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- Creators: Arizona State University
Description
Cognitive Radios (CR) are designed to dynamically reconfigure their transmission and/or reception parameters to utilize the bandwidth efficiently. With a rapidly fluctuating radio environment, spectrum management becomes crucial for cognitive radios. In a Cognitive Radio Ad Hoc Network (CRAHN) setting, the sensing and transmission times of the cognitive radio play a more important role because of the decentralized nature of the network. They have a direct impact on the throughput. Due to the tradeoff between throughput and the sensing time, finding optimal values for sensing time and transmission time is difficult. In this thesis, a method is proposed to improve the throughput of a CRAHN by dynamically changing the sensing and transmission times. To simulate the CRAHN setting, ns-2, the network simulator with an extension for CRAHN is used. The CRAHN extension module implements the required Primary User (PU) and Secondary User (SU) and other CR functionalities to simulate a realistic CRAHN scenario. First, this work presents a detailed analysis of various CR parameters, their interactions, their individual contributions to the throughput to understand how they affect the transmissions in the network. Based on the results of this analysis, changes to the system model in the CRAHN extension are proposed. Instantaneous throughput of the network is introduced in the new model, which helps to determine how the parameters should adapt based on the current throughput. Along with instantaneous throughput, checks are done for interference with the PUs and their transmission power, before modifying these CR parameters. Simulation results demonstrate that the throughput of the CRAHN with the adaptive sensing and transmission times is significantly higher as compared to that of non-adaptive parameters.
ContributorsBapat, Namrata Arun (Author) / Syrotiuk, Violet R. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2012
Description
Photosystem II (PSII) is a large protein-cofactor complex. The first step in
photosynthesis involves the harvesting of light energy from the sun by the antenna (made
of pigments) of the PSII trans-membrane complex. The harvested excitation energy is
transferred from the antenna complex to the reaction center of the PSII, which leads to a
light-driven charge separation event, from water to plastoquinone. This phenomenal
process has been producing the oxygen that maintains the oxygenic environment of our
planet for the past 2.5 billion years.
The oxygen molecule formation involves the light-driven extraction of 4 electrons
and protons from two water molecules through a multistep reaction, in which the Oxygen
Evolving Center (OEC) of PSII cycles through 5 different oxidation states, S0 to S4.
Unraveling the water-splitting mechanism remains as a grant challenge in the field of
photosynthesis research. This requires the development of an entirely new capability, the
ability to produce molecular movies. This dissertation advances a novel technique, Serial
Femtosecond X-ray crystallography (SFX), into a new realm whereby such time-resolved
molecular movies may be attained. The ultimate goal is to make a “molecular movie” that
reveals the dynamics of the water splitting mechanism using time-resolved SFX (TRSFX)
experiments and the uniquely enabling features of X-ray Free-Electron Laser
(XFEL) for the study of biological processes.
This thesis presents the development of SFX techniques, including development of
new methods to analyze millions of diffraction patterns (~100 terabytes of data per XFEL
experiment) with the goal of solving the X-ray structures in different transition states.
ii
The research comprises significant advancements to XFEL software packages (e.g.,
Cheetah and CrystFEL). Initially these programs could evaluate only 8-10% of all the
data acquired successfully. This research demonstrates that with manual optimizations,
the evaluation success rate was enhanced to 40-50%. These improvements have enabled
TR-SFX, for the first time, to examine the double excited state (S3) of PSII at 5.5-Å. This
breakthrough demonstrated the first indication of conformational changes between the
ground (S1) and the double-excited (S3) states, a result fully consistent with theoretical
predictions.
The power of the TR-SFX technique was further demonstrated with proof-of principle
experiments on Photoactive Yellow Protein (PYP) micro-crystals that high
temporal (10-ns) and spatial (1.5-Å) resolution structures could be achieved.
In summary, this dissertation research heralds the development of the TR-SFX
technique, protocols, and associated data analysis methods that will usher into practice a
new era in structural biology for the recording of ‘molecular movies’ of any biomolecular
process.
photosynthesis involves the harvesting of light energy from the sun by the antenna (made
of pigments) of the PSII trans-membrane complex. The harvested excitation energy is
transferred from the antenna complex to the reaction center of the PSII, which leads to a
light-driven charge separation event, from water to plastoquinone. This phenomenal
process has been producing the oxygen that maintains the oxygenic environment of our
planet for the past 2.5 billion years.
The oxygen molecule formation involves the light-driven extraction of 4 electrons
and protons from two water molecules through a multistep reaction, in which the Oxygen
Evolving Center (OEC) of PSII cycles through 5 different oxidation states, S0 to S4.
Unraveling the water-splitting mechanism remains as a grant challenge in the field of
photosynthesis research. This requires the development of an entirely new capability, the
ability to produce molecular movies. This dissertation advances a novel technique, Serial
Femtosecond X-ray crystallography (SFX), into a new realm whereby such time-resolved
molecular movies may be attained. The ultimate goal is to make a “molecular movie” that
reveals the dynamics of the water splitting mechanism using time-resolved SFX (TRSFX)
experiments and the uniquely enabling features of X-ray Free-Electron Laser
(XFEL) for the study of biological processes.
This thesis presents the development of SFX techniques, including development of
new methods to analyze millions of diffraction patterns (~100 terabytes of data per XFEL
experiment) with the goal of solving the X-ray structures in different transition states.
ii
The research comprises significant advancements to XFEL software packages (e.g.,
Cheetah and CrystFEL). Initially these programs could evaluate only 8-10% of all the
data acquired successfully. This research demonstrates that with manual optimizations,
the evaluation success rate was enhanced to 40-50%. These improvements have enabled
TR-SFX, for the first time, to examine the double excited state (S3) of PSII at 5.5-Å. This
breakthrough demonstrated the first indication of conformational changes between the
ground (S1) and the double-excited (S3) states, a result fully consistent with theoretical
predictions.
The power of the TR-SFX technique was further demonstrated with proof-of principle
experiments on Photoactive Yellow Protein (PYP) micro-crystals that high
temporal (10-ns) and spatial (1.5-Å) resolution structures could be achieved.
In summary, this dissertation research heralds the development of the TR-SFX
technique, protocols, and associated data analysis methods that will usher into practice a
new era in structural biology for the recording of ‘molecular movies’ of any biomolecular
process.
ContributorsBasu, Shibom, 1988- (Author) / Fromme, Petra (Thesis advisor) / Spence, John C.H. (Committee member) / Wolf, George (Committee member) / Ros, Robert (Committee member) / Fromme, Raimund (Committee member) / Arizona State University (Publisher)
Created2015
Description
Composite materials are widely used in various structural applications, including within the automotive and aerospace industries. Unidirectional composite layups have replaced other materials such as metals due to composites’ high strength-to-weight ratio and durability. Finite-element (FE) models are actively being developed to model response of composite systems subjected to a variety of loads including impact loads. These FE models rely on an array of measured material properties as input for accuracy. This work focuses on an orthotropic plasticity constitutive model that has three components – deformation, damage and failure. The model relies on the material properties of the composite such as Young’s modulus, Poisson’s ratio, stress-strain curves in the principal and off-axis material directions, etc. This thesis focuses on two areas important to the development of the FE model – tabbing of the test specimens and data processing of the tests used to generate the required stress-strain curves. A comparative study has been performed on three candidate adhesives using double lap-shear testing to determine their effectiveness in composite specimen tabbing. These tests determined the 3M DP460 epoxy performs best in shear. The Loctite Superglue with 80% the ultimate stress of the 3M DP460 epoxy is acceptable when test specimens have to be ready for testing within a few hours. JB KwikWeld is not suitable for tabbing. In addition, the Experimental Data Processing (EDP) program has been improved for use in post-processing raw data from composites test. EDP has improved to allow for complete processing with the implementation of new weighted least squares smoothing options, curve averaging techniques, and new functionality for data manipulation.
ContributorsSchmidt, Nathan William (Author) / Rajan, Subramaniam D. (Thesis advisor) / Neithalath, Narayanan (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2016
Description
The superior brightness and ultra short pulse duration of X-ray free electron laser
(XFEL) allows it to outrun radiation damage in coherent diffractive imaging since elastic scattering terminates before photoelectron cascades commences. This “diffract-before-destroy” feature of XFEL opened up new opportunities for biological macromolecule imaging and structure studies by breaking the limit to spatial resolution imposed by the maximum dose that is allowed before radiation damage. However, data collection in serial femto-second crystallography (SFX) using XFEL is affected by a bunch of stochastic factors, which pose great challenges to the data analysis in SFX. These stochastic factors include crystal size, shape, random orientation, X-ray photon flux, position and energy spectrum. Monte-Carlo integration proves effective and successful in extracting the structure factors by merging all diffraction patterns given that the data set is sufficiently large to average out all stochastic factors. However, this approach typically requires hundreds of thousands of patterns collected from experiments. This dissertation explores both experimental and algorithmic methods to eliminate or reduce the effect of stochastic factors in data acquisition and analysis. Coherent convergent X-ray beam diffraction (CCB) is discussed for possibilities of obtaining single-shot angular-integrated rocking curves. It is also shown the interference between Bragg disks helps ab-initio phasing. Two-color diffraction scheme is proposed for time-resolved studies and general data collection strategies are discussed based on error metrics. A new auto-indexing algorithm for sparse patterns is developed and demonstrated for both simulated and experimental data. Statistics show that indexing rate is increased by 3 times for I3C data set collected from beam time LJ69 at Linac coherent light source (LCLS). Finally, dynamical inversion from electron diffraction is explored as an alternative approach for structure determination.
(XFEL) allows it to outrun radiation damage in coherent diffractive imaging since elastic scattering terminates before photoelectron cascades commences. This “diffract-before-destroy” feature of XFEL opened up new opportunities for biological macromolecule imaging and structure studies by breaking the limit to spatial resolution imposed by the maximum dose that is allowed before radiation damage. However, data collection in serial femto-second crystallography (SFX) using XFEL is affected by a bunch of stochastic factors, which pose great challenges to the data analysis in SFX. These stochastic factors include crystal size, shape, random orientation, X-ray photon flux, position and energy spectrum. Monte-Carlo integration proves effective and successful in extracting the structure factors by merging all diffraction patterns given that the data set is sufficiently large to average out all stochastic factors. However, this approach typically requires hundreds of thousands of patterns collected from experiments. This dissertation explores both experimental and algorithmic methods to eliminate or reduce the effect of stochastic factors in data acquisition and analysis. Coherent convergent X-ray beam diffraction (CCB) is discussed for possibilities of obtaining single-shot angular-integrated rocking curves. It is also shown the interference between Bragg disks helps ab-initio phasing. Two-color diffraction scheme is proposed for time-resolved studies and general data collection strategies are discussed based on error metrics. A new auto-indexing algorithm for sparse patterns is developed and demonstrated for both simulated and experimental data. Statistics show that indexing rate is increased by 3 times for I3C data set collected from beam time LJ69 at Linac coherent light source (LCLS). Finally, dynamical inversion from electron diffraction is explored as an alternative approach for structure determination.
ContributorsLi, Chufeng (Author) / Spence, John CH (Thesis advisor) / Spence, John (Committee member) / Kirian, Richard (Committee member) / Weierstall, Uwe (Committee member) / Schmidt, Kevin (Committee member) / Arizona State University (Publisher)
Created2016
Description
According to the Center for Disease Control and Prevention report around 29,668 United States residents aged greater than 65 years had died as a result of a fall in 2016. Other injuries like wrist fractures, hip fractures, and head injuries occur as a result of a fall. Certain groups of people are more prone to experience falls than others, one of which being individuals with stroke. The two most common issues with individuals with strokes are ankle weakness and foot drop, both of which contribute to falls. To mitigate this issue, the most popular clinical remedy given to these users is thermoplastic Ankle Foot Orthosis. These AFO's help improving gait velocity, stride length, and cadence. However, studies have shown that a continuous restraint on the ankle harms the compensatory stepping response and forward propulsion. It has been shown in previous studies that compensatory stepping and forward propulsion are crucial for the user's ability to recover from postural perturbations. Hence, there is a need for active devices that can supply a plantarflexion during the push-off and dorsiflexion during the swing phase of gait. Although advancements in the orthotic research have shown major improvements in supporting the ankle joint for rehabilitation, there is a lack of available active devices that can help impaired users in daily activities. In this study, our primary focus is to build an unobtrusive, cost-effective, and easy to wear active device for gait rehabilitation and fall prevention in individuals who are at risk. The device will be using a double-acting cylinder that can be easily incorporated into the user's footwear using a novel custom-designed powered ankle brace. The device will use Inertial Measurement Units to measure kinematic parameters of the lower body and a custom control algorithm to actuate the device based on the measurements. The study can be used to advance the field of gait assistance, rehabilitation, and potentially fall prevention of individuals with lower-limb impairments through the use of Active Ankle Foot Orthosis.
ContributorsRay, Sambarta (Author) / Honeycutt, Claire (Thesis advisor) / Dasarathy, Gautam (Thesis advisor) / Redkar, Sangram (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020
Description
To date, there is not a standardized method for consistently quantifying the performance of an automated driving system (ADS)-equipped vehicle (AV). The purpose of this dissertation is to contribute to a framework for such an approach referred to throughout as the operational safety assessment (OSA) methodology. Through this research, safety metrics are identified, researched, and analyzed to capture aspects of the operational safety of AVs, interacting with other salient objects. This dissertation outlines the approach for developing this methodology through a series of key steps including: (1) comprehensive literature review; (2) research and refinement of OSA metrics; (3) generation of MATLAB script for metric calculations; (4) generation of simulated events for analysis; (5) collection of real-world data for analysis; (6) review of OSA methodology results; and (7) discussion of future work to expand complexity, fidelity, and relevance aspects of the OSA methodology. The detailed literature review includes the identification of metrics historically used in both traditional and more recent evaluations of vehicle performance. Subsequently, the metric formulations are refined, and robust severity evaluations are proposed. A MATLAB script is then presented which was generated to calculate the metrics from any given source assuming proper formatting of the data. To further refine the formulations and the MATLAB script, a variety of simulated scenarios are discussed including car-following, intersection, and lane change situations. Additionally, a data collection activity is presented, leveraging the SMARTDRIVE testbed operated by Maricopa County Department of Transportation in Anthem, AZ to collect real-world data from an active intersection. Lastly, the efficacy of the OSA methodology with respect to the evaluation of vehicle performance for a set of scenarios is evaluated utilizing both simulated and real-world data. This assessment provides a demonstration of the ability and robustness of this methodology to evaluate vehicle performance for a given scenario. At the conclusion of this dissertation, additional factors including fidelity, complexity, and relevance are explored to contribute to a more comprehensive evaluation.
ContributorsComo, Steven Gerard (Author) / Wishart, Jeffrey (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Chen, Yan (Committee member) / Favaro, Francesca (Committee member) / Arizona State University (Publisher)
Created2022
Description
Serial femtosecond crystallography (SFX) uses diffraction patterns from crystals delivered in a serial fashion to an X-Ray Free Electron Laser (XFEL) for structure determination. Typically, each diffraction pattern is a snapshot from a different crystal. SFX limits the effect of radiation damage and enables the use of nano/micro crystals for structure determination. However, analysis of SFX data is challenging since each snapshot is processed individually.
Many photosystem II (PSII) dataset have been collected at XFELs, several of which are time-resolved (containing both dark and laser illuminated frames). Comparison of light and dark datasets requires understanding systematic errors that can be introduced during data analysis. This dissertation describes data analysis of PSII datasets with a focus on the effect of parameters on later results. The influence of the subset of data used in the analysis is also examined and several criteria are screened for their utility in creating better subsets of data. Subsets are compared with Bragg data analysis and continuous diffuse scattering data analysis.
A new tool, DatView aids in the creation of subsets and visualization of statistics. DatView was developed to improve the loading speed to visualize statistics of large SFX datasets and simplify the creation of subsets based on the statistics. It combines the functionality of several existing visualization tools into a single interface, improving the exploratory power of the tool. In addition, it has comparison features that allow a pattern-by-pattern analysis of the effect of processing parameters. \emph{DatView} improves the efficiency of SFX data analysis by reducing loading time and providing novel visualization tools.
Many photosystem II (PSII) dataset have been collected at XFELs, several of which are time-resolved (containing both dark and laser illuminated frames). Comparison of light and dark datasets requires understanding systematic errors that can be introduced during data analysis. This dissertation describes data analysis of PSII datasets with a focus on the effect of parameters on later results. The influence of the subset of data used in the analysis is also examined and several criteria are screened for their utility in creating better subsets of data. Subsets are compared with Bragg data analysis and continuous diffuse scattering data analysis.
A new tool, DatView aids in the creation of subsets and visualization of statistics. DatView was developed to improve the loading speed to visualize statistics of large SFX datasets and simplify the creation of subsets based on the statistics. It combines the functionality of several existing visualization tools into a single interface, improving the exploratory power of the tool. In addition, it has comparison features that allow a pattern-by-pattern analysis of the effect of processing parameters. \emph{DatView} improves the efficiency of SFX data analysis by reducing loading time and providing novel visualization tools.
ContributorsStander, Natasha (Author) / Fromme, Petra (Thesis advisor) / Zatsepin, Nadia (Thesis advisor) / Kirian, Richard (Committee member) / Liu, Wei (Committee member) / Arizona State University (Publisher)
Created2019
Description
Evidence is mounting to address and reverse the effects of environmental neglect. Perhaps the greatest evidence for needing environmental stewardship originates from the ever-increasing extreme weather events ranging from the deadly wildfires scorching Greece and California to the extreme heatwaves in Japan. Scientists have concluded that the probability and severity for about two thirds of such extreme natural events that occurred between 2004 and 2018 is contributed by rising global temperatures.
Operations management literature regarding environmental issues have typically focused on the “win-win” approach with a multitude of papers investigating a link between sustainability and firm performance. This dissertation seeks to take a different approach by investigating firm responses to climate change. The first two essays explore firm emissions goals and the last essay investigates firm emissions performance.
The first essay identifies firm determinants of greenhouse gas (GHG) reduction targets. The essay leverages Behavioral Theory of the Firm (BTOF) and argues for two additional determinants, Data Stratification and Science-Based Targets, unique to GHG emissions. Utilizing system generalized method of moments on a dataset from Carbon Disclosure Project for years 2011-2017, the paper finds partial confirmation for BTOF and support for the two additional determinants of firm GHG emission goals.
The second essay is an exploratory study that seeks to understand factors for firm participation in the Science-Based Targets (SBT) initiative by combining both primary and secondary data analysis. The study is a working paper with primary data still needing to be completed. Secondary data analysis begins with a review of the literature which suggested four potential factors: ISO 14001 certification, Customer Engagement, Emission Credit Purchases, and presence of Absolute Emissions Targets. Preliminary results using panel logistic regression suggest that Emissions Credit Purchases and Absolute Emissions Targets influence SBT participation.
The third essay seeks to understand whether stakeholder pressure drives firm GHG emissions reductions. This relies on Stakeholder Theory and classification schemes proposed in Management literature to divide stakeholders, based on their relationship with the firm, into three groups: primary, secondary, and public. Random effects estimation results provide evidence for primary and public stakeholder pressure impacting firm GHG emissions.
ContributorsHsu, Ta Kang (Author) / Dooley, Kevin J (Thesis advisor) / Rabinovich, Elliot (Committee member) / Corbett, Charles J. (Committee member) / Arizona State University (Publisher)
Created2020
Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021