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Parental care provides many benefits to offspring. One widely realized benefit is enhanced regulation of offspring's thermal environment. The developmental thermal environment during development can be optimized behaviorally through nest site selection and brooding, and it can be further enhanced by physiological heat production. In fact, enhancement of the developmental thermal environment has been proposed as the initial driving force for the evolution of endothermy in bird and mammals. I used pythons (Squamata: Pythonidae) to expand existing knowledge of behavioral and physiological parental tactics used to regulate offspring thermal environment. I first demonstrated that brooding behavior in the Children's python (Antaresia childreni) is largely driven by internal mechanisms, similar to solitary birds, suggesting that the early evolution of the parent-offspring association was probably hormonally driven. Two species of python are known to be facultatively thermogenic (i.e., are endothermic during reproduction). I expand current knowledge of thermogenesis in Burmese pythons (Python molurus) by demonstrating that females use their own body temperature to modulate thermogenesis. Although pythons are commonly cited as thermogenic, the actual extent of thermogenesis within the family Pythonidae is unknown. Thus, I assessed the thermogenic capability of five previously unstudied species of python to aid in understanding phylogenetic, morphological, and distributional influences on thermogenesis in pythons. Results suggest that facultative thermogenesis is likely rare among pythons. To understand why it is rare, I used an artificial model to demonstrate that energetic costs to the female likely outweigh thermal benefits to the clutch in species that do not inhabit cooler latitudes or lack large energy reserves. In combination with other studies, these results show that facultative thermogenesis during brooding in pythons likely requires particular ecological and physiological factors for its evolution.
ContributorsBrashears, Jake (Author) / DeNardo, Dale (Thesis advisor) / Harrison, Jon (Committee member) / Deviche, Pierre (Committee member) / McGraw, Kevin (Committee member) / Smith, Andrew (Committee member) / Arizona State University (Publisher)
Created2012
Description
Visualizations are an integral component for communicating and evaluating modern networks. As data becomes more complex, info-graphics require a balance between visual noise and effective storytelling that is often restricted by layouts unsuitable for scalability. The challenge then rests upon researchers to effectively structure their information in a way that allows for flexible, transparent illustration. We propose network graphing as an operative alternative for demonstrating community behavior over traditional charts which are unable to look past numeric data. In this paper, we explore methods for manipulating, processing, cleaning, and aggregating data in Python; a programming language tailored for handling structured data, which can then be formatted for analysis and modeling of social network tendencies in Gephi. We implement this data by applying an algorithm known as the Fruchterman-Reingold force-directed layout to datasets of Arizona State University’s research and collaboration network. The result is a visualization that analyzes the university’s infrastructure by providing insight about community behaviors between colleges. Furthermore, we highlight how the flexibility of this visualization provides a foundation for specific use cases by demonstrating centrality measures to find important liaisons that connect distant communities.
ContributorsMcMichael, Jacob Andrew (Author) / LiKamWa, Robert (Thesis director) / Anderson, Derrick (Committee member) / Goshert, Maxwell (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transporation. In past literature, automobile crash data is analyzed using time-series prediction technicques to identify road segments and/or intersections likely to experience future crashes (Lord & Mannering, 2010). After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publically-available crash and traffic data in Allgeheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Methods for evaluating routes based on the presented historical safety metric are included using the Mann Whitney U Test to evaluate the significance of routing decisions. The evaluation method presented requires routes have at least 20 crashes to be compared with significance testing. The safety of the road network is visualized using a heatmap to present distribution of the metric throughout Allgeheny County.
ContributorsGupta, Ariel Meron (Author) / Bansal, Ajay (Thesis director) / Sodemann, Angela (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The purpose of this project was to program a Raspberry Pi to be able to play music from both local storage on the Pi and from internet radio stations such as Pandora. The Pi also needs to be able to play various types of file formats, such as mp3 and FLAC. Finally, the project is also to be driven by a mobile app running on a smartphone or tablet. To achieve this, a client server design was employed where the Raspberry Pi acts as the server and the mobile app is the client. The server functionality was achieved using a Python script that listens on a socket and calls various executables that handle the different formats of music being played. The client functionality was achieved by programming an Android app in Java that sends encoded commands to the server, which the server decodes and begins playing the music that command dictates. The designs for both the client and server are easily extensible and allow for any future modifications to the project to be easily made.
ContributorsStorto, Michael Olson (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Agent Based modeling has been used in computer science to simulate complex phenomena. The introduction of Agent Based Models into the field of economics (Agent Based Computational Economics ACE) is not new, however work on making model environments simpler to design for individuals without a background in computer science or computer engineering is a constantly evolving topic. The issue is a trade off of how much is handled by the framework and how much control the modeler has, as well as what tools exist to allow the user to develop insights from the behavior of the model. The solutions looked at in this thesis are the construction of a simplified grammar for model construction, the design of an economic based library to assist in ACE modeling, and examples of how to construct interactive models.
ContributorsAnderson, Brandon David (Author) / Bazzi, Rida (Thesis director) / Kuminoff, Nicolai (Committee member) / Roberts, Nancy (Committee member) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A growing number of stylists \u2014 cosmetologists \u2014 are finding it harder to afford the basic necessities such as rent. However, the ever-increasing presence of smartphones and the increasing need for on-demand services like Uber and Uber Eats creates a unique opportunity for stylists \u2014 Clippr. Clippr is an application that aims to connect individual stylists directly to their customers. The application gives stylists a platform to create and display their own prices, services, and portfolio. Customers get the benefit of finding a stylist that suits them and booking instantly. This project outlines the backend for the Clippr application. It goes over the framework, REST API, and various functionalities of the application. Additionally, the project also covers the work that is still needed to successfully launch the application.
ContributorsKamath, Sanketh (Author) / Olsen, Christopher (Thesis director) / Sebold, Brent (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
I, Christopher Negrich, am the sole author of this paper, but the tools described were designed in collaboration with Andrew Hoetker. ConstrictR (constrictor) and ConstrictPy are an R package and python tool designed together. ConstrictPy implements the functions and methods defined in ConstrictR and applies data handling, data parsing, input/output (I/O), and a user interface to increase usability. ConstrictR implements a variety of common data analysis methods used for statistical and subnetwork analysis. The majority of these methods are inspired by Lionel Guidi's 2016 paper, Plankton networks driving carbon export in the oligotrophic ocean. Additional methods were added to expand functionality, usability, and applicability to different areas of data science. Both ConstrictR and ConstrictPy are currently publicly available and usable, however, they are both ongoing projects. ConstrictR is available at github.com/cnegrich and ConstrictPy is available at github.com/ahoetker. Currently, ConstrictR has implemented functions for descriptive statistics, correlation, covariance, rank, sparsity, and weighted correlation network analysis with clustering, centrality, profiling, error handling, and data parsing methods to be released soon. ConstrictPy has fully implemented and integrated the features in ConstrictR as well as created functions for I/O and conversion between pandas and R data frames with a full feature user interface to be released soon. Both ConstrictR and ConstrictPy are designed to work with minimal dependencies and maximum available information on the algorithms implemented. As a result, ConstrictR is only dependent on base R (v3.4.4) functions with no libraries imported. ConstrictPy is dependent upon only pandas, Rpy2, and ConstrictR. This was done to increase longevity and independence of these tools. Additionally, all mathematical information is documented alongside the code, increasing the available information on how these tools function. Although neither tool is in its final version, this paper documents the code, mathematics, and instructions for use, in addition to plans for future work, for of the current versions of ConstrictR (v0.0.1) and ConstrictPy (v0.0.1).
ContributorsNegrich, Christopher Alec (Author) / Can, Huansheng (Thesis director) / Hansford, Dianne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The Internet of Things (IoT) is term used to refer to the billions of Internet connected, embedded devices that communicate with one another with the purpose of sharing data or performing actions. One of the core usages of the proverbial network is the ability for its devices and services to interact with one another to automate daily tasks and routines. For example, IoT devices are often used to automate tasks within the household, such as turning the lights on/off or starting the coffee pot. However, designing a modular system to create and schedule these routines is a difficult task.
Current IoT integration utilities attempt to help simplify this task, but most fail to satisfy one of the requirements many users want in such a system ‒ simplified integration with third party devices. This project seeks to solve this issue through the creation of an easily extendable, modular integrating utility. It is open-source and does not require the use of a cloud-based server, with users hosting the server themselves. With a server and data controller implemented in pure Python and a library for embedded ESP8266 microcontroller-powered devices, the solution seeks to satisfy both casual users as well as those interested in developing their own integrations.
Current IoT integration utilities attempt to help simplify this task, but most fail to satisfy one of the requirements many users want in such a system ‒ simplified integration with third party devices. This project seeks to solve this issue through the creation of an easily extendable, modular integrating utility. It is open-source and does not require the use of a cloud-based server, with users hosting the server themselves. With a server and data controller implemented in pure Python and a library for embedded ESP8266 microcontroller-powered devices, the solution seeks to satisfy both casual users as well as those interested in developing their own integrations.
ContributorsBeagle, Bryce Edward (Author) / Acuna, Ruben (Thesis director) / Jordan, Shawn (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The traditional early design phase of an aircraft involves a design approach in which the model's characteristics are defined before the CAD model is built. This thesis discusses an alternative to the early design process employing the use of a parametric model. A parametric model is one in which its characteristics are defined as functions of input parameters that a user will choose, as opposed to being pre-defined. This allows for faster iterations of the CAD design of an aircraft going through its first design phases. In order to demonstrate the feasibility and efficiency, a tool was developed in the form of a script written in Python that compiles into a plugin that a user can install into Rhino. With a full template of about 70 parameters that have significant effects on the performance characteristics of an aircraft, a user with the plugin can generate a full model. The overall design phase and development of the script into a publicly available installation file is discussed below. Results for the thesis took the form of insight gained into the field of parametric modeling. After development and implementation, emphasis points such as generation time, focus on parameters with large effect on aircraft performance, and interpolation of parameters dependent upon others were concluded.
ContributorsElliott, Steven Joseph (Author) / Takahashi, Tim (Thesis director) / Middleton, James (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The purpose of this thesis was to develop a tool to provide information and data for design teams to use throughout the mobile application design process. Ideally, this would enable teams to see patterns in iterative design, and ultimately use data-driven analysis to make their own decisions. The initial problem was a lack of available information offered by mobile application design teams—the initial goal being to work closely with design teams to learn their decision-making methodology. However, every team that was reached out to responded with rejection, presenting a new problem: a lack of access to quality information regarding the decision-making process for mobile applications. This problem was addressed by the development of an ethical hacking script that retrieves reviews in bulk from the Google Play Store using Python. The project was a success—by feeding an application’s unique Play Store ID, the script retrieves a user-specified amount of reviews (up to millions) for that mobile application and the 4 “recommended” applications from the Play Store. Ultimately, this thesis proved that protected reviews on the Play Store can be ethically retrieved and used for data-driven decision making and identifying patterns in an application’s iterative design. This script provides an automated tool for teams to “put a finger on the pulse” of their target applications.
ContributorsDyer, Mitchell Patrick (Author) / Lin, Elva (Thesis director) / Giles, Charles (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12