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Comics: Why do we study them and how?
No full textComics are everywhere, particularly in contemporary US society: grocery stores, back of cereal boxes, hidden within bubblegum wrappers, doctor’s offices, any number of bookshops, etc. They’ve been around for over 150 years, appearing in newspapers circulated around the globe, floppy cheap pulpy comics that cost a nickel, Pulitzer Prize-winning graphic novels, Japanese manga that have run for over 1,000 issues, and even appearing digitally on our phones. We also, of course, live in a golden age of comics on the silver screen: Marvel and DC both have produced some of the world’s biggest blockbuster flicks around, comic book shows regularly appear on network television, and children have a treasure trove of superhero-themed cartoons.
But – why? Why have comics stuck around for so long? Why do we continue to read them as consumers? Why do they matter as a product of popular culture? How can we use technology to study over 100,000 comic strips all at once? And why might comics belong not just in college classrooms, but at the center of academic study by professors?
In this lecture Dr. Justin Wigard, Assistant Professor of English at University of North Dakota, answers some of these questions while talking through his own work in comics studies, writing about characters like Deadpool, Green Arrow, Black Panther, Calvin and Hobbes, along with how he teaches comics for both literature majors and STEM students
“Grow your plants, but most importantly, grow yourself”: Women, Resilience, and Cultural Continuity in Jamaican and Diaspora Gardens
Get PDFThis essay explores the cultural, personal, and transformative power of gardening through the stories of four women—Brenda, Linda, Keisha, and Janice—whose lives span Jamaica and its diaspora. Their narratives reveal gardening as more than food cultivation but the practice of resilience, healing, and cultural continuity. Brenda honors her ancestral legacy in St. Elizabeth; Linda finds solace after loss; Keisha fosters socioemotional growth among urban students; and Janice embraces sustainability in suburban America. Together, their experiences illustrate how gardening nurtures identity, strengthens communities, and connects people to land, memory, and future generations
the meadow is a teacher
Get PDFThis poem invites the reader into a layered experience of nature through the eyes of a mindful observer. From a single wildflower to a grove of aspens, the poet gathers impressions from the meadow and its inhabitants: birds, trees, water, fish. The speaker’s act of closing their eyes and listening becomes a moment of learning—not through books or instruction, but through quiet immersion in the living world. Written in free verse, this poem uses short lines that mimic the motion of breeze, birds, and water. There is no punctuation, creating a seamless flow. The structure echoes the gentle, undemanding way that nature teaches: simply by being
Cruel Memories, Song Lyrics, undated
No full textSong lyrics for Cruel Memories by Bill Gessner. Seven pages total, including one double-sided page. The typewritten pages include multiple versions of the lyrics, with some pages including handwritten marks and notes; some notes and suggested revisions appear on yellow post-it notes affixed to the sheets of paper. The song is at least partly concerned with changes and restrictions to diet.https://commons.und.edu/gessner-lyrics/1006/thumbnail.jp
Grand and a Glorious Day, Song Lyrics, 1994
No full textLyrics for Grand and a Glorious Day by Bill Gessner. The song was written in 1994 during the Holiday season. Gessner (1947-2019) graduated from UND in 1969 and was a major figure in the co-op grocery industry. He was also a musician and guitar player who composed many songs.https://commons.und.edu/gessner-lyrics/1007/thumbnail.jp
Integrated Machine Learning For Predicting Reservoir Properties And Experimental Approach For Evaluating Co2-Brine-Rock Interactions In Carbonate Formations
No full textUnderstanding subsurface properties is fundamental for optimizing hydrocarbon recovery and ensuring the long-term integrity of carbon capture and storage (CCS) operations. Conventional methods such as empirical correlations, advanced logging, and laboratory measurements, though widely used, are costly, time-consuming, and often limited in accuracy within formations. This study develops an ML framework to enhance the prediction of petrophysical and geomechanical properties and experimental investigation to evaluate time-dependent alterations in petrophysical and geomechanical rock properties under CO₂-brine exposure.In the first phase, six ML algorithms Decision Tree, Random Forest, Extra Trees, XGBoost, LightGBM, and K-Nearest Neighbor were applied to predict key petrophysical parameters (porosity, permeability, and water saturation) in the Bakken formation using well log and core analysis data. Ensemble-based models, particularly Extra Trees, delivered superior predictive accuracy, achieving R² values of 0.98, 0.89, and 0.86 for water saturation, porosity, and permeability, respectively.
The second phase used a hybrid ML framework to predict geomechanical properties in the Williston Basin. Four optimized algorithms Random Forest, Extra Trees, XGBoost, and LightGBM were trained on well log data to estimate shear (DTSM) and compressional (DTCO) wave transit times, along with derived parameters including Poisson’s ratio, Young’s modulus, and shear modulus. Extra Trees achieved the highest accuracy, with DTSM and DTCO predictions yielding R² values of 0.97. Compared to Castagna’s empirical correlation, ML-based predictions provided closer alignment with measured logs, underscoring the practical value of ML workflows as cost-effective and reliable alternatives to laboratory-based methods in geomechanical property estimation.
The third phase experimentally investigated the effects of supercritical CO2-brine exposure on Madison carbonate rocks under simulated reservoir conditions (140°F and 4,000 psi). Samples exposed for 10 days exhibited increased porosity and enlarged pore structures due to calcite dissolution, while those exposed for 30 days showed reduced porosity linked to secondary mineral precipitation (dolomite, quartz, halite, magnesium oxide). SEM and XRD confirmed dissolution–precipitation processes, while NMR revealed time-dependent pore evolution. Mechanical testing indicated increased Young’s modulus and Poisson’s ratio, suggesting matrix stiffening through mineral re-cementation. These results highlight a dual-phase mechanism of initial weakening followed by structural strengthening, with important implications for long-term CO2 storage security.
Collectively, this dissertation demonstrates that machine learning offers a powerful and cost-effective framework for predicting subsurface properties, while experimental insights into CO₂-brine-rock interactions provide critical understanding of subsurface propoerties evolution under storage conditions. The novelty of this work lies in bridging data-driven prediction with experimental validation to deliver an integrated approach that advances unconventional reservoir characterization and supports safer, more predictable CCS implementation
Elucidating The Oxidation Mechanisms In Cr2AlB2 Using Density Functional Theory And Thermodynamic Modeling
No full textThis thesis investigates the oxidation behavior of Cr2AlB2 surfaces using density functional theory calculations combined with thermodynamic modeling. Our analysis encompassed surface stability assessments, oxygen adsorption studies on various surfaces, and thermodynamic insights into oxidation mechanisms. Surface stability assessments reveal that the (010) surface terminated with an Al layer exhibits the lowest surface energy. Oxygen adsorption studies on the (010), (111), and (021) surfaces demonstrate strong dissociative adsorption with binding energies ranging from -4.72 to -4.22 eV depending on coverage. Thermodynamic analysis shows that elevated temperatures reduce oxidation favorability, while increased oxygen partial pressure enhances it. The study reveals that low coverage surfaces favor oxidation, providing practical control strategies through manipulation of temperature and pressure conditions. This computational study provides fundamental insights into corrosion processes in Cr2AlB2 and establishes a framework for understanding oxidation mechanisms in aluminum containing MAB phase materials
A Review Of Remote Sensing-Based Approaches For 3D Building Geometry And Thermal Loss Detection In Urban Energy Efficiency Applications
No full textBuildings contribute significantly to worldwide energy use and greenhouse gas emissions, underscoring the importance of precise evaluation of building geometry and envelope performance for efficient energy management and climate mitigation initiatives. Recent advances in high-resolution satellite remote sensing, uncrewed aircraft systems (UAS), and deep-learning-based data analysis have produced a growing body of scholarly work focused on three-dimensional building extraction and thermal loss detection. However, these methods are often evaluated in isolation or under controlled conditions, limiting their demonstrated applicability to real-world, cold-region urban environments where seasonal variability and heterogeneous building stocks present unique challenges. This dissertation addresses this gap through a structured synthesis and applied evaluation of remote sensing-based approaches for 3D building geometry extraction and thermal performance assessment.
This research develops and applies an integrated remote sensing framework that combines GaoFen-7 (GF-7) high-resolution stereo satellite imagery, UAS-based thermal photogrammetry, and deep-learning-driven multi-feature data fusion methods drawn from the contemporary literature. Rather than proposing a single novel algorithm, the study critically reviews and contextualizes three anchor journal methodologies corresponding to building geometry extraction, multi-feature fusion networks, and thermal photogrammetric analysis. These approaches are systematically applied to residential and commercial buildings at the University of North Dakota, across the City of Grand Forks, and within representative building stocks in the State of North Dakota. The framework integrates spectral, geometric, and thermal information to generate three-dimensional building models and spatially resolved indicators of building-envelope performance suitable for energy analysis and decision support.
The results demonstrate that literature-established remote sensing and deep-learning techniques can be cohesively applied across sensing platforms and spatial scales to produce reliable 3D building representations in cold-region environments. Multi-feature fusion approaches show increased robustness under challenging conditions such as snow cover, low solar angles, and mixed land use, while the integration of UAS thermal photogrammetry with three-dimensional geometry enhances the identification and interpretation of insulation defects, air leakage, and thermal inefficiencies. The findings indicate that an integrated, literature-grounded remote sensing framework can support scalable energy efficiency assessment, retrofit prioritization, and energy management planning for institutional, municipal, and state-level applications. This dissertation contributes to the advancement of applied geospatial energy analysis by bridging established remote sensing research with practical implementation in cold-region built environments
The Role Of Attitude Ambivalence In Extending The Theory Of Planned Behavior: A Mixed-Methods Study Of Factors Predicting Influenza Vaccine Uptake Intentions Among Active-Duty Military Parents For Themselves And Their Children
No full textThis dissertation is a mixed-methods study examining how U.S. military parents form intentions to vaccinate themselves and their children against influenza. The study applies the Theory of Planned Behavior (TPB) and the construct of ambivalence tounderstand how attitudes, social norms, and perceived behavioral control influence vaccination decisions within the unique institutional and cultural environment of military life. Quantitative data were collected from 341 military parents through an online survey distributed across more than 50 military-affiliated Facebook groups. Regression models tested TPB constructs, flu knowledge, and social media use as predictors of vaccination intention and ambivalence. Results showed that flu shot history and social norms predicted ambivalence, while attitudes, norms, perceived behavioral control, and ambivalence significantly influenced vaccination intention. Concurrently, 11 military parents were recruited for individual in-depth interviews to discuss their beliefs, experiences, and decision-making processes regarding flu vaccination. Fifteen themes were identified. Practical and theoretical implications are discussed, emphasizing the need for public health campaigns that address ambivalence and reflect the lived realities of military family life
Genetic Engineering Of Mouse Models To Understand Gene Functions And Disease Mechanisms
No full textMouse models are indispensable for life sciences research in order to understand gene functions and disease mechanisms. Lack of appropriate models has hampered the robust understanding of biological processes, animal physiology, and pathophysiology. For example, most of the existing approaches to mouse models, such as expressing (or knocking out) a gene of interest constitutively and throughout the whole animal body, have limited our ability to deconstruct human disease and behavior, in a tissue- and cell-type specific manner, and one cell type at a time. Fortuitously, biological engineering (bioengineering) approaches could be developed and harnessed to generate robust animal models of disease that allow dissecting the biological mechanisms in a tissue- and cell-type-specific manner, for disease understanding and research. In this dissertation, we developed mouse models expressing genes of interest in a tissue- and cell-type-specific manner, enabling precise determination of how particular cell and tissue types contribute to disease mechanisms, with a specific focus on the brain as the target of interest. First, we investigated a virus vector-based approach for targeted gene delivery to the mouse brain using stereotaxic injections and specialized instruments, in order to obtain brain-wide but cell-type-specific gene expression. We validated this approach using molecular and histological approaches. Second, we employed a cell-type-specific transgenic mouse model. We tested the ability of the transgenic model to recapitulate brain disease symptoms observed in patients and to decipher disease mechanisms, using COVID-19 as a proof-of-concept model, restricting the infection to a particular tissue, therefore enabling determine the role of these tissues in disease pathology. Through this work, we were able to advance the fields of bioengineering and life sciences in general, with new approaches to gene manipulation in vivo and mouse model development. This work has significant and broad impacts on understanding biological and physiological mechanisms and on understanding and treating human disease