Although the petroleum industry has imaged reflections from the sides of salt domes, steeply dipping structures have not been imaged as reflectors outside of sedimentary basins; to do so requires appropriate data acquisition, prestack depth migration, and an excellent seismic velocity model. Poststack time migrated seismic images, normal moveout velocity analysis, well logs, and other geologic information are used to build the velocity model. In regions of interest outside of sedimentary basins, such as major strike-slip faults, seismic reflectivity is often sparse and little is known of detailed subsurface geology. Alternate methods of velocity model construction must be used. First arrival (refraction and turning ray) traveltime tomography is proposed to construct the preliminary velocity model for steep-dip prestack depth migration in settings with little a priori subsurface information. A densely spaced synthetic seismic data set with long-offset recording, modeled after a real survey across the San Andreas Fault, was constructed using a finite-difference algorithm. First arrival traveltimes were picked from the data and a velocity model was constructed using tomography. The velocity model was used to perform a Kirchhoff prestack depth migration of the synthetic shot gathers. The subsurface structure was sufficiently reconstructed that the velocity model could be refined through migration velocity analysis. A series of tomography tests was used to determine the spatial resolution limits of the velocity model. Isolated erroneous anomalies with sizes near the resolution limits were added to the velocity model derived from tomography and used as input for migration. This pessimistic test provided an adequate image and identifiable arrivals in migrated common image gathers, allowing the velocity model to be improved through migration moveout analysis. Data acquisition requirements for tomography include long recording offsets and times, larger sources, and dense spacings, very similar to the requirements for steep-dip reflection imaging. / Master of Science

Indigenous (Métis, First Nation, and Inuit) peoples and communities in Canada, especially in the prairies, continue to experience disproportionate levels of tuberculosis (TB) compared to the rest of the Canadian born population. This inequitable distribution of TB disease burden demands effective policy, program, and practice responses. These have so far failed to materialize, perhaps in part because of limitations in the approaches we have taken to understanding the issue. As well, these responses have largely been grounded in western scientific paradigms. Science is the search and the re-search for knowledge and this varies according to the perspectives and paradigms of the researcher(s) and stakeholders. In this project, the student researcher collaborated with the Métis Nation-Saskatchewan (MN-S) and two volunteer health researchers to adapt and ground a western paradigm and methodology (System Dynamics and Group Model Building) to a Métis research paradigm to understand experiences of tuberculosis (TB) among Métis people. Data collection took place in a 2-day Métis-adapted group model building (GMB) workshop. The outcome is a causal loop diagram with associated stories co-created by the team and the workshop participants. The workshop was evaluated using a storytelling and story listening method that explored the appropriateness of adapting GMB within a Métis research context. The approach was determined to be successful methodologically, and substantively new knowledge was created in our Métis community about the determinants of TB. This research was a journey of diversity, working at the intersection of knowledge systems to produce a new understanding of a health issue as complex as TB.

This thesis focuses on the critical development of friction and blended braking capabilities for an electric driveline model intended for multiscale road-cargo simulations. In the rapidly evolving landscape of transportation, electric driveline technology has emerged as a key player in achieving sustainability and efficiency goals. This study addresses the pivotal aspect of braking performance within this context, aiming to optimize energy utilization and enhance road safety. Using a systems engineering approach, MATLAB, and Simulink, the work in this thesis focused on the construction of a comprehensive model of a Volvo FH truck with friction/blended braking and an Anti-lock Braking System (ABS). The model underwent rigorous validation against the New European Driving Cycle (NEDC) and subsequent simulation under NEDC, US06, WLTC3 driving cycles. The primary objective was to assess the impact of activating or deactivating the blended control system on the State of Charge (SOC) and energy consumption. Through iterative refinement, the model's energy consumption per kilometer closely approximated actual FH truck test values. Activating the blended control system consistently led to reduced energy consumption across all driving cycles, underscoring its potential to enhance sustainable and safe transportation solutions. Importantly, the ABS system functioned effectively throughout the simulations, ensuring vehicle safety, while the model maintained a consistent output speed aligned with the input speed. This abstract summarizes the research's focus, methodology, and key findings, highlighting the successful development and validation of a Volvo FH truck model with friction/blended braking and ABS systems. The results underscore the blended control system's capacity to improve energy efficiency, contributing to the pursuit of sustainable and efficient transportation solutions in the evolving landscape of electric driveline technology. / Detta examensarbete fokuserar på utvecklingen av en elektrisk drivlinemodell som inkluderar mixning av konventionell friktionsbromssystem och den elektriska drivlinans moment under regenerering. Modellen är avsedd för multiskaliga väg-och godstransportsimuleringar. I den snabbt föränderliga transportsektorn är elektrisk drivlineteknologi en viktig del för att uppnå hållbarhets- och effektivitetsmål. Detta arbete behandlar den avgörande aspekten av bromsprestanda i detta sammanhang och har som mål att optimera energianvändningen och kontrollera vägsäkerheten. Genom att använda s.k. systems engineering-metodik, MATLAB och Simulink, utvecklades en dynamisk modell av en Volvo FH-lastbil med friktions-/mixad bromsning och system för låsningsfria bromsar (ABS). Modellen genomgick en rigorös validering mot New European Driving Cycle (NEDC) och efterföljande simuleringar enligt NEDC, US06 och WLTC3-körcykler. Det primära målet var att bedöma effekten av att aktivera eller inaktivera det mixade kontrollsystemet på laddningsnivå (SOC) och energiförbrukning. Genom iterativ förbättring approximerade modellen energiförbrukningen per kilometer nära de faktiska provvärdena för FH-lastbilar. Aktivering av det blandade kontrollsystemet ledde konsekvent till minskad energiförbrukning för alla körcykler och underströk dess potential att förbättra hållbara och säkra transportsystem. Det är viktigt att notera att ABS-systemet fungerade effektivt genom alla simuleringar och säkerställde fordonssäkerheten, samtidigt som modellen beräknade en hastighet i linje med körcykelns specificerade hastigheten. Denna sammanfattning sammanfattar forskningens fokus, metodik och huvudsakliga resultat och lyfter fram den framgångsrika utvecklingen och valideringen av en Volvo FH-lastbilsmodell med friktions-/mixad bromsning och ABS-system. Resultaten understryker det blandade kontrollsystemets förmåga att förbättra energieffektiviteten och bidra till strävan efter hållbara och effektiva transportsystem i den snabbt föränderliga världen av elektrisk drivlineteknologi.

<p>Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy. </p><p>The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics. </p><p>As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified. </p><p>In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community. </p><p>Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.</p>

Although patterns of African American and white women breast cancer incidence and mortality in St. Louis, Missouri is consistent with those seen elsewhere in the United States, rates vary greatly across zip codes within the city of St. Louis. North St. Louis, whose neighborhoods are primarily African American, exhibits rates of breast cancer mortality that are among the highest in the city and higher than the state as a whole. Based on information that up to 50% of women in North St. Louis with a suspicious diagnosis of breast cancer never enter treatment, we conducted three 2-hour group model building sessions with 34 community stakeholders (e.g., breast cancer survivors or family members or caregivers and community support members such as navigators) to identify the reasons why African American women do not begin or delay breast cancer treatment. Participant sessions produced a very rich and dynamic causal loop diagram of the system producing disparities in breast cancer mortality in St. Louis. The diagram includes 8 major subsystems, causal links between system factors, and feedback loops, all of which shed light on treatment delays/initiation. Our work suggests that numerous intersecting factors contribute to not seeking treatment, which in turn may contribute to African American and white disparities in mortality.

No description available.

Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy. The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics. As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified. In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community. Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.

This paper presents the whole process of building up a high precision, high dynamical MEMS acceleration sensor. The first samples have achieved a resolution of better than 500 micro g and a bandwidth of more than 200 Hz. The sensor fabrication technology is shortly covered in the paper. A theoretical model is built from the physical principles of the complete sensor system, consisting of the MEMS sensor, the charge amplifier and the PWM driver for the sensor element. The mathematical modeling also covers problems during startup. A reduced order model of the entire system is used to design a robust control with the Mixed-Sensitivity H-infinity Approach. Since the system has an unstable pole, imposed by the electrostatic field and time delay, caused by A/D-D/A conversation delay and DSP computing time, limitations for the control design are given. The theoretical model might be inaccurate or lacks of completeness, because the parameters for the theoretical model building vary from sample to sample or might be not known. A new identification scheme for open or closed-loop operation is deployed to obtain directly from the samples the parameters of the mechanical system and the voltage dependent gains. The focus of this paper is the complete system development and identification process including practical tests in a DSP TI-TMS320C3000 environment.

O vírus da diarreia viral bovina (BVDV) causa uma das doenças mais importantes de bovinos em termos de custos econômicos e sociais, uma vez que é largamente disseminado na população de gado leiteiro. Os objetivos do trabalho foram estimar a prevalência em nível de rebanho e investigar fatores associados aos níveis de anticorpos em leite de tanque através de um estudo de caso-controle pareado, bem como discutir estratégias de construção de modelos. Para estimar a prevalência de rebanho, amostras de tanque de leite foram selecionadas aleatoriamente (n = 314) de uma população (N = 1604). A prevalência real de BVDV foi de 24,3% (IC95% = 20,1-29,3%). Para o estudo de caso-controle, rebanhos positivos para BVDV (altos níveis de anticorpos) foram classificados como casos (n = 21) e pareados (n = 63) por produção de leite com rebanhos que apresentaram baixos títulos de anticorpos (razão 1:3). Para análise, três modelos multivariáveis foram construídos: 1) modelo completo, onde todas as 21 variáveis independentes foram oferecidas, e dois modelos foram criados de acordo com conhecimento empírico e similaridades entre as variáveis independentes, 2) modelo de fatores animais e 3) modelo de biossegurança. Um questionário foi aplicado (n = 84) para obtenção de informações a respeito de possíveis fatores de risco para BVDV. O modelo completo (Modelo 1) identificou as seguintes variáveis: idade com critério de eliminação (OR = 0,10; IC95% = 0,02 – 0,39; P < 0,01); propriedades que forneceram leite a outras cooperativas anteriormente (OR = 4,13; IC95% = 1,17 - 14,49; P = 0,02) e a presença de piquete de isolamento para animais doentes (OR = 0,14; IC95% = 0,01 – 0,26; P = 0,02). O modelo de biossegurança (Modelo 3) revelou uma associação significativa com o uso de monta natural (OR = 9,03; IC95% = 2,14 – 38,03; P < 0,01); presença de piquete de isolamento para animais doentes (OR = 0,06; IC95% = 0,05 – 0,83; P = 0,03); anos fornecendo leite para a mesma cooperativa (OR = 0,94; IC95% = 0,91 – 0,97; P < 0,01) e contato direto pela cerca entre bovinos de propriedades vizinhas (OR = 5,78; IC95% = 1,41 – 23,67; P = 0,04). O modelo de biossegurança pode ser considerado o “melhor”, pois obteve AIC = 43,880 e BIC = 48,058 menores quando comparado com o modelo completo que obteve AIC = 50,445 e BIC = 53,779. Esta dissertação tem a intenção de promover a discussão sobre as estratégias de construção de modelos especialmente quando se trata de saúde animal. Recomenda-se a aplicação de agrupamento de variáveis independentes como uma boa alternativa na construção de modelos, uma vez que este processo pode levar a uma melhor compreensão a respeito da associação entre cause e efeito de doenças. / Bovine viral diarrhea virus (BVDV) causes one of the most important diseases of cattle in terms of economic costs and welfare, since it is widespread in the dairy cattle population. The aims were to estimate herd prevalence and investigate factors associated with antibodies in bulk tank milk (BTM) in dairy herds through a matched case-control design as well as discuss model-building strategies. To estimate herd prevalence, BTM samples were randomly selected (n = 314) from a population (N = 1604). The true prevalence of BVDV was 24.3% (CI95% = 20.1 - 29.3%). For the case-control study, BVDV antibody-positive herds (high antibody titers) were classified as cases (n = 21) and matched (n = 63) by milk production with herds presenting low antibody titers (ratio of 1:3). For analysis, three multivariable models were built: 1) full model, holding all 21 independent variables; and two models divided according to empirical knowledge and similarity among independent variables, i.e., 2) animal factor model and 3) biosecurity model. A questionnaire was applied (n = 84) to get information about possible BVDV risk factors. The full model (model 1) identified the following variables: age as a culling criteria (OR = 0.10; IC95% = 0.02 – 0.39; P < 0.01); farms that provided milk to other industries previously (OR = 4.13; IC95% = 1.17 – 14.49; P = 0.02); and isolation paddocks for ill animals (OR = 0.14; IC95% = 0.01 – 0.26; P = 0.02). The biosecurity model (model 3) revealed a significant association with the use of natural mating (OR = 9.03; IC95% = 2.14 – 38.03; P < 0.01); isolation paddocks for ill animals (OR = 0.06; IC95% = 0.05 – 0.83; P = 0.03); years providing milk for the same industry (OR = 0.94; CI95% = 0.89 - 0.99; P = 0.02); and direct contact over fences among cattle of neighboring farms (OR = 5.78; IC95% = 1.41 – 23.67; P = 0.04). The biosecurity model could be considered the “best” since AIC = 43,880 and BIC = 48,058 when compared with the full model where AIC = 50,445 and BIC = 53,579. This paper intends to promote discussion about the model-building strategy when animal-health-modeling is on the line. We recommend the application of grouping predictors as a good choice for model building since it could lead to a better understanding of disease-exposure associations.

Although patterns of African American and white women breast cancer incidence and mortality in St. Louis, Missouri is consistent with those seen elsewhere in the United States, rates vary greatly across zip codes within the city of St. Louis. North St. Louis, whose neighborhoods are primarily African American, exhibits rates of breast cancer mortality that are among the highest in the city and higher than the state as a whole. Based on information that up to 50% of women in North St. Louis with a suspicious diagnosis of breast cancer never enter treatment, we conducted three 2-hour group model building sessions with 34 community stakeholders (e.g., breast cancer survivors or family members or caregivers and community support members such as navigators) to identify the reasons why African American women do not begin or delay breast cancer treatment. Participant sessions produced a very rich and dynamic causal loop diagram of the system producing disparities in breast cancer mortality in St. Louis. The diagram includes 8 major subsystems, causal links between system factors, and feedback loops, all of which shed light on treatment delays/initiation. Our work suggests that numerous intersecting factors contribute to not seeking treatment, which in turn may contribute to African American and white disparities in mortality.