at or above the glass transition temperature due to stress relaxation. The effect is also absent at temperatures far below Tg due to the frozen molecular motion with a much higher activation energy. [ABSTRACT FROM AUTHOR]

Two‐stage randomized experiments become an increasingly popular experimental design for causal inference when the outcome of one unit may be affected by the treatment assignments of other units in the same cluster. In this paper, we provide a methodological framework for general tools of statistical inference and power analysis for two‐stage randomized experiments. Under the randomization‐based framework, we consider the estimation of a new direct effect of interest as well as the average direct and spillover effects studied in the literature. We provide unbiased estimators of these causal quantities and their conservative variance estimators in a general setting. Using these results, we then develop hypothesis testing procedures and derive sample size formulas. We theoretically compare the two‐stage randomized design with the completely randomized and cluster randomized designs, which represent two limiting designs. Finally, we conduct simulation studies to evaluate the empirical performance of our sample size formulas. For empirical illustration, the proposed methodology is applied to the randomized evaluation of the Indian National Health Insurance Program. An open‐source software package is available for implementing the proposed methodology. [ABSTRACT FROM AUTHOR]

This consideration is critical in our study because judges' decisions can affect individual arrestees. Instead of engaging with this philosophical debate, we examine Dawid's recommendation to use the statistical decision theory for answering the following questions: (1) 'should we present PSA to the judge before she makes her decision, or not? As explained above, to decide on the use of PSA, policy makers need to understand if PSA helps judges achieve their goal of preventing a negative outcome while avoiding unnecessarily harsh decisions. Finally, several commentators asked why our study design did not involve blinding of judges either to the intervention or to the fact of the study. [Extracted from the article]

Manski (Monotone treatment response. Econometrica 1997;65:1311–34) and Manski and Pepper (Monotone instrumental variables: with an application to the returns to schooling. Econometrica 2000;68:997–1010) gave sharp bounds on causal effects under the assumptions of monotone treatment response (MTR) and monotone treatment selection (MTS). VanderWeele (The sign of the bias of unmeasured confounding. Biometrics 2008;64:702–6) provided bounds for binary treatment under an assumption of monotone confounding (MC). We discuss the relation between MC and MTS and provide bounds under various combinations of these assumptions. We show that MC and MTS coincide for a binary treatment, but MC does not imply MTS for a treatment variable with more than two levels.