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Description
As technology has advanced in recent years, tablet devices have started to make their way into all walks of life. Yet, many medical documentation processes still see the use of paper. Though the paper based documentation method has been shown to be effective for some purposes, the introduction of tablet devices has the potential to make the documentation processes a lot smoother. In this thesis, tablet based documentation systems are reviewed, and based on this, a new custom application is developed that medical staff can use with ease. This new application, developed for an iPad is one where users can fully customize their own forms for different uses in the intensive care unit for resuscitation scenarios. The thesis discusses the architecture behind this application along with designing different elements of the system. Through this thesis project, the application was evaluated to see if such a complex documentation process can be easily used and created on a tablet device. The medical staff surveyed, responded positively to the use of the application and agreed that the electronic documentation usage and creation is a powerful tool that could help improve resuscitation practice by making it more efficient.
ContributorsDamania, Harsh (Author) / Patel, Vimla (Thesis advisor) / Nelson, Brian C (Thesis advisor) / Balasooriya, Janaka (Committee member) / Sen, Ayan (Committee member) / Arizona State University (Publisher)
Created2016
Description
The need for sustainability in construction has encouraged scientists to develop novel environmentally friendly materials. The use of supplementary cementitious materials was one such initiative which aided in enhancing the fresh and hardened concrete properties. This thesis aims to explore the understanding of the early age rheological properties of such cementitious systems.
The first phase of the work investigates the influence of supplementary cementitious materials (SCM) in combination with ordinary Portland cement (OPC) on the rheological properties of fresh paste with and without the effect of superplasticizers. Yield stress, plastic viscosity and storage modulus are the rheological parameters which were evaluated for all the design mixtures to fundamentally understand the synergistic effects of the SCM. A time-dependent study was conducted on these blends to explore the structure formation at various time intervals which explains the effect of hydration in conjecture to its physical stiffening. The second phase focuses on the rheological characterization of novel iron powder based binder system.
The results of this work indicate that the rheological characteristics of cementitious suspensions are complex, and strongly dependent on several key parameters including: the solid loading, inter-particle forces, shape of the particle, particle size distribution of the particles, and rheological nature of the media in which the particles are suspended. Chemical composition and reactivity of the material play an important role in the time-dependent rheological study.
A stress plateau method is utilized for the determination of rheological properties of concentrated suspensions, as it better predicts the apparent yield stress and is shown to correlate well with other viscoelastic properties of the suspensions. Plastic viscosity is obtained by calculating the slope of the stress-strain rate curve of ramp down values of shear rates. In oscillatory stress measurements the plateau obtained within the linear visco-elastic region was considered to be the value for storage modulus.
Between the different types of fly ash, class F fly ash indicated a reduction in the rheological parameters as opposed to class C fly ash that is attributable to the enhanced ettringite formation in the latter. Use of superplasticizer led to a huge influence on yield stress and storage modulus of the paste due to the steric hindrance effect.
In the study of iron based binder systems, metakaolin had comparatively higher influence than fly ash on the rheology due to its tendency to agglomerate as opposed to the ball bearing effect observed in the latter. Iron increment above 60% resulted in a decrease in all the parameters of rheology discussed in this thesis. In the OPC-iron binder, the iron behaved as reinforcements yielding higher yield stress and plastic viscosity.
The first phase of the work investigates the influence of supplementary cementitious materials (SCM) in combination with ordinary Portland cement (OPC) on the rheological properties of fresh paste with and without the effect of superplasticizers. Yield stress, plastic viscosity and storage modulus are the rheological parameters which were evaluated for all the design mixtures to fundamentally understand the synergistic effects of the SCM. A time-dependent study was conducted on these blends to explore the structure formation at various time intervals which explains the effect of hydration in conjecture to its physical stiffening. The second phase focuses on the rheological characterization of novel iron powder based binder system.
The results of this work indicate that the rheological characteristics of cementitious suspensions are complex, and strongly dependent on several key parameters including: the solid loading, inter-particle forces, shape of the particle, particle size distribution of the particles, and rheological nature of the media in which the particles are suspended. Chemical composition and reactivity of the material play an important role in the time-dependent rheological study.
A stress plateau method is utilized for the determination of rheological properties of concentrated suspensions, as it better predicts the apparent yield stress and is shown to correlate well with other viscoelastic properties of the suspensions. Plastic viscosity is obtained by calculating the slope of the stress-strain rate curve of ramp down values of shear rates. In oscillatory stress measurements the plateau obtained within the linear visco-elastic region was considered to be the value for storage modulus.
Between the different types of fly ash, class F fly ash indicated a reduction in the rheological parameters as opposed to class C fly ash that is attributable to the enhanced ettringite formation in the latter. Use of superplasticizer led to a huge influence on yield stress and storage modulus of the paste due to the steric hindrance effect.
In the study of iron based binder systems, metakaolin had comparatively higher influence than fly ash on the rheology due to its tendency to agglomerate as opposed to the ball bearing effect observed in the latter. Iron increment above 60% resulted in a decrease in all the parameters of rheology discussed in this thesis. In the OPC-iron binder, the iron behaved as reinforcements yielding higher yield stress and plastic viscosity.
ContributorsInbasekaran, Aditya (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Two thirds of the U.S. power systems are operated under market structures. A good market design should maximize social welfare and give market participants proper incentives to follow market solutions. Pricing schemes play very important roles in market design.
Locational marginal pricing scheme is the core pricing scheme in energy markets. Locational marginal prices are good pricing signals for dispatch marginal costs. However, the locational marginal prices alone are not incentive compatible since energy markets are non-convex markets. Locational marginal prices capture dispatch costs but fail to capture commitment costs such as startup cost, no-load cost, and shutdown cost. As a result, uplift payments are paid to generators in markets in order to provide incentives for generators to follow market solutions. The uplift payments distort pricing signals.
In this thesis, pricing schemes in electric energy markets are studied. In the first part, convex hull pricing scheme is studied and the pricing model is extended with network constraints. The subgradient algorithm is applied to solve the pricing model. In the second part, a stochastic dispatchable pricing model is proposed to better address the non-convexity and uncertainty issues in day-ahead energy markets. In the third part, an energy storage arbitrage model with the current locational marginal price scheme is studied. Numerical test cases are studied to show the arguments in this thesis.
The overall market and pricing scheme design is a very complex problem. This thesis gives a thorough overview of pricing schemes in day-ahead energy markets and addressed several key issues in the markets. New pricing schemes are proposed to improve market efficiency.
Locational marginal pricing scheme is the core pricing scheme in energy markets. Locational marginal prices are good pricing signals for dispatch marginal costs. However, the locational marginal prices alone are not incentive compatible since energy markets are non-convex markets. Locational marginal prices capture dispatch costs but fail to capture commitment costs such as startup cost, no-load cost, and shutdown cost. As a result, uplift payments are paid to generators in markets in order to provide incentives for generators to follow market solutions. The uplift payments distort pricing signals.
In this thesis, pricing schemes in electric energy markets are studied. In the first part, convex hull pricing scheme is studied and the pricing model is extended with network constraints. The subgradient algorithm is applied to solve the pricing model. In the second part, a stochastic dispatchable pricing model is proposed to better address the non-convexity and uncertainty issues in day-ahead energy markets. In the third part, an energy storage arbitrage model with the current locational marginal price scheme is studied. Numerical test cases are studied to show the arguments in this thesis.
The overall market and pricing scheme design is a very complex problem. This thesis gives a thorough overview of pricing schemes in day-ahead energy markets and addressed several key issues in the markets. New pricing schemes are proposed to improve market efficiency.
ContributorsLi, Chao (Author) / Hedman, Kory (Thesis advisor) / Sankar, Lalitha (Committee member) / Scaglione, Anna (Committee member) / Arizona State University (Publisher)
Created2016
Description
Three dilemmas plague governance of scientific research and technological
innovation: the dilemma of orientation, the dilemma of legitimacy, and the dilemma of control. The dilemma of orientation risks innovation heedless of long-term implications. The dilemma of legitimacy grapples with delegation of authority in democracies, often at the expense of broader public interest. The dilemma of control poses that the undesirable implications of new technologies are hard to grasp, yet once grasped, all too difficult to remedy. That humanity has innovated itself into the sustainability crisis is a prime manifestation of these dilemmas.
Responsible innovation (RI), with foci on anticipation, inclusion, reflection, coordination, and adaptation, aims to mitigate dilemmas of orientation, legitimacy, and control. The aspiration of RI is to bend the processes of technology development toward more just, sustainable, and societally desirable outcomes. Despite the potential for fruitful interaction across RI’s constitutive domains—sustainability science and social studies of science and technology—most sustainability scientists under-theorize the sociopolitical dimensions of technological systems and most science and technology scholars hesitate to take a normative, solutions-oriented stance. Efforts to advance RI, although notable, entail one-off projects that do not lend themselves to comparative analysis for learning.
In this dissertation, I offer an intervention research framework to aid systematic study of intentional programs of change to advance responsible innovation. Two empirical studies demonstrate the framework in application. An evaluation of Science Outside the Lab presents a program to help early-career scientists and engineers understand the complexities of science policy. An evaluation of a Community Engagement Workshop presents a program to help engineers better look beyond technology, listen to and learn from people, and empower communities. Each program is efficacious in helping scientists and engineers more thoughtfully engage with mediators of science and technology governance dilemmas: Science Outside the Lab in revealing the dilemmas of orientation and legitimacy; Community Engagement Workshop in offering reflexive and inclusive approaches to control. As part of a larger intervention research portfolio, these and other projects hold promise for aiding governance of science and technology through responsible innovation.
ContributorsBernstein, Michael J. (Author) / Wiek, Arnim (Thesis advisor) / Wetmore, Jameson M. (Thesis advisor) / Grimm, Nancy (Committee member) / Anderies, John M (Committee member) / Arizona State University (Publisher)
Created2016
Description
A roofing manufacturer wants to differentiate themselves from other roofing manufacturers based on performance information. However, construction industry has revealed poor performance documentation in the last couple of decades. With no current developed performance measurement model in the industry, two roofing manufacturers approached the research group to implement a warranty program that measures the performance information of their systems and applicators. Moreover, the success of any project in the construction industry heavily relies upon the capability of the contractor(s) executing the project. Low-performing contractors are correlated with increased cost and delayed schedules, resulting in end-user dissatisfaction with the final product. Hence, the identification and differentiation of the high performing contractors from their competitors is also crucial. The purpose of this study is to identify and describe a new model for measuring manufacturer performance and differentiating contractor performance and capability for two roofing manufacturers (Manufacturer 1 and Manufacturer 2) in the roofing industry. The research uses multiple years of project data and customer satisfaction data collected for two roofing manufacturers for over 1,000 roofing contractors. The performance and end-user satisfaction was obtained for over 7,000 manufacturers' projects and each contractor associated with that project for cost, schedule, and quality metrics. The measurement process was successfully able to provide a performance measurement for the manufacturer based on the customer satisfaction and able to identify low performing contractors. This study presents the research method, the developed measurement model, and proposes a new performance measurement process that entities in the construction industry can use to measure performance.
ContributorsGajjar, Dhaval (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Thesis advisor) / Kashiwagi, Jacob (Committee member) / Arizona State University (Publisher)
Created2016
Description
The Partition of Variance (POV) method is a simplistic way to identify large sources of variation in manufacturing systems. This method identifies the variance by estimating the variance of the means (between variance) and the means of the variance (within variance). The project shows that the method correctly identifies the variance source when compared to the ANOVA method. Although the variance estimators deteriorate when varying degrees of non-normality is introduced through simulation; however, the POV method is shown to be a more stable measure of variance in the aggregate. The POV method also provides non-negative, stable estimates for interaction when compared to the ANOVA method. The POV method is shown to be more stable, particularly in low sample size situations. Based on these findings, it is suggested that the POV is not a replacement for more complex analysis methods, but rather, a supplement to them. POV is ideal for preliminary analysis due to the ease of implementation, the simplicity of interpretation, and the lack of dependency on statistical analysis packages or statistical knowledge.
ContributorsLittle, David John (Author) / Borror, Connie (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
Description
Machine learning has demonstrated great potential across a wide range of applications such as computer vision, robotics, speech recognition, drug discovery, material science, and physics simulation. Despite its current success, however, there are still two major challenges for machine learning algorithms: limited robustness and generalizability.
The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neural networks can be totally different for input images that are visually indistinguishable to human eyes. Based on such property, hackers can reversely engineer the input to trick machine learning systems in targeted ways. These adversarial attacks have shown to be surprisingly effective, which has raised serious concerns over safety-critical applications like autonomous driving. In the meantime, many established defense mechanisms have shown to be vulnerable under more advanced attacks proposed later, and how to improve the robustness of neural networks is still an open question.
The generalizability of neural networks refers to the ability of networks to perform well on unseen data rather than just the data that they were trained on. Neural networks often fail to carry out reliable generalizations when the testing data is of different distribution compared with the training one, which will make autonomous driving systems risky under new environment. The generalizability of neural networks can also be limited whenever there is a scarcity of training data, while it can be expensive to acquire large datasets either experimentally or numerically for engineering applications, such as material and chemical design.
In this dissertation, we are thus motivated to improve the robustness and generalizability of neural networks. Firstly, unlike traditional bottom-up classifiers, we use a pre-trained generative model to perform top-down reasoning and infer the label information. The proposed generative classifier has shown to be promising in handling input distribution shifts. Secondly, we focus on improving the network robustness and propose an extension to adversarial training by considering the transformation invariance. Proposed method improves the robustness over state-of-the-art methods by 2.5% on MNIST and 3.7% on CIFAR-10. Thirdly, we focus on designing networks that generalize well at predicting physics response. Our physics prior knowledge is used to guide the designing of the network architecture, which enables efficient learning and inference. Proposed network is able to generalize well even when it is trained with a single image pair.
The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neural networks can be totally different for input images that are visually indistinguishable to human eyes. Based on such property, hackers can reversely engineer the input to trick machine learning systems in targeted ways. These adversarial attacks have shown to be surprisingly effective, which has raised serious concerns over safety-critical applications like autonomous driving. In the meantime, many established defense mechanisms have shown to be vulnerable under more advanced attacks proposed later, and how to improve the robustness of neural networks is still an open question.
The generalizability of neural networks refers to the ability of networks to perform well on unseen data rather than just the data that they were trained on. Neural networks often fail to carry out reliable generalizations when the testing data is of different distribution compared with the training one, which will make autonomous driving systems risky under new environment. The generalizability of neural networks can also be limited whenever there is a scarcity of training data, while it can be expensive to acquire large datasets either experimentally or numerically for engineering applications, such as material and chemical design.
In this dissertation, we are thus motivated to improve the robustness and generalizability of neural networks. Firstly, unlike traditional bottom-up classifiers, we use a pre-trained generative model to perform top-down reasoning and infer the label information. The proposed generative classifier has shown to be promising in handling input distribution shifts. Secondly, we focus on improving the network robustness and propose an extension to adversarial training by considering the transformation invariance. Proposed method improves the robustness over state-of-the-art methods by 2.5% on MNIST and 3.7% on CIFAR-10. Thirdly, we focus on designing networks that generalize well at predicting physics response. Our physics prior knowledge is used to guide the designing of the network architecture, which enables efficient learning and inference. Proposed network is able to generalize well even when it is trained with a single image pair.
ContributorsYao, Houpu (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2019
Description
Falls are the leading cause of fatal and non-fatal injuries in the older adult population with more than 27,000 fall related deaths reported every year[1]. Adults suffering from lower extremity arthritis have more than twice the likelihood of experiencing multiple falls resulting in increased fall-related injuries compared to healthy adults. People with lower extremity end-stage osteoarthritis(KOA), experience a number of fall risk factors such as knee instability, poor mobility, and knee pain/stiffness. At end-stage knee OA, the space between the bones in the joint of the knee is significantly reduced, resulting in bone to bone frictional wearing causing bone deformation. In addition, an impaired stepping response during a postural perturbation is seen in people with OA related knee instability. The most common treatment for end-stage knee osteoarthritis is a surgical procedure called, total knee replacement (TKR). It is known that TKR significantly reduces pain, knee stiffness, and restores musculoskeletal functions such as range of motion. Despite studies concluding that knee OA increases fall-risk, it remains unknown if standard treatments, such as TKR, can effectively decrease fall-risk. Analyzing the compensatory step response during a fall is a significant indicator of whether a fall or a recovery will occur in the event of a postural disturbance and is key to determining fall risk among people. Studies have shown reduced trunk stability and step length, as well as increased trunk velocities, correspond to an impaired compensatory step. This study looks at these populations to determine whether TKR significantly enhances compensatory stepping response by analyzing trunk velocities and flexions among other kinematic/kinetic variable analysis during treadmill induced perturbations and clinical assessments.
ContributorsMeza, Estefania (Author) / Honeycutt, Claire (Thesis advisor) / Lockhart, Thurmon E (Committee member) / Hodge, William A (Committee member) / Arizona State University (Publisher)
Created2019
Description
This dissertation studies two outstanding microscale fluid mechanics problems: 1) mechanisms of gas production from the nanopores of shale; 2) enhanced mass flow rate in steady compressible gas flow through a micro-conduit.
The dissertation starts with a study of a volumetric expansion driven drainage flow of a viscous compressible fluid from a small capillary and channel in the low Mach number limit. An analysis based on the linearized compressible Navier-Stokes equations with no-slip condition shows that fluid drainage is controlled by the slow decay of the acoustic wave inside the capillary and the no-slip flow exhibits a slip-like mass flow rate. Numerical simulations are also carried out for drainage from a small capillary to a reservoir or a contraction of finite size. By allowing the density wave to escape the capillary, two wave leakage mechanisms are identified, which are dependent on the capillary length to radius ratio, reservoir size and acoustic Reynolds number. Empirical functions are generated for an effective diffusive coefficient which allows simple calculations of the drainage rate using a diffusion model without the presence of the reservoir or contraction.
In the second part of the dissertation, steady viscous compressible flow through a micro-conduit is studied using compressible Navier-Stokes equations with no-slip condition. The mathematical theory of Klainerman and Majda for low Mach number flow is employed to derive asymptotic equations in the limit of small Mach number. The overall flow, a combination of the Hagen-Poiseuille flow and a diffusive velocity shows a slip-like mass flow rate even through the overall velocity satisfies the no-slip condition. The result indicates that the classical formulation includes self-diffusion effect and it embeds the Extended Navier-Stokes equation theory (ENSE) without the need of introducing additional constitutive hypothesis or assuming slip on the boundary. Contrary to most ENSE publications, the predicted mass flow rate is still significantly below the measured data based on an extensive comparison with thirty-five experiments.
The dissertation starts with a study of a volumetric expansion driven drainage flow of a viscous compressible fluid from a small capillary and channel in the low Mach number limit. An analysis based on the linearized compressible Navier-Stokes equations with no-slip condition shows that fluid drainage is controlled by the slow decay of the acoustic wave inside the capillary and the no-slip flow exhibits a slip-like mass flow rate. Numerical simulations are also carried out for drainage from a small capillary to a reservoir or a contraction of finite size. By allowing the density wave to escape the capillary, two wave leakage mechanisms are identified, which are dependent on the capillary length to radius ratio, reservoir size and acoustic Reynolds number. Empirical functions are generated for an effective diffusive coefficient which allows simple calculations of the drainage rate using a diffusion model without the presence of the reservoir or contraction.
In the second part of the dissertation, steady viscous compressible flow through a micro-conduit is studied using compressible Navier-Stokes equations with no-slip condition. The mathematical theory of Klainerman and Majda for low Mach number flow is employed to derive asymptotic equations in the limit of small Mach number. The overall flow, a combination of the Hagen-Poiseuille flow and a diffusive velocity shows a slip-like mass flow rate even through the overall velocity satisfies the no-slip condition. The result indicates that the classical formulation includes self-diffusion effect and it embeds the Extended Navier-Stokes equation theory (ENSE) without the need of introducing additional constitutive hypothesis or assuming slip on the boundary. Contrary to most ENSE publications, the predicted mass flow rate is still significantly below the measured data based on an extensive comparison with thirty-five experiments.
ContributorsShen, Di (Author) / Chen, Kangping (Thesis advisor) / Herrmann, Marcus (Committee member) / Huang, Huei-Ping (Committee member) / Calhoun, Ronald (Committee member) / Lopez, Juan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Background – Among influential education reports, there is clear consensus that an expansive range of intrapersonal (e.g. self-regulation) and interpersonal competencies (e.g. empathy) highly influence educational and career success. Research on teaching and learning these competencies is limited in engineering education.
Purpose/Hypothesis – This dissertation study explores the impacts of a mindfulness training program on first-year engineering students and aims to understand potential impacts on the development of intrapersonal and interpersonal competencies.
Design/Method – A four-session mindfulness-based training program was designed, developed, and facilitated to cultivate intrapersonal and interpersonal competencies. This study employed a multiphase mixed method design in which quantitative and qualitative data was collected from a total of 35 different students through a post survey (n=31), 3-month follow-up survey (n=29), and interviews (n=18). t-tests were used to evaluate the statistical significance of the program and a rigorous thematic analysis process was utilized to help explain the quantitative data.
Results – The results suggest that the majority of students became more mindful, which led to improved intrapersonal competencies (i.e. self-management, critical-thinking, focus, resilience, and well-being) and interpersonal competencies (i.e. empathy, communication, teamwork, and leadership).
Discussion / Conclusions – The study provides compelling evidence that mindfulness training can support the development of intrapersonal and interpersonal skills among engineering students, which can support their overall academic experience, as well as personal and professional development. Future design and development work will be needed to evaluate the integration and scalability potential of mindfulness training within engineering programs.
Purpose/Hypothesis – This dissertation study explores the impacts of a mindfulness training program on first-year engineering students and aims to understand potential impacts on the development of intrapersonal and interpersonal competencies.
Design/Method – A four-session mindfulness-based training program was designed, developed, and facilitated to cultivate intrapersonal and interpersonal competencies. This study employed a multiphase mixed method design in which quantitative and qualitative data was collected from a total of 35 different students through a post survey (n=31), 3-month follow-up survey (n=29), and interviews (n=18). t-tests were used to evaluate the statistical significance of the program and a rigorous thematic analysis process was utilized to help explain the quantitative data.
Results – The results suggest that the majority of students became more mindful, which led to improved intrapersonal competencies (i.e. self-management, critical-thinking, focus, resilience, and well-being) and interpersonal competencies (i.e. empathy, communication, teamwork, and leadership).
Discussion / Conclusions – The study provides compelling evidence that mindfulness training can support the development of intrapersonal and interpersonal skills among engineering students, which can support their overall academic experience, as well as personal and professional development. Future design and development work will be needed to evaluate the integration and scalability potential of mindfulness training within engineering programs.
ContributorsHuerta, Mark Vincent (Author) / McKenna, Anna (Thesis advisor) / Pipe, Teri (Committee member) / Carberry, Adam (Committee member) / Arizona State University (Publisher)
Created2019