MARS

MARS is a repository service of Mason Publishing and the Data and Digital Scholarship Services (DDSS) at the George Mason University Libraries. MARS provides enduring, stable, well-indexed access to a wide range of scholarship from the Mason community, such as Electronic Theses and Dissertations (ETDs), articles, presentations, reports, and creative work. Learn more about publishing, sharing, and preserving research data with the George Mason University Institutional Dataverse, and our other repository services.

To start publishing your content in MARS, please contact us by using our online form. Questions? Please email publish@gmu.edu.

 

Recent Submissions

Publication
Improving the Representation of Human Health Behavior in Spatial Agent-Based Models of Disease Spread
(2023-08-03) Jose, Roberto Siasoco; Anderson, Taylor M
Given the significant threat of infectious respiratory diseases to global health, epidemiological models are essential tools for better understanding disease transmission. Of the many modeling approaches, Agent-Based Models (ABMs) are ideal for simulating disease spread because of their ability to examine and predict disease outcomes based on individual-level behaviors and interactions, which are key drivers of disease spread. However, many existing ABMs either ignore or generalize the behavioral component due to several challenges, such as the lack of data for informing agent behaviors, difficulties with implementing behavioral computational frameworks, or limited interdisciplinary collaboration between the broader ABM community and domain experts. The objective of the thesis is to advance the representation of human health behaviors in ABMs of infectious respiratory disease spread. To achieve this, a systematic literature review is conducted to assess the extent to which health behavior is modeled in existing ABMs of infectious respiratory disease spread. Building upon the findings from the literature review, a data-driven agent decision framework of health behaviors for spatial ABMs of disease spread is developed. The framework is then integrated into a geospatial ABM that simulates the spread of COVID-19 and mask-use behavior among the student population at George Mason University (GMU) during the Fall 2021 semester. The advancements made in this thesis will ultimately provide the public and decision-makers with greater insight into disease transmission, accurate predictions on disease outcomes, and preparation for future infectious respiratory disease outbreaks.
Publication
Wildfire Burn Area and Severity Mapping by Using GIS and Remote Sensing Data
(2023-08-04) Atakul, Canan; Di, Liping
Wildfires globally have impacted the environment socially, economically, and ecologically. A precise evaluation of burn severity is fundamental for productive post-fire management and strategic planning. The primary objective of this study is to evaluate commonly used satellite indices in detecting burn severity and compare them with a widely used field index, the composite burn index (CBI). Sentinel-2 Level-1C satellite images, providing a spatial resolution of 10 meters, were utilized in this research from the European Space Agency (ESA) within the Copernicus program. The study focused on three wildfire incidents in the United States, namely Legion Lake, Fuller, and Chimney Tops 2, each with available CBI measurements. SNAP software, an open-source software developed by ESA, was employed for image analysis and metric calculations. Using ArcGIS Pro, the burn indices results, and CBI values were compared, followed by statistical analysis in Excel to assess the correlation between burn severity categories derived from the differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), differenced Normalized Difference Vegetation Index (dNDVI), and the newly established differenced Normalized Difference Burn Index (dNDBI) with CBI measurements. The study findings revealed that the performance of satellite burn severity indices, compared to CBI, is contingent upon the specific wildfire incident. Factors such as topography, shadow, and vegetation cover significantly influenced the results. For the ChimneyTops2 fire, the dNDVI index exhibited the most substantial correlation (R²=0.72) with the CBI, suggesting its superior predictive potential for burn severity. Conversely, the Legion Lake fire demonstrated the strongest correlation (R²=0.64) between the dNDBI index and CBI, indicating its potential as the optimal predictor of burn severity. In contrast, the Fuller fire showed generally lower correlation coefficients, with the RBR index showcasing the highest correlation with the CBI (R²=0.26). The observed significant correlation between the indices and CBI measurements highlights their value in evaluating burn severity across various regions. Additionally, the created burn severity mapping using Sentinel-2 dNBR with the proposed USGS threshold was compared to the Monitoring Trends in Burn Severity (MTBS) provided burn severity map using Landsat 8 data, yielding similar results despite some limitations, such as differences in pre- and post-fire image dates. Future research should continue to explore the applicability of these indices in other wildfire-prone regions, further enhancing our understanding of wildfire dynamics and its impact on the environment.
Publication
A Systematic and Comparative Study in Deep Learning Approaches in Automated Extraocular Muscle Segmentation and Analysis in Orbit Magnetic Resonance Images
(2023-08-08) Qureshi, Amad Aamir; Wei, Qi
Strabismus is an ocular condition characterized by binocular misalignment, which impacts about 5% of the global population. It can cause double vision, reduced vision, and impair the quality of life. Accurate diagnosis and treatment planning often benefits from the anatomical evaluation of the extraocular muscles (EOMs) that can be obtained by imaging modalities, such as magnetic resonance imaging (MRI). Such image-based examination requires segmenting the ocular structures from images, which is a labor and time-intensive task, subject to error when done manually. Deep learning-based segmentation has shown promise to outline anatomical structures automatically and objectively. We performed three sets of experimentation for EOM segmentation via DL-methods. Furthermore, we analyzed the performance of the deep learning methods through F-measure-based metrics, intersection over union (IoU) and Dice coefficient, and estimation of the EOM centroid (centroid offset). We first investigated the performance of U-Net, U-NeXt, DeepLabV3+, and ConResNet in multi-class pixel-based segmentation of the EOMs from ocular MRI taken in the quasi-coronal plane. Based on the performance evaluation (visual and the quantitative metrics mentioned), the U-Net model achieved the highest overall segmentation accuracy, and lowest centroid offset. It was noted that segmentation accuracy varied in spatially different image plane – relative to the middle slice (optic nerve junction point) in the MRI stack. In the second set of experiments, we compared the performance of the U-Net model with its variants, U-NeXt, Attention U-Net and FD-UNet and subjected the prediction outputs to the same evaluation as before, with U-Net achieving the best performance. We also explored methods in an attempt to improve the model performance – particularly with data augmentation and enhancement, where methods such as Adaptive Gamma Correction and CLAHE enhancement were used with the U-Net model. No significant difference was observed when CLAHE, Adaptive Gamma Correction and a dataset with unenhanced, CLAHE, and adaptive gamma corrected images were tested against unenhanced data, however, did result in better quantitative performance than the standard augmentation technique. Our study provides the insights into the factors that impact the accuracy of deep learning models in segmenting the EOMs, such as spatial slice location, image quality, and contrast and demonstrate the potential of these models in translating into 3D space for potential diagnosis and treatment planning for patients with strabismus and other ocular conditions.
Publication
Improving Engagement by Creating a Peer Environment
(Common Ground Research Networks, 2013) Stitz, Tammy; Tammy Stitz
It is well documented in literature that engineering students seek information by asking their peers before seeking information from other sources, such as asking a professor or performing a literature search. Social networking and other technologies, such as short message service, instant messaging, and even e-mail can be used to foster a peer type of relationship between educators and students. An online student survey can be used for this purpose as well. Assessment of library instruction sessions is important to ensure that students are learning what is intended. Surveying students can ensure instructional relevancy, reveal missing lecture information, and highlight points that create confusion. In addition to improving the instruction, implementing feedback can be used as a collaborative tool between the presenter and the students. This paper will illustrate, by example, the value of using several tools, some traditional and some not, to create an engaging, collaborative learning experience.
Publication
Transitioning from Marketing-Oriented Design to User-Oriented Design: A Case Study
(Taylor & Francis, 2011) Stitz, Tammy
The transition to a new architecture and design for an academic library Web site does not always proceed smoothly. This case study describes the experiences of a library at a large research university that hired an outside Web development contractor to create a new architecture and design for the university’s Web site using dotCMS, an open source content management system. The library participated in the design and development process along with other campus units. Because the university-wide process focused on marketing the university to prospective students, parents, and donors, the fact-finding process that the contractor used for the library’s site design focused on how the design could incorporate Web 2.0 technologies. The outcome was a library Web site that showcased Web 2.0 technology more than it provided users with access to library resources. The library’s users quickly communicated their dissatisfaction and confusion, which led to some immediate changes and a commitment to redesign the site based on expressed and demonstrated user needs. Therefore, the library hired another contractor to conduct iterative usability testing on both the new site and prototypes for a redesigned version. The testing outcome showed that Web 2.0 technology that does not meet existing user needs creates obstacles for both novice and experienced users. In collaboration with the university’s information technology unit, the library developed and launched a revised Web site that helped users connect to the resources they need. This upgrade included the deployment of the Google Search Appliance to replace the native dotCMS search functionality. This case study demonstrates that libraries may need to advocate for different Web design priorities than those in practice at the university-wide level, and that working with outside contractors presents different challenges and opportunities depending on the contractor’s hiring unit. These experiences also demonstrate that libraries can do a better job learning about their users when they lead the fact-finding process. Following these experiences, the library has made a commitment to conducting iterative usability testing on a regular basis.