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A little more than a decade ago, CRISPR-Cas system was identified as a potential gene editing tool. This RNA-guided DNA cleavage system, which naturally provides immunity to the prokaryotic host cell, has been engineered since for its application in agriculture, genomic screening, and hereditary genetic disorder treatment and cures. Recently, a eukaryotic CRISPR-Cas-like system, Fanzor, was reported, under a new class of the RNA-guided system (termed OMEGA). The discovery has increased the potentials of genetic modification more than ever, while simultaneously increasing the need for ethical considerations and guidelines. The history, structure, and functions of the two RNA-guided systems, as well as their current and prospective applications, and the ethical and social issues surrounding genomic modification through specific case studies are discussed in this thesis.

The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.

Experimental results and simulations of imploding fusion concepts have identified the Rayleigh-Taylor (RT) instability as one of the largest inhibitors to achieving fusion. Understanding the origin and development of the RT instability will allow for the development of mitigating measures to dampen the instability growth, thus improving the chance that fusion concepts such as inertial confinement fusion (ICF) are successful. A study of 1D and 2D simulations are presented for investigating RT instability growth in deceleration stage of imploding geometries. Two cases of laser-driven implosion geometry, Cartesian and cylindrical, are used to study late stage deceleration-phase RT instability development on the interior surface of imploding targets. FLASH's hydrodynamic (HD) and magnetohydrodynamic (MHD) modeling capabilities are used for different laser and target parameters in order to study the RT instability and the impact of externally applied magnetic fields on their evolution. Several simulation regimes have been identified that provide novel insight into the impact that a seeded magnetic field can have on RT instability growth and the conditions under which magnetic field stabilization of the RT instability is observable. Finally, future work and recommendations are made.

Under high pressure, Zr undergoes a transformation from its ambient equilibrium hexagonal close packed α phase to a simple hexagonal ω phase. Subsequent unloading to ambient conditions does not see a full reversal to the α phase, but rather a retention of significant ω. The thermal stability of the ω phase was investigated via in-situ synchrotron X-ray diffraction analysis of the isothermal annealing of Zr samples shocked to 8 and 10.5 GPa at temperatures 443, 463, 483, and 503 K. The phase volume fractions were tracked quantitatively and the dislocation densities were tracked semi-quantitatively. Trends included a rapid initial (transient) transformation rate from ω→α followed by a plateau to a new metastable state with lesser retained ω (asymptotically stable). Samples shocked to a higher peak pressure experienced relatively higher transformation rates and extent of transformation. A significant reduction in dislocation densities in the ω phase was observed prior to initiation of an earnest reverse transformation, leading to the hypothesis that the ω→α transformation from is being hindered by defects in the ω phase. Additional electron backscatter diffraction analysis of the samples affirmed the expected (0001)α || (10-11)ω and [10-10]α || [11-2-3]ω orientation relationship is maintained during the nucleation and growth of the α phase during annealing.In an effort to build on the hypothesis from the experimental studies, this work also presents a temperature dependent model that couples the removal of dislocations in the ω phase and the reverse transformation via a barrier energy that is associated with the free energy of remaining dislocations. The reduction of dislocations in the ω phase occur as a sum of glide and climb controlled processes, both of which dictate the transient and asymptotic behavior of the annealing process respectively. Furthermore, a single crystal plasticity framework is introduced as a venue toward future work on the reverse transformation. The model incorporates phase transformation, slip, twinning and an equation of state for Ti in order to model dynamic deformation and shock wave propagation. The model is able to serve as a basis toward future work and was part of my contribution toward a VUMAT user subroutine implementation in Abaqus.

Oracles are distributed abstract constructs employed in distributed algorithm design that encapsulate the portion of a solution that is not solvable in a particular distributed system model. We consider two complementary research directions: oracle use and oracle implementation. The first research direction addresses a fundamental problem of peer-to-peer networks: maintaining connectivity while peer processes are leaving. Increasing the level of difficulty of this problem, is to allow the network to be in an arbitrary initial state, yet connected, and provide a self-stabilizing solution to exclude leaving processes. The second research direction is the study of Omega oracle implementation under the assumption that a message may come to its destination through other processes; that is, multiple hop communication may occur.

The use of aluminum alloys for commercial and military applications has increased substantially due to the alloys' low areal density, toughness, and processability. It has recently been shown that an aluminum alloy, Al 2139, with copper, magnesium, and silver can be significantly toughened and strengthened by combinations of θ' and Ω precipitates and dispersed manganese particles. What has not been quantified are how these precipitates and dispersed particles affect behavior and what the material mechanisms and microstructural characteristics are that control the behavior of Al 2139 for strain-rates that span the quasi-static to high rates of strain. Hence, in this investigation, detailed transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM), scanning electron microscopy (SEM), orientation imaging microscopy (OIM), and optical microscopy (OM) were used to delineate the different physical scales that range from the nano for the precipitates and dislocations to the micron for the dispersed particles, grain orientations and texture, grain-sizes, slip-bands, and grain-boundary orientations. The deformed specimens were from an Al 2139 plate that was impacted by 4340 steel fragmentation stimulating projectiles (FSPs) at impact velocities ranging from 813 to 1043 m⁄s. The majority of the projectiles were defeated by the Al 2139 plate, which is another indication of the alloy's potential for damage mitigation and projectile defeat and resistance. Based on this detailed microstructural characterization, mechanisms for projectile defeat and full penetration are proposed. Deformation and damage modes include petalling on the impact face, shear cracking through the middle section of the plate due to projectile penetration, and discing due to bending stresses at a spall plane near the back of the plate. Shear cracking appears to be GB related, and the discing is dependent on the rolling direction. The extent of these modes for cross-sections where the target was penetrated was greater than that in regions where the projectile was defeated. For projectile defeat, large and elongated grains and precipitate deformation due to dislocation interaction can lead to highly ductile performance, which resists discing failure and plate penetration. Large grains significantly reduce the fraction of GBs, which then reduces the amount of GB cracking due to intense shear accumulation and spall. The elongation of the grains due to rolling also increased the dislocation densities, and subsequently the ductility of the grains, which reduced tensile failure due to the bending in the discing regions. High angle GB's can also limit heterogeneous θ' precipitation at the GB's, which would reduce intergranular fracture. Precipitation of Ω also increases the spall strength and decreases localized shear through its multiple cutting interactions with dislocations at the matrix interface. Dispersed particles also increase the strength of the alloy in high strain-rate applications by resisting localized shear. The results of this study are a first step in developing a tailored methodology that can be used to optimize microstructural characteristics and behavior of aluminum alloys for optimal strength and toughness.

Microstructural evolution in Beta Titanium alloys is an important factor that governs the properties exhibited by them. Intricate understanding of complex phase transformations in these alloys is vital to tailor their microstructures and in turn their properties to our advantage. One such important subject of study is the nucleation and growth of alpha precipitates triggered by the compositional instabilities in the beta matrix, instilled in them during non equilibrium heat treatments. The present work is an effort to investigate such a phenomenon.Here studies have been conducted primarily on two different Beta-Titanium alloys of commercial relevance- Ti5553 (Ti-5Al-5Mo-5V-3Cr-0.5Fe), an alloy used in the aerospace industry for landing gear applications and, TNZT (Ti-35Nb-7Zr-5Ta), a potential load bearing orthopedic implant alloy. Apart from the effect of thermal treatment on these alloys, the focus of this work is to study the interplay between different alpha and beta stabilizers present in them. For this, advanced nano-scale characterization tools such as High Resolution STEM, High Resolution TEM, EFTEM and 3D Atom Probe have been used to determine the structure, distribution and composition of the non equilibrium instabilities such as beta phase separation and omega phase, and also to investigate the subsequent nucleation of stable alpha.Thus in this work, very early stages of phase separation via spinodal decomposition and second phase nucleation in titanium alloys are successfully probed at an atomic resolution. For the first time, atomically resolved HRSTEM Z-contrast image is recorded showing modulated structures within the as-quenched beta matrix. Also in the same condition HRTEM results showed the presence of nanoscale alpha regions. These studies are revalidated by conventional selected area diffraction and 3D atom probe reconstruction results. Also TEM dark field and selected area diffraction studies are conducted to understand the effect of quenching and subsequent aging of omega precipitates. Using 3D atom probe tomography, the elemental partitioning involved in coarsening of omega is investigated in detail. Finally by performing a series of well planned heat treatments, an effort is made to reason out the influence of these instabilities on the morphology, volume fraction and nucleation site of alpha.

Radiative shocks are notoriously difficult to produce in a laboratory setting, due to the extreme demands of the production of relatively high temperatures over sufficient extent for radiation and matter to interact. This dissertation describes a fundamental radiation hydrodynamics experiment, using a low- Z piston to drive a strong shock through low density gas, creating a driven, radiatively collapsed shock. Using 10 beams of the 60-beam Omega Laser facility at the University of Rochester, a low-Z disk is irradiated with an intensity of approximately 5 x 1014 W/cm2 in a 1 ns square pulse over a 1 mm spot size. The laser disturbance first shocks then accelerates the disk via laser ablation pressure, achieving velocities well over 200 km/sec. This accelerated piston drives a shock into gas, usually xenon. The strong shock heats the gas to several hundred eV, which then radiates away a significant amount of energy, leading the shocked plasma to collapse to very high compression. This experiment is compared to astrophysical systems with similar optical depth profiles. Radiative shocks are common in astrophysical settings, where it is easy to generate fast shocks in low-density media. This experiment has applications to laser-driven dynamic hohlraums, which would take advantage of the radiative shock as a smooth implosion source while still reaping the benefits of a converging geometry.

The present study examined the relationship between Alpha strategies of persuasion, Omega strategies of persuasion, and different levels of threat to attitudinal freedom (reactance) on agreement with a communicator. Specifically, the Alpha strategy of similarity, the Omega strategy of depersonalization, and high/low threat levels were studied to test the hypothesis that agreement could be significantly higher in the similar, depersonalized, and high threat condition compared to the other conditions. Participants were randomly assigned to one of eight conditions: high threat/similar/depersonalized, high threat/similar/personalized, high threat/non-similar/depersonalized, high threat/non-similar/personalized, low threat/similar/depersonalized, low threat/similar/personalized, low threat/non-similar/depersonalized, and low threat/non-similar/personalized. Participants were told that they were either similar or non-similar to a paragraph author, read an opinionated paragraph containing differing levels of threats and personalization, and then answered a questionnaire to measure agreement. Although the expected 2 (Alpha strategy of similarity: high vs. low) _ 2 (Threat to attitudinal freedom: high vs. low) _ 2 (Omega strategy of depersonalization: personalized vs. depersonalized) three-way interaction was not found, results did demonstrate that both similarity and depersonalization were significant predictors of agreement with the communicator across all conditions.