Modified Optimal Prediction and its Application to a Particle-Method Problem

The paper is concerned with system reduction by statistical methods and, in particular, by the optimal prediction method introduced in (Chorin, A.J., Hald, O.H., Kupferman, R., Optimal prediction with memory, Phys. D 166:239–257, 2002). The optimal prediction method deals with problems that possess large and small scales and uses the conditional expectation to model the influence of the small scales on the large ones. In the current paper, we develop a different variant of the optimal prediction method as well as introduce and compare several approximations of this method. We apply the original and modified optimal prediction methods to a system of ODEs obtained from a particle method discretization of a hyperbolic PDE and demonstrate their performance in a number of numerical experiments.     

Positron Emission Profiling Study of the Diffusion of 3-Methylpentane in Silicalite-1

The influence of the loading on the self-diffusion of 3-methylpentane within silicalite-1 has been studied with Positron Emission Profiling technique. A monotonous decrease in the self-diffusivities of alkane was observed with zeolite loading, which could be approximated by a stretched exponential. As a consequence, the apparent activation energy increases with partial pressure.     

A non-parametric segmentation methodology for oral videocapillaroscopic images

We aim to describe a new non-parametric methodology to support the clinician during the diagnostic process of oral videocapillaroscopy to evaluate peripheral microcirculation. Our methodology, mainly based on wavelet analysis and mathematical morphology to preprocess the images, segments them by minimizing the within-class luminosity variance of both capillaries and background. Experiments were carried out on a set of real microphotographs to validate this approach versus handmade segmentations provided by physicians. By using a leave-one-patient-out approach, we pointed out that our methodology is robust, according to precision–recall criteria (average precision and recall are equal to 0.924 and 0.923, respectively) and it acts as a physician in terms of the Jaccard index (mean and standard deviation equal to 0.858 and 0.064, respectively).     

Goal hierarchy: Improving asset data quality by improving motivation

Many have recognized the need for high quality data on assets and the problems in obtaining them, particularly when there is a need for human observation and manual recording. Yet very few have looked at the role of the data collectors themselves in the data quality process. This paper argues that there are benefits to more fully understanding the psychological factors that lay behind data collection and we use goal hierarchy theory to understand these factors. Given the myriad of potential reasons for poor-quality data it has previously proven difficult to identify and successfully deploy employee-driven interventions; however, the goal hierarchy approach looks at all of the goals that an individual has in their life and the connections between them. For instance, does collecting data relate to whether or not they get a promotion? Stay safe? Get a new job? and so on. By eliciting these goals and their connections we can identify commonalities across different groups, sites or organizations that can influence the quality of data collection. Thus, rather than assuming what the data collectors want, a goal hierarchy approach determines that empirically. Practically, this supports the development of customized interventions that will be much more effective and sustainable than previous efforts.     

Lead adsorption onto exfoliated graphitic nanoplatelets in aqueous solutions

Exfoliated graphitic nanoplatelets (xGnPs) show adsorption capability and adsorption efficiency for lead removal from water, the adsorption being influenced by the solution pH value and the xGnPs surface characteristics, which are controlled by their treatment processing. The adsorption isotherms were described by both Langmuir and Freundlich models. These results suggest that xGnPs are remarkable lead adsorbents, having potential applications in environmental protection.