International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The Integrated Monitoring and Control COTS System (IMACCS) was developed as a proof-of-concept to show that commercial off-the-shelf (COTS) products could be integrated to provide spacecraft ground support faster and cheaper than current practices. A key component of IMACCS is the Altair Mission Control System (AMCS), one of several commercial packages available for satellite command and control. It is distinguished from otherwise similar tools by its implementation of Finite State Modeling as part of its expert system capability. Using the Finite State Modeling and State Transition capabilities of the ALTAIR Mission Control System (AMCS), IMACCS was enhanced to provide automated monitoring, routine pass support, anomaly resolution, and emergency "lights on again" response. Orbit determination and production of typical flight dynamics products, such as acquisition times and vectors, have also been automated.

This dissertation focuses on several issues pertaining to three-state progressive stochastic processes. Casting survival data within a three-state framework is an effective way to incorporate intermediate events into an analysis. These events can yield valuable insights into treatment interventions and the natural history of a process, especially when the right censoring is heavy. Exploiting the uni-directional nature of these processes allows for more effective modelling of the types of incomplete data commonly encountered in practice, as well as time-dependent explanatory variables and different time scales. In Chapter 2, we extend the model developed by Frydman (1995) by incorporating explanatory variables and by permitting interval censoring for the time to the terminal event. The resulting model is quite general and combines features of the models proposed by Frydman (1995) and Kim <i>et al</i>. (1993). The decomposition theorem of Gu (1996) is used to show that all of the estimating equations arising from Frydman's log likelihood function are self-consistent. An AIDS data set analyzed by these authors is used to illustrate our regression approach. Estimating the standard errors of our regression model parameters, by adopting a piecewise constant approach for the baseline intensity parameters, is the focus of Chapter 3. We also develop data-driven algorithms which select changepoints for the intervals of support, based on the Akaike and Schwarz Information Criteria. A sensitivity study is conducted to evaluate these algorithms. The AIDS example is considered here once more; standard errors are estimated for several piecewise constant regression models selected by the model criteria. Our results indicate that for both the example and the sensitivity study, the resulting estimated standard errors of certain model parameters can be quite large. Chapter 4 evaluates the goodness-of-link function for the transition intensity between states 2 and 3 in the regression model we introduced in chapter 2. By embedding this hazard function in a one-parameter family of hazard functions, we can assess its dependence on the specific parametric form adopted. In a simulation study, the goodness-of-link parameter is estimated and its impact on the regression parameters is assessed. The logistic specification of the hazard function from state 2 to state 3 is appropriate for the discrete, parametric-based data sets considered, as well as for the AIDS data. We also investigate the uniqueness and consistency of the maximum likelihood estimates based on our regression model for these AIDS data. In Chapter 5 we consider the possible efficiency gains realized in estimating the survivor function when an intermediate auxiliary variable is incorporated into a time-to-event analysis. Both Markov and hybrid time scale frameworks are adopted in the resulting progressive three-state model. We consider three cases for the amount of information available about the auxiliary variable: the observation is completely unknown, known exactly, or known to be within an interval of time. In the Markov framework, our results suggest that observing subjects at just two time points provides as much information about the survivor function as knowing the exact time of the intermediate event. There was generally a greater loss of efficiency in the hybrid time setting. The final chapter identifies some directions for future research.

Renewable energy applications in electric power systems have undergone rapid development and increased use due to global environmental concerns associated with conventional energy sources. Photovoltaics and wind energy sources are considered to be very promising alternatives for power generation because of their tremendous environmental, social and economic benefits, together with public support. </p> <p>Electrical power generation from wind and solar energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of these facilities therefore affect power system reliability in a different manner than those of conventional systems. The research work presented in this thesis is focused on the development of appropriate models and techniques for wind energy conversion and photovoltaic conversion systems to assess the adequacy of composite power systems containing wind or solar energy.</p> <p>This research shows that a five-state wind energy conversion system or photovoltaic conversion system model can be used to provide a reasonable assessment in practical power system adequacy studies using an analytical method or a state sampling simulation approach. The reliability benefits of adding single or multiple wind/solar sites in a composite generation and transmission system are examined in this research. The models, methodologies, results and discussion presented in this thesis provide valuable information for system planners assessing the adequacy of composite electric power systems incorporating wind or solar energy conversion systems.

This dissertation focuses on several issues pertaining to three-state progressive stochastic processes. Casting survival data within a three-state framework is an effective way to incorporate intermediate events into an analysis. These events can yield valuable insights into treatment interventions and the natural history of a process, especially when the right censoring is heavy. Exploiting the uni-directional nature of these processes allows for more effective modelling of the types of incomplete data commonly encountered in practice, as well as time-dependent explanatory variables and different time scales. In Chapter 2, we extend the model developed by Frydman (1995) by incorporating explanatory variables and by permitting interval censoring for the time to the terminal event. The resulting model is quite general and combines features of the models proposed by Frydman (1995) and Kim <i>et al</i>. (1993). The decomposition theorem of Gu (1996) is used to show that all of the estimating equations arising from Frydman's log likelihood function are self-consistent. An AIDS data set analyzed by these authors is used to illustrate our regression approach. Estimating the standard errors of our regression model parameters, by adopting a piecewise constant approach for the baseline intensity parameters, is the focus of Chapter 3. We also develop data-driven algorithms which select changepoints for the intervals of support, based on the Akaike and Schwarz Information Criteria. A sensitivity study is conducted to evaluate these algorithms. The AIDS example is considered here once more; standard errors are estimated for several piecewise constant regression models selected by the model criteria. Our results indicate that for both the example and the sensitivity study, the resulting estimated standard errors of certain model parameters can be quite large. Chapter 4 evaluates the goodness-of-link function for the transition intensity between states 2 and 3 in the regression model we introduced in chapter 2. By embedding this hazard function in a one-parameter family of hazard functions, we can assess its dependence on the specific parametric form adopted. In a simulation study, the goodness-of-link parameter is estimated and its impact on the regression parameters is assessed. The logistic specification of the hazard function from state 2 to state 3 is appropriate for the discrete, parametric-based data sets considered, as well as for the AIDS data. We also investigate the uniqueness and consistency of the maximum likelihood estimates based on our regression model for these AIDS data. In Chapter 5 we consider the possible efficiency gains realized in estimating the survivor function when an intermediate auxiliary variable is incorporated into a time-to-event analysis. Both Markov and hybrid time scale frameworks are adopted in the resulting progressive three-state model. We consider three cases for the amount of information available about the auxiliary variable: the observation is completely unknown, known exactly, or known to be within an interval of time. In the Markov framework, our results suggest that observing subjects at just two time points provides as much information about the survivor function as knowing the exact time of the intermediate event. There was generally a greater loss of efficiency in the hybrid time setting. The final chapter identifies some directions for future research.

Renewable energy applications in electric power systems have undergone rapid development and increased use due to global environmental concerns associated with conventional energy sources. Photovoltaics and wind energy sources are considered to be very promising alternatives for power generation because of their tremendous environmental, social and economic benefits, together with public support. </p> <p>Electrical power generation from wind and solar energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of these facilities therefore affect power system reliability in a different manner than those of conventional systems. The research work presented in this thesis is focused on the development of appropriate models and techniques for wind energy conversion and photovoltaic conversion systems to assess the adequacy of composite power systems containing wind or solar energy.</p> <p>This research shows that a five-state wind energy conversion system or photovoltaic conversion system model can be used to provide a reasonable assessment in practical power system adequacy studies using an analytical method or a state sampling simulation approach. The reliability benefits of adding single or multiple wind/solar sites in a composite generation and transmission system are examined in this research. The models, methodologies, results and discussion presented in this thesis provide valuable information for system planners assessing the adequacy of composite electric power systems incorporating wind or solar energy conversion systems.

No description available.

Pelo fato da maioria das mortes e morbidades associadas à gravidez ocorrerem em torno do parto, a qualidade do cuidado nesse período é crucial para as mães e seus bebês. Para acompanhar as mulheres nessa etapa, o partograma tem sido a ferramenta mais utilizada nas últimas décadas e, devido à sua simplicidade, é frequentemente usado em países com baixa e média renda. No entanto, sua utilização é altamente questionada devido à ausência de evidências que justifiquem uma contribuição ao parto. Para melhorar a qualidade do parto nessas circunstâncias, o projeto BOLD tem sido desenvolvido com o intuito de reduzir a ocorrência de problemas indesejados e com a finalidade desenvolver uma ferramenta moderna, chamada de SELMA, que projetase como uma alternativa ao partograma. Com a finalidade de associar características fixas e dinâmicas avaliadas no parto e identificar quais elementos intra parto podem ser utilizados como gatilhos para realização de uma intervenção e, assim, prevenir um desfecho indesejado, propomos nesta tese a utilização de modelos de sobrevivência com covariáveis dependentes do tempo. Inicialmente, consideramos a modelagem de dados longitudinais e de sobrevivência utilizando funções de risco paramétricas flexíveis. Nesse caso, propomos a utilização de cinco generalizações da distribuição Weibull, da distribuição Nagakami e utilizamos um procedimento geral de seleção de modelos paramétricos usuais via distribuição Gamma generalizada, inédito na modelagem conjunta. Realizamos um extenso estudo de simulação para avaliar as estimativas de máxima verossimilhança e os critérios de discriminação. Além disso, a própria natureza do parto nos leva a um contexto de eventos múltiplos, nos remetendo à utilização dos modelos multiestados. Eles são definidos como modelos para um processo estocástico que a qualquer momento ocupa um conjunto discreto de estados. De uma forma geral, são os modelos mais comuns para descrever o desenvolvimento de dados de tempo de falha longitudinais e são frequentemente utilizados em aplicações médicas. Considerando o contexto de eventos múltiplos, propomos a inclusão de uma covariável dependente do tempo no modelo multiestados a partir de uma modificação dos dados, o que nos trouxe resultados satisfatórios e similares ao esperado na prática clínica. / As most pregnancy-related deaths and morbidities are clustered around the time of child birth, the quality of care during this period is crucial for mothers and their babies. To monitor the women at this stage, the partograph has been the central tool used in recent decades and, motivated by its simplicity, is frequently used in low-and middle-income countries. However, its use is highly questioned due to lack of evidence to justify a contribution to labor. To improve the quality of labor in these circumstances, the BOLD project has been developed in order to reduce the occurrence of pregnancy-related problems and in order to develop a modern tool, called SELMA, which is projected as an alternative to partograph. Aiming to associate fixed and dynamic characteristics evaluated in the delivery and to identify which elements can be used as triggers for performing an intervention, and thus preventing a bad outcome, this thesis proposes the use of survival models with time dependent covariates. Initially, we consider the joint modeling of survival and longitudinal data using flexible parametric hazard functions. In this sense, we propose the use of five generalizations of Weibull distribution, the Nagakami model and an inedited framework to discriminate usual parametric models via the generalized Gamma distribution, performing an extensive simulation study to evaluate the maximum likelihood estimations and the proposed discrimination criteria. Indeed, by its own nature, the birth leads us to a context of multiple events, referring to the use of multi-state models. These are models for a stochastic process which at any time occupies one of a few possible states. In general, they are the most common models to describe the development of longitudinal failure time data and are often used in medical applications. Considering this context, we proposed the inclusion of a time dependent covariate in the multi-state model using a modified version of the input data, which gave us satisfactory results similar to those expected in clinical practice.

Migrationsverket är en myndighet som prövar ansökningar från personer som vill söka skydd, ha medborgarskap, studera eller vill jobba i Sverige. Då det på senare tid varit en stor ökning i dessa ansökningar har tiden för vilket ett beslut tar ökat. Varje typ av ansökning (exempelvis medborgarskap) är en process som består av flera steg. Hur beslutet går igenom dessa steg kallas för flöde. Migrationsverket vill därför öka sin flödeseffektivitet. När beslutet är klart och personen tagit del av det men inte är nöjd kan denne överklaga. Detta är en av de mest komplexa processerna på Migrationsverket. Syftet är analysera hur lång tid denna process tar och vilka steg i processen som påverkar tiden. Ett steg (som senare visar sig ha en stor effekt på tiden) är yttranden. Det är när domstolen begär information om vad personen som överklagar har att säga om varför denne överklagar. För att analysera detta var två metoder relevanta, accelerated failure time (AFT) och \multi-state models (MSM). Den ena kan predicera tid till händelse (AFT) medan den andra kan analysera effekten av tidspåverkan (MSM) i stegen. Yttranden tidigt i processen har stor betydelse för hur snabbt en överklagan får en dom samtidigt som att antal yttranden ökar tiden enormt. Det finns andra faktorer som påverkar tiden men inte i så stor grad som yttranden. Då yttranden tidigt i processen samtidigt som antal yttranden har betydelse kan flödeseffektiviteten ökas med att ta tid på sig att skriva ett informativt yttrande som gör att domstolen inte behöver begära flera yttranden. / The Swedish Migration Board is an agency that review applications from individuals who wish to seek shelter, have citizenship, study or want to work in Sweden. In recent time there has been a large increase in applications and the time for which a decision is made has increased. Each type of application (such as citizenship) is a process consisting of several stages. How the decision is going through these steps is called flow. The Swedish Migration Board would therefore like to increase their flow efficiency. When the decision is made and the person has take part of it but is not satisfied, he can appeal. This is one of the most complex processes at the Board. The aim is to analyze how long this process will take and what steps in the process affects the time. One step (which was later found to have a significant effect on time) is opinions. This is when the court requests information on what the person is appealing has to say about why he is appealing. To analyze this, two methods were relevant, accelerated failure time (AFT) and the multi-state models (MSM). One can predict time to event (AFT), the other to analyze the effect of time-manipulation (MSM) in the flow. Opinions early in the process is crucial to how quickly an appeal get judgment while the number of opinions increases the time enormously. There are other factors that affect the time but not so much as opinions. The flow efficiency can be increased by taking time to write an informative opinion which allows the court need not to ask for more opinions.

Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models. Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was proposed to handle uneven follow-up assessments or skipped visits, resulting in the interval censored data. Simulations were used to compare the performance of the proposed model with the traditional discrete time stationary Markov chain under different types of observation schemes. We applied these two methods to the well-known Nun study, a longitudinal study of 672 participants aged ≥ 75 years at baseline and followed longitudinally with up to ten cognitive assessments per participant. Secondly, we constructed a non-homogenous Markov model for this type of panel data. The baseline intensity was assumed to be Weibull distributed to accommodate the non-homogenous property. The proportional hazards method was used to incorporate risk factors into the transition intensities. Simulation studies showed that the Weibull assumption does not affect the accuracy of the parameter estimates for the risk factors. We applied our model to data from the BRAiNS study, a longitudinal cohort of 531 subjects each cognitively intact at baseline. Last, we presented a parametric method of fitting semi-Markov models based on Weibull transition intensities with interval censored cognitive data with death as a competing risk. We relaxed the Markov assumption and took interval censoring into account by integrating out all possible unobserved transitions. The proposed model also allowed for incorporating time-dependent covariates. We provided a goodness-of-fit assessment for the proposed model by the means of prevalence counts. To illustrate the methods, we applied our model to the BRAiNS study.

Gene transcription in individual living cells is inevitably a stochastic and dynamic process. Little is known about how cells and organisms learn to balance the fidelity of transcriptional control and the stochasticity of transcription dynamics. In an effort to elucidate the contribution of environmental signals to this intricate balance, a Three State Model was recently proposed, and the transcription system was assumed to transit among three different functional states randomly. In this work, we employ this model to demonstrate how the stochastic dynamics of gene transcription can be characterized by the three transition parameters. We compute the probability distribution of a zero transcript event and its conjugate, the distribution of the time durations in gene on or gene off periods, the transition frequency between system states, and the transcriptional bursting frequency. We also exemplify the mathematical results by the experimental data on prokaryotic and eukaryotic transcription. The analysis reveals that no promoters will be definitely turned on to transcribe within a finite time period, no matter how strong the induction signals are applied, and how abundant the activators are available. Although stronger extrinsic signals could enhance promoter activation rate, the promoter creates an intrinsic ceiling that no signals could cross over in a finite time. Consequently, among a large population of isogenic cells, only a portion of the cells, but not the whole population, could be induced by environmental signals to express a particular gene within a finite time period. We prove that the gene on duration follows an exponential distribution, and the gene off intervals show a local maximum that is best described by assuming two sequential exponential process. The transition frequencies are determined by a system of stochastic differential equations, or equivalently, an iterative scheme of integral operators. We prove that for each positive integer n , there associates a unique time, called the peak instant, at which the nth transcript synthesis cycle since time zero proceeds most likely. These moments constitute a time series preserving the nature order of n.