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Nitrile rubbers (copolymers of butadiene and acrylonitrile) are widely used throughout the aerospace, automobile, construction and footwear industries. The useful properties of nitrile rubbers include excellent resistance to the action of oils, petroleum fuels and solvents, combined with good heat and abrasion resistance. These properties derive from the polar nature of the cyanide group on the polymer chain and rubbers with high oil resistance contain 40 - 50 mole % acrylonitrile. Unfortunately, at low temperatures the elastomeric properties of such rubbers are poor. One of the objectives of this work was to investigate whether the working temperature range of nitrile rubbers could be extended by chemicalmodification, in particular by grafting poly (tetrahydrofuran) (PTHF) onto the nitrile rubber. PTHF is a low melting, crystalline polymer with a glass transition temperature, Tg, of -84°C, above which it is rubbery. The polymerisation of THF can be initiated by the generation of suitable carbonium ions which attack the ethereal oxygen of the THF molecule forming an oxonium ion. Propagation occurs via this oxonium ion intermediate by a cationic, ring-opening mechanism. This 'living' polymerisation can be terminated by many nucleophilic reagents such as water, methanol, etc. In view of the polymerisation characteristics of THF, it can be grafted to a nitrile rubber backbone by two distinct methods. The first is a 'grafting from' technique, which involves initiation of the THF polymerisation from carbonium ion sites generated on the polymer backbone. In these experiments, described in Chapter 2, the parent rubbers were first functionalised by dihalocarbenation, bromination and allyl bromination, and on reaction with a suitable silver salt cationic centres were generated on the backbone. This method proved to be unsuitable for preparing well-defined graft copolymers of PTHF. The second process, a 'grafting onto' technique, involved a 'coupling' reaction between 'living' PTHF and functional groups previously attached to the nitrile rubber backbone. This method, which was finally chosen to prepare a range of graft copolymers of PTHF, is described in Chapters 3 and 4. One to 3 mole % of the diene residues in the nitrile rubber were first epoxidised then treated with HC1 to open the epoxide rings to yield chlorohydrin groups. The hydroxyl substituents functioned as terminators in a 'coupling' reaction with the 'living' PTHF to form a graft copolymer. Since polymerisation of THF at room temperature is fairly slow the lengths of the grafted chains could be controlled by monitoring the time from initiation to termination. The graft copolymers and precursors were examined by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and for solvent resistance. The DSC results show that grafting with PTHF reduces the Tg of low nitrile rubbers but has little effect on the Tg of high nitrile rubbers. This is believed to be due to a greater degree of phase separation of the PTHF component in the high nitrile rubbers. Consequently, the physical properties of these PTHF-high nitrile rubber grafts are not those of a homogeneous amorphous blend of the two components, TGA of the PTHF graft copolymers suggest that the PTHF graft destabilises the nitrile rubber to heat. However, PTHF is well known to be susceptible to hydroperoxidation and it is likely that thisc effect is attributable to peroxides rather than the PTHF chains per se. An antioxidant should be capable of inhibiting hydroperoxidation. Finally, the solvent resistance of the PTHF graft copolymers, especially from high nitrile rubbers, is lower than that of the original nitrile rubber.

A process allowing for the intuitive use of SQL queries on dense multidimensional data stored in Network Common Data Format (netCDF) files is developed using advanced bitmap indexing provided by the FastBit bitmap indexing tool. A method for netCDF data extraction and FastBit index creation is presented and a geospatial Range and pseudo-KNN search based on the haversine function is implemented via SQL. A two step filtering algorithm is shown to greatly enhance the speed of these geospatial queries, allowing for extremely efficient processing of the netCDF data in bitmap indexed form.

This is a comprehensive report based on my examination of the National Museum of Korea after completing an internship in the International Relations & PR Division from September 1, 2010 through February 28, 2011. This report is divided into five chapters. Chapter I introduces the overall organizational profile of the museum beginning with its history, mission, vision, goals, management structure, programming, and funding. Chapter II specifically focuses on the International Relations & PR Division, where I have successfully completed a six-month internship. It provides a description of the internship including an overview of the International Relation & PR Division as well as my duties and responsibilities as an intern. In Chapter III, a SWOT analysis of the organization‟s internal and external issues is performed including its strengths, weaknesses, opportunities and threats. Best practices of other institutions are demonstrated in Chapter IV which may serve as a reference for further development of the museum. In Chapter V, I conclude this report by providing my own insights and recommendations.

<p>I explore and analyze a problem of finding the socially optimal capital requirements for financial institutions considering two distinct channels of contagion: direct exposures among the institutions, as represented by a network and fire sales externalities, which reflect the negative price impact of massive liquidation of assets.These two channels amplify shocks from individual financial institutions to the financial system as a whole and thus increase the risk of joint defaults amongst the interconnected financial institutions; this is often referred to as systemic risk. In the model, there is a trade-off between reducing systemic risk and raising the capital requirements of the financial institutions. The policymaker considers this trade-off and determines the optimal capital requirements for individual financial institutions. I provide a method for finding and analyzing the optimal capital requirements that can be applied to arbitrary network structures and arbitrary distributions of investment returns.</p><p>In particular, I first consider a network model consisting only of direct exposures and show that the optimal capital requirements can be found by solving a stochastic linear programming problem. I then extend the analysis to financial networks with default costs and show the optimal capital requirements can be found by solving a stochastic mixed integer programming problem. The computational complexity of this problem poses a challenge, and I develop an iterative algorithm that can be efficiently executed. I show that the iterative algorithm leads to solutions that are nearly optimal by comparing it with lower bounds based on a dual approach. I also show that the iterative algorithm converges to the optimal solution.</p><p>Finally, I incorporate fire sales externalities into the model. In particular, I am able to extend the analysis of systemic risk and the optimal capital requirements with a single illiquid asset to a model with multiple illiquid assets. The model with multiple illiquid assets incorporates liquidation rules used by the banks. I provide an optimization formulation whose solution provides the equilibrium payments for a given liquidation rule.</p><p>I further show that the socially optimal capital problem using the ``socially optimal liquidation" and prioritized liquidation rules can be formulated as a convex and convex mixed integer problem, respectively. Finally, I illustrate the results of the methodology on numerical examples and</p><p>discuss some implications for capital regulation policy and stress testing.</p> / Dissertation

<p> A mixed-integer linear program is proposed to model the dynamic network expansion problem of improving oil spill response capabilities to support energy exploration in the Arctic. Oil spill response operations in this region can be hampered by a lack of existing infrastructure, limited pre-positioned response equipment, and the possibility that response equipment might not arrive in time to mitigate the impact of a spill because of distance and infrastructure limitations. These considerations are modeled by two inter-related constraint sets with the objective of minimized total weighted response time for a set of potential oil spill incidents. One constraint set determines how to dynamically allocate response equipment and improve the infrastructures necessary to stockpile them within a network of response sites. The other set determines how to utilize this stockpile to respond to each task necessary for an incident by scheduling the equipment to complete tasks. These task completion times are subject to deadlines which, if not met, can, instead, require costlier follow-on tasks to be scheduled. The model, its assumptions, and data requirements were assessed by subject matter experts in the United States (U.S.) Coast Guard and a major Oil Spill Response Organization in the context of oil spill response logistics to support energy exploration initiatives in the U.S. Arctic.</p>

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 203-213). / We study marketplaces in which participants arrive over time, looking to interact with each other. While such interactions have historically been decentralized, the past few years have seen a dramatic increase in the number of internet-enabled platforms which facilitate the process of connecting together, or matching, sets of two or more participants. We will focus mainly on centralized matching markets such as kidney exchange and carpooling platforms. In such platforms, the algorithm which determines whom to match and when to do so plays an important role in the efficiency of the marketplace. In the first part, we study the interface between the participant heterogeneity, the types of matchings that are allowed, and the frequency at which the platform computes the allocations. We provide an empirical analysis of the effect of match frequency based on data from major US Kidney exchange programs. We then study models that enable us to compare the participants' match rates and waiting times under varying matching policies. We show both in theory and in practice that matching quickly can be beneficial, compared to policies which try to increase opportunities for optimization through artificial waiting. Until now, the theory of matching algorithms has focused mostly on static environments and little is known in the case where all participants arrive and depart dynamically. In our second part, we help bridge this gap by introducing a new theoretical problem for dynamic matching when anyone can arrive online. We provide new algorithms with state-of-the-art theoretical guarantees, both in the case of adversarial and random order inputs. Finally, we show that these algorithms perform well on kidney exchange and carpooling data. / by Maximilien Burq. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 251-256). / Finding optimal product offerings is a fundamental operational issue in modern retailing, exemplified by the development of recommendation systems and decision support tools. The challenge is that designing an accurate predictive choice model generally comes at the detriment of efficient algorithms, which can prescribe near-optimal decisions. This thesis attempts to resolve this disconnect in the context of assortment and inventory optimization, through theoretical and empirical investigation. First, we tightly characterize the complexity of general nonparametric assortment optimization problems. We reveal connections to maximum independent set and combinatorial pricing problems, allowing to derive strong inapproximability bounds. We devise simple algorithms that achieve essentially best-possible factors with respect to the price ratio, size of customers' consideration sets, etc. Second, we develop a novel tractable approach to choice modeling, in the vein of nonparametric models, by leveraging documented assumptions on the customers' consider-then-choose behavior. We show that the assortment optimization problem can be cast as a dynamic program, that exploits the properties of a bi-partite graph representation to perform a state space collapse. Surprisingly, this exact algorithm is provably and practically efficient under common consider-then-choose assumptions. On the estimation front, we show that a critical step of standard nonparametric estimation methods (rank aggregation) can be solved in polynomial time in settings of interest, contrary to general nonparametric models. Predictive experiments on a large purchase panel dataset show significant improvements against common benchmarks. Third, we turn our attention to joint assortment optimization and inventory management problems under dynamic customer choice substitution. Prior to our work, little was known about these optimization models, which are intractable using modern discrete optimization solvers. Using probabilistic analysis, we unravel hidden structural properties, such as weak notions of submodularity. Building on these findings, we develop efficient and yet conceptually-simple approximation algorithms for common parametric and nonparametric choice models. Among notable results, we provide best-possible approximations under general nonparametric choice models (up to lower-order terms), and develop the first constant-factor approximation under the popular Multinomial Logit model. In synthetic experiments vis-a-vis existing heuristics, our approach is an order of magnitude faster in several cases and increases revenue by 6% to 16%. / by Ali Aouad. / Ph. D.