New Releases by Aaron Seth

This study used various statistical tools to identify key factors contributing to adverse patient outcomes at a mid-western hospital. Nurses working in an acute care hospital setting are faced with a broad set of task demands that can vary several times during a normal work shift. Task demands presented by patients must be actively monitored to ensure that proper care is being provided. In addition to challenges presented by patients, nurse fatigue can pose a threat to patient outcomes. Understanding risk factors that contribute to increased nurse fatigue can provide solutions to reduce the impact that fatigue has on nurse performance and patient outcome. This was comprised of two phases: 1) a database analysis of current data collected at a mid-western hospital, and 2) three focus groups to identify nurse perceptions pertaining to task demand and fatigue. The adverse patient outcomes (APOs) analyzed in this study were medical administration errors (MAEs) and patient falls.

What is life worth, when nothing is left? Young Aharon, no more than twelve, tells us his struggles during the Holocaust. Despite the atmosphere and religious differences, he was able to relive the Passover Seder with a gentile friend. How can a young boy subsist when he lost almost everyone he loved? Did he find solace through his friend''s words? Even though, the days were long and harsh Aharon gave strength when he had none left. Even though, the nights were dark and scary. Aharon gave hope when he had none left. Even though the days and nights turned into years of tears, Aharon gave courage when all else fails....

This dissertation examines the rise in what I call, in an approximate sense, "global unemployment," since the 1950s. The category of unemployment is difficult to apply precisely at the global level, since in most countries either there is no unemployment insurance, or that insurance covers only a small portion of the unemployed. As a result, the unemployed must work even though they are unable to find regular work. Some of these unemployed individuals end up underemployed in the formal sector of the economy. Globally, many more find their way into the informal sector. I develop a category of "surplus population," drawn from 19th century political economy, to describe the diverse situations of these unemployed and marginally employed individuals. I also offer a dynamic theory of how people come to find themselves in those situations--due to processes leading, on the one hand, to an oversupply of labor and, on the other hand, to an under-demand for labor. And I estimate the size of the global surplus population, at present. The dissertation then goes on to provide an historical account of the rise of surplus populations worldwide, mainly in the period since World War II. My argument is that the rise of surplus populations was largely the result of three processes that unfolded globally: (1) a massive, demographically driven increase in the world''s working population; (2) a global "Green Revolution," which significantly reduced the price of food but also resulted in a worldwide wave of agricultural exit, making for the further rise in the population needing jobs; and (3) a global wave of deindustrialization, which unfolded not only in the high-income countries, but also across the low-income world. The result was a huge increase in the global supply of labor relative to a weakening demand for labor, and this brought into being a gigantic population of unemployed people--whether in open or hidden forms. I try to buttress this account by reference to the exception that proves the rule: the unique developmental path of East Asia, where a combination of a more slowly growing labor supply and a larger demand for labor reduced the scale of the surplus-population problem, as compared to other developing regions.

(Cont.) In contrast to many suggested watermarking techniques that view the original data as interference, we find that the capacity increases with the uncertainty in the original data. Indeed, we find that out of all distributions with the same variance, a Gaussian distribution on the original data results in the highest capacity. Furthermore, for Gaussian data, the capacity increases with its variance. One surprising result is that with Gaussian data the capacity does not increase if the original data can be used to decode the watermark. This is reminiscent of a similar model, Costa''s "writing on dirty paper", in which the attacker simply adds independent Gaussian noise. Unlike with a more sophisticated attacker, we show that the capacity does not change for Costa''s model if the original data is not Gaussian.