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In the interest of expediting future pilot line start-ups for solar cell research, the development of Arizona State University's student-led pilot line at the Solar Power Laboratory is discussed extensively within this work. Several experiments and characterization techniques used to formulate and optimize a series of processes for fabricating diffused-junction, screen-printed silicon solar cells are expounded upon. An experiment is conducted in which the thickness of a PECVD deposited anti-reflection coating (ARC) is varied across several samples and modeled as a function of deposition time. Using this statistical model in tandem with reflectance measurements for each sample, the ARC thickness is optimized to increase light trapping in the solar cells. A response surface model (RSM) experiment is conducted in which 3 process parameters are varied on the PECVD tool for the deposition of the ARCs on several samples. A contactless photoconductance decay (PCD) tool is used to measure the dark saturation currents of these samples. A statistical analysis is performed using JMP in which optimum deposition parameters are found. A separate experiment shows an increase in the passivation quality of the a-SiNx:H ARCs deposited on the solar cells made on the line using these optimum parameters. A RSM experiment is used to optimize the printing process for a particular silver paste in a similar fashion, the results of which are confirmed by analyzing the series resistance of subsequent cells fabricated on the line. An in-depth explanation of a more advanced analysis using JMP and PCD measurements on the passivation quality of 3 aluminum back-surface fields (BSF) is given. From this experiment, a comparison of the means is conducted in order to choose the most effective BSF paste for cells fabricated on the line. An experiment is conducted in parallel which confirms the results via Voc measurements. It is shown that in a period of 11 months, the pilot line went from producing a top cell efficiency of 11.5% to 17.6%. Many of these methods used for the development of this pilot line are equally applicable to other cell structures, and can easily be applied to other solar cell pilot lines.
ContributorsPickett, Guy (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Committee member) / Bertoni, Mariana (Committee member) / Arizona State University (Publisher)
Created2014
Description
Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
ContributorsSong, Yang (Author) / Gurney, Kevin (Thesis advisor) / Kuby, Michael (Committee member) / Golub, Aaron (Committee member) / Chester, Mikhail (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2018
Description
This study focuses on three major Maghreb states (Algeria, Morocco and Tunisia) with distinct institutional, political and socioeconomic patterns. It essentially tackles the issue of technological development particularly investments, trade, human capital and patents in a socially and politically sensitive environment. The researcher assumes that government stability, law and order, GDP growth and ICT usage are related to technological innovation in the Maghreb. The stated hypotheses indicate that these political, institutional and socioeconomic factors have significant effect on technological innovation in the Maghreb. Based on a two equations' empirical model, our researcher attempts to test these effects and explore the interactions between the different dependent and independent variables through a set of hypotheses. Data analysis covers three countries from 1996 to 2010. The study identifies significant effects of key covariates on technological innovation in the Maghreb. Although not every predictor effect is consistent, the results indicate that they matter for technological innovation in the Maghreb. Empirical findings might constitute essential evidence for technology and innovation policies in this Middle East and North African region.
ContributorsOubaiden, Mohamed (Author) / Grossman, Gary (Thesis advisor) / Waissi, Gary (Committee member) / Parmentier, Mary Jane (Committee member) / Arizona State University (Publisher)
Created2012
Description
Current popular NBA mobile applications do little to provide information about the NBA's players, usually providing limited statistical information or news and completely ignoring players' presence on social media. For fans, especially fans who are unfamiliar with the NBA, finding this information by themselves can be a daunting task, one which requires extensive knowledge about how the NBA provides media related to its players. NBA PlayerTrack has been designed to centralize player information from a variety of media streams, making it easier for fans to learn about and stay up-to-date with players and enabling fan discussion about those players and the NBA in general. By providing a variety of references to the locations of player information, NBA PlayerTrack also serves as a tool for learning about how and where the NBA presents player-related media, allowing fans to more easily locate information they desire as they become more invested in the NBA.
ContributorsSethia, Sumbhav (Author) / Davulcu, Hasan (Thesis director) / Faucon, Philippe (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Background: Recurrent glioblastoma (GBM) is resistant to available treatments and continued growth of the tumor is inevitable; this process is facilitated by the expression of genes regulated by the Signal Transducer and Activator of Transcription (STAT) family of transcription factors, namely STAT5, active in the invasive rim of GBM tumors. Currently, there are no targeted therapies for recurrent GBM that increase the overall patient survival rate. This study aims to analyze the differential expression of genes regulated by STAT5 between primary and recurrent GBM.<br/>Methods: Analysis of whole exome and RNA sequencing were performed on matched bulk primary and multiple recurrent tumor samples from GBM patients who received the current standard care to determine significant changes in gene expression of STAT3/5 targets. <br/>Results: Statistical analysis reveals a decrease in Synaptotagmin 2 (SYT2) and Pleckstrin Homology Domain Containing A3 (PLEKHA3) at recurrence, previously identified as potential STAT5 targets. <br/>Conclusions: To get a better understanding of the roles of STAT5 in GBM recurrence, their downstream effects need to be better understood. The transcriptomic program initiated by STAT5 activation is distinct from that of STAT3 activation. The roles of STAT5 target genes in GBM are poorly characterized, so further research should focus on understanding the effects of altered expression of these genes as they relate to STAT3/5 in GBM recurrence.
ContributorsPennett, Maya E (Author) / Martin, Thomas W. (Thesis director) / Tran, Nhan L. (Committee member) / Blomquist, Mylan (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Sports analytics refers to the implementation of data science and analytics techniques within the sports industry. Several sports analysts and team managers have utilized analytical tools to boost overall team and player performance, often through the analysis of historical data. One of the most common techniques employed in sports analytics is that of data mining–the extensive practice of analyzing data in order to extract and deliver insights and findings. Data mining projects are frequently guided with the six-step Cross Industry Standard Process for Data Mining (CRISP-DM) framework. One such sport that has extensively used data science and analytics, and data mining specifically, is that of Formula One (F1). Given the sports’ reliance on technology, race engineers working for F1 constructors often develop statistical models analyzing historical race performance to derive insight of drivers’ success. For the purposes of this project, the perspective of a race engineer working for the F1 constructor McLaren was considered. As the constructor is seeking to gain a competitive advantage for the upcoming F1 season, race performance data concerning previous seasons was collected and analyzed as part of a larger data mining project utilizing the CRISP-DM framework. Statistical models, such as linear regression and random forest, were developed to predict the number of points scored by McLaren racers and the variables most strongly contributed to such scored points. The final results point to specific lap times having to be aimed for as the most important variable in determining the number of points gained, although specific locations also seem prone to McLaren race success. These results in turn will be utilized to develop race strategies for the upcoming season to ensure McLaren has high efficiency against its competitors.
ContributorsImam, Amir (Author) / Simon, Alan (Thesis director) / Sha, Xiqing (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2023-05
Description
Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.
ContributorsAhsan, Faiyaz (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Kwon, Beomjin (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this work, we explore the potential for realistic and accurate generation of hourly traffic volume with machine learning (ML), using the ground-truth data of Manhattan road segments collected by the New York State Department of Transportation (NYSDOT). Specifically, we address the following question– can we develop a ML algorithm that generalizes the existing NYSDOT data to all road segments in Manhattan?– by introducing a supervised learning task of multi-output regression, where ML algorithms use road segment attributes to predict hourly traffic volume. We consider four ML algorithms– K-Nearest Neighbors, Decision Tree, Random Forest, and Neural Network– and hyperparameter tune by evaluating the performances of each algorithm with 10-fold cross validation. Ultimately, we conclude that neural networks are the best-performing models and require the least amount of testing time. Lastly, we provide insight into the quantification of “trustworthiness” in a model, followed by brief discussions on interpreting model performance, suggesting potential project improvements, and identifying the biggest takeaways. Overall, we hope our work can serve as an effective baseline for realistic traffic volume generation, and open new directions in the processes of supervised dataset generation and ML algorithm design.
ContributorsOtstot, Kyle (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Weather radars provide quantitative precipitation estimates (QPEs) with seamless spatial coverage that can complement limitations of sparse rain gage measurements, including those affecting intensity-duration-frequency (IDF) relations used for infrastructure design. The goal of this M.S. thesis is to assess the ability of 4-km, 1-h QPEs from the Stage IV analysis of the Next-Generation Radar (NEXRAD) network to reproduce the statistics of extreme precipitation (P) in central Arizona, USA, using a dense network of 257 rain gages as reference. The generalized extreme value (GEV) distribution is used to model the frequency of annual P maximum series observed at gages and radar pixels for durations, d, from 1 to 24 h. Estimates of P quantiles from radar QPEs are negatively biased (-20% – -30%) for d = 1 h. The bias tends to 0 and errors are small for d ≥ 6 h, independently of the return period. The presence of scaling for the GEV location and scale parameters, needed to apply IDF scaling models, was found for both radar and gage products. Regional frequency analysis methods combined with bias correction of the GEV shape parameter allow reducing the statistical uncertainty and providing seamless spatial distribution of P quantiles at daily and subdaily durations that address limitations of current IDF relations in southwestern U.S. based on NOAA Atlas 14.
ContributorsSrivastava, Nehal Ansh (Author) / Mascaro, Giuseppe (Thesis advisor) / Chester, Mikhail (Committee member) / Garcia, Margaret (Committee member) / Papalexiou, Simon Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
Differences between cultures have been (and continue to be) examined by researchers all over the world. Prominent studies performed by organizations such as GLOBE and Hofstede have created a foundation for our understanding of how culture affects business in different countries. They also inspired our study, which investigates how employment benefits vary in different cultures. We examined the difference in employee benefit preference of Austria and Germany compared to America and how that affects their perception of the organization. Specifically, we studied how employees in those countries would react to an increase in wage or an increase in vacation time. Each participant read a hypothetical scenario in which they received one of the two benefits. The alternative benefit was not disclosed to them. After reading about the reward, they were asked various questions about the company. These questions gauged their belief in the ability of the organization, their benevolence toward the organization, their perception of the integrity of the organization, their trust in the organization, their turnover intentions, and their obligation felt towards the organization.
Two of the six variables tested yielded statistically significant results after we performed a univariate analysis of variance test on each of the variables. The two variables that yielded statistically significant results were belief in the integrity of the organization and benevolence toward the organization. Americans expressed more benevolence and belief in the integrity of their organization when they received more vacation time, while Europeans exhibited the opposite reaction (to a lesser degree). These results could provide insight to companies that are looking to strengthen company culture or increase motivation of employees. The variables with non-significant results could be attributed to globalization, limitations of our study, or the concept of scarcity.
Two of the six variables tested yielded statistically significant results after we performed a univariate analysis of variance test on each of the variables. The two variables that yielded statistically significant results were belief in the integrity of the organization and benevolence toward the organization. Americans expressed more benevolence and belief in the integrity of their organization when they received more vacation time, while Europeans exhibited the opposite reaction (to a lesser degree). These results could provide insight to companies that are looking to strengthen company culture or increase motivation of employees. The variables with non-significant results could be attributed to globalization, limitations of our study, or the concept of scarcity.
ContributorsMackey, Henry Aloysius (Author) / Baer, Mike (Thesis director) / Hom, Peter (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Management and Entrepreneurship (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05