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- Creators: Barrett, The Honors College
- Creators: Kavazanjian, Edward
- Creators: Allenby, Braden
- Creators: Cadillo – Quiroz, Hinsby
Description
Natural Language Processing and Virtual Reality are hot topics in the present. How can we synthesize these together in order to make a cohesive experience? The game focuses on users using vocal commands, building structures, and memorizing spatial objects. In order to get proper vocal commands, the IBM Watson API for Natural Language Processing was incorporated into our game system. User experience elements like gestures, UI color change, and images were used to help guide users in memorizing and building structures. The process to create these elements were streamlined through the VRTK library in Unity. The game has two segments. The first segment is a tutorial level where the user learns to perform motions and in-game actions. The second segment is a game where the user must correctly create a structure by utilizing vocal commands and spatial recognition. A standardized usability test, System Usability Scale, was used to evaluate the effectiveness of the game. A survey was also created in order to evaluate a more descriptive user opinion. Overall, users gave a positive score on the System Usability Scale and slightly positive reviews in the custom survey.
ContributorsOrtega, Excel (Co-author) / Ryan, Alexander (Co-author) / Kobayashi, Yoshihiro (Thesis director) / Nelson, Brian (Committee member) / Computing and Informatics Program (Contributor) / School of Art (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Currently conventional Subtitle D landfills are the primary means of disposing of our waste in the United States. While this method of waste disposal aims at protecting the environment, it does so through the use of liners and caps that effectively freeze the breakdown of waste. Because this method can keep landfills active, and thus a potential groundwater threat for over a hundred years, I take an in depth look at the ability of bioreactor landfills to quickly stabilize waste. In the thesis I detail the current state of bioreactor landfill technologies, assessing the pros and cons of anaerobic and aerobic bioreactor technologies. Finally, with an industrial perspective, I conclude that moving on to bioreactor landfills as an alternative isn't as simple as it may first appear, and that it is a contextually specific solution that must be further refined before replacing current landfills.
ContributorsWhitten, George Avery (Author) / Kavazanjian, Edward (Thesis director) / Allenby, Braden (Committee member) / Houston, Sandra (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
Description
Background: As the growth of social media platforms continues, the use of the constantly increasing amount of freely available, user-generated data they receive becomes of great importance. One apparent use of this content is public health surveillance; such as for increasing understanding of substance abuse. In this study, Facebook was used to monitor nicotine addiction through the public support groups users can join to aid their quitting process. Objective: The main objective of this project was to gain a better understanding of the mechanisms of nicotine addiction online and provide content analysis of Facebook posts obtained from "quit smoking" support groups. Methods: Using the Facebook Application Programming Interface (API) for Python, a sample of 9,970 posts were collected in October 2015. Information regarding the user's name and the number of likes and comments they received on their post were also included. The posts crawled were then manually classified by one annotator into one of three categories: positive, negative, and neutral. Where positive posts are those that describe current quits, negative posts are those that discuss relapsing, and neutral posts are those that were not be used to train the classifiers, which include posts where users have yet to attempt a quit, ads, random questions, etc. For this project, the performance of two machine learning algorithms on a corpus of manually labeled Facebook posts were compared. The classification goal was to test the plausibility of creating a natural language processing machine learning classifier which could be used to distinguish between relapse (labeled negative) and quitting success (labeled positive) posts from a set of smoking related posts. Results: From the corpus of 9,970 posts that were manually labeled: 6,254 (62.7%) were labeled positive, 1,249 (12.5%) were labeled negative, and 2467 (24.8%) were labeled neutral. Since the posts labeled neutral are those which are irrelevant to the classification task, 7,503 posts were used to train the classifiers: 83.4% positive and 16.6% negative. The SVM classifier was 84.1% accurate and 84.1% precise, had a recall of 1, and an F-score of 0.914. The MNB classifier was 82.8% accurate and 82.8% precise, had a recall of 1, and an F-score of 0.906. Conclusions: From the Facebook surveillance results, a small peak is given into the behavior of those looking to quit smoking. Ultimately, what makes Facebook a great tool for public health surveillance is that it has an extremely large and diverse user base with information that is easily obtainable. This, and the fact that so many people are actually willing to use Facebook support groups to aid their quitting processes demonstrates that it can be used to learn a lot about quitting and smoking behavior.
ContributorsMolina, Daniel Antonio (Author) / Li, Baoxin (Thesis director) / Tian, Qiongjie (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.
ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
Description
This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.
ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
Description
This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.
ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
Description
This Honors thesis was written in partial fulfillment of the requirements for a Bachelor of Science in Human Systems Engineering with Honors. The project consists of a literature review that explores the uses and applications of Machine Learning and Artificial Intelligence techniques in the field of commercial aviation. After a brief introduction and explanation of the most commonly used algorithms in the field of aviation, it explores the applications of Machine Learning techniques for risk reduction, and for the betterment of in-flight operations, and pilot selection, training, and assessment.
ContributorsInderberg, Laura (Author) / Gray, Robert (Thesis director) / Demir, Mustafa (Committee member) / Barrett, The Honors College (Contributor) / Human Systems Engineering (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2023-12
Characterization and Manipulation of Microbiomes From Arid Landfills for Improved Methane Production
Description
Environmentally harmful byproducts from solid waste’s decomposition, including methane (CH4) emissions, are managed through standardized landfill engineering and gas-capture mechanisms. Yet only a limited number of studies have analyzed the development and composition of Bacteria and Archaea involved in CH4 production from landfills. The objectives of this research were to compare microbiomes and bioactivity from CH4-producing communities in contrasting spatial areas of arid landfills and to tests a new technology to biostimulate CH4 production (methanogenesis) from solid waste under dynamic environmental conditions controlled in the laboratory. My hypothesis was that the diversity and abundance of methanogenic Archaea in municipal solid waste (MSW), or its leachate, play an important role on CH4 production partially attributed to the group’s wide hydrogen (H2) consumption capabilities. I tested this hypothesis by conducting complementary field observations and laboratory experiments. I describe niches of methanogenic Archaea in MSW leachate across defined areas within a single landfill, while demonstrating functional H2-dependent activity. To alleviate limited H2 bioavailability encountered in-situ, I present biostimulant feasibility and proof-of-concepts studies through the amendment of zero valent metals (ZVMs). My results demonstrate that older-aged MSW was minimally biostimulated for greater CH4 production relative to a control when exposed to iron (Fe0) or manganese (Mn0), due to highly discernable traits of soluble carbon, nitrogen, and unidentified fluorophores found in water extracts between young and old aged, starting MSW. Acetate and inhibitory H2 partial pressures accumulated in microcosms containing old-aged MSW. In a final experiment, repeated amendments of ZVMs to MSW in a 600 day mesocosm experiment mediated significantly higher CH4 concentrations and yields during the first of three ZVM injections. Fe0 and Mn0 experimental treatments at mesocosm-scale also highlighted accelerated development of seemingly important, but elusive Archaea including Methanobacteriaceae, a methane-producing family that is found in diverse environments. Also, prokaryotic classes including Candidatus Bathyarchaeota, an uncultured group commonly found in carbon-rich ecosystems, and Clostridia; All three taxa I identified as highly predictive in the time-dependent progression of MSW decomposition. Altogether, my experiments demonstrate the importance of H2 bioavailability on CH4 production and the consistent development of Methanobacteriaceae in productive MSW microbiomes.
ContributorsReynolds, Mark Christian (Author) / Cadillo-Quiroz, Hinsby (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Wang, Xuan (Committee member) / Kavazanjian, Edward (Committee member) / Arizona State University (Publisher)
Created2022
Description
Zero-Valent Metals (ZVM) are highly reactive materials and have been proved to be effective in contaminant reduction in soils and groundwater remediation. In fact, zero-Valent Iron (ZVI) has proven to be very effective in removing, particularly chlorinated organics, heavy metals, and odorous sulfides. Addition of ZVI has also been proved in enhancing the methane gas generation in anaerobic digestion of activated sludge. However, no studies have been conducted regarding the effect of ZVM stimulation to Municipal Solid Waste (MSW) degradation. Therefore, a collaborative study was developed to manipulate microbial activity in the landfill bioreactors to favor methane production by adding ZVMs. This study focuses on evaluating the effects of added ZVM on the leachate generated from replicated lab scale landfill bioreactors. The specific objective was to investigate the effects of ZVMs addition on the organic and inorganic pollutants in leachate. The hypothesis here evaluated was that adding ZVM including ZVI and Zero Valent Manganese (ZVMn) will enhance the removal rates of the organic pollutants present in the leachate, likely by a putative higher rate of microbial metabolism. Test with six (4.23 gallons) bioreactors assembled with MSW collected from the Salt River Landfill and Southwest Regional Landfill showed that under 5 grams /liter of ZVI and 0.625 grams/liter of ZVMn additions, no significant difference was observed in the pH and temperature data of the leachate generated from these reactors. The conductivity data suggested the steady rise across all reactors over the period of time. The removal efficiency of sCOD was highest (27.112 mg/lit/day) for the reactors added with ZVMn at the end of 150 days for bottom layer, however the removal rate was highest (16.955 mg/lit/day) for ZVI after the end of 150 days of the middle layer. Similar trends in the results was observed in TC analysis. HPLC study indicated the dominance of the concentration of heptanoate and isovalerate were leachate generated from the bottom layer across all reactors. Heptanoate continued to dominate in the ZVMn added leachate even after middle layer injection. IC analysis concluded the chloride was dominant in the leachate generated from all the reactors and there was a steady increase in the chloride content over the period of time. Along with chloride, fluoride, bromide, nitrate, nitrite, phosphate and sulfate were also detected in considerable concentrations. In the summary, the addition of the zero valent metals has proved to be efficient in removal of the organics present in the leachate.
ContributorsPandit, Gandhar Abhay (Author) / Cadillo – Quiroz, Hinsby (Thesis advisor) / Olson, Larry (Thesis advisor) / Boyer, Treavor (Committee member) / Arizona State University (Publisher)
Created2019
Description
While there are many existing systems which take natural language descriptions and use them to generate images or text, few systems exist to generate 3d renderings or environments based on natural language. Most of those systems are very limited in scope and require precise, predefined language to work, or large well tagged datasets for their models. In this project I attempt to apply concepts in NLP and procedural generation to a system which can generate a rough scene estimation of a natural language description in a 3d environment from a free use database of models. The primary objective of this system, rather than a completely accurate representation, is to generate a useful or interesting result. The use of such a system comes in assisting designers who utilize 3d scenes or environments for their work.
ContributorsHann, Jacob R. (Author) / Kobayashi, Yoshihiro (Thesis director) / Srivastava, Siddharth (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05