Faculty and Staff

In 2014/2015, Arizona State University (ASU) Libraries, the Labriola National American Indian Data Center, and the ASU American Indian Studies Department completed an ASU Institute for Humanities Research (IHR) seed grant entitled “Carlos Montezuma’s Wassaja Newsletter: Digitization, Access and Context” to digitize all ASU held issues of the newsletter Wassaja Freedom’s Signal for the Indian, which Yavapai activist-intellectual Carlos Montezuma, MD (1866-1923) self-published during 1916-1922. The grant team additionally selected a portion of the ASU Libraries Carlos Montezuma archival collection for digitization to provide a more complete picture of Dr. Carlos Montezuma’s life and work.

The ASU grant team produced a searchable online collection on the ASU Digital Repository and created an online exhibition in conjunction with the IHR Nexus Lab’s Developing Wassaja Project. The Nexus Lab’s role at ASU is to grow the digital humanities through interdisciplinary collaborations bringing together humanities, science, and technology. The Nexus Lab partnered with the grant team to create the Developing Wassaja Project which provided an opportunity for faculty, staff, and students at ASU to engage in electronic publication through web application development.

The resulting web platform, Wassaja: A Carlos Montezuma Project, provides context for this digitized collection and facilitates community interaction, including a partnership with Dr. Montezuma’s home community the Fort McDowell Yavapai Nation. In this webcast, Digital Projects Librarian Matthew Harp, Developing Wassaja Project team member Joe Buenker (subject librarian), and grant team member Joyce Martin (librarian and curator of the Labriola National American Indian Data Center) will discuss and demonstrate the resources created and the resulting partnership with the Fort McDowell Yavapai Nation. The webcast will focus on identifying collaborators and needed skills to engage in Digital Humanities research and on identifying the stages of a collaborative project.

Participants will gain insight on working directly with diverse communities; overcoming technical limitations of traditional institutional repositories; collaborative strategies with faculty, research centers, and cultural heritage societies; solutions for moving hidden collections into an engaging digital exhibition; integrating digital humanities research and instruction with library curation; and preparing for long term costs and management issues.

Anthropology librarian Juliann Couture and Joyce Martin, curator of the Labriola National American Indian Data Center, looking at the Center's display of unique Hopi Kachina dolls. Four of the kachinas (Navan Kachina; Talavi Kachina; Flute Kachina; and Ahöla Kachina) were created by artist, carver, and former ASU employee Tony Dukepoo as a gift to the libraries in 1979. The kachina dolls are on display in the Labriola Center located on the 2nd floor of the Hayden Library on ASU's Tempe campus.

The Simon Ortiz and Labriola Center Lecture on Indigenous Land, Culture, and Community addresses topics and issues across disciplines in the arts, humanities, sciences, and politics. Underscoring Indigenous American experiences and perspectives, this Series seeks to create and celebrate knowledge that evolves from an Indigenous worldview that is inclusive and that is applicable to all walks of life.” Professor Simon Ortiz discussed the overall nature of the Series, especially emphasizing the global nature of Indigenous concerns. Joyce Martin and Matthew Harp elaborated on the contributions of the Labriola National American Indian Data Center and ASU Libraries to the Series.

The Labriola Center hosts an informal event in Hayden Library which facilitates close interaction between the featured speaker and audience members. The ASU Libraries records the evening lectures which take place at the Heard Museum and presents both an audio podcast and streaming video of each lecture on the ASU Library Channel webpage. This lecture series provides not only a chance for community discussion at the events themselves, but through the innovative use of technology the ASU Libraries enables additional forums for discussion in blogs and web pages which choose to link to the streaming videos.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

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.