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Invasions of exotic species are one of the primary causes of biodiversity loss on our planet (National Research Council 1995). In the marine environment, all habitat types including estuaries, coral reefs, mud flats, and rocky intertidal shorelines have been impacted (e.g. Bertness et al. 2001). Recently, the topic of invasive species has caught the public's attention. In particular, there is worldwide concern about the aquarium strain of the green alga Caulerpa taxifolia (Vahl) C. Agardh that was introduced to the Mediterranean Sea in 1984 from the Monaco Oceanographic Museum. Since that time, it has flourished in thousands of hectares of near-shore waters. More recently, C. taxifolia has invaded southern Californian and Australian waters. Since the waters of Florida are similar to the waters of the Mediterranean Sea and other invasive sites my study will focus on determining potential invasion locations in Florida. I will look at the present distribution of C. taxifolia - native strain in Florida as well as the distribution of the whole genus around the state. During this study, I address three questions: 1) What is the current distribution of Caulerpa spp. in Florida? 2) Can I predict the location of potential Caulerpa spp. invasions using a set of environmental parameters and correlate them to the occurrence of the algae with the support of Geographic Information System (GIS) maps? 3) Using the results of part two, is there an ecological preferred environment for one or all Caulerpa spp. in Florida? To answer these questions, I surveyed 24 areas in each of 6 zones chosen in a stratified manner along the Floridian coastline to evaluate the association of potential indicators Caulerpa. Latitude, presence or absence of seagrass beds, human population density, and proximity to marinas were chosen as the 4 parameters expected to correlate to Caulerpa occurrences. A logistic regression model assessing the association of Caulerpa occurrence with measured variables has been developed to predict current and future probabilities of Caulerpa spp. presence throughout the state. Fourteen different species of Caulerpa spp. were found in 26 of the 132 sites visited. There was a positive correlation between Caulerpa spp. and seagrass beds presence and proximity to marinas. There was a negative correlation with latitude and human population density. C. taxifolia – aquarium strain wasn't found. Percent correct for our model was of 61.5% for presence and 98.1% for absence. This prediction model will allow us to focus on particular areas for future surveys.

Water distribution utilities are responsible for supplying both clean and safe drinking water, while under constraints of operating at an efficient and acceptable performance level. The City of Kingston, Ontario is currently experiencing elevated costs to repair its aging buried water main assets. Utilities Kingston is opting for a more efficient and practical means of forecasting pipe breaks and the application of a predictive water main break models allows Utilities Kingston to forecast future pipe failures and plan accordingly. The objective of this thesis is to develop an artificial neural network (ANN) model to forecast pipe breaks in the Kingston water distribution network. Data supplied by Utilities Kingston was used to develop the predictive ANN water main break model incorporating multiple variables including pipe age, diameter, length, and surrounding soil type. The constructed ANN model from historical break data was utilized to forecast pipe breaks for 1-year, 2-year, and 5-year planning periods. Simulated results were evaluated by statistical performance metrics, proving the overall model to be adequate for testing and forecasting. Predicted breaks were as follows, 33 breaks for 2011-2012, 22 breaks for 2012-2013 and 35 breaks for 2013-2016. Additionally, GIS plots were developed to highlight areas in need of potential rehabilitation for the distribution system. The goal of the model is to provide a practical means to assist in the management and development of Kingston’s pipe rehabilitation program, and to enable Utilities Kingston to reduce water main repair costs and to improve water quality at the customer's tap. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-10-21 15:30:10.288

Doctor of Philosophy / Department of Civil Engineering / Sunanda Dissanayake / Malgorzata J. Rys / Driver workload increases on horizontal curves due to more complicated navigation compared to navigation on straight roadway sections. Although only a small portion of roadways are horizontal curve sections, approximately 25% of all fatal highway crashes occur at horizontal curve sections. According to the Fatality Analysis Reporting System (FARS) database, fatalities associated with horizontal curves were more than 25% during last years from 2008 to 2014, reinforcing that investigation of horizontal curve crashes and corresponding safety improvements are crucial study topics within the field of transportation safety. Improved safety of horizontal curve sections of rural transportation networks can contribute to reduced crash severities and frequencies. Statistical methods can be utilized to develop crash prediction models in order to estimate crashes at horizontal curves and identify contributing factors to crash occurrences, thereby correlating to the primary objectives of this research project. Primary data analysis for 221 randomly selected horizontal curves on undivided two-lane two-way highways with Poisson regression method revealed that annual average daily traffic (AADT), heavy vehicle percentage, degree of curvature, and difference between posted and advisory speeds affect crash occurrence at horizontal curves. The data, however, were relatively overdispersed, so the negative binomial (NB) regression method was utilized. Results indicated that AADT, heavy vehicle percentage, degree of curvature, and long tangent length significantly affect crash occurrence at horizontal curve sections. A new dataset consisted of geometric and traffic data of 5,334 horizontal curves on the entire state transportation network including undivided and divided highways provided by Kansas Department of Transportation (KDOT) Traffic Safety Section as well as crash data from the Kansas Crash and Analysis Reporting System (KCARS) database were used to analyze the single vehicle (SV) crashes. An R software package was used to write a code and combine required information from aforementioned databases and create the dataset for 5,334 horizontal curves on the entire state transportation network. Eighty percent of crashes including 4,267 horizontal curves were randomly selected for data analysis and remaining 20% horizontal curves (1,067 curves) were used for data validation. Since the results of the Poisson regression model showed overdispersion of crash data and many horizontal curves had zero crashes during the study period from 2010 to 2014, NB, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) methods were used for data analysis. Total number of crashes and severe crashes were analyzed with the selected methods. Results of data analysis revealed that AADT, heavy vehicle percentage, curve length, degree of curvature, posted speed, difference between posted and advisory speed, and international roughness index influenced single vehicle crashes at 4,267 randomly selected horizontal curves for data analysis. Also, AADT, degree of curvature, heavy vehicle percentage, posted speed, being a divided roadway, difference between posted and advisory speeds, and shoulder width significantly influenced severe crash occurrence at selected horizontal curves. The goodness-of-fit criteria showed that the ZINB model more accurately predicted crash numbers for all crash groups at the selected horizontal curve sections. A total of 1,067 horizontal curves were used for data validation, and the observed and predicted crashes were compared for all crash groups and data analysis methods. Results of data validation showed that ZINB models for total crashes and severe crashes more accurately predicted crashes at horizontal curves. This study also investigated the effect of speed limit change on horizontal curve crashes on K-5 highway in Leavenworth County, Kansas. A statistical t-test proved that crash data from years 2006 to 2012 showed only significant reduction in equivalent property damage only (EPDO) crash rate for adverse weather condition at 5% significance level due to speed limit reduction in June 2009. However, the changes in vehicles speeds after speed limit change and other information such as changes in surface pavement condition were not available. According to the results of data analysis for 221 selected horizontal curves on undivided two-lane highways, tangent section length significantly influenced total number of crashes. Therefore, providing more information about upcoming changes in horizontal alignment of the roadway via doubling up warning sings, using bigger sings, using materials with higher retroreflectivity, or flashing beacons were recommended for horizontal curves with long tangent section lengths and high number of crashes. Also, presence of rumble strips and wider shoulders significantly and negatively influenced severe SV crashes at horizontal curve sections; therefore, implementing rumble strips and widening shoulders for horizontal curves with high number of severe SV crashes were recommended.

Millions of somatic mutations in human cancers have been identified by sequenc- ing. Identifying and distinguishing cancer driver genes amongst the millions of candi- date mutations remains a major challenge. Accurate identification of driver genes and mutations is essential for the progress of cancer research and personalizing treatment based on accurate stratification of patients. Because of inter-tumor genetic hetero- geneity, numerous driver mutations within a gene can be found at low frequencies. This makes them difficult to differentiate from other non-driver mutations. Inspired by these challenges, we devised a novel way of identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, function, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In ad- dition to identifying known driver genes, we identify several novel candidate driver genes. We provide an external evaluation of the predicted genes using a dataset of 26 nasopharyngeal cancer samples that underwent whole exome sequencing. We find that the predicted driver genes have a significantly higher rate of mutation than non-driver genes, both in publicly available data and in the nasopharyngeal cancer samples we use for validation. Additionally, we characterize sub-networks of genes that are jointly involved in specific tumors.

This study was conducted to investigate and model the high traffic collision frequencies in the City of Edmonton, Canada. Consistent collision spikes were observed on Fridays compared to the other days of the week. The first Negative Binomial model was formulated to establish a relation between the collision frequency and the independent variables. The second Multinomial logistic regression model was formulated to examine the probability of age categories and gender involved in collision for each day of week considering collision has happened. The proposed collision prediction models were found good. They could provide a realistic estimate of expected collision frequency and properties of collision for a particular day as a function of number of hours of daylight, number of hours of snowfall, visibility, age and gender. It is hoped that predicted collision frequency will help the decision maker to quantify traffic safety of Edmonton and improve the scenario. / Transportation Engineering

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Companies allocate the majority of the acquisition price to goodwill, which has resulted in goodwill to become a prominent asset on companies balance sheets. Research shows that goodwill impairments lag behind the economic reality between two to four years and that the current accounting regime does not provide adequate disclosures to predict future impairments. The purpose of this paper is to examine what factors that can predict the occurrence of future goodwill impairments. We carry out our investigation by choosing several pre-acquisition and post-performance indicators, which we hand-collect from companies’ annual reports. Our sample includes acquisitions made by Swedish listed companies during the period 2005 to 2011. To examine the predictability of goodwill impairments we carry out a series of binary logistic regressions in which goodwill write-offs are predicted by our acquisition and performance indicators. Our results suggest that information on acquisition activity, change in segment-level return on assets and firm-level return on assets are useful to predict goodwill impairments. Although our findings indicate that information surrounding the acquisition and subsequent performance can be helpful in predicting future impairments there is still difficulties for external stakeholders to predict goodwill write-offs. This is due the fact that a majority of acquisitions lack adequate information on the acquired goodwill. Consequently, our findings have implications for the accounting literature and standard setters since it is questionable whether financial statements and their disclosures provide sufficient and relevant information to evaluate the economic reality of goodwill balances.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22478号 / 農博第2382号 / 新制||農||1074(附属図書館) / 学位論文||R2||N5258(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 中村 公人, 教授 星野 敏, 教授 藤原 正幸 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第22835号 / 社医博第111号 / 新制||社医||11(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 佐藤 俊哉, 教授 川上 浩司, 教授 木村 剛 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM