Anatomy of a financial center\u27s global competitiveness in the context of Michael Porter\u27s model of national competitive advantage a theoretical analysis

Throughout history, a number of financial centers have risen and fallen. While the significance of some centers have deteriorated, a small number of centers have emerged as today\u27s leading financial centers by meeting a specific set of necessary conditions needed to successfully address the growing financial needs of the regions they are located. Furthermore, an even smaller number of financial centers have been able to sustain and expand their initial dominance in the financial industry by continuously satisfying a more focused set of conditions and factors. This thesis focuses on adapting Michael Porter\u27s Diamond Model in determining, clustering, and expanding key factors that have historically given cities such as London, New York, Hong Kong, Singapore, and Tokyo their current status at the pinnacle of the financial centers of the world. This thesis begins by taking Porter\u27s model that addresses national competitive advantage nations from a macroeconomic point of view, and adapting it to the development of financial centers at a microeconomic level. It utilizes Michael Porter\u27s established grouping corners for identifying a vast array of macroeconomic and microeconomic factors that have historically played critical roles in increasing productivity and efficiency within a center\u27s financial industry. Additionally, this thesis categorizes these factors into parameters that form a theoretical model designed to showcase the path to global financial dominance for an aspiring financial center. With the adaptation of Porter\u27s model outlined in this thesis, financial centers are given a figurative blueprint of what constitutes a successful financial center. The theoretical model analyzes the necessary conditions and environments that a center needs to recreate within itself, or are endowed with, in order to be a globally competitive financial center

Pre-diagnosis plasma immune markers and risk of non-Hodgkin lymphoma in two prospective cohort studies

Inflammation and B-cell hyperactivation have been associated with non-Hodgkin lymphoma development. This prospective analysis aimed to further elucidate pre-diagnosis plasma immune marker profiles associated with non-Hodgkin lymphoma risk. We identified 598 incident lymphoma cases and 601 matched controls in Nurses\u27 Health Study and Health Professionals Follow-up Study participants with archived pre-diagnosis plasma samples and measured 13 immune marker levels with multiplexed immunoassays. Using multivariable logistic regression we calculated odds ratios and 95% confidence intervals per standard deviation unit increase in biomarker concentration for risk of non-Hodgkin lymphoma and major histologic subtype, stratifying additional models by years ( \u3c 5, 5 to \u3c 10, \u3e /=10) after blood draw. Soluble interleukin-2 receptor-alpha, CXC chemokine ligand 13, soluble CD30, and soluble tumor necrosis factor receptor-2 were individually positively associated, and B-cell activating factor of the tumor necrosis factor family inversely associated, with all non-Hodgkin lymphoma and one or more subtypes. The biomarker combinations associated independently with lymphoma varied somewhat by subtype and years after blood draw. Of note, the unexpected inverse association between B-cell activating factor and chronic lymphocytic leukemia/small lymphocytic lymphoma risk (odds ratio: 95% confidence interval: 0.51, 0.43-0.62) persisted more than 10 years after blood draw (odds ratio: 0.70; 95% confidence interval: 0.52-0.93). In conclusion, immune activation precedes non-Hodgkin lymphoma diagnosis by several years. Decreased B-cell activating factor levels may denote nascent chronic lymphocytic leukemia many years pre-diagnosis

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available

From QFT to DCC

A quantum field theoretical model for the dynamics of the disoriented chiral condensate is presented. A unified approach to relate the quantum field theory directly to the formation, decay and signals of the DCC and its evolution is taken. We use a background field analysis of the O(4) sigma model keeping one-loop quantum corrections (quadratic order in the fluctuations). An evolution of the quantum fluctuations in an external, expanding metric which simulates the expansion of the plasma, is carried out. We examine, in detail, the amplification of the low momentum pion modes with two competing effects, the expansion rate of the plasma and the transition rate of the vacuum configuration from a metastable state into a stable state.We show the effect of DCC formation on the multiplicity distributions and the Bose-Einstein correlations.Comment: 34 pages, 10 figure

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

Improving the Classification of Landsat-8 OLI Images using Neighborhood Median Pixel Values

Image classification in remote sensing is defined by categorizing image pixels or raw data sensed by satellites into a distinct set of labels. In this paper, an improved technique for classifying pixels from satellite images is proposed. The technique makes use of the median value of the pixels in the rectangular neighborhood centered at the given pixel to be classified. A scoring system was developed that compares this median value in relation to the expected median values for each of the different classes. The proposed method was tested on Landsat-8 Operational Land Imager (OLI) bands 1 to 7 images and three index images-Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The experimental results showed an overall accuracy of 94%, a remarkable improvement from the 84% accuracy of the previous work that uses a distance-based classifier. The obtained results indicate that the proposed method can be a better alternative way to classify images in remote sensing