A Transportation Safety Planning Tool for the City of Ames, August 2011

The historically-reactive approach to identifying safety problems and mitigating them involves selecting black spots or hot spots by ranking locations based on crash frequency and severity. The approach focuses mainly on the corridor level without taking the exposure rate (vehicle miles traveled) and socio-demographics information of the study area, which are very important in the transportation planning process, into consideration. A larger study analysis unit at the Transportation Analysis Zone (TAZ) level or the network planning level should be used to address the needs of development of the community in the future and incorporate safety into the long-range transportation planning process. In this study, existing planning tools (such as the PLANSAFE models presented in NCHRP Report 546) were evaluated for forecasting safety in small and medium-sized communities, particularly as related to changes in socio-demographics characteristics, traffic demand, road network, and countermeasures. The research also evaluated the applicability of the Empirical Bayes (EB) method to network-level analysis. In addition, application of the United States Road Assessment Program (usRAP) protocols at the local urban road network level was investigated. This research evaluated the applicability of these three methods for the City of Ames, Iowa. The outcome of this research is a systematic process and framework for considering road safety issues explicitly in the small and medium-sized community transportation planning process and for quantifying the safety impacts of new developments and policy programs. More specifically, quantitative safety may be incorporated into the planning process, through effective visualization and increased awareness of safety issues (usRAP), the identification of high-risk locations with potential for improvement, (usRAP maps and EB), countermeasures for high-risk locations (EB before and after study and PLANSAFE), and socio-economic and demographic induced changes at the planning-level (PLANSAFE).

Predição em modelos de tempo de falha acelerado com efeito aleatório para avaliação de riscos de falha em poços petrolíferos

We considered prediction techniques based on models of accelerated failure time with random e ects for correlated survival data. Besides the bayesian approach through empirical Bayes estimator, we also discussed about the use of a classical predictor, the Empirical Best Linear Unbiased Predictor (EBLUP). In order to illustrate the use of these predictors, we considered applications on a real data set coming from the oil industry. More speci - cally, the data set involves the mean time between failure of petroleum-well equipments of the Bacia Potiguar. The goal of this study is to predict the risk/probability of failure in order to help a preventive maintenance program. The results show that both methods are suitable to predict future failures, providing good decisions in relation to employment and economy of resources for preventive maintenance.

A useful empirical bayesian method to analyse industrial data from saturated factorial designs

The use of saturated two-level designs is very popular, especially in industrial applications where the cost of experiments is too high. Standard classical approaches are not appropriate to analyze data from saturated designs, since we could only get the estimates of the main factor effects and we would not have degrees of freedom to estimate the variance of the error. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs. The proposed methodology is illustrated assuming a simulated data set. © 2013 Growing Science Ltd. All rights reserved.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Multiple Testing With an Empirical Alternative Hypothesis

An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for use of an empirical alternative hypothesis to increase by 50% or more the number of true positives identified at a given significance level.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

Development of safety performance functions for SafetyAnalyst applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Spatial analysis of notified dengue fever infections

This study aimed to investigate the spatial clustering and dynamic dispersion of dengue incidence in Queensland, Australia. We used Moran’s I statistic to assess the spatial autocorrelation of reported dengue cases. Spatial empirical Bayes smoothing estimates were used to display the spatial distribution of dengue in postal areas throughout Queensland. Local indicators of spatial association (LISA) maps and logistic regression models were used to identify spatial clusters and examine the spatio-temporal patterns of the spread of dengue. The results indicate that the spatial distribution of dengue was clustered during each of the three periods of 1993–1996, 1997–2000 and 2001–2004. The high-incidence clusters of dengue were primarily concentrated in the north of Queensland and low-incidence clusters occurred in the south-east of Queensland. The study concludes that the geographical range of notified dengue cases has significantly expanded in Queensland over recent years.

Culture as a driver of entrepreneurship : contrasting independent entrepreneurship versus employee entrepreneurship

Principal Topic Counties in Northern Europe, such as Sweden, Finland and Denmark, have comparatively low per capita rates of entrepreneurship as measured by independent new venture start-up rates – as for example measured by the Global Entrepreneurship Monitor (GEM) Total entrepreneurial activity (TEA) rate. However, the latest 2011 GEM data reveals that these same countries have comparatively very high Employee Entrepreneurship Activity (EEA) rates – that is a high rate per capita of employees involved in new product development or new enterprise activities. This observation has prompted us to investigate the role of national culture in driving independent versus employee entrepreneurial activities. Prior research has established that national (and regional) culture plays an important role in forming an “entrepreneurial culture” that encourages (or discourages) independent business start-ups and TEA (e.g. Davidsson, 1995; Beugelsdijk, 2007). However, the relationship of culture and EEA has not received research attention. Moreover, empirical relationships between elements of national culture and independent entrepreneurship have revealed some surprising results. For example, Wildeman et al. (1999) report an unexpected higher share of individual business ownership in countries that have higher uncertainty avoidance, higher power distance and lower individualism according to Hofstede’s dimensions of culture. They speculate that dissatisfaction can be a source of entrepreneurship: in countries with a high power distance, a high uncertainty avoidance and low individualism, there may be relatively more business owners since enterprising individuals cannot satisfy their needs within existing organizations. Yet it remains a rather open question whether entrepreneurial behaviour in existing organisations provides a satisfactory explanation for these empirical findings. Methods We will conduct a cross sectional study of the influence of national culture according to the five / six dimensions of Hofstede (1980; 2001) on both TEA and EEA for the 54 countries that participated in GEM 2011. Since it is well established that the opportunities for entrepreneurship vary substantially with a country’s level of economic development, we intend to conduct separate analyses for the three categories of development – innovation driven economies, efficient driven economies and factor driven economies. We also intend to restrict our assessment of TEA to opportunity driven entrepreneurship, as necessity driven entrepreneurship has a different relationship to the “entrepreneurial culture” that is the focus of our study. We will control for a range of factors such as GDP growth, ease of doing business index and unemployment. Results and Implications Descriptive analyses of the GEM TEA and EEA data reveal clusters of countries that appear to be have similar national culture. We are yet to conduct regression analyses.

A review of modern computational algorithms for Bayesian optimal design

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

Occupational Health and Safety, Courts and Crime: the legal construction of occupational health and safety offences in Victoria

This book reports on an empirically-based study of the manner in which the Magistrates' Courts in Victoria, construct occupational health and safety (OHS) issues when hearing prosecutions for offences under the Victorian OHS legislation. Prosecution has always been a controversial element in the enforcement armoury of OHS regulators, but at the same time it has long been argued that the low level of fines imposed by courts has had an important chilling effect on the OHS inspectorate's enforcement approaches, and on the impact of OHS legislation. Using a range of empirical research methods, including three samples of OHS prosecutions carried out in the Victorian Magistrates' Courts, Professor Johnstone shows how courts, inspectors, prosecutors and defence counsel are involved in filtering or reshaping OHS issues during the prosecution process, both pre-trial and in court. He argues that OHS offences are constructed by focusing on "events", in most cases incidents resulting in injury or death. This "event-focus" ensures that the attention of the parties is drawn to the details of the incident, and away from the broader context of the event. During the court-based sentencing process defence counsel is able to adopt a range of techniques which isolate the incident from its micro and macro contexts, thereby individualising and decontextualising the incident.

The court and the factory the legal construction of occupational health and safety offences in Victoria

This thesis reports on an empirically based study of the manner in which Victorian Magistrates Courts constructed occupational health and safety (OHS) issues when hearing prosecutions for offences under the Industrial Safety, Health and Welfare Act 1981 (the ISHWA) and the Occupational Health and Safety Act 1985 (OHSA) from 1983 to 1991. These statutes established OHS standards for employers and other relevant parties. The State government enforced these standards through an OHS inspectorate which had a range of enforcement powers, including prosecution. After outlining the historical development of Victoria’s OHS legislation, the magistracy’s historical role in its enforcement, and the development of an enforcement culture in which inspectors viewed prosecution as a last resort, the study shows how the key provisions of the ISHWA and OHSA required occupiers of workplaces and employers to provide and maintain safe systems of work, including the guarding of dangerous machinery. Using a wide range of empirical research methods and legal materials, it shows how the enforcement policies, procedures and practices of the inspectorate heavily slanted inspectors workplace investigations and hence prosecutions towards a restricted and often superficial, analysis of incidents (or “events”) most of which involved injuries on machinery. There was evidence, however, that after the establishment of the Central Investigation Unit in 1989 cases were more thoroughly investigated and prosecuted. From 1990 the majority of prosecutions were taken under the employer’s general duty provisions, and by 1991 there was evidence that prosecutions were focusing on matters other than machinery guarding.

Spatial incidence of dengue infections in Queensland, Australia - reply

This study aimed to investigate the spatial clustering and dynamic dispersion of dengue incidence in Queensland, Australia. We used Moran's I statistic to assess the spatial autocorrelation of reported dengue cases. Spatial empirical Bayes smoothing estimates were used to display the spatial distribution of dengue in postal areas throughout Queensland. Local indicators of spatial association (LISA) maps and logistic regression models were used to identify spatial clusters and examine the spatio-temporal patterns of the spread of dengue. The results indicate that the spatial distribution of dengue was clustered during each of the three periods of 1993–1996, 1997–2000 and 2001–2004. The high-incidence clusters of dengue were primarily concentrated in the north of Queensland and low-incidence clusters occurred in the south-east of Queensland. The study concludes that the geographical range of notified dengue cases has significantly expanded in Queensland over recent years.

Purification of pharmaceuticals and nutraceutical compounds by sub- and supercritical extraction and chromatography

This thesis discusses the use of sub- and supercritical fluids as the medium in extraction and chromatography. Super- and subcritical extraction was used to separate essential oils from herbal plant Angelica archangelica. The effect of extraction parameters was studied and sensory analyses of the extracts were done by an expert panel. The results of the sensory analyses were compared to the analytically determined contents of the extracts. Sub- and supercritical fluid chromatography (SFC) was used to separate and purify high-value pharmaceuticals. Chiral SFC was used to separate the enantiomers of racemic mixtures of pharmaceutical compounds. Very low (cryogenic) temperatures were applied to substantially enhance the separation efficiency of chiral SFC. The thermodynamic aspects affecting the resolving ability of chiral stationary phases are briefly reviewed. The process production rate which is a key factor in industrial chromatography was optimized by empirical multivariate methods. General linear model was used to optimize the separation of omega-3 fatty acid ethyl esters from esterized fish oil by using reversed-phase SFC. Chiral separation of racemic mixtures of guaifenesin and ferulic acid dimer ethyl ester was optimized by using response surface method with three variables per time. It was found that by optimizing four variables (temperature, load, flowate and modifier content) the production rate of the chiral resolution of racemic guaifenesin by cryogenic SFC could be increased severalfold compared to published results of similar application. A novel pressure-compensated design of industrial high pressure chromatographic column was introduced, using the technology developed in building the deep-sea submersibles (Mir 1 and 2). A demonstration SFC plant was built and the immunosuppressant drug cyclosporine A was purified to meet the requirements of US Pharmacopoeia. A smaller semi-pilot size column with similar design was used for cryogenic chiral separation of aromatase inhibitor Finrozole for use in its development phase 2.