Thesis (M.S.)--Ohio University, August, 2000. / Title from PDF t.p.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 44-48). Also available via the Internet.

Thesis (Ph. D.)--University of Chicago, Department of Astronomy & Astrophysics, August 2002. / Includes bibliographical references. Also available on the Internet.

Extratropical cyclones are responsible for a substantial portion of midlatitude climate variability and contribute to widespread impacts. The characteristics of extratropical cyclones, such as their spatial distribution and intensity, are thought to be dependent on the large scale circulation. The relationship between cyclone characteristics and modes of large-scale climate variability has been investigated in previous studies, but interactions between modes of climate variability have largely been ignored. Since extratropical cyclone characteristics may be related to interactions between phases, quantifying these relationships is an important step in improving the climatology of extratropical cyclones. The goal of this study is to quantify relationships between modes of climate variability and characteristics of strong cyclones in the contiguous United States. Using historical sea-level pressure data, cyclone intensity, frequency, and spatial distribution are investigated using a cyclone definition that combines the requirement for low pressure (1000 hPa or lower) and positive (cyclonic) vorticity. The large scale modes of climate variability considered include El Niño Southern Oscillation (ENSO), the Pacific North American (PNA) mode, and the Arctic Oscillation (AO). The analysis is divided into three phases focusing on (1) establishing a background cyclone climatology within the study area, (2) quantifying differences in cyclone characteristics between the positive and negative phases of the individual modes of climate variability, and (3) examining the interactions between the modes of climate variability as they relate to extratropical cyclone characteristics. The results are expected to provide an improved baseline for evaluation of coupled climate models and also have the potential to improve seasonal climate predictability.

We study the orientations of satellite galaxies in redMaPPer clusters constructed from the Sloan Digital Sky Survey at 0.1 < z < 0.35 to determine whether there is any preferential tendency for satellites to point radially towards cluster centres. We analyse the satellite alignment (SA) signal based on three shape measurement methods (re-Gaussianization, de Vaucouleurs, and isophotal shapes), which trace galaxy light profiles at different radii. The measured SA signal depends on these shape measurement methods. We detect the strongest SA signal in isophotal shapes, followed by de Vaucouleurs shapes. While no net SA signal is detected using re-Gaussianization shapes across the entire sample, the observed SA signal reaches a statistically significant level when limiting to a subsample of higher luminosity satellites. We further investigate the impact of noise, systematics, and real physical isophotal twisting effects in the comparison between the SA signal detected via different shape measurement methods. Unlike previous studies, which only consider the dependence of SA on a few parameters, here we explore a total of 17 galaxy and cluster properties, using a statistical model averaging technique to naturally account for parameter correlations and identify significant SA predictors. We find that the measured SA signal is strongest for satellites with the following characteristics: higher luminosity, smaller distance to the cluster centre, rounder in shape, higher bulge fraction, and distributed preferentially along the major axis directions of their centrals. Finally, we provide physical explanations for the identified dependences and discuss the connection to theories of SA.

This international workshop entitled “How to integrate geochemistry at affordable costs into reac-tive transport for large-scale systems” was organized by the Institute of Resource Ecology of the Helmholtz-Zentrum Dresden Rossendorf in Feb-ruary 2020. A mechanistic understanding and building on that an appropriate modelling of geochemical processes is essential for reliably predicting contaminant transport in groundwater systems, but also in many other cases where migration of hazardous substances is expected and consequently has to be assessed and limited. In case of already present contaminations, such modelling may help to quantify the threads and to support the development and application of suitable remediation measures. Typical application areas are nuclear waste disposal, environmental remediation, mining and milling, carbon capture & storage, or geothermal energy production. Experts from these fields were brought together to discuss large-scale reactive transport modelling (RTM) because the scales covered by such pre-dictions may reach up to one million year and dozens of kilometers. Full-fledged incorporation of geochemical processes, e.g. sorption, precipitation, or redox reactions (to name just a few important basic processes) will thus create inacceptable long computing times. As an effective way to integrate geochemistry at affordable costs into RTM different geochemical concepts (e.g. multidimensional look-up tables, surrogate functions, machine learning, utilization of uncertainty and sensitivity analysis etc.) exist and were extensively discussed throughout the workshop. During the 3-day program of the workshop keynote and regular lectures from experts in the field, a poster session, and a radio lab tour had been offered. In total, 40 scientists from 28 re-search institutes and 8 countries participated.

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and costeffective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms.

High Efficiency Particulate Air (HEPA) filters are defined as extended-medium, dry-type filters with: (1) a minimum particle removal efficiency of no less than 99.97 percent for 0.3 micrometer particles, (2) a maximum, clean resistance of 1.0 inch water column (in. WC) when operated at 1,000 cubic feet per minute (CFM), and (3) a rigid casing that extends the full depth of the medium. Specifically, ceramic media HEPA filters provide better performance at elevated temperatures, are moisture resistant and nonflammable, can perform their function if wetted and exposed to greater pressures, and can be cleaned and reused. This paper describes the modification and design of a large scale test stand which properly evaluates the filtration characteristics of a range of ceramic media filters challenged with a nuclear aerosol agent in order to develop Section FO of ASME AG-1.

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