Matching Items (125)
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Description

Adaptive comanagement endeavors to increase knowledge and responsiveness in the face of uncertainty and complexity. However, when collaboration between agency and nonagency stakeholders is mandated, rigid institutions may hinder participation and ecological outcomes. In this case study we analyzed qualitative data to understand how participants perceive strengths and challenges within

Adaptive comanagement endeavors to increase knowledge and responsiveness in the face of uncertainty and complexity. However, when collaboration between agency and nonagency stakeholders is mandated, rigid institutions may hinder participation and ecological outcomes. In this case study we analyzed qualitative data to understand how participants perceive strengths and challenges within an emerging adaptive comanagement in the Agua Fria Watershed in Arizona, USA that utilizes insight and personnel from a long-enduring comanagement project, Las Cienegas. Our work demonstrates that general lessons and approaches from one project may be transferable, but particular institutions, management structures, or projects must be place-specific. As public agencies establish and expand governance networks throughout the western United States, our case study has shed light on how to maintain a shared vision and momentum within an inherently murky and shared decision-making environment.

ContributorsChilds, Cameron (Author) / York, Abigail (Author) / White, Dave (Author) / Schoon, Michael (Author) / Bodner, Gitanjali S. (Author) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2013
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Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a

The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
Collaborative research is not only a form of social and human capital and a public good, but also a fundamental elicitor of positive Collective Action. Collaborative Research Networks can serve as models of proactive and purposive Collective Action and catalysts of societal change, if they function as more than hubs

Collaborative research is not only a form of social and human capital and a public good, but also a fundamental elicitor of positive Collective Action. Collaborative Research Networks can serve as models of proactive and purposive Collective Action and catalysts of societal change, if they function as more than hubs of research and knowledge. It is the goal of this Honors Thesis to examine the current nature under which collaborative research networks, focused on matters of Global Health or Sustainability, operate., how they are organized, what type of collaboration they engage in, and who collaborates with whom. A better understanding of these types of networks can lead to the formation of more effective networks that can develop innovative solutions to our collective Global Health and Sustainability problems.
ContributorsHodzic, Mirna (Author) / Van Der Leeuw, Sander (Thesis director) / Janssen, Marco (Committee member) / Schoon, Michael (Committee member) / Barrett, The Honors College (Contributor)
Created2012-05
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Description

Background:
Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.

Objectives:
The first objective of this work was to catalyze discussion of the role of personal heat exposure

Background:
Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.

Objectives:
The first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.

Discussion:
We define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated indirectly through the combination of time–activity and meteorological data sets. Complementary information to understand individual-scale drivers of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches, and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment concerning extreme heat.

Conclusions:
Personal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when, where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of heat exposure.

ContributorsKuras, Evan R. (Author) / Richardson, Molly B. (Author) / Calkins, Mirian M. (Author) / Ebi, Kristie L. (Author) / Gohlke, Julia M. (Author) / Hess, Jeremy J. (Author) / Hondula, David M. (Author) / Kintziger, Kristina W. (Author) / Jagger, Meredith A. (Author) / Middel, Ariane (Author) / Scott, Anna A. (Author) / Spector, June T. (Contributor) / Uejio, Christopher K. (Author) / Vanos, Jennifer K. (Author) / Zaitchik, Benjamin F. (Author)
Created2017-08
Description
Urban centers worldwide face the escalating challenge of urban heat islands (UHIs), which exacerbate public health issues and energy consumption due to increased temperatures. This thesis focuses on the Phoenix metropolitan area, recognized for its high summer temperatures, to explore innovative computational strategies for mitigating urban heat through optimized tree

Urban centers worldwide face the escalating challenge of urban heat islands (UHIs), which exacerbate public health issues and energy consumption due to increased temperatures. This thesis focuses on the Phoenix metropolitan area, recognized for its high summer temperatures, to explore innovative computational strategies for mitigating urban heat through optimized tree placement. The research integrates high-fidelity microclimate modeling with advanced computational techniques to strategically position trees and enhance urban climate resilience. Utilizing the SOLWEIG and TreePlanter models, this study simulates the effects of tree planting on mean radiant temperature (MRT), crucial for thermal comfort in outdoor spaces. The models process geospatial data, including LiDAR and high-resolution thermal maps, to produce actionable insights for reducing urban temperatures. Results indicate that strategic tree planting significantly lowers MRT, enhancing urban livability and sustainability. This thesis contributes to urban planning by demonstrating how targeted greening interventions can alleviate the heat burden in cities, providing a replicable framework for other urban areas experiencing similar challenges.
ContributorsGarg, Shrey (Author) / Middel, Ariane (Thesis director) / Buo, Isaac (Committee member) / Barrett, The Honors College (Contributor)
Created2024-05