Structural transformations of double-walled carbon nanotube bundle under hydrostatic pressure

Three kinds of the response mechanisms to the external pressure have been found for double-walled carbon nanotube (DWNT) bundle, depending strongly on their average radius and symmetry. The small-diameter DWNT bundle undergoes a small discontinuous volume change, and then deform continuously. The intermediate-diameter DWNT bundle collapses completely after a structure phase transition (SPT). Significantly, two SPTs exist for the larger-diameter DWNT bundle if the outer tube has no $C_{6}$ or $C_{3}$ symmetry. It would be interesting to search for signatures of these different structural transformations by experimentally investigating mechanical, optical and thermal response functions of DWNT bundle.Comment: 9 pages, 2 figure

Nursing Staff Compliance with Hand Hygiene Protocol in NICU in a Regional Perinatal Center

Proper hand hygiene protocols are a way to prevent infections in neonates admitted to the NICU. A total of 771 observations on hand sanitizer use before and after patient contact and 208 observations for level of touch were made. Adherence increased significantly since the introduction of waterless individual bedside hand sanitizers: 73% vs. 80% before and 83% vs. 90% after patient contact. The suboptimal adherence to the hand hygiene protocol could not be fully explained by the emergency nature of patient contact, suggesting that time and convenience are not the main concerns that infection control needs to address. The system improvement should focus on providing the tools for perfect hygiene, on optimizing staff education and on improving staff motivation

Thompson sampling based Monte-Carlo planning in POMDPs

Monte-Carlo tree search (MCTS) has been drawinggreat interest in recent years for planning under uncertainty. One of the key challenges is the tradeoffbetween exploration and exploitation. To addressthis, we introduce a novel online planning algorithmfor large POMDPs using Thompson sampling basedMCTS that balances between cumulative and simple regrets.The proposed algorithm — Dirichlet-Dirichlet-NormalGamma based Partially Observable Monte-Carlo Planning (D2NG-POMCP) — treats the accumulatedreward of performing an action from a beliefstate in the MCTS search tree as a random variable followingan unknown distribution with hidden parameters.Bayesian method is used to model and infer theposterior distribution of these parameters by choosingthe conjugate prior in the form of a combination of twoDirichlet and one NormalGamma distributions. Thompsonsampling is exploited to guide the action selection inthe search tree. Experimental results confirmed that ouralgorithm outperforms the state-of-the-art approacheson several common benchmark problems