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- Creators: Barrett, The Honors College
- Creators: Armbruster, Dieter
Description
The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.
ContributorsHaghnevis, Moeed (Author) / Askin, Ronald G. (Thesis advisor) / Armbruster, Dieter (Thesis advisor) / Mirchandani, Pitu (Committee member) / Wu, Tong (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2013
Description
The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)
ContributorsKrell, Abby Elizabeth (Co-author) / Bruner, Ashley (Co-author) / Ramesh, Frankincense (Co-author) / Lewis, Gabriel (Co-author) / Barwey, Ishna (Co-author) / Myers, Jack (Co-author) / Hymer, William (Co-author) / Reagan, Sage (Co-author) / Compton, Carolyn (Thesis director) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cellular and molecular biologists often perform cellular assays to obtain a better understanding of how cells work. However, in order to obtain a measurable response by the end of an experiment, the cells must reach an ideal cell confluency. Prior to conducting the cellular assays, range-finding experiments need to be conducted to determine an initial plating density that will result in this ideal confluency, which can be costly. To help alleviate this common issue, a mathematical model was developed that describes the dynamics of the cell population used in these experiments. To develop the model, images of cells from different three-day experiments were analyzed in Photoshop®, giving a measure of cell count and confluency (the percentage of surface area covered by cells). The cell count data were then fitted into an exponential growth model and were correlated to the cell confluency to obtain a relationship between the two. The resulting mathematical model was then evaluated with data from an independent experiment. Overall, the exponential growth model provided a reasonable and robust prediction of the cell confluency, though improvements to the model can be made with a larger dataset. The approach used to develop this model can be adapted to generate similar models of different cell-lines, which will reduce the number of preliminary range-finding experiments. Reducing the number of these preliminary experiments can save valuable time and experimental resources needed to conduct studies using cellular assays.
ContributorsGuerrero, Victor Dominick (Co-author) / Guerrero, Victor (Co-author) / Watanabe, Karen (Thesis director) / Jurutka, Peter (Committee member) / School of Mathematical and Natural Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The Hippo signaling pathway is responsible for regulating organ size through cell proliferation, stemness, and apoptosis. Through targeting proteins Yes-associated kinase 1(YAP) and transcriptional co-activator with a PDZ-binding domain(TAZ), YAP/TAZ are unable to enter the nucleus and bind with coactivators to express target genes. To understand YAP/TAZ dynamics and its role in tumorigenesis, tissue regeneration, and tissue degeneration, a regulatory network was modeled by ordinary differential equations. Using MATLAB, the deterministic behavior of the network was observed to determine YAP/TAZ activity in different states. Performing the bifurcation analysis of the system through Oscill8, three states were identified: tumorigenic/regenerative, degenerative, and homeostatic states. Further analysis through parameter modification allowed a better understanding of which proteins can be targeted for cancer and degenerative disease.
ContributorsBarra Avila, Diego Rodrigo (Author) / Tian, Xiaojun (Thesis director) / Wang, Xiao (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Ultimate Frisbee or "Ultimate," is a fast growing field sport that is being played competitively at universities across the country. Many mid-tier college teams have the goal of winning as many games as possible, however they also need to grow their program by training and retaining new players. The purpose of this project was to create a prototype statistical tool that maximizes a player line-up's probability of scoring the next point, while having as equal playing time across all experienced and novice players as possible. Game, player, and team data was collected for 25 different games played over the course of 4 tournaments during Fall 2017 and early Spring 2018 using the UltiAnalytics iPad application. "Amount of Top 1/3 Players" was the measure of equal playing time, and "Line Efficiency" and "Line Interaction" represented a line's probability of scoring. After running a logistic regression, Line Efficiency was found to be the more accurate predictor of scoring outcome than Line Interaction. An "Equal PT Measure vs. Line Efficiency" graph was then created and the plot showed what the optimal lines were depending on what the user's preferences were at that point in time. Possible next steps include testing the model and refining it as needed.
ContributorsSpence, Andrea Nicole (Author) / McCarville, Daniel R. (Thesis director) / Pavlic, Theodore (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Solid oxide fuel cells have become a promising candidate in the development of high-density clean energy sources for the rapidly increasing demands in energy and global sustainability. In order to understand more about solid oxide fuel cells, the important step is to understand how to model heterogeneous materials. Heterogeneous materials are abundant in nature and also created in various processes. The diverse properties exhibited by these materials result from their complex microstructures, which also make it hard to model the material. Microstructure modeling and reconstruction on a meso-scale level is needed in order to produce heterogeneous models without having to shave and image every slice of the physical material, which is a destructive and irreversible process. Yeong and Torquato [1] introduced a stochastic optimization technique that enables the generation of a model of the material with the use of correlation functions. Spatial correlation functions of each of the various phases within the heterogeneous structure are collected from a two-dimensional micrograph representing a slice of a solid oxide fuel cell through computational means. The assumption is that two-dimensional images contain key structural information representative of the associated full three-dimensional microstructure. The collected spatial correlation functions, a combination of one-point and two-point correlation functions are then outputted and are representative of the material. In the reconstruction process, the characteristic two-point correlation functions is then inputted through a series of computational modeling codes and software to generate a three-dimensional visual model that is statistically similar to that of the original two-dimensional micrograph. Furthermore, parameters of temperature cooling stages and number of pixel exchanges per temperature stage are utilized and altered accordingly to observe which parameters has a higher impact on the reconstruction results. Stochastic optimization techniques to produce three-dimensional visual models from two-dimensional micrographs are therefore a statistically reliable method to understanding heterogeneous materials.
ContributorsPhan, Richard Dylan (Author) / Jiao, Yang (Thesis director) / Ren, Yi (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The crew planning problem in the airline industry presents a very computationally complex problem of high importance to the business. Airlines must schedule crew members to ensure that all flights are staffed while remaining in compliance with the business needs and regulatory requirements set by entities such as unions and FAA. With the magnitude of operation of the prominent players in the airline industry today, the crew staffing problem proves very large and has become heavily reliant on operations research solution methodologies. An area of opportunity that has not yet been extensively researched lies in the planning of crew vacation. This paper develops a model driven by the idea of system risk that constructs an optimal vacation grid for the time period of one year. The model generates a daily allocation that maximizes vacation offering while ensuring a given level of system reliability. The model is then implemented using data from US Airways and model improvements are provided for practical application in the airline industry based on the output.
ContributorsFisher, Tignes Noel (Author) / Gel, Esma (Thesis director) / Jacobs, Tim (Committee member) / Clough, Michael (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor)
Created2015-05
Description
The purpose of this paper is to provide a new and improved design method for the Formula Society of Automotive Engineering (FSAE) team. There are five tasks that I accomplish in this paper: 1. I describe how the FSAE team is currently designing their car. This allows the reader to understand where the flaws might arise in their design method. 2. I then describe the key aspects of systems engineering design. This is the backbone of the method I am proposing, and it is important to understand the key concepts so that they can be applied to the FSAE design method. 3. I discuss what is available in the literature about race car design and optimization. I describe what other FSAE teams are doing and how that differs from systems engineering design. 4. I describe what the FSAE team at Arizona State University (ASU) should do to improve their approach to race car design. I go into detail about how the systems engineering method works and how it can and should be applied to the way they design their car. 5. I then describe how the team should implement this method because the method is useless if they do not implement it into their design process. I include an interview from their brakes team leader, Colin Twist, to give an example of their current method of design and show how it can be improved with the new method. This paper provides a framework for the FSAE team to develop their new method of design that will help them accomplish their overall goal of succeeding at the national competition.
ContributorsPickrell, Trevor Charles (Author) / Trimble, Steven (Thesis director) / Middleton, James (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05