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No description available.

Rheumatoid arthritis (RA) is a chronic disease characterized by an overactive immune system and joint inflammation. Population-based administrative health data (AHD) are widely used for RA outcomes research and surveillance. However, AHD may not completely capture all cases of RA in the population. Capture-recapture (CR) methods have been proposed to describe the completeness of AHD for estimating disease population size, but AHD may not conform to the assumptions that underlie CR models. A Monte Carlo simulation study was used to investigate the effects of violations of the assumptions for two-source CR methods: dependence between data sources and heterogeneity of capture probabilities. We compared the Chapman estimator and an estimator based on the multinomial logistic regression model (MLRM) to study relative bias (RB), coverage probability (CP) of 95% confidence intervals, width of 95% confidence intervals (WCI), and root-mean-square-error (RMSE) in prevalence estimates. The effects of misspecification of the MLRM were also investigated. In addition, the Chapman and MLRM estimators were used to estimate RA prevalence using AHD data from Saskatchewan, Canada. Population sizes were consistently underestimated for CR methods when the assumptions were violated. The estimated population size for both of the estimators did not differ substantially except for the RMSE values. Parameter estimates became biased when the MLRM model was misspecified, but there was little impact on population size estimates. In conclusion, CR methods are recommended to reduce bias in prevalence estimates based on AHDS. Because these methods may be sensitive to assumption violations, researchers should consider potential dependence between data sources. As well, sufficient overlap in the cases captured by each data source (e.g., 50% of the cases are captured by both data sources) or balanced capture probability in each data source is needed to effectively implement these methods. Researchers who estimate population size using CR methods in AHDs should favour the MLRM estimator over the Chapman estimator.

Capture-recapture (CRC) models use two or more samples, or lists, to estimate the size of a population. In the canonical example, a researcher captures, marks, and releases several samples of fish in a lake. When the fish that are captured more than once are few compared to the total number that are captured, one suspects that the lake contains many more uncaptured fish. This basic intuition motivates CRC models in fields as diverse as epidemiology, entomology, and computer science. We use simulations to study the performance of conventional log-linear models for CRC. Specifically we evaluate model selection criteria, model averaging, an asymptotic variance formula, and several small-sample data adjustments. Next, we argue that interpretable models are essential for credible inference, since sets of models that fit the data equally well can imply vastly different estimates of the population size. A secondary analysis of data on survivors of the World Trade Center attacks illustrates this issue. Our main chapter develops local log-linear models. Heterogeneous populations tend to bias conventional log-linear models. Post-stratification can reduce the effects of heterogeneity by using covariates, such as the age or size of each observed unit, to partition the data into relatively homogeneous post-strata. One can fit a model to each post-stratum and aggregate the resulting estimates across post-strata. We extend post-stratification to its logical extreme by selecting a local log-linear model for each observed point in the covariate space, while smoothing to achieve stability. Local log-linear models serve a dual purpose. Besides estimating the population size, they estimate the rate of missingness as a function of covariates. Simulations demonstrate the superiority of local log-linear models for estimating local rates of missingness for special cases in which the generating model varies over the covariate space. We apply the method to estimate bird species richness in continental North America and to estimate the prevalence of multiple sclerosis in a region of France.

Carbon Capture and Storage (CCS) is an old technology matrix with new concept to mitigate climate change while utilizing fossil fuels by advancing the technology. The various level of advancement in technology has been successfully demonstrated in some part of the world. However the technology has inherent uncertainty of not having commercial CCS plant. Efforts to make CCS commercially viable unfold uncertainties in numerous aspects of CCS technology. Beside the uncertainties in technology many barriers restrain CCS to become a successful climate mitigation technology. However the growing energy demand and urgent need to mitigate climate change through emission reduction favours CSS as transition to clean energy production. FutureGen 2.0 is the only large commercial scale CCS project, initiated in 2003 to test the commercial viability of the technology and to meet the U.S energy demands besides emission reductions target. The project resurrection in 2010 as FutureGen 2.0 after FutureGen termination in 2008 provides an opportunity to understand and analyse numerous uncertainties. However through document analysis only major three uncertainties i.e. policy and regulatory, economic and financial and public acceptance uncertainties are identified and analysed. The interlinkages between these uncertainties are also analysed. The study results show that above uncertainties constrained the project engendering new uncertainties i.e. timeframe uncertainties. This study also provides an insight about the sustainability implication of CCS by evaluating economic, environmental and social impact of CCS technology. It is still early to term the CCS as Sustainable technological innovation however for many years CCS would upset and restrain investment in other clean energy technologies like Renewable technology system. This study gives an input in sustainability of CCS and technological assessment study. This study is helpful in managing uncertainties and planning new CCS projects.

The primary objective of this work is to develop an approach for evaluating GHG mitigation strategies that considers the detailed operation of the electricity system in question and to ascertain whether considering the detailed operation of the electricity system materially affects the assessment. A secondary objective is to evalute the potential benefit of flexible CO2 capture and storage. An electricity system simlator is developed based upon a deregulated electricity system containing markets for both real and reserve power. Using the IEEE RTS ???96 as a test case, the performance of the electricity system is benchmarked with GHG regulation. Two different implementations of CO2 capture are added to the electricity system ??? fixed CO2 capture and flexible CO2 capture ??? and the impact of having CCS is assessed. The results indicate that: - the assessment of GHG mtigation strategies for the electricity generation subsector should consider the detailed operation of the electricity system in question, - cost of generation alone is not necessarily a good indicator of the economic impact of GHG regulation or the deployment of a GHG mitigation strategy, - adding CCS, at even a single generating unit, can significantly reduce GHG emissions and moderate the ecnomic impact of GHG regulation relative to the cases where CCS is not present, and - a generating unit with a flexible CCS processes participates preferentially in the reserve market enabling it to increase its net energy benefit. It is conclued that there is a significant potential advantage to generating units with flexible CCS processes. The flexibiity of existing and novel CCS process should be an assessment and design criterion, respectively, and the development of novel CCS processes with optimial operability is a suggested area of future research activity. A reduced-order model of a coal-fired generating unit with flexible CO2 capture is developed and integrated into the MINLP formulation of an economic dispatch model. Both of these efforts, not observed previously in the literature, constitute an important contribution of the work as the methodology provides a template for future assessmments of CCS and other electricity mitigation strategies in the electricity generation subsector.

Greenhouse gas (GHG) emission control has been extensively studied over the past decade. One GHG mitigation alternative is post-combustion carbon dioxide (CO2) capture using chemical absorption, which is a promising alternative due to its proven technology and the relative ease to install on existing coal-fired power plants. Nevertheless, the implementation of commercial-scale CO2 capture plants faces several challenges, such as high energy consumption, commercial availability, and geological CO2 storage. Therefore, there is a great incentive to develop studies that provide insights needed to design and dynamically operate industrial-scale CO2 capture plants for coal-fired power plants. This work presents a mechanistic dynamic model of a pilot plant of a post-combustion CO2 capture plant using the monoethanolamine (MEA) absorption processes. This model was implemented in gPROMS. The process insights gained from the sensitivity analysis, on six manipulated variables and six potential controlled variables, was used to determine promising control schemes for this pilot plant. This study then proposed three decentralized control structures. The first control scheme was designed based on the traditional-RGA (Relative Gain Array) analysis, whereas the other two control schemes were designed using heuristics. The performance evaluation of those control structures were conducted under eight scenarios, e.g. changes in flue gas composition, set point tracking, valve stiction, reboiler heat duty constraint, and flue gas flow rate. Under the condition where the reboiler temperature is to be controlled, a control scheme obtained from the heuristic showed faster response to achieve the process control objectives (90% CO2 capture rate and 95 mol% CO2 purity in the CO2 product stream) than the RGA-based control scheme. Furthermore, this study describes a step-by-step method to scale-up an MEA absorption plant for CO2 capture from a 750 MW supercritical coal-fired power plants. This industrial-scale CO2 capture plant consists of three absorbers (11.8 m diameter, 34 m bed height) and two strippers (10.4 m diameter, 16 m bed height) to achieve 87% CO2 captured rate and 95% CO2 purity in the CO2 product stream. It was calculated that the reboiler heat duty of 4.1GJ is required to remove 1 tonne of CO2 at the base case condition (20 kmol/s of flue gas flow rate with 16.3 mol% of CO2). The mechanistic model of an industrial-scale CO2 capture plant including a proposed control structure was evaluated using different scenarios. The performance evaluation result revealed that this plant can accommodate a maximum flue gas flow rate of +22% from the nominal condition due to absorbers??? flooding constraints. Moreover, it is able to handle different disturbances and offers prompt responses (After a plant is disturbed by an external perturbation, control variables in that plant are able to return to their set points in timely fashion using the adjustment of manipulated variables.) without significant oscillating signal or offset. In addition, this study highlights that the poor wetting in the strippers can be avoided by the implementation of a process scheduling, which has not been presented in any publications. Based on the above, the mechanistic models of CO2 absorption plants and proposed control structures provide insights regarding dynamic behaviour and controllability of these plants. In addition, the industrial-scale CO2 capture plant model can be used for future studies, i.e. integration of power plant and CO2 capture plant, feasibility of plant operation, and controllability improvement.

This study geochemically characterized a proposed Carbon Capture and Storage project in northeast British Columbia, and presents new dissolution kinetics data for the proposed saline aquifer storage reservoir, the Keg River Formation. The Keg River Formation is a carbonate reservoir (89-93% Dolomite, 5-8% Calcite) at approximately 2200 m depth, at a pressure of 190 bar, and temperature of 105 °C. The Keg River brine is composed of Na, Cl, Ca, K, Mg, S, Si, and HCO3 and is of approximately 0.4 M ionic strength. Fluid analysis found the Keg River brine to be relatively fresh compared with waters of the Keg River formation in Alberta, and to also be distinct from waters in overlying units. These findings along with the physical conditions of the reservoir make the Keg River Formation a strong candidate for CO2 storage. Further work measured the dissolution rates of Keg River rock that will occur within the Keg River formation. This was performed in a new experimental apparatus at 105 °C, and 50 bar pCO2 with brine and rock sampled directly from the reservoir. Dissolution rate constants (mol!m-2s-1) for Keg River rock were found to be Log KMg 9.80 ±.02 and Log KCa -9.29 ±.04 for the Keg River formation. These values were found to be significantly lower compared to rate constants generated from experiments involving synthetic brines with values of Log KMg -9.43 ±.09, and Log KCa -9.23 ±.21. Differences in rates were posited as due to influences of other element interactions with the >MgOH hydration site, which was tested through experiments with brines spiked with SrCl2 and ZnCl2. Results for the SrCl2 spiked solution showed little impact on dissolution rates with rate constants of Log KMg -9.43 ±.09, and Log KCa -9.15 ±.21, however the ZnCl2 spiked solution did show some inhibition with rate constants of Log KMg -9.67 ±.04, and Log KCa -9.30 ±.04. Rate constants generated in this work are among the first presented which can actually be tested by full-scale injection of CO2. / Graduate

Drug development in general and clinical trials in particular is expensive and time consuming processes. One mandatory procedure in clinical trials are data collection, about 15 years ago almost all data were collected with a paper based approach but with new digitalised technology for data collection the process were about to become more efficient in regard to time, cost and quality of data. However the adoption rate of these systems for data collection were much lower than anticipated and most previous research points toward poorly developed products as the main reason for the adoption failure. Nevertheless, these systems have become more user friendly and efficient and today almost all studies use Electronic Data Capture (EDC) as the primary method for data collection. This project aim to investigate if the reason for the slow diffusion was a result of poorly developed products or if there are external factors such as social or organisational aspects that caused this delay. Semi structured interviews were conducted with 15 informants who works with EDC systems daily and are professionals within this industry. The result indicates that the slow diffusion is partly caused by initially bad systems that in turn might have caused a resistance among the end users and partly caused by slow decision organisations such as multinational pharmaceutical companies. The advice given to the project owner who intends to acquire this market is to focus on electronic Patient Reported Outcome (ePRO), which is a tool used by individual patients for self-reporting of data in clinical trials. ePRO is an extension of the EDC systems and must be user friendly for the patients and easy to connect to other systems. The company should rather focus on small Contract Research Organisation (CRO) as main customers rather than Big Pharma. Big Pharma often conduct multinational studies and decisions regarding the protocol and how data is to be collected are centrally decided. Since the project owner is a newly started, small firm with limited experience of clinical trials my advice would be to target CROs that conduct smaller studies.

A multidisciplinary approach has been applied to the development of sustainable technologies for three industrially relevant projects. Reversible ionic liquids are novel carbon dioxide capture solvents. These non-aqueous silylamines efficiently capture carbon dioxide through chemical and physical absorption and release carbon dioxide with minimal addition of heat. The development of these capture agents aims to eliminate the need for a co-solvent, while minimizing energy loss and achieving solvent recyclability. Also presented is the use of carbon dioxide for amine protection during chemical syntheses. Amine protection is widely used in almost all sectors of chemical and pharmaceutical industries. The use of carbon dioxide as a reversible protecting group reduces solvent waste during protection and deprotection and improves the atom economy of existing processes. Sustainable chemistry has also been applied to the use of reversible ionic liquids as switchable surfactants for nanoparticle synthesis. The reversible ionic liquid system offers two significant advantages toward a more efficient synthesis and deposition of nanoparticles in that an additional surfactant is not required, and due to the reversible nature of the ionic liquids, a facile and waste-reduced deposition method exists.