O mercado da indústria petrolífera tem aumentado consideravelmente seus investimentos em projetos, dada a importância desse setor para o desenvolvimento de um país. O elevado número de projetos faz com que seus objetivos sejam cada vez mais difíceis de serem alcançados, seja pela competição de recursos, pela alta complexidade ou mesmo pela interferência entre eles. A área de logística, um dos elos da cadeia produtiva desse mercado especialmente a de transporte marítimo, possui projetos caracterizados pela manutenção preventiva dos navios existentes, denominada de docagem, e pela construção de novos navios. Verifica-se, nos projetos de docagem, uma diferença entre os objetivos planejados e os alcançados, indicando a necessidade de melhorias e, por se tratar de projetos, podem-se utilizar os conceitos e ferramentas de gestão de projetos e de gestão de riscos para essa finalidade. Este trabalho apresenta um estudo dos eventos de risco do projeto de docagem de navios petroleiros, indicando os de maior relevância, suas causas e conseqüências, e a metodologia, ferramentas e técnicas utilizadas. / The oil industry has substantially increased its investments on projects given the importance of this sector to the development of a country. The large quantity of projects has turning their objectives more and more hard to be achieved either by the competition for resources or by their high complexity or even by interference among the projects themselves. At one of the links of the production chain of this industry, the logistic area, more specifically the maritime transportation, the projects are related to the preventive maintenance of the existing ships, which is called docking, and to the building of new ships. A difference is noticed on the docking projects regarding the planned objectives and those achieved which indicates the necessity of improvements. As dockings are considered projects, the concepts and tools of project and risk management can be used to fulfill such a necessity. This paper presents a study of the risk events of the docking projects for oil tankers identifying the most important ones, shows their causes and consequences and the used methodology, tools and techniques.

Medical imaging is a powerful tool for clinical practice allowing in-vivo insight into a patient’s disease state. Many modalities exist, allowing for the collection of diverse information about the underlying tissue structure and/or function. Traditionally, medical professionals use visual assessment of scans to search for disease, assess relevant disease predictors and propose clinical intervention steps. However, the imaging data contain potentially useful information beyond visual assessment by trained professional. To better use the full depth of information contained in the image sets, quantitative imaging characteristics (QICs), can be extracted using mathematical and statistical operations on regions or volumes of interests. The process of using QICs is a pipeline typically involving image acquisition, segmentation, feature extraction, set qualification and analysis of informatics. These descriptors can be integrated into classification methods focused on differentiating between disease states. Lung cancer, a leading cause of death worldwide, is a clear application for advanced in-vivo imaging based classification methods. We hypothesize that QICs extracted from spatially-linked and size-standardized regions of surrounding lung tissue can improve risk assessment quality over features extracted from only the lung tumor, or nodule, regions. We require a robust and flexible pipeline for the extraction and selection of disease QICs in computed tomography (CT). This includes creating an optimized method for feature extraction, reduction, selection, and predictive analysis which could be applied to a multitude of disease imaging problems. This thesis expanded a developmental pipeline for machine learning using a large multicenter controlled CT dataset of lung nodules to extract CT QICs from the nodule, surrounding parenchyma, and greater lung volume and explore CT feature interconnectivity. Furthermore, it created a validated pipeline that is more computationally and time efficient and with stability of performance. The modularity of the optimized pipeline facilitates broader application of the tool for applications beyond CT identified pulmonary nodules. We have developed a flexible and robust pipeline for the extraction and selection of Quantitative Imaging Characteristics for Risk Assessment from the Tumor and its Environment (QIC-RATE). The results presented in this thesis support our hypothesis, showing that classification of lung and breast tumors is improved through inclusion of peritumoral signal. Optimal performance in the lung application achieved with the QIC-RATE tool incorporating 75% of the nodule diameter equivalent in perinodular parenchyma with a development performance of 100% accuracy. The stability of performance was reflected in the maintained high accuracy (98%) in the independent validation dataset of 100 CT from a separate institution. In the breast QIC-RATE application, optimal performance was achieved using 25% of the tumor diameter in breast tissue with 90% accuracy in development, 82% in validation. We address the need for more complex assessments of medically imaged tumors through the QIC-RATE pipeline; a modular, scalable, transferrable pipeline for extracting, reducing and selecting, and training a classification tool based on QICs. Altogether, this research has resulted in a risk assessment methodology that is validated, stable, high performing, adaptable, and transparent.

Balance disorders and falls are prevalent among multiple pathologies that affect the musculoskeletal or sensorineural systems. Annually, fall-related injuries put excessive economic burden on society and yet, current clinical balance assessment tools based on functional tests are inaccurate and have limited association with fall risk. Therefore, there is a growing need of an accurate balance and fall risk assessment tool for clinical use. The primary purpose of this research was to develop an accurate Center of Pressure (COP) based balance assessment tool to study postural instability and fall risk. Chapter 1 aimed at development of the COP based tool using cost effective equipment- a Wii Balance Board (WBB) and testing its accuracy and errors. The result of this study indicated that the WBB tool is reliable in assessing balance and the linearity and hysteresis errors in WBB tool are higher than force plates but it compares well in terms of cost, portability and availability. Chapter 2 aimed at assessing the relation between the radiographic parameters of balance, COP metrics, and health related quality of life in adults with spinal deformities. The results of this investigation revealed that just like radiographic parameters, COP metrics could help explain some variability in symptoms in patients with comparable extent of deformity. Chapter 3 attempted to establish a threshold value of COP metrics for binary classification of fall risk in patients with multiple sclerosis (MS). The findings of this study highlighted path length as an excellent predictor of future falls with high test accuracy, sensitivity and specificity. This dissertation concludes that the WBB tool has the potential to revolutionize balance and fall risk assessment in clinical fields such as geriatrics, rehabilitation, neurology, and orthopedics.

Degradation product toxicity is a critical quality issue for a small group of useful drug products--e.g. lidocaine, isoniazid, chlorhexidine, gabapentin. In the traditional risk assessment approaches, a no-observed-adverse-effect level (NOAEL) derived from animal data is determined with the use of generic (and arbitrary) uncertainty factors to obtain an acceptable daily intake. The effects of compound-specific biological complexities and pharmacokinetics are typically not part of the risk calculations. The selection of uncertainty factors that account for interspecies or intraspecies difference concerning biokinetics and biodynamics has also generally failed to consider chemical-specific mechanism information or pharmacokinetics data. The use of combining in-vitro biopharmaceutical characterization methods and physiologically-based pharmacokinetic modeling has undergone extensive study and validation for predicting clinical drug blood level time profiles. The rationale for the proposed research is that a PBPK modeling utilizing rat to human scaling for target tissue toxicity in combination with the Monte Carlo method for estimating human target exposure distributions provides a rational basis for assessing drug stability safety issues for drug substances that potentially degrade to toxic compounds. PBPK models for rats and humans were developed to simulate drug exposure time profiles after oral administration of model compounds including aniline, p-chloroaniline, 2,6-dimethylaniline, o-toluidine and p-aminophenol. The PBPK models were parameterized using a combination of literature values, computational models and standard in vitro experiments. Microsomal and hepatocyte metabolism studies were used to estimate the metabolic constants, and ultrafiltration was used to measure protein binding. Intestinal permeability was predicted using a set of related compound data to correlate measured Caco-2 permeability with molecular descriptors by multivariate regression. Sensitivity analyses were conducted to evaluate the impact of PBPK model parameters on plasma level predictions. To evaluate patient population effects on exposure profiles, the PBPK model parameters were varied in meaningful ways using Monte Carlo methods. Based on population PBPK models, distributions of target tissue exposure in rats and humans were simulated and compared to derive human safe dose. As results, rat PBPK model-predicted aniline concentration time profiles were in reasonable agreement with published profiles. Distributions of target tissue exposure in rats and humans were generated and compared based on a criterion. A human reference dose was then selected at a value of 1% criteria. This approach was compared to traditional risk assessment calculations. In conclusion, the PBPK modeling approach resulted in drug degradation product risk specifications that were less stringent than those estimated by conventional risk assessment approach. The PBPK modeling approach provides a rational basis for drug instability risk assessment by focusing on target tissue exposure and leveraging physiological, biochemical, biophysical knowledge of compounds and species.

Environmental airborne benzene is a ubiquitous hazardous air pollutant whose emissions are generated from multiple sources, including industrial emissions, fuel station emissions, and automobile emissions. Chronic occupational exposures to elevated levels of benzene are known to be associated with leukemic cancers, in particular, acute myeloid leukemia (AML), though epidemiological evidence regarding environmental exposures and subsequent AML development is lacking. This investigation uses historical airborne monitoring data from six counties in the State of Florida to characterize the environmental cancer risk from airborne benzene concentrations using current Federal and State regulatory analysis methodology, and a comparative analysis based on occupational epidemiological evidence. Airborne benzene concentrations were collected from 24 air toxics monitoring stations in Broward, Duval, Orange, Miami-Dade, Hillsborough, and Pinellas counties. From the years 2003 - 2006, 3,794 air samples were collected using 8, 12, and 24 hr samples with sub-ambient pressure canister collectors consistent with EPA benzene methodological protocols 101 and 176. Mean benzene concentrations, by site, ranged from 0.18 - 3.58 ppb. Using risk analysis methodology consistent with the EPA and the Florida Department of Environmental Protection (FLDEP) the resulting cancer risk estimates ranged from 4.37 x 10-6 to 8.56 x 10-5, exceeding the FLDEP's acceptable cancer risk level, 1 x 10-6 for all monitoring sites. The cumulative lifetime exposures were calculated in ppm-years by site, ranging from 0.036 - 0.702 ppmyears. A comparative analysis with available epidemiological literature revealed that associations between benzene exposure and cancer outcomes were related to cumulative lifetime exposures in great excess of 1 ppm-years. The results of this investigation indicate that it is not reasonable to expect additional cancer outcomes in Florida residents as a result of airborne benzene exposures consistent with measured concentrations, despite the fact that all regulatory risk calculations exceed acceptable cancer risk levels in the State of Florida.

Companies, across the globe are concerned with risks that impair their ability to produce quality products at a low cost and deliver them to customers on time. Risk assessment, comprising of both external and internal elements, prepares companies to identify and manage the risks affecting them. Although both external/supply chain and internal/production line risk assessments are necessary, internal risk assessment is often ignored. Internal risk assessment helps companies recognize vulnerable sections of production operations and provide opportunities for risk mitigation. In this research, a novel production line risk assessment methodology is proposed. Traditional simulation techniques fail to capture the complex relationship amongst risk events and the dynamic interaction between risks affecting a production line. Bayesian- integrated System Dynamics modelling can help resolve this limitation. Bayesian Belief Networks (BBN) effectively capture risk relationships and their likelihoods. Integrating BBN with System Dynamics (SD) for modelling production lines help capture the impact of risk events on a production line as well as the dynamic interaction between those risks and production line variables. The proposed methodology is applied to an industrial case study for validation and to discern research and practical implications.

Significant challenges in water resources management arise because of the ever-increasing pressure on the world’s heavily exploited and limited water resources. These stressors include demographic growth, intensification of agriculture, climate variability, and climate change. These challenges to water resources are usually tackled using a top-down approach, which suffers from many limitations including the use of a limited set of climate change scenarios, the lack of methodology to rank these scenarios, and the lack of credibility, particularly on extremes. The bottom-up approach, the recently introduced approach, reverses the process by assessing vulnerabilities of water resources systems to variations in future climates and determining the prospects of such wide range of changes. While it solves some issues of the top-down approach, several issues remain unaddressed. The current project seeks to provide end-users and the research community with an improved version of the bottom-up framework for streamlining climate variability into water resources management decisions. The improvement issues that are tackled are a) the generation of a sufficient number of climate projections that provide better coverage of the risk space; b) a methodology to quantitatively estimate the plausibility of a future desired or undesired outcome and c) the optimization of the size of the projections pool to achieve the desired precision with the minimum time and computing resources. The results will hopefully help to cope with the present-day and future challenges induced mainly by climate. In the first part of the study, the adequacy of stochastically generated climate time series for water resources systems risk and performance assessment is investigated. A number of stochastic weather generators (SWGs) are first used to generate a large number of realizations (i.e. an ensemble of climate outputs) of precipitation and temperature time series. Each realization of the generated climate time series is then used individually as an input to a hydrological model to obtain streamflow time series. The usefulness of weather generators is evaluated by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space (CSS), and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space (RPIS). The performance of the weather generator is assessed using visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out using five different weather generators: two versions of WeaGETS, two versions of MulGETS and the k-nearest neighbor weather generator (knn). In the second part of the thesis, the impacts of climate change, on the other hand, was evaluated by generating a large number of representative climate projections. Large ensembles of future series are created by perturbing downscaled regional climate models’ outputs with a stochastic weather generator, then used as inputs to a hydrological model that was calibrated using observed data. Risk indices calculated with the simulated streamflow data are converted into probability distributions using Kernel Density Estimations. The results are dimensional joint probability distributions of risk-relevant indices that provide estimates of the likelihood of unwanted events under a given watershed configuration and management policy. The proposed approach offers a more complete vision of the impacts of climate change and opens the door to a more objective assessment of adaptation strategies. The third part of the thesis deals with the estimation of the optimal size of SWG realizations needed to calculate risk and performance indices. The number of realizations required to reach is investigated utilizing Relative Root Mean Square Error and Relative Error. While results indicate that a single realization is not enough to adequately represent a given stochastic weather generator, results generally indicate that there is no major benefit of generating more than 100 realizations as they are not notably different from results obtained using 1000 realizations. Adopting a smaller but carefully chosen number of realizations can significantly reduce the computational time and resources and therefore benefit a larger audience particularly where high-performance machines are not easily accessible. The application was done in one pilot watershed, the South Nation Watershed in Eastern Ontario, yet the methodology will be of interest for Canada and beyond. Overall, the results contribute to making the bottom-up more objective and less computationally intensive, hence more attractive to practitioners and researchers.

In response to increased greenhouse gases and global temperatures, changes to the hydrologic cycle are projected to occur and new precipitation characteristics are expected to emerge. The study of these characteristics is facilitated by common indices to measure precipitation and temperature developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices can be used to describe the likely consequences of climate change such as increased daily precipitation intensity (SDII) and heavier rainfall events (R95p). This study calculates a subset of these indices from observed and modelled precipitation data in Portland, Oregon. Five rainfall gages from a high resolution rain gage network and projections from three downscaled global climate models including CanESM2, CESM1, CNRM-CM5 are used to calculate precipitation indices. Mann-Kendall's tau is used to detect monotonic trends in indices. The observational record is compared with models for the historic period (1977-2005) and these past trends are compared with projected future trends (2006-2100). The influence of study unit on trend detection is analyzed by computing trends at the annual and monthly scale. Study unit is shown to be important for trend detection. When the annual study unit is used, projected future trends towards increased precipitation intensity and event volumes are not observed in the historic data. However, when analyzed with a monthly study unit, trends towards increased precipitation intensity and event volumes are observed in the historic data. These trends are shown to be important for Portland area flooding, as precipitation indices are shown to significantly correlate with 40 maximum peak flow events that occurred during the period of study.

Drought is among the costliest natural hazards developing slowly and affecting large areas, which imposes severe consequences on society and economy. Anthropogenic climate change is expected to exacerbate drought in various regions of the globe, making its associated socioeconomic impacts more severe. Such impacts are of higher concern in Africa, which is mainly characterized by arid climate and lacking infrastructure as well as social development. Furthermore, the continent is expected to experience vast population growth, which will make it more vulnerable to the adverse effects of drought. This study provides the first comprehensive multi-dimensional assessment of drought risk across the African continent as a function of hazard, vulnerability, and exposure. A multi-model and multi-scenario approach is employed to quantify drought hazard using the most recent ensemble of regional climate models and a multi-scalar drought index. Moreover, a rigorous framework is proposed and applied to assess drought vulnerability based on various sectors of economy, energy and infrastructure, health, land use, society, and water resources. Drought risk is then projected for different population scenarios and the changes of drought risk and the role of each component are investigated. In addition, the impacts of climate change on heat-stress mortality risk is assessed across the Middle East and North Africa. The results indicate vast increase for the projected drought risk with varied spatiotemporal patterns. Population growth and climate change will significantly escalate drought risk, especially in distant future. Therefore, climate change mitigation and adaptation planning as well as social development strategies should be carried out immediately in order to reduce the projected adverse risks on human life and society.

Background. CRC risk can be reduced though lifestyle modification and regular screenings. Providing CRC risk feedback that promotes preventive behaviors to those at average risk has the potential to significantly reduce CRC morbidity and mortality. Purpose. The purpose of this dissertation was to examine the impact of CRC risk assessment feedback among adults aged 50-75 with no personal or family history of the disease. The specific aims were to: (1a) test personalized (vs. generic) risk assessment feedback on individuals’ risk perceptions and intentions to engage in three risk-reducing behaviors (e.g., physical activity, diet, and screening); (1b) determine if the provision of CRC risk information influences breast cancer risk perceptions and mammography intentions; (2a) examine individuals’ accuracy of perceived lifetime risk of CRC; (2b) assess whether improved accuracy following risk assessment was associated with changes in behavioral intentions; and finally, (3) evaluate the use of a unique sampling procedure designed to increase diversity of survey respondents. Methods. A pre-post parallel, two arm randomized controlled trial examined the effects of providing CRC risk assessment feedback that included lifetime risk estimates and information about CRC risk factors that was either personalized (treatment) or generic (control). N=419 average risk adults between the ages of 50-75 were recruited from a commercial online panel. Results. There were no differences in risk perception between study arms. Overall participants, perceived lifetime risk of CRC lowered at post-test and seemingly produced a spillover effect in lowered perceived lifetime risk of breast cancer among females. CRC screening intentions increased in both study arms and mammography intentions increased in the control arm. Accuracy of lifetime risk improved at post-test, but was not associated with changes in intentions to perform risk reducing behaviors. Quota sampling acquired a targeted and diverse sample quickly and efficiently. Conclusion. Communicating CRC risk information to average risk adults can improve CRC risk perception accuracy and enhance colorectal and mammography screening intentions. Risk assessment feedback did not consistently influence intentions to improve diet and physical activity.