ASU Scholarship Showcase

Background: Diet-derived carotenoid pigments are concentrated in the retinas of birds and serve a variety of functions, including photoprotection. In domesticated bird species (e.g., chickens and quail), retinal carotenoid pigmentation has been shown to respond to large manipulations in light exposure and provide protection against photodamage. However, it is not known if or how wild birds respond to ecologically relevant variation in sun exposure.

Methods: We manipulated the duration of natural sunlight exposure and dietary carotenoid levels in wild-caught captive House Finches (Haemorhous mexicanus), then measured carotenoid accumulation and oxidative stress in the retina.

Results: We found no significant effects of sun exposure on retinal levels of carotenoids or lipid peroxidation, in replicate experiments, in winter (Jan–Mar) and spring/summer (May–June). Dietary carotenoid supplementation in the spring/summer experiment led to significantly higher retinal carotenoid levels, but did not affect lipid peroxidation. Carotenoid levels differed significantly between the winter and spring/summer experiments, with higher retinal and lower plasma carotenoid levels in birds from the later experiment.

Conclusion: Our results suggest that variation in the duration of exposure to direct sunlight have limited influence on intraspecific variation in retinal carotenoid accumulation, but that accumulation may track other seasonal–environmental cues and physiological processes.

The United State generates the most waste among OECD countries, and there are adverse effects of the waste generation. One of the most serious adverse effects is greenhouse gas, especially CH4, which causes global warming. However, the amount of waste generation is not decreasing, and the United State recycling rate, which could reduce waste generation, is only 26%, which is lower than other OECD countries. Thus, waste generation and greenhouse gas emission should decrease, and in order for that to happen, identifying the causes should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling waste influences carbon dioxide emissions from the waste sector. The annual-based U.S. data from 1990 to 2012 were used. The data were collected from various data sources, and the Granger causality test was applied for identifying the causal relationships. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generation significantly cause positive and negative greenhouse gas emissions from the waste sector, respectively. This implies that the waste generation will not decrease even if GDP increases. And, if waste generation decreases or recycling rate increases, the greenhouse gas emission will decrease. Based on these results, it is expected that the waste generation and carbon dioxide emission from the waste sector can decrease more efficiently.

Construction waste management has become extremely important due to stricter disposal and landfill regulations, and a lesser number of available landfills. There are extensive works done on waste treatment and management of the construction industry. Concepts like deconstruction, recyclability, and Design for Disassembly (DfD) are examples of better construction waste management methods. Although some authors and organizations have published rich guides addressing the DfD's principles, there are only a few buildings already developed in this area. This study aims to find the challenges in the current practice of deconstruction activities and the gaps between its theory and implementation. Furthermore, it aims to provide insights about how DfD can create opportunities to turn these concepts into strategies that can be largely adopted by the construction industry stakeholders in the near future.

Previous studies in building energy assessment clearly state that to meet sustainable energy goals, existing buildings, as well as new buildings, will need to improve their energy efficiency. Thus, meeting energy goals relies on retrofitting existing buildings. Most building energy models are bottom-up engineering models, meaning these models calculate energy demand of individual buildings through their physical properties and energy use for specific end uses (e.g., lighting, appliances, and water heating). Researchers then scale up these model results to represent the building stock of the region studied.

Studies reveal that there is a lack of information about the building stock and associated modeling tools and this lack of knowledge affects the assessment of building energy efficiency strategies. Literature suggests that the level of complexity of energy models needs to be limited. Accuracy of these energy models can be elevated by reducing the input parameters, alleviating the need for users to make many assumptions about building construction and occupancy, among other factors. To mitigate the need for assumptions and the resulting model inaccuracies, the authors argue buildings should be described in a regional stock model with a restricted number of input parameters. One commonly-accepted method of identifying critical input parameters is sensitivity analysis, which requires a large number of runs that are both time consuming and may require high processing capacity.

This paper utilizes the Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS) model, which calculates the net energy demand of buildings and presents aggregated and individual- building-level, demand for specific end uses, e.g., heating, cooling, lighting, hot water and appliances. The model has already been validated using the Swedish, Spanish, and UK building stock data. This paper discusses potential improvements to this model by assessing the feasibility of using stepwise regression to identify the most important input parameters using the data from UK residential sector. The paper presents results of stepwise regression and compares these to sensitivity analysis; finally, the paper documents the advantages and challenges associated with each method.

As the construction continue to be a leading industry in the number of injuries and fatalities annually, several organizations and agencies are working avidly to ensure the number of injuries and fatalities is minimized. The Occupational Safety and Health Administration (OSHA) is one such effort to assure safe and healthful working conditions for working men and women by setting and enforcing standards and by providing training, outreach, education and assistance. Given the large databases of OSHA historical events and reports, a manual analysis of the fatality and catastrophe investigations content is a time consuming and expensive process. This paper aims to evaluate the strength of unsupervised machine learning and Natural Language Processing (NLP) in supporting safety inspections and reorganizing accidents database on a state level. After collecting construction accident reports from the OSHA Arizona office, the methodology consists of preprocessing the accident reports and weighting terms in order to apply a data-driven unsupervised K-Means-based clustering approach. The proposed method classifies the collected reports in four clusters, each reporting a type of accident. The results show the construction accidents in the state of Arizona to be caused by falls (42.9%), struck by objects (34.3%), electrocutions (12.5%), and trenches collapse (10.3%). The findings of this research empower state and local agencies with a customized presentation of the accidents fitting their regulations and weather conditions. What is applicable to one climate might not be suitable for another; therefore, such rearrangement of the accidents database on a state based level is a necessary prerequisite to enhance the local safety applications and standards.

Though many animal ornaments and signals are sensitive to and encode information about the oxidative balance (OB) of individuals (e.g., antioxidant supplies/activity, reactive oxygen species, cellular oxidative damage/repair), often the environmental and/or physiological sources of such OB are unknown. Urban development is among the most recent, pervasive, and persistent human stressors on the planet and impacts many environmental and physiological parameters of animals. Here we review the mechanistic underpinnings and functional consequences of how human urbanization drives antioxidant/oxidative status in animals and how this affects signal expression and use. Although we find that urbanization has strong negative effects on signal quality (e.g., visual, auditory, chemical) and OB across a range of taxa, few urban ecophysiological studies address signals and oxidative stress in unison, and even fewer in a fitness context. We also highlight particular signal types, taxa, life-histories, and anthropogenic environmental modifications on which future work integrating OB, signals, and urbanization could be centered. Last, we examine the conceptual and empirical framework behind the idea that urban conditions may disentangle signal expression from honesty and affect plasticity and adaptedness of sexually selected traits and preferences in the city.

Vertebrates cannot synthesize carotenoid pigments de novo, so to produce carotenoid-based coloration they must ingest carotenoids. Most songbirds that deposit red carotenoids in feathers, bills, eyes, or skin ingest only yellow or orange dietary pigments, which they oxidize to red pigments via a ketolation reaction. It has been hypothesized that carotenoid ketolation occurs in the liver of vertebrates, but this hypothesis remains to be confirmed. To better understand the role of hepatocytes in the production of ketolated carotenoids in songbirds, we measured the carotenoid content of subcellular components of hepatocytes from wild male house finches (Haemorhous mexicanus) that were molting red, ketocarotenoid-containing feathers (e.g., 3-hydroxy-echinenone). We homogenized freshly collected livers of house finches and isolated subcellular fractions, including mitochondria. We found the highest concentration of ketocarotenoids in the mitochondrial fraction. These observations are consistent with the hypothesis that carotenoid pigments are oxidized on or within hepatic mitochondria, esterified, and then transported to the Golgi apparatus for secretory processing.