Applicability of Mean Field Approximation to Numerical Experiments of Thermal Convection during the Cooling Period of Lake Biwa

This paper describes the applicability of a stochastic model to the numerical experiments of thermal convection carried out under the condition of the northern part of Lake Biwa, Shiga Prefecture, Japan. It was shown in the previous study that the temporal changes of vertical water temperature distributions during the cooling period between September and February can be reproduced by a simple 3D-CFD model. It was also pointed out that the spatial distributions of cooled water body sinking to the bottom due to water surface cooling represent similar features of forest gap distribution, which can be clarified by a stochastic model. The basic features of numerical experiments on thermal convection such as the spatial distribution of cooled water body are firstly shown with several cooling rates at water surface. Then, a stochastic model, which was originally introduced to explain forest gap dynamics, is shown with its MFA （mean field approximation） as first approximation of stochastic model. It is pointed out through the comparison of theoretical results by MFA with tuned model constants to numerical experiments that MFA with some refinement can be applicable to reproduce the basic features of simulated results to some extent, although further investigations are required to clarify the applicability of the model to more detailed mechanism of thermal convection such as size distribution of cooled water body, phase change of flow pattern, etc..     

Safety at work: A meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes.

In this article, we develop and meta-analytically test the relationship between job demands and resources and burnout, engagement, and safety outcomes in the workplace. In a meta-analysis of 203 independent samples (N = 186,440), we found support for a health impairment process and for a motivational process as mechanisms through which job demands and resources relate to safety outcomes. In particular, we found that job demands such as risks and hazards and complexity impair employees' health and positively relate to burnout. Likewise, we found support for job resources such as knowledge, autonomy, and a supportive environment motivating employees and positively relating to engagement. Job demands were found to hinder an employee with a negative relationship to engagement, whereas job resources were found to negatively relate to burnout. Finally, we found that burnout was negatively related to working safely but that engagement motivated employees and was positively related to working safely. Across industries, risks and hazards was the most consistent job demand and a supportive environment was the most consistent job resource in terms of explaining variance in burnout, engagement, and safety outcomes. The type of job demand that explained the most variance differed by industry, whereas a supportive environment remained consistent in explaining the most variance in all industries. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Robust and Efficient Implicit Surface Reconstruction for Point Clouds Based on Convexified Image Segmentation

We present an implicit surface reconstruction algorithm for point clouds. We view the implicit surface reconstruction as a three dimensional binary image segmentation problem that segments the entire space $\mathbb R ^3$ or the computational domain into an interior region and an exterior region while the boundary between these two regions fits the data points properly. The key points with using an image segmentation formulation are: (1) an edge indicator function that gives a sharp indicator of the surface location, and (2) an initial image function that provides a good initial guess of the interior and exterior regions. In this work we propose novel ways to build both functions directly from the point cloud data. We then adopt recent convexified image segmentation models and fast computational algorithms to achieve efficient and robust implicit surface reconstruction for point clouds. We test our methods on various data sets that are noisy, non-uniform, and with holes or with open boundaries. Moreover, comparisons are also made to current state of the art point cloud surface reconstruction techniques.     

Generation and Characterization of Ultrafine Chain Aggregate Aerosols

Continuously stable, well-characterized chain aggregate aerosol sources generated from a flame aerosol generator are described. To characterize the length of the chain aggregate, a new image processing software was developed to measure the length distribution of aggregates. This program measures the path length of the aggregate that has been skeletonized into one pixel in width while maintaining its length and shape. In order to generate monodisperse chain aggregate sources, a differential mobility analyzer was used to classify aggregates according to electrical mobility. A series of experiments were conducted to generate singly charged, monodisperse chain aggregate aerosol sources. The operational range of the system for generation of monodisperse chain aggregates is described. Single-mode chain aggregates with a geometric mean particle length between 1.50 and 3.20 μm and a length standard deviation around 1.3 were generated.     

Studies with High Activity Catalysts for Olefin Polymerization

Catalysis continues to play a vital role in polymerization of such olefins as ethylene and propylene. A voluminous patent and scientific literature describing transition metal catalysts for olefin polymerization has emerged since the original discoveries by Ziegler, Natta, and other workers [1–6], Significant progress in polymerization catalysis has been made in the last 15 years, particularly with the development of methods to increase the efficiency of transition metal catalysts in olefin polymerization. Success in this area has provided the basis of simplified, less costly plant operations which do not require removal of residual catalyst from the polymer [3–9].     

Hydrothermal carbonization of microalgae

Hydrothermal carbonization is a process in which biomass is heated in water under pressure to create a char product. With higher plants, the chemistry of the process derives primarily from lignin, cellulose and hemicellulose components. In contrast, green and blue-green microalgae are not lignocellulosic in composition, and the chemistry is entirely different, involving proteins, lipids and carbohydrates (generally not cellulose). Employing relatively moderate conditions of temperature (ca. 200 °C), time (<1 h) and pressure (<2 MPa), microalgae can be converted in an energy efficient manner into an algal char product that is of bituminous coal quality. Potential uses for the product include creation of synthesis gas and conversion into industrial chemicals and gasoline; application as a soil nutrient amendment; and as a carbon neutral supplement to natural coal for generation of electrical power.