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.

Atmospheric boundary layer flows around wind turbines distributed in a large wind farm can be examined by the use of large eddy simulation (LES), which is based on the assumption that large eddies in the flow are anisotropic and depend on the mean flow and the configuration geometry, while smaller eddies are isotropic and homogeneous, and can be modeled via subgrid scale models. In this thesis, a pseudo-spectral LES code with inflow conditions imposed through a precursor concurrent simulation is utilized to model the flow around a single wind turbine or a large wind farm operating in thermally-stratified conditions. The effect of the wind turbines on humidity is monitored through an additional scalar convection equation. It is found that on average, the effect of an individual wind turbine on the humidity is less than 1%, while the effect of the wind farm on humidity can reach 1-2% in the cumulative wakes.

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.

Reservoirs represent a relatively young element of the U.S. landscape, with most reservoirs being built within the last century. Despite their recreational, ecological, and socioeconomic importance, reservoirs nationwide are suffering from severe habitat degradation. Habitat impairments related to siltation, eutrophication, poor water quality, water regime, lack of submerged structure, and macrophyte invasions affect reservoirs to differing degrees in different reservoirs. To adequately assess these issues, we needed to develop a classification system within which an assessment mechanism could function. I collected data for large reservoirs across the conterminous U.S. regarding fish habitat impairments and status of the fish community and recreational fishery. Using these data, I developed a fish habitat classification system for large U.S. reservoirs, which can be used to better understand differences among reservoirs, develop habitat management expectations, and prioritize conservation efforts.

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One goal of applied linguistics is to learn languages better and faster. Second language (L2) learners need to acquire large vocabularies to approach native-like proficiency in their targeted language. A number of studies have explored the factors that facilitate and hinder word learning using highly controlled experiments, however, these lack ecological validity and the findings may not generalize to real-world learning. The studies in this thesis respond to this gap in the literature. The studies leverage big data from a popular language learning app called Lingvist to explore how understudied semantic factors such as valence (positivity/negativity) and concreteness impact adult L2 word learning. Chapter 2 explores the shape of valence effects on learning, the interaction between the semantics of the target word and the linguistic context in which the word is learned, and how these effects unfold over multiple exposures to the target word. Users learn both positive and negative words better than neutral ones, and learning improves by 7% when target words appear in emotionally congruent contexts (i.e., positive words in positive sentences, negative words in negative sentences). These effects are strongest on the learner’s second encounter with the word and diminish over subsequent encounters. Chapter 3 examines the interaction between target word valence and concreteness. Increased positivity increased accuracy for concrete words by up to 13%, but had little impact on learning abstract words. On the theoretical front, findings provide support for embodied cognition, the lexical quality hypothesis, and the multimodal induction hypothesis. On the applied front, they indicate that context valence can be manipulated to facilitate learning and identify which words will be most difficult to learn. / Thesis / Master of Science (MSc) / Language learners need to know tens of thousands of words to communicate fluently in a language. These studies use data from a popular language learning app called Lingvist to understand how the emotionality of words and the sentences we see them in impact learning. Negative words (e.g., murder) and positive words (e.g., vaccation) were learned better than neutral words. Positive words were learned better when they are part of a positive sentence and negative words are learned better in more negative sentences. The second study found that concrete words like brick or table are easier to learn when they are positive, but emotions have little impact on learning abstract words like hope. These findings help researchers understand how words are represented in the mind and point to ways to make language learning faster and easier.

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