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An initial study into the use of the large chamber scanning electron microscope (LCSEM) to interrogate the surface microstructure evolution of metallic specimens has been carried out. The LC-SEM located at Western Kentucky University is the largest instrument of its type at any university in the world. As such, unique measurements can be performed due to the size of its chamber and extended view of its optic system. Strain was varied for each individual specimen, and imaged using Secondary Electrons within the gauge length as well as near the grip position. Results will show progression of surface microstructures and nickel content of metallic specimens. Additionally, results will demonstrate the capability of the LC-SEM to carry out these types of measurements. Future measurements will include the incorporation of an in-situ uniaxial load frame for dynamic studies.

This paper documents a major shift in market microstructure during the period 1990 through 1999. In particular, a dramatic change in the pattern of cash flows by individual and institutional investors is documented. The question becomes, what effect this change has on day-of-the-week return patterns for the Dow Jones Industrial Average, Standard and Poor's 500 index, and Standard and Poor's 500 index futures. I find Monday's return pattern has changed in the decade of the 1990's. Not only is Monday's mean return significantly large and positive for all indices, the entire anomalous pattern occurs from Monday's open to Monday's close - an intraday effect. In addition, I find evidence that trading volume is a factor in explaining the anomalous behaviour of Monday's returns. New York Stock Exchange trading volume is significantly lower on Mondays from the trading volume of other days of the week but the trading activity of individual investors is significantly higher. More recently, individual investors have increased their buying activity on Mondays relative to prior periods. Finally, Monday exhibits the largest returns in the first two trading hours when the Dow Jones Industrial Average returns are decomposed into hourly returns. The research emphasizes the dynamic nature of the time series patterns of stock returns and the suggestion day-of-the-week return patterns are not robust over time. Therefore, familiarity with market microstructure issues is just as important as the statistical techniques utilized.

Friction stir processing (FSP) is one of the new and promising thermomechanical processing techniques that alters the microstructural and mechanical properties of the material in single pass to achieve maximum performance with low production cost in less time using a simple and inexpensive tool. Preliminary studies of different FS processed alloys report the processed zone to contain fine grained, homogeneous and equiaxed microstructure. Several studies have been conducted to optimize the process and relate various process parameters like rotational and translational speeds to resulting microstructure. But there is only a little data reported on the effect of the process parameters on the forces generated during processing, and the resulting microstructure of aluminum alloys especially AA5052 which is a potential superplastic alloy. In the present work, sheets of aluminum alloys were friction stir processed under various combinations of rotational and translational speeds. The processing forces were measured during the process and the resulting microstructure was analyzed using TEM. The results indicate that the processing forces and the microstructure evolved during FSP are sensitive to the rotational and translational speed. It is observed that the forces generated increase with the increasing rotational speed. The grain refinement was observed to vary directly with rotational speed and inversely with the translational speed. Also these forces generated were proportional to the grain refinement i.e., greater refinement of grains occurred at lower forces. Thus the choice of process parameters especially the rotational speed has a significant effect on the control and optimization of the process.

A recent study on the Post-Formed properties of Superplastically Formed Magnesium AZ31B has shown that the heating time prior to testing has a major effect on the Post Forming properties of the superplastically material. To this point, there has been very little examination into the effect of pre-heating or annealing on superplastic forming (SPF) properties. In this work, the effects of annealing prior to the SPF of Mg AZ31 alloy were examined. Both high temperature SPF tensile and bulge specimens were formed after annealing. Multiple annealing temperatures were examined to produce specimens with grain sizes ranging from 8 andamp;igrave;m to 15 andamp;igrave;m for comparison with traditional SPF results. The results show that the effect of annealing can be suitable for the improvement of thinning and possibly the forming time of superplastically formed Magnesium alloys through the control of the microstructure.

In this paper, we model price dispersion effects in over-the-counter (OTC) markets to show that, in the presence of inventory risk for dealers and search costs for investors, traded prices may deviate from the expected market valuation of an asset. We interpret this devia- tion as a liquidity effect and develop a new liquidity measure quantifying the price dispersion in the context of the US corporate bond market. This market offers a unique opportunity tofstudy liquidity effects since, from October 2004 onwards, all OTC transactions in this marketfhave to be reported to a common database known as the Trade Reporting and CompliancefEngine (TRACE). Furthermore, market-wide average price quotes are available from MarkitGroup Limited, a financial information provider. Thus, it is possible, for the first time, to directly observe deviations between transaction prices and the expected market valuation of securities. We quantify and analyze our new liquidity measure for this market and find significant price dispersion effects that cannot be simply captured by bid-ask spreads. Wefshow that our new measure is indeed related to liquidity by regressing it on commonly-usedfliquidity proxies and find a strong relation between our proposed liquidity measure and bond characteristics, as well as trading activity variables. Furthermore, we evaluate the reliability of end-of-day marks that traders use to value their positions. Our evidence suggests that the price deviations from expected market valuations are significantly larger and more volatile than previously assumed. Overall, the results presented here improve our understanding of the drivers of liquidity and are important for many applications in OTC markets, in general. (authors' abstract)

Market makers are unique entities in a market ecosystem. Unlike other participants that have exposure (either speculative or endogenous) to potential future states of the world, market making agents either endeavor to secure a risk-free profit or to facilitate trade that would otherwise not occur. In this thesis we present a principled theoretical framework for market making along with applications of that framework to different contexts. We begin by presenting a synthesis of two concepts—automated market making from the artificial intelligence literature and risk measures from the finance literature—that were developed independently. This synthesis implies that the market making agents we develop in this thesis also correspond to better ways of measuring the riskiness of a portfolio—an important application in quantitative finance. We then present the results of the Gates Hillman Prediction Market (GHPM), a fielded large-scale test of automated market making that successfully predicted the opening date of the new computer science buildings at CMU. Ranging over 365 possible opening days, the market’s large event partition required new advances like a novel span-based elicitation interface. The GHPM uncovered some practical flaws of automated market makers; we investigate how to rectify these failures by describing several classes of market makers that are better at facilitating trade in Internet prediction markets. We then shift our focus to notions of profit, and how a market maker can trade to maximize its own account. We explore applying our work to one of the largest and most heavily-traded markets in the world by recasting market making as an algorithmic options trading strategy. Finally, we investigate optimal market makers for fielding wagers when good priors are known, as in sports betting or insurance.