The use of Artificial Neural Networks (ANNs) in aquatic ecology

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Structural vulnerability to narcotics-driven firearm violence: An ethnographic and epidemiological study of Philadelphia's Puerto Rican inner-city.

BackgroundThe United States is experiencing a continuing crisis of gun violence, and economically marginalized and racially segregated inner-city areas are among the most affected. To decrease this violence, public health interventions must engage with the complex social factors and structural drivers-especially with regard to the clandestine sale of narcotics-that have turned the neighborhood streets of specific vulnerable subgroups into concrete killing fields. Here we present a mixed-methods ethnographic and epidemiological assessment of narcotics-driven firearm violence in Philadelphia's impoverished, majority Puerto Rican neighborhoods.MethodsUsing an exploratory sequential study design, we formulated hypotheses about ethnic/racial vulnerability to violence, based on half a dozen years of intensive participant-observation ethnographic fieldwork. We subsequently tested them statistically, by combining geo-referenced incidents of narcotics- and firearm-related crime from the Philadelphia police department with census information representing race and poverty levels. We explored the racialized relationships between poverty, narcotics, and violence, melding ethnography, graphing, and Poisson regression.FindingsEven controlling for poverty levels, impoverished majority-Puerto Rican areas in Philadelphia are exposed to significantly higher levels of gun violence than majority-white or black neighborhoods. Our mixed methods data suggest that this reflects the unique social position of these neighborhoods as a racial meeting ground in deeply segregated Philadelphia, which has converted them into a retail endpoint for the sale of astronomical levels of narcotics.ImplicationsWe document racial/ethnic and economic disparities in exposure to firearm violence and contextualize them ethnographically in the lived experience of community members. The exceptionally concentrated and high-volume retail narcotics trade, and the violence it generates in Philadelphia's poor Puerto Rican neighborhoods, reflect unique structural vulnerability and cultural factors. For most young people in these areas, the narcotics economy is the most readily accessible form of employment and social mobility. The performance of violence is an implicit part of survival in these lucrative, illegal narcotics markets, as well as in the overcrowded jails and prisons through which entry-level sellers cycle chronically. To address the structural drivers of violence, an inner-city Marshall Plan is needed that should include well-funded formal employment programs, gun control, re-training police officers to curb the routinization of brutality, reform of criminal justice to prioritize rehabilitation over punishment, and decriminalization of narcotics possession and low-level sales

Multimodal, Multidimensional Models of Mouse Brain

Naturally occurring mutants and genetically manipulated strains of mice are widely used to model a variety of human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison and to facilitate the integration of anatomic, genetic, and physiologic observations from multiple subjects and experiments. We have developed digital atlases of the C57BL/6J mouse brain (adult and neonate) as comprehensive frameworks for storing and accessing the myriad types of information about the mouse brain. Along with raw and annotated images, these contain database management systems and a set of tools for comparing information from different techniques and different animals. Each atlas establishes a canonical representation of the mouse brain and provides the tools for the manipulation and analysis of new data. We describe both these atlases and discuss how they may be put to use in organizing and analyzing data from mouse models of epilepsy

One Year Later: September 11 and the Internet

Presents findings from a survey that looks at how the terror attacks affected Americans' views about access to online information, Internet use, and the Web after September 11. Contains scholarly studies built around analysis of hundreds of Web sites

Using visualization, variable selection and feature extraction to learn from industrial data

Although the engineers of industry have access to process data, they seldom use advanced statistical tools to solve process control problems. Why this reluctance? I believe that the reason is in the history of the development of statistical tools, which were developed in the era of rigorous mathematical modelling, manual computation and small data sets. This created sophisticated tools. The engineers do not understand the requirements of these algorithms related, for example, to pre-processing of data. If algorithms are fed with unsuitable data, or parameterized poorly, they produce unreliable results, which may lead an engineer to turn down statistical analysis in general. This thesis looks for algorithms that probably do not impress the champions of statistics, but serve process engineers. This thesis advocates three properties in an algorithm: supervised operation, robustness and understandability. Supervised operation allows and requires the user to explicate the goal of the analysis, which allows the algorithm to discover results that are relevant to the user. Robust algorithms allow engineers to analyse raw process data collected from the automation system of the plant. The third aspect is understandability: the user must understand how to parameterize the model, what is the principle of the algorithm, and know how to interpret the results. The above criteria are justified with the theories of human learning. The basis is the theory of constructivism, which defines learning as construction of mental models. Then I discuss the theories of organisational learning, which show how mental models influence the behaviour of groups of persons. The next level discusses statistical methodologies of data analysis, and binds them to the theories of organisational learning. The last level discusses individual statistical algorithms, and introduces the methodology and the algorithms proposed by this thesis. This methodology uses three types of algorithms: visualization, variable selection and feature extraction. The goal of the proposed methodology is to reliably and understandably provide the user with information that is related to a problem he has defined interesting. The above methodology is illustrated by an analysis of an industrial case: the concentrator of the Hitura mine. This case illustrates how to define the problem with off-line laboratory data, and how to search the on-line data for solutions. A major advantage of algorithmic study of data is efficiency: the manual approach reported in the early took approximately six man months; the automated approach of this thesis created comparable results in few weeks.reviewe

Affects of Elimination: Foundations of Collectivity

This paper examines specific indigenous social movements in the United States. Two examples are considered: the occupation of the decommissioned Fort-Lawton, Seattle military base in 1970 and the contemporary movement for missing and murdered indigenous women (MMIW). Both are examples of resistance to assimilation and ‘elimination’ in the form of collective action by indigenous persons. The paper explores the relation between coming together as a group and responding to the experience of violence, injury, or suffering. This dynamic between collective formation and shared affective experience constructs the foundation upon which these movements imagine and work to enact a social and political ‘alternative.\u2

GSU View, 2008-12

Newsletter published by Governors State University 2007-current

Pattern Formation in Drying Drops of Colloidal Solutions

The deposited residue from evaporated drops of colloidal solutions can exhibit a wide variety of interesting and reproducible patterns. A familiar example is the ring-like residue associated with the "coffee ring effect"[1]. This phenomenon was first explained by Deegan, et al as due to outward (radial) flows introduced by evaporation driving the suspended particles to the self-pinned edge, creating a "coffee ring" structure at the drop perimeter. This behavior has drawn the attention of a number of other researchers interested in its applications and relevance to industrial processes and technology (i.e. printing, coating, and medical diagnostics). Recently, certain diseases have been associated with specific patterns in dried drops of blood serum [2]. Important studies on the key mechanisms of the process (e.g. evaporation kinetics, internal flow motions, triple line dynamics role in evaporative self-assembly phenomena) have also appeared. During the evaporation of colloidal sessile droplets, capillary and Marangoni flow and Van der Waals forces work together. With so many factors effecting pattern formation, small changes in solution composition can lead to significant changes in the resulting patterns. In this research project, model solutions are made using a base lysozyme protein aqueous solution mixed with aqueous sodium chloride solutions of various concentrations. Droplet patterns from these colloidal solutions are examined by atomic force microscope and digital optical microscope. The sodium chloride concentration dependent patterns are analyzed and the temporal stability of these patterns in air at room temperature is monitored over several months