Matching Items (7)

135971-Thumbnail Image.png

NBA PlayerTrack: A Mobile Application Providing NBA Fans with Statistics, News, and Information about their Favorite Players

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

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.

Contributors

Agent

Created

Date Created
  • 2015-12

131311-Thumbnail Image.png

Predicting Outcome of a Pitch Given the Type of Pitch for any Baseball Scenario

Description

This thesis serves as a baseline for the potential for prediction through machine learning (ML) in baseball. Hopefully, it also will serve as motivation for future work to expand and

This thesis serves as a baseline for the potential for prediction through machine learning (ML) in baseball. Hopefully, it also will serve as motivation for future work to expand and reach the potential of sabermetrics, advanced Statcast data and machine learning. The problem this thesis attempts to solve is predicting the outcome of a pitch. Given proper pitch data and situational data, is it possible to predict the result or outcome of a pitch? The result or outcome refers to the specific outcome of a pitch, beyond ball or strike, but if the hitter puts the ball in play for a double, this thesis shows how I attempted to predict that type of outcome. Before diving into my methods, I take a deep look into sabermetrics, advanced statistics and the history of the two in Major League Baseball. After this, I describe my implemented machine learning experiment. First, I found a dataset that is suitable for training a pitch prediction model, I then analyzed the features and used some feature engineering to select a set of 16 features, and finally, I trained and tested a pair of ML models on the data. I used a decision tree classifier and random forest classifier to test the data. I attempted to us a long short-term memory to improve my score, but came up short. Each classifier performed at around 60% accuracy. I also experimented using a neural network approach with a long short-term memory (LSTM) model, but this approach requires more feature engineering to beat the simpler classifiers. In this thesis, I show examples of five hitters that I test the models on and the accuracy for each hitter. This work shows promise that advanced classification models (likely requiring more feature engineering) can provide even better prediction outcomes, perhaps with 70% accuracy or higher! There is much potential for future work and to improve on this thesis, mainly through the proper construction of a neural network, more in-depth feature analysis/selection/extraction, and data visualization.

Contributors

Agent

Created

Date Created
  • 2020-05

147743-Thumbnail Image.png

Evaluating STAT5 Signature in Recurrent Glioblastoma

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

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.

Contributors

Agent

Created

Date Created
  • 2021-05

132329-Thumbnail Image.png

Pay or Play: Austria and Germany Versus America. Examining Differences In Employment Benefit Preferences and Their Effects on Employees

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

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.

Contributors

Created

Date Created
  • 2019-05

151449-Thumbnail Image.png

Analysis of the effects of socioeconomic, political and institutional determinants on technological innovation in the Maghreb

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,

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.

Contributors

Agent

Created

Date Created
  • 2012

152518-Thumbnail Image.png

Development of a diffused junction silicon solar cell pilot line

Description

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

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.

Contributors

Agent

Created

Date Created
  • 2014

156901-Thumbnail Image.png

Relationships between on-road FFCO₂ emission and socio-economics/urban form factors

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.,

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.

Contributors

Agent

Created

Date Created
  • 2018