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In the latter half of the 20th-century and into the 21st, William Burroughs, Samuel Delany, and bioartists such as Oron Catts, Orlan, and Stelarc have all attempted to create works which respond to the increasing biopoliticization of contemporary society. The biopolitics of today seek to regularize life and structure it according to the imperatives of economic thought, a process by which the human becomes the Foucauldian homo oeconomicus. This restricted logic of biopolitics desperately tries to cover the explosive excess of the world today, what Bataille calls general economy. The artists under consideration in this work attempt to uncover this state of excess. While they are typically seen as exploring fantastic realms of the transgressive or, in the case of bioartists, attempting to emulate science fiction, in fact it is their realism which provokes. These artists reveal the heterological body, that which cannot be contained or described by the biopolitical regime. In so doing, they rewrite our standards of taste and point the way to understandings of the human that have been otherwise unavailable to us. William Burroughs in Naked Lunch highlights the manipulability of affect in contemporary society through the reduction of the human to bare life. He uses the figure of flesh/meat as a way of depicting the heterogeneous body and to generate a counter-affect, or free-floating affect, which unlike typical affect, is not worked up into emotion. Samuel Delany, too, describes the heterogeneous or destabilized body in the heterotopia of his novel Dhalgren. While Burroughs is unable or unwilling to gesture towards the potentially radical implications of the heterogeneous body, Delany proposes a new model of community that rests upon the revelation of the heterogeneous body, a community which acts as one informed by an affirmative biopolitics. Bioart, a somewhat vexed genre of art, attempts to construct artworks that both utilize and critique new science and technology of the body. The life sciences are complicit in the rise of the biopolitical state and further the view of the human as constrained by its material substrate. Fetishistic bioart problematically reproduces a fascination with the life sciences and advanced technology. However, the bioart which I call sacred has a demystifying effect and attempts to use the knowledge gained by the life sciences to expand our understanding of the human, going beyond the bounds of that very knowledge itself. / English

This thesis is missing pages 152 and 194, neither of which are in the other copies of this thesis. -Digitization Centre / Restricted Hartree-Fock calculations for light even-even nuclei have been carried out using simple effective interactions. The primary emphasis is on the nature of the intrinsic states, and, in particular, on the deformation of these states. In order to find the equilibrium deformations a representation of deformed cylindrically symmetric states is used. A self-consistent technique for finding the equilibrium size and shape is proposed. There is a strong secondary emphasis on the role of the effective interaction, and four rather different interactions are used. Two of these incorporate a dependence on the density on the nuclear system, and this density dependence improves the systematic behaviour of the energies and sizes of light nuclei. / Thesis / Doctor of Philosophy (PhD)

In 2010, there were 6,714,288 total registered drivers in New Jersey. Approximately 4% (267,485) of these drivers had a suspended driver's license. The intent of suspending a driver's license is to keep hazardous drivers off of the roads, in hopes of having a safer driving environment for others on the road. Drivers in New Jersey can have their driver's license suspended for a number of reasons. These include dangerous driving behaviors such as reckless driving and driving under the influence of alcohol or drugs. However, there are also reasons for suspension that have little or nothing to do with driver behavior, such as failure to pay child support, failure to pay MVC insurance surcharge, or failure to appear in court. While these offenses are all due of consequence, they have little or nothing to do with driver behavior. This research program will conduct an analysis of the issues and implications of implementing a restricted-use license program for suspended New Jersey drivers, detailing key issues associated with restricted-use license programs. It was found that over two-thirds of suspended drivers in New Jersey receive driver's license suspensions for both driving and non-driving-related offenses, whereas only about four-percent of suspended drivers in New Jersey receive a driver's license suspension for driving-related reasons only. It was also found that drivers suspended for non-driving related reasons have different driver behavior than drivers suspended for driving related reasons. Surveying both New Jersey police chiefs, as well as U.S. state motor vehicle agencies, it was found that there is a generally positive perception of restricted driver's license programs. Overall, it is recommended that the New Jersey Motor Vehicle Commission implement a restricted driver's license program in New Jersey. / Master of Science

Yes / Guided by academic literature, industry practice and policy recommendations, we analyze a wide range of option and restricted stock plans with exercise and vesting conditions that may be contingent on stock price performance. To assess the effectiveness of these plans at attracting and providing incentives to executives, we create compensation plans with fixed firm cost and executive valuation and calculate their expected total lifetime incentives. We show that performance vesting targets provide the least cost effective incentives, performance exercise targets provide the largest risk incentives, option plans are generally superior to restricted stock plans, and calendar vesting is only efficient up to a maximum of three years. Performance exercise targets can increase the expected total lifetime incentives provided by compensation plans, but in general, standard options with short vesting periods provide the most cost effective pay-for-performance incentives.

Experimental methods based on the magnetic resonance phenomenon belong to the most widely used experimental techniques for investigations of molecular and electronic structure. The difficulty with such experiments, usually a proper interpretation of data obtained from high-resolution spectra, opens new challenges for pure theoretical methods. One of these methods is density functional theory (DFT), that now has an advanced position among a whole variety of computational techniques. This thesis constitutes an effort in this respect, as it presents theory and discusses calculations of electron paramagnetic resonance (EPR) and nuclear magnetic resonance (NMR) parameters of paramagnetic molecules. It is known that the experimental determination of the magnetic resonance parameters of such molecules, especially in the case of NMR, is quite complicated and requires special techniques of spectral detection. On the other hand, paramagnetics play an important role in many areas, such as molecular magnets, active centers in biological systems, and defects in inorganic conductive materials. Therefore, they have spurred great interest among experimentalists, motivating us to facilitate the interpretation of spectral data through theoretical calculations. This thesis describes new methodologies for the determination of magnetic properties of paramagnetic molecules in the framework of DFT, which have been developed in our laboratory, and their applications in calculations of a wide range of molecular systems. The first two papers of this thesis deal with the theoretical determination of NMRparameters, such as nuclear shielding tensors and chemical shifts, in paramagnetic nitroxides that form core units in molecular magnets. The developed methodology is aimed to realize a high calculational accuracy for these systems. The effects of hydrogen bonding are also described in that context. Our theory for the evaluation of nuclear shielding tensors in paramagnetic molecules is consistent up to second order in the fine structure constant and considers orbital, fully anisotropic dipolar, and isotropic contact contributions to the shielding tensor. The next projects concern electron paramagnetic resonance. The well-known EPR parameters, such as the g-tensors and the hyperfine coupling constants are explored. Calculations of electronic g-tensors were carried out in the framework of a spin-restricted open-shell Kohn-Sham method combined with the linear response theory recently developed in our laboratory and allowing us to avoid by definition the spin-contamination problem. The inclusion of solvent effects, described by the polarizable continuum model, extends the possibility to treat molecular systems often investigated in solution. For calculations of the hyperfine coupling constants a so-called restricted-unrestricted approach to account for the spin polarization effect has been developed in the context of DFT. To examine the validity of the approximations implicit in this scheme, the neglect ii of singlet operators, a generalized RU methodology was implemented, which includes a fully unrestricted treatment with both singlet and triplet operators. The small magnitude of the changes in hyperfine coupling constants confirms the validity of the original scheme. / QC 20100923

There is a growing need for accurate distribution data for both common and rare plant species for conservation planning and ecological research purposes. A database of more than 500 observations for nine tree species with different ecological and geographical distributions and a range of frequencies of occurrence in south-eastern New South Wales (Australia) was used to compare the predictive performance of logistic regression models, generalised additive models (GAMs) and classification tree models (CTMs) using different data restriction regimes and several model-building strategies. Environmental variables (mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature, mean maximum summer temperature, mean minimum winter temperature, mean daily radiation, mean daily summer radiation, mean daily June radiation, lithology and topography) were used to model the distribution of each of the plant species in the study area. Model predictive performance was measured as the area under the curve of a receiver operating characteristic (ROC) plot. The initial predictive performance of logistic regression models and generalised additive models (GAMs) using unrestricted, temperature restricted, major gradient restricted and climatic domain restricted data gave results that were contrary to current practice in species distribution modelling. Although climatic domain restriction has been used in other studies, it was found to produce models that had the lowest predictive performance. The performance of domain restricted models was significantly (p = 0.007) inferior to the performance of major gradient restricted models when the predictions of the models were confined to the climatic domain of the species. Furthermore, the effect of data restriction on model predictive performance was found to depend on the species as shown by a significant interaction between species and data restriction treatment (p = 0.013). As found in other studies however, the predictive performance of GAM was significantly (p = 0.003) better than that of logistic regression. The superiority of GAM over logistic regression was unaffected by different data restriction regimes and was not significantly different within species. The logistic regression models used in the initial performance comparisons were based on models developed using the forward selection procedure in a rigorous-fitting model-building framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding framework involved testing for the significant reduction in model deviance (p = 0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter estimates and their standard errors were inspected because large estimates and/or standard errors are an indication of model degradation from overfilling or effecls such as mullicollinearily. For additional variables to be included in a model, they had to contribule significantly (p = 0.025) to the model prediclive performance. An attempt to improve the performance of species distribution models using logistic regression models in a rigorousfitting model-building framework, the backward elimination procedure was employed for model selection, bul it yielded models with reduced performance. A liberal-filling model-building framework that used significant model deviance reduction at p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the major criterion for variable selection was employed for the development of logistic regression models using the forward selection and backward elimination procedures. Liberal filling yielded models that had a significantly greater predictive performance than the rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the former models was comparable to that of GAM and classification tree models (CTMs). The low significance liberal-filling models had a much larger number of variables than the high significance liberal-fitting models, but with no significant increase in predictive performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size of a full CTM for a given species. The 10-fold cross-validated model deviance for the subtrees was plotted against the size of the subtree as a means of selecting an appropriate tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance. Species geographical range and species prevalence within the study area were used to categorise the tree species into different distributional forms. These were then used, to compare the effect of plant species rarity on the predictive performance of logistic regression models, GAMs and CTMs. The distributional forms included restricted and rare (RR) species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC) species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides), widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC) species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were significant differences (p = 0.076) in predictive performance among the distributional forms for the logistic regression and GAM. The predictive performance for the WR distributional form was significantly lower than the performance for the other plant species distributional forms. The predictive performance for the RC and RR distributional forms was significantly greater than the performance for the WC distributional form. The trend in model predictive performance among plant species distributional forms was similar for CTMs except that the CTMs had poor predictive performance for the RR distributional form. This study shows the importance of data restriction to model predictive performance with major gradient data restriction being recommended for consistently high performance. Given the appropriate model selection strategy, logistic regression, GAM and CTM have similar predictive performance. Logistic regression requires a high significance liberal-fitting strategy to both maximise its predictive performance and to select a relatively small model that could be useful for framing future ecological hypotheses about the distribution of individual plant species. The results for the modelling of plant species for conservation purposes were encouraging since logistic regression and GAM performed well for the restricted and rare species, which are usually of greater conservation concern.

This thesis investigates improved estimators of the parameters of the linear regression models with normal errors, under sample and non-sample prior information about the value of the parameters. The estimators considered are the unrestricted estimator (UE), restricted estimator (RE), shrinkage restricted estimator (SRE), preliminary test estimator (PTE), shrinkage preliminary test estimator (SPTE), and shrinkage estimator (SE). The performances of the estimators are investigated with respect to bias, squared error and linex loss. For the analyses of the risk functions of the estimators, analytical, graphical and numerical procedures are adopted. In Part I the SRE, SPTE and SE of the slope and intercept parameters of the simple linear regression model are considered. The performances of the estimators are investigated with respect to their biases and mean square errors. The efficiencies of the SRE, SPTE and SE relative to the UE are obtained. It is revealed that under certain conditions, SE outperforms the other estimators considered in this thesis. In Part II in addition to the likelihood ratio (LR) test, the Wald (W) and Lagrange multiplier (LM) tests are used to define the SPTE and SE of the parameter vector of the multiple linear regression model with normal errors. Moreover, the modified and size-corrected W, LR and LM tests are used in the definition of SPTE. It is revealed that a great deal of conflict exists among the quadratic biases (QB) and quadratic risks (QR) of the SPTEs under the three original tests. The use of the modified tests reduces the conflict among the QRs, but not among the QBs. However, the use of the size-corrected tests in the definition of the SPTE almost eliminates the conflict among both QBs and QRs. It is also revealed that there is a great deal of conflict among the performances of the SEs when the three original tests are used as the preliminary test statistics. With respect to quadratic bias, the W test statistic based SE outperforms that based on the LR and LM test statistics. However, with respect to the QR criterion, the LM test statistic based SE outperforms the W and LM test statistics based SEs, under certain conditions. In Part III the performance of the PTE of the slope parameter of the simple linear regression model is investigated under the linex loss function. This is motivated by increasing criticism of the squared error loss function for its inappropriateness in many real life situations where underestimation of a parameter is more serious than its overestimation or vice-versa. It is revealed that under the linex loss function the PTE outperforms the UE if the nonsample prior information about the value of the parameter is not too far from its true value. Like the linex loss function, the risk function of the PTE is also asymmetric. However, if the magnitude of the scale parameter of the linex loss is very small, the risk of the PTE is nearly symmetric.