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

No description available.

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

Globally, there is an alarming increase in the incidence of Type II diabetes mellitus (T2DM). It is well recognized that women who develop gestational diabetes (GDM) in their pregnancies are at increased risk of T2DM in later life. In addition, poor glycaemic control in pregnancy impacts adversely on the neonatal outcome, as well as the long term disease risks of that child. The risk of these outcomes increases continuously as maternal fasting plasma glucose levels increases. Several adverse outcomes have been associated with DM during pregnancy. These include pre-eclampsia, polyhydramnios, fetal macrosomia, fetal hepatomegaly and cardiomegaly, birth trauma, operative delivery, perinatal mortality and neonatal respiratory problems and metabolic complications such as hypoglycaemia, hyperbilirubinaemia, hypocalcaemia and polycythaemia. Despite five decades of research there is little consensus regarding the optimal approach to screening for GDM. Recently most international organisations have recommended that all women should be screened for GDM. South Africa is a diverse multi-racial society with an increasing burden of non-communicable diseases. The health system is already overburdened, and the optimal approach to screening for GDM remains unclear. A prospective cohort observational study was conducted at the Eyethu Yarona clinic (Lion Park Clinic), in Johannesburg, South Africa (SA). One thousand (1000) consecutive non-diabetic women who were less than 26 weeks pregnant were recruited. At recruitment the women completed a demographic questionnaire, and had a random glucose and glycated haemoglobin (HbA1c) drawn. A fasting blood glucose was assessed within 2 weeks, and a serum specimen was frozen at -40°C for further testing at a later stage. Patients had a 75 g 2-hour oral glucose tolerance test (OGTT) and HbA1c between 24 – 28 weeks gestation. All glucose measurements were done at the laboratory using standardized tests (venous blood) and on a Roche Accuchek Active® glucometer (Roche Diagnostics, Mannheim, Germany) (capillary blood). GDM was diagnosed according to the International Federation of Gynecology and Obstetrics (FIGO) criteria, i.e. any one abnormal reading was diagnostic of GDM: 0-hour ≥5.1 mmol/l, 1-hour ≥10 mmol/l, or 2-hour ≥8.5 mmol/l. Thereafter a nested cohort study of HIV negative patients was conducted to investigate the association between the concentrations of biomarkers associated with glucose homeostasis and GDM in a South African population. C-reactive protein (CRP), adiponectin, and fasting insulin were measured on the stored serum samples. The Insulin Sensitivity Index (HOMA-IR = fasting insulin (microU/L) x fasting glucose (mmol/L) / 22.5), and Quantitative Insulin Sensitivity Check Index (QUICKI = 1 / [log (I0) + log (G0)]) were calculated for further evaluation of markers of insulin sensitivity. The significance of this research was to assess the burden of disease of GDM in a South African population. The different diagnostic criteria were also compared, as well as the universal versus the traditional risk-factor based screening approach to GDM. Screening methods were compared so as to propose a simple, effective, cost efficient screening and diagnostic tool that may be implemented at primary health care level, which will in turn identify those pregnant women who warrant referral to a high care obstetric unit, thus improving both maternal and neonatal outcomes in our population. / Thesis (PhD) - University of Pretoria, 2017. / SEMDSA / SASA / Roche / Obstetrics and Gynaecology / PhD / Unrestricted

This study develops a bankruptcy prediction model for South African companies listed on the Johannesburg Stock Exchange. The model is of considerable efficiency and the findings reported extend bankruptcy literature to developing countries. 64 financial ratios for 28 companies, grouped into failed and non-failed companies, were tested using multiple discriminant analysis after conducting normality tests. Three variables were found to be significant which are: Times Interest Earned, Cash to Debt and Working Capital to Turnover. The model correctly classified about 75% of failed and non-failed in the original and cross validation procedures. This study went on to conduct an external validation of the model superiority by introducing a sample of failed companies, which showed that the model predictive accuracy is more than chance. Despite the popularity of the topic among researchers this study highlighted the importance and relevance of the topic to corporate managers, policy makers and to investors especially in a developing market perspective, thereby contributing significantly towards understanding the factors that lead to corporate bankruptcy.

Unemployment Insurance serves as a vital economic stabiliser, offering financial assistance and promoting workforce reintegration. In Sweden, occupation-specific unemployment funds, known as "Arbetslöshetskassan" (A-KASSAN), manage these claims. New complex challenges pertaining to A-KASSAN's decision-making process and unemployment insurance claims necessitate a holistic data analytics framework, innovative modelling approaches, and effective implementation strategies.  This study aims to establish a comprehensive approach to data analytics for unemployment insurance claims to provide a more accurate prediction model to aid A-KASSAN's decision-making. It accomplishes this through three main objectives: the development of a thorough framework employing management data analytics for claim analysis; advancement in modelling approaches to predict unemployment trends; and deliberation on effective strategies to visualise the developed solutions.  Drawing on Data Science, Computer Science, and Economics and Management Science, this study has crafted a four-tiered comprehensive framework encompassing descriptive, diagnostic, predictive, and prescriptive analytics. It has explored novel methodologies, formulated a model library, devised rules for result integration, and validated these through case studies. The model library showcases diverse models from Economic modelling, Statistical modelling, Big Data analytics with Machine Learning and Deep Learning, alongside hybrid modelling strategies. This study primarily concentrates on developing visualisation tools as an implementation strategy. In a summary, this study provides A-KASSAN with an approach to overcome two central issues: the lack of a comprehensive data analytics approach for unemployment insurance claims, including a framework and predictive modelling, and a dearth of visualisation solutions for management data analytics pertinent to these claims.