3 resultados para generalized linear mixed models

em DigitalCommons@University of Nebraska - Lincoln


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Mycobacterium bovis infects the wildlife species badgers Meles meles who are linked with the spread of the associated disease tuberculosis (TB) in cattle. Control of livestock infections depends in part on the spatial and social structure of the wildlife host. Here we describe spatial association of M. bovis infection in a badger population using data from the first year of the Four Area Project in Ireland. Using second-order intensity functions, we show there is strong evidence of clustering of TB cases in each the four areas, i.e. a global tendency for infected cases to occur near other infected cases. Using estimated intensity functions, we identify locations where particular strains of TB cluster. Generalized linear geostatistical models are used to assess the practical range at which spatial correlation occurs and is found to exceed 6 in all areas. The study is of relevance concerning the scale of localized badger culling in the control of the disease in cattle.

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Aim To assess the distribution, group size, seasonal occurrence and annual trends of cetaceans. Location The study area included all major inland waters of Southeast Alaska. Methods Between 1991 and 2007, cetacean surveys were conducted by observers who kept a constant watch when the vessel was underway and recorded all cetaceans encountered. For each species, we examined distributional patterns, group size, seasonal occurrence and annual trends. Analysis of variance (anova F) was used to test for differences in group sizes between multiple means, and Student’s t-test was used to detect differences between pairwise means. Cetacean seasonal occurrence and annual trends were investigated using a generalized linear model framework. Results Humpback whales (Megaptera novaeangliae) were seen throughout the region, with numbers lowest in spring and highest in the fall. Fin whale (Balaenoptera physalus) and minke whale (Balaenoptera acutorostrata) distributions were more restricted than that reported for humpback whales, and the low number of sightings precluded evaluating seasonal trends. Three killer whale (Orcinus orca) eco-types were documented with distributions occurring throughout inland waters. Seasonal patterns were not detected or could not be evaluated for resident and offshore killer whales, respectively; however, the transient eco-type was more abundant in the summer. Dall’s porpoise (Phocoenoides dalli) were distributed throughout the region, with more sightings in spring and summer than in fall. Harbour porpoise (Phocoena phocoena) distribution was clumped, with concentrations occurring in the Icy Strait/Glacier Bay and Wrangell areas and with no evidence of seasonality. Pacific white-sided dolphins (Lagenorhynchus obliquidens) were observed only occasionally, with more sightings in the spring. For most species, group size varied on both an annual and seasonal basis. Main conclusions Seven cetacean species occupy the inland waters of Southeast Alaska, with distribution, group size, seasonal occurrence and annual trends varying by species. Future studies that compare spatial and temporal patterns with other features (e.g. oceanography, prey resources) may help in identifying the key factors that support the high density and biodiversity of cetaceans found in this region. An increased understanding of the region’s marine ecology is an essential step towards ensuring the long-term conservation of cetaceans in Southeast Alaska.

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INVESTIGATION INTO CURRENT EFFICIENCY FOR PULSE ELECTROCHEMICAL MACHINING OF NICKEL ALLOY Yu Zhang, M.S. University of Nebraska, 2010 Adviser: Kamlakar P. Rajurkar Electrochemical machining (ECM) is a nontraditional manufacturing process that can machine difficult-to-cut materials. In ECM, material is removed by controlled electrochemical dissolution of an anodic workpiece in an electrochemical cell. ECM has extensive applications in automotive, petroleum, aerospace, textile, medical, and electronics industries. Improving current efficiency is a challenging task for any electro-physical or electrochemical machining processes. The current efficiency is defined as the ratio of the observed amount of metal dissolved to the theoretical amount predicted from Faraday’s law, for the same specified conditions of electrochemical equivalent, current, etc [1]. In macro ECM, electrolyte conductivity greatly influences the current efficiency of the process. Since there is a certain limit to enhance the conductivity of the electrolyte, a process innovation is needed for further improvement in current efficiency in ECM. Pulse electrochemical machining (PECM) is one such approach in which the electrolyte conductivity is improved by electrolyte flushing in pulse off-time. The aim of this research is to study the influence of major factors on current efficiency in a pulse electrochemical machining process in macro scale and to develop a linear regression model for predicting current efficiency of the process. An in-house designed electrochemical cell was used for machining nickel alloy (ASTM B435) by PECM. The effects of current density, type of electrolyte, and electrolyte flow rate, on current efficiency under different experimental conditions were studied. Results indicated that current efficiency is dependent on electrolyte, electrolyte flow rate, and current density. Linear regression models of current efficiency were compared with twenty new data points graphically and quantitatively. Models developed were close enough to the actual results to be reliable. In addition, an attempt has been made in this work to consider those factors in PECM that have not been investigated in earlier works. This was done by simulating the process by using COMSOL software. However, it was found that the results from this attempt were not substantially different from the earlier reported studies.