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- Creators: Srinivasan, Devarajan
- Creators: Gardner, Carl
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
As the photovoltaic (PV) power plants age in the field, the PV modules degrade and generate visible and invisible defects. A defect and statistical degradation rate analysis of photovoltaic (PV) power plants is presented in two-part thesis. The first part of the thesis deals with the defect analysis and the second part of the thesis deals with the statistical degradation rate analysis. In the first part, a detailed analysis on the performance or financial risk related to each defect found in multiple PV power plants across various climatic regions of the USA is presented by assigning a risk priority number (RPN). The RPN for all the defects in each PV plant is determined based on two databases: degradation rate database; defect rate database. In this analysis it is determined that the RPN for each plant is dictated by the technology type (crystalline silicon or thin-film), climate and age. The PV modules aging between 3 and 19 years in four different climates of hot-dry, hot-humid, cold-dry and temperate are investigated in this study.
In the second part, a statistical degradation analysis is performed to determine if the degradation rates are linear or not in the power plants exposed in a hot-dry climate for the crystalline silicon technologies. This linearity degradation analysis is performed using the data obtained through two methods: current-voltage method; metered kWh method. For the current-voltage method, the annual power degradation data of hundreds of individual modules in six crystalline silicon power plants of different ages is used. For the metered kWh method, a residual plot analysis using Winters’ statistical method is performed for two crystalline silicon plants of different ages. The metered kWh data typically consists of the signal and noise components. Smoothers remove the noise component from the data by taking the average of the current and the previous observations. Once this is done, a residual plot analysis of the error component is performed to determine the noise was successfully separated from the data by proving the noise is random.
In the second part, a statistical degradation analysis is performed to determine if the degradation rates are linear or not in the power plants exposed in a hot-dry climate for the crystalline silicon technologies. This linearity degradation analysis is performed using the data obtained through two methods: current-voltage method; metered kWh method. For the current-voltage method, the annual power degradation data of hundreds of individual modules in six crystalline silicon power plants of different ages is used. For the metered kWh method, a residual plot analysis using Winters’ statistical method is performed for two crystalline silicon plants of different ages. The metered kWh data typically consists of the signal and noise components. Smoothers remove the noise component from the data by taking the average of the current and the previous observations. Once this is done, a residual plot analysis of the error component is performed to determine the noise was successfully separated from the data by proving the noise is random.
ContributorsSundarajan, Prasanna (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2016
Description
This is a two-part thesis. Part 1 presents the seasonal and tilt angle dependence of soiling loss factor of photovoltaic (PV) modules over two years for Mesa, Arizona (a desert climatic condition). Part 2 presents the development of an indoor artificial soil deposition chamber replicating natural dew cycle. Several environmental factors affect the performance of PV systems including soiling. Soiling on PV modules results in a decrease of sunlight reaching the solar cell, thereby reducing the current and power output. Dust particles, air pollution particles, pollen, bird droppings and other industrial airborne particles are some natural sources that cause soiling. The dust particles vary from one location to the other in terms of particle size, color, and chemical composition. The thickness and properties of the soil layer determine the optical path of light through the soil/glass interface. Soil accumulation on the glass surface is also influenced by environmental factors such as dew, wind speeds and rainfall. Studies have shown that soil deposition is closely related to tilt angle and exposure period before a rain event. The first part of this thesis analyzes the reduction in irradiance transmitted to a solar cell through the air/soil/glass in comparison to a clean cell (air/glass interface). A time series representation is used to compare seasonal soiling loss factors for two consecutive years (2014-2016). The effect of tilt angle and rain events on these losses are extensively analyzed. Since soiling is a significant field issue, there is a growing need to address the problem, and several companies have come up with solutions such as anti-soiling coatings, automated cleaning systems etc. To test and validate the effectiveness of these anti-soiling coating technologies, various research institutes around the world are working on the design and development of artificial indoor soiling chambers to replicate the natural process in the field. The second part of this thesis work deals with the design and development of an indoor artificial soiling chamber that replicates natural soil deposition process in the field.
ContributorsVirkar, Shalaim (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Kuitche, Joseph (Committee member) / Arizona State University (Publisher)
Created2017