Dogbane beetle, Chrysochus auratus, was studied for its biological control potential against spreading dogbane, Apocynum androsaemifolium, a native perennial weed in lowbush blueberry (Vaccinium angustifolium). No-choice host range experiments were conducted with common milkweed (Asclepias syriaca), periwinkle (Vinca minor), wild raisin (Viburnum cassenoides), and lowbush blueberry. There was no significant feeding on these plant species by adult dogbane beetles. Significant decreases in foliar dry weight were achieved with 16 beetles per ramet. In Nova Scotia, beetles were present in the field for 8-12 weeks beginning in late June or early July (225-335 growing degree days). Peak beetle abundance occured at 357-577 growing degree days and varied from 4-7 beetles/m2. The fecundity and fertility, timing of pupation, and number of instars were also examined. Females deposited approximately 100 eggs over a 20 day period, with an egg viability of over 90%. Pupae were found on June 1st and June 15th. / A unique project exploring biological control of a native plant species using a native insect species

A wide area assessment of forecast changes in wintertime synoptic conditions over western North America is combined with a meso-scale alpine hydrometeorology model to evaluate the joint impact(s) of forecast climate change on snowpack conditions in an alpine watershed in the southern Canadian Rockies. The synoptic analysis was used to generate long-term climate time series scenarios using the CCCma CGCM1. An alpine hydrometerology model is used to predict changes in wintertime precipitation at the watershed scale. A mass balance snow model is utilized to predict the overall snow accumulation throughout a watershed. A vapour transfer model has been incorporated in the snow model to estimate snow volumes more accurately. The synoptic analysis and GCM output forecasts a modest increase in both winter precipitation and temperatures in the study area, resulting in a decline of winter snow accumulations, and hence an expected decline in spring runoff. / ix, 87 leaves : ill. ; 28 cm.

This report presents a thesis project conducted by two students at Mälardalens högskola. The project covers 15 points in industrial design and was conducted during April to June 2014. The principal of the thesis works at Plastic Produkter. The assignment was to develop equipment for an effective way to dose, mix silicon, fill a special designed plastic bag and then seal it. The bag is then formed into a breast model.When a customer for various reasons decides to do a breast augmentation or a breast reconstruction, there may be doubt or uncertainty about the result. The idea of this product is to show the client and the client’s environment how the result of a future breast surgery would look like.The process began with a specification of demands and a functional analysis to define the issue. Once the problem was defined a competitor analysis was made to get an idea of how the competitors solve similar problems. The authors concluded that there were no obvious competitors, but there were products with similar purpose.The authors chose to start focus on how the mechanism would work. A partition of the machine made that a generation of ideas could be made on each separate part and then they could be combined into several concepts. A Pugh’s matrix and a QFD were made on the concepts for the mechanism to decide which concepts that should be developed. The winning mechanism concept was then combined with the remaining concepts parts that were selected. The final concept was a machine that is simple and easy to use, which meets the specified requirements.Tests and calculations were made to support the decisions that were taken. When the final concept was fully developed and tested, a casing was created which was formed after the inside of the machine. The casing is designed to fit into the customer environment and is developed for a first production. A 3D sketch of the final concept was made in SolidWorks to easily get a visual image of the concept. The entire machine and its components are presented in the results. Concept care was made on the machine to get low production costs and to make it easy to assembly. To locate and eliminate any future risks, a failure modes and effects analysis called FMEA was made. To easily evaluate the dynamic of the team and the process of the project, the team used an evaluation tool called PIPS. This gave an overall look of that the group assignment worked well throughout the process. Even if the project was in industrial design, it was a lot of construction that was needed to process. This meant that the project took a different and better direction than expected and gave the authors a greater overview of how the machine would work. The authors believe that the goals of the thesis were achieved well in this project.

The planet is experiencing a dramatic loss of species. The majority of species are unknown to science, and it is usually infeasible to conduct a census of a region to acquire a complete inventory of all life forms. Therefore, it is important to estimate and conduct statistical inference on the total number of species in a region based on samples obtained from field observations. Such estimates may suggest the number of species new to science and at potential risk of extinction. In this thesis, we develop novel methodology to conduct statistical inference, based on abundance-based data collected from multiple sampling locations, on the number of species within a taxonomic group residing in a region. The primary contribution of this work is the formulation of novel statistical methodology for analysis in this setting, where abundances of species are recorded at multiple sampling units across a region. This particular area has received relatively little attention in the literature. In the first chapter, the problem of estimating the number of species is formulated in a broad context, one that occurs in several seemingly unrelated fields of study. Estimators are commonly developed from statistical sampling models. Depending on the organisms or objects under study, different sampling techniques are used, and consequently, a variety of statistical models have been developed for this problem. A review of existing estimation methods, categorized by the associated sampling model, is presented in the second chapter. The third chapter develops a new negative binomial mixture model. The negative binomial model is employed to account for the common tendency of individuals of a particular species to occur in clusters. An exponential mixing distribution permits inference on the number of species that exist in the region, but were in fact absent from the sampling units. Adopting a classical approach for statistical inference, we develop the maximum likelihood estimator, and a corresponding profile-log-likelihood interval estimate of species richness. In addition, a Gaussian-based confidence interval based on large-sample theory is presented. The fourth chapter further extends the hierarchical model developed in Chapter 3 into a Bayesian framework. The motivation for the Bayesian paradigm is explained, and a hierarchical model based on random effects and discrete latent variables is presented. Computing the posterior distribution in this case is not straight-forward. A data augmentation technique that indirectly places priors on species richness is employed to compute the model using a Metropolis-Hastings algorithm. The fifth chapter examines the performance of our new methodology. Simulation studies are used to examine the mean-squared error of our proposed estimators. Comparisons to several commonly-used non-parametric estimators are made. Several conclusions emerge, and settings where our approaches can yield superior performance are clarified. In the sixth chapter, we present a case study. The methodology is applied to a real data set of oribatid mites (a taxonomic order of micro-arthropods) collected from multiple sites in a tropical rainforest in Panama. We adjust our statistical sampling models to account for the varying masses of material sampled from the sites. The resulting estimates of species richness for the oribatid mites are useful, and contribute to a wider investigation, currently underway, examining the species richness of all arthropods in the rainforest. Our approaches are the only existing methods that can make full use of the abundance-based data from multiple sampling units located in a single region. The seventh and final chapter concludes the thesis with a discussion of key considerations related to implementation and modeling assumptions, and describes potential avenues for further investigation. / Graduate

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

Diss. (sammanfattning) Umeå : Univ., 2006. / Härtill 5 uppsatser.

Thesis (M.S.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains v, 49 p. Includes abstract. Includes bibliographical references (p. 37-49).

Universiẗat, Diss., 2004--Osnabrück.

Thesis (M.S.)--University of Alabama at Birmingham, 2007. / Title from first page of PDF file (viewed Oct. 30, 2007). Includes bibliographical references (p. 45-56).