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This thesis investigates the viability of a solar still for desalination of a personal water supply. The end goal of the project is to create a design that meets the output requirement while tailoring the components to focus on low cost so it would be feasible in the impoverished areas of the world. The primary requirement is an output of 3 liters of potable water per day, the minimum necessary for an adult human. The study examines the effect of several design parameters, such as the basin material, basin thickness, starting water depth, basin dimensions, cover material, cover angle, and cover thickness. A model for the performance of a solar still was created in MATLAB to simulate the system's behavior and sensitivity to these parameters. An instrumented prototype solar still demonstrated viability of the concept and provided data for validation of the MATLAB model.
ContributorsRasmussen, Dylan James (Author) / Wells, Valana (Thesis director) / Trimble, Steven (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
The listing price of residential rental real estate is dependent upon property specific attributes. These attributes involve data that can be tabulated as categorical and continuous predictors. The forecasting model presented in this paper is developed using publicly available, property specific information sourced from the Zillow and Trulia online real estate databases. The following fifteen predictors were tracked for forty-eight rental listings in the 85281 area code: housing type, square footage, number of baths, number of bedrooms, distance to Arizona State University’s Tempe Campus, crime level of the neighborhood, median age range of the neighborhood population, percentage of the neighborhood population that is married, median year of construction of the neighborhood, percentage of the population commuting longer than thirty minutes, percentage of neighborhood homes occupied by renters, percentage of the population commuting by transit, and the number of restaurants, grocery stores, and nightlife within a one mile radius of the property. Through regression analysis, the significant predictors of the listing price of a rental property in the 85281 area code were discerned. These predictors were used to form a forecasting model. This forecasting model explains 75.5% of the variation in listing prices of residential rental real estate in the 85281 area code.
ContributorsSchuchter, Grant (Author) / Clough, Michael (Thesis director) / Escobedo, Adolfo (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Within recent years, the drive for increased sustainability within large corporations has drastically increased. One critical measure within sustainability is the diversion rate, or the amount of waste diverted from landfills to recycling, repurposing, or reselling. There are a variety of different ways in which a company can improve their diversion rate, such as repurposing paper. A conventional method would be to simply have a recycling bin for collecting all paper, but the concern for large companies then becomes a security issue as confidential papers may not be safe in a traditional recycling bin. Salt River Project (SRP) has tackled this issue by hiring a third-party vendor (TPV) and having all paper placed into designated, secure shredding bins whose content is shredded upon collection and ultimately recycled into new material. However, while this effort is improving their diversion, the question has arisen of how to make the program viable in the long term based on the costs required to sustain it. To tackle this issue, this thesis will focus on creating a methodology and sampling plan to determine the appropriate level of a third-party recycling service required and to guide efficient bin-sizing solutions. This will in turn allow for SRP to understand how much paper waste is being produced and how accurately they are being charged for TPV services.
ContributorsHolladay, Amy E. (Author) / Escobedo, Adolfo (Thesis director) / Kucukozyigit, Ali (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
ContributorsKemmer, Ryan Wyeth (Author) / Escobedo, Adolfo (Thesis director) / Maciejewski, Ross (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The outbreak of the coronavirus has impacted retailers and the food industry after they were forced to switch to delivery services due to social distancing measures. During these times, online sales and local deliveries started to see an increase in their demand - making these methods the new way of staying in business. For this reason, this research seeks to identify strategies that could be implemented by delivery service companies to improve their operations by comparing two types of p-median models (node-based and edge-based). To simulate demand, geographical data will be analyzed for the cities of San Diego and Paris. The usage of districting models will allow the determination on how balance and compact the service regions are within the districts. After analyzing the variability of each demand simulation run, conclusions will be made on whether one model is better than the other.
ContributorsAguilar, Sarbith Anabella (Author) / Escobedo, Adolfo (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12