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FACULTY AND STAFF COLLABORATION AT VERY SMALL HIGHER EDUCATION INSTITUTIONS
No full textThesis (Ph.D.)--Michigan State University. Higher, Adult, and Lifelong Education - Doctor of Philosophy, 2025This dissertation utilizes generic qualitative methodology (Kostere & Kostere, 2022) to study faculty and staff collaboration at very small higher education institutions. In the fall of 2020, very small higher education institutions, where there are less than 1000 students, made up 37% of higher education schools in the United States (The Carnegie Classification of Institutions of Higher Education, n.d.). Yet, most research on faculty and staff collaboration is focused on larger institutions. This study looked to answer two research questions on what ways and why faculty and staff collaborate at very small schools. Through data collection and analysis, 10 themes were identified around what ways and why collaboration exists at very small schools: formal structures; informal interactions and special interest projects; pressing campus projects; small campus and close proximity; culture and community; integrated structures; meeting student and external needs; common respect and understanding; knowledge and expertise; and barriers to collaboration. Research findings can help other small institutions assess their own collaborative efforts and potentially provide insight to larger institutions on different approaches to faculty and staff relationships.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Building Trust in and through Big Data Technologies? A Psychometric Exploration of Artificial Intelligence Adoption at a Research University
No full textThesis (Ph.D.)--Michigan State University. Higher, Adult, and Lifelong Education - Doctor of Philosophy, 2025Artificial intelligence technologies are increasingly commonplace. These digital tools are capable of producing prose, computer code, images, or videos that are indistinguishable from the outputs of skilled humans. The immense potential and danger inherent in this technology has fueled a fierce debate and growing distrust. In postsecondary education, discussions have largely revolved around how pedagogy must change in response to AI. Yet, building on decades of reform movements, there is also interest amongst policy actors in using AI to alter how colleges and universities function as organizations. This dissertation utilizes postsecondary policy scholarship and literature on the role of trust in organizations and technology adoption to explore the possibilities and challenges of embedding AI within a university\u2019s student success mission. Specifically, I asked (a) to what extent students, staff, and faculty think AI is trustworthy and (b) where these participants think AI use can be trusted. Through descriptive statistics and structural equation modeling (SEM), I explored the answers to these questions and compared the responses of students and university employees. Overall, I found that personnel were less likely to perceive AI as benevolent (i.e., operating with their interests in mind) and transparent than students. Both students and employees were less likely to trust AI in contexts where students are more vulnerable, such as admissions, financial aid, and course evaluations. Through my SEM analyses, I found that measures of AI trustworthiness were strongly associated with trust in AI uses. Additionally, I found in several AI use cases that the trustworthiness of faculty had a significant relationship to students\u2019 trust in AI while the trustworthiness of senior leaders had a significant relationship to personnel\u2019s trust in AI. These findings have implications for organizational governance practices and future research as postsecondary education wrestles with where AI can and should be ethically used.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
MOLECULAR ANALYSES OF BLUEBERRY ASSOCIATED MICROBIOMES AND THE INFECTION PROCESS OF THE FRUIT ROT PATHOGEN COLLETOTRICHUM FIORINIAE
No full textThesis (Ph.D.)--Michigan State University. Plant Pathology - Doctor of Philosophy, 2025Blueberry anthracnose, caused by Colletotrichum species such as Colletotrichum fioriniae, is a post-harvest fruit rot that can cause substantial yield losses. The pathogen infects blossoms from sporulating bud tissue before entering a quiescent phase, only to show signs of infection when fruit are ripe, at which point numerous secondary infections of ripe fruit can occur. With a lack of any blueberry cultivars demonstrating complete resistance, management is primarily achieved with synthetic fungicides. This research aimed to characterize the ecological and molecular context in which blueberry anthracnose occurs, as well as explore a novel management option. Utilizing amplicon sequencing of the internal transcribed spacer (ITS) region, the fungal microbial communities associated with blueberries were characterized. Fungicide applications were identified to mediate changes in the fungal communities associated with blueberry fruits, with differential effects in the skin or pulp of the fruits. Temporal patterns in diversity of various aerial portions of the blueberry plant were examined, identifying high diversity in the early season associated with bud and flower tissue that decreases when blueberry fruits set, but recovers with ripening. RNA-sequencing was leveraged to investigate molecular events occurring in C. fioriniae over the course of infection, elucidating the arsenal of carbohydrate active enzymes (CAZymes) utilized during the necrotrophic stage of infection, as well as identifying a previously uncharacterized role of certain polyketides late in the infection process of blueberry fruits. Spray-induced gene silencing (SIGS) was evaluated for its efficacy against C. fioriniae to investigate the viability of this biopesticide approach in the species, demonstrating the uptake and subsequent knockdown of gene expression in C. fioriniae when treated with exogenous double-stranded RNA.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Choice and Constraint Behind Bars : A Thematic Analysis of Women\u2019s Experiences in Voluntary and Mandatory Prison Programs
No full textThesis (M.S.)--Michigan State University. Criminal Justice - Master of Science, 2025Most research on prison programming focuses on quantitative outcomes such as recidivism rates or technical violations, often overlooking the subjective experiences of incarcerated women. Few studies center women\u2019s own voices or explore how they define meaningful change during incarceration. This qualitative study addresses that gap by examining how 26 incarcerated women perceive the effectiveness of prison programs, particularly the differences between voluntary and mandatory participation. Through thematic analysis, the findings reveal that women often define effectiveness in terms of personal growth, emotional healing and preparation for reentry rather than institutional metrics of success. Participants emphasized the importance of pro-social communication, skill building, and emotional wellbeing within programming while also identifying barriers such as program duration, superficial content, and lack of relevance post-release. Collectively, these insights challenge traditional measures of success and offer a participant-driven guide to more responsive correctional programming.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
BRAIN ACTIVITY AND CONNECTIVITY ANALYSIS AND EARLY DETECTION OF COGNITIVE DECLINE
No full textThesis (Ph.D.)--Michigan State University. Electrical and Computer Engineering - Doctor of Philosophy, 2025Early detection and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. In literature, it has been shown that the pathology of Alzheimer's disease (AD) is often associated with the weakening or alterations of the connectivity between critical brain regions. Along the line, in this dissertation, we focus on the modeling and analysis of brain activity and connectivity, including both functional and effective connectivity, with applications to the early detection and prediction of cognitive impairment based on neuroimaging techniques. First, we conduct multiscale analysis on dynamic brain functional connectivity. In literature, functional connectivity between two brain regions is often taken as a static parameter and is represented as a constant, such as the Pearson correlation of two time series. More recently, however, it has been observed that in fact, functional connectivity varies significantly with time, and the dynamic variation of functional connectivity may indicate changes in neural activity patterns in cognitive and behavioral aspects. In this research, relying on joint time-frequency-spatial analysis of dynamic brain functional connectivity, we develop a soft discrimination model of normal cognition (NC) and mild cognitive impairment (MCI). In addition to a binary NC or MCI decision, our model also comes with an EEG-based score for each participant. The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.Second, we investigate brain effective connectivity (also known as causality) and aim to find a reliable and easy to implement approach to quantify the directed information transfer in the brain network. We start with convergent cross mapping (CCM), a state-space reconstruction approach which has attracted increased attention due to its capability to detect causality in non-separable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. In this research, we first causalize CCM so that it aligns with the presumption in causality analysis---the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and functional magnetic resonance imaging (fMRI)-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. Our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings.Finally, we evaluate and compare the neural activity of NC and aMCI based on task-fMRI data collected in the object-location associations paradigm. Our analysis shows that NC and MCI exhibit significant differences in brain activity, especially in some regions in the visuospatial network and the executive control network. We further develop a discrimination model for NC and aMCI based on the neural activity patterns of the two groups, and obtain a classification accuracy of 88%. Our results demonstrate that the biomarker generated from the neural activity extracted from the task-fMRI data can serve as a promising tool in the early detection of aMCI.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
COMPARING CAPILLARY HYDROPONIC AND VARIABLE DEPTH ROOTZONES FOR SUSTAINABLE PUTTING GREEN MANAGEMENT
No full textThesis (M.S.)--Michigan State University. Crop and Soil Sciences - Master of Science, 2025Irrigation requirements have become a focal point of golf turf management. The objective of the study was to evaluate two golf course putting green construction methods for irrigation inputs, playability, organic matter content, turfgrass quality, health and growth. The experiment was conducted in the summer 2023 and 2024 at the Michigan State University Hancock Turfgrass Research Center. Six putting greens were constructed and seeded in June 2022 with sand conforming to USGA specifications, with three constructed as Variable Depth Rootzone (VDR)and the other three as Capillary Hydroponic System (CHS), resulting in a randomized complete block design with three replications of each construction type. All greens were managed following typical maintenance practices and irrigation was provided by overhead in VDR and subsurface irrigation by CHS to maintain a volumetric water content of 8%. Volumetric water content was monitored continuously with inground soil moisture sensors and across greens surfaces at 7.62 cm weekly with a handheld moisture meter. Surface firmness and greens speed were measured twice monthly and clipping yield was collected once per month. There was no difference in surface firmness, greens speed, or clippings yield. The CHS required 59% less irrigation water than the VDR for the entirety of the trials.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
How Power Consolidation Harms Women's Rights : The Gender Dynamics of Democratic Backsliding
No full textThesis (Ph.D.)--Michigan State University. Political Science - Doctor of Philosophy, 2025This dissertation examines how left-wing incumbents in Latin America strategically engage with women\u2019s rights as part of broader efforts to consolidate power. Contrary to conventional wisdom that associates the left with progress on gender equality, I argue that left-wing incumbents with power consolidation aspirations often engage in the strategic use of women's rights. While they will support some aspects of women's rights, such as women's descriptive representation, they will nonetheless undermine others, such as women's rights activism. I develop a theoretical framework that links the strategic use of women's rights to both macro level (e.g., institutional and partisan) and micro level (e.g., electoral) benefits. To test this argument, I use a mixed-methods design across three empirical chapters. Chapter 3 compares the presidencies of Luiz In\ue1cio Lula da Silva and Andr\ue9s Manuel L\uf3pez Obrador, showing that only the latter, who had power consolidation aspirations, strategically used women's rights, using novel text analysis techniques to analyze presidential rhetoric. Chapter 4 examines the macro-level implications of this strategy, demonstrating across a small-N sample how incumbents with power consolidation aspirations strategically used women's rights to promote partisan unity, protect their reputations, weaken diagonal accountability, and redirect resources. Chapter 5 turns to the micro level, using an original conjoint experiment in Mexico to show that voters reward women\u2019s descriptive representation, and that they associate attacks on women's rights activism and undemocratic behavior with masculinity.This dissertation contributes to the literature on women's rights and authoritarianism by showing that different aspects of women's rights are more compatible with authoritarian rule than others. Additionally, it illuminates how attacks on women's rights can come from the left. In doing so, it illuminates how, wherever democracy is at risk, women's rights are also more likely to be attacked, regardless of how progressive an incumbent seems.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
AUTONOMOUS VEHICLE BEHAVIOR IN MIXED TRAFFIC
No full textThesis (Ph.D.)--Michigan State University. Civil Engineering - Doctor of Philosophy, 2025ABSTRACTAutonomous Vehicle (AV) technology has been maturing over the last decade through continuous testing on controlled test tracks, public roadways, and simulation environments. Understanding the driving behavior of AVs in mixed-traffic streams by analyzing their interactions with other human-driven vehicles (HDVs) is essential for ensuring AVs\u2019 seamless integration into existing transportation systems. This Ph.D. dissertation utilized publicly available AV driving datasets from Waymo, Lyft, and Argoverse to investigate AVs' car-following (CF), merging, and lateral crossing conflict resolution behaviors in a mixed traffic environment. The specific research objectives are to- (i) investigate CF behavior (e.g., headway, gap time) of AVs; (ii) analyze crossing conflicts at intersections based on driving volatility measures; and (iii) investigate the merging behavior of AVs in mixed traffic environments. AV\u2019s rear-end collision risk for different CF scenarios in mixed traffic (i.e., AV as a follower or leader vehicle) was investigated to establish a relationship between CF conflict indicators and rear-end crash risks. Relative crash risks between different CF pairs, estimated from the EVT parameters, indicate that CF scenarios involving an HDV following an AV had two to three times higher rear-end crash risk than those involving an HDV following another HDV. Empirical analysis of time-to-collision (TTC) and deceleration rate to avoid a collision (DRAC) measure revealed that AVs\u2019 CF behavior was more cautious and maintained higher time gaps and headways than HDVs. When AV was the follower vehicle in a CF pair, the relative crash risk was below one compared to the HDV following HDV scenario, indicating that AVs reduced the rear-end crash risk when following HDVs. Crossing conflict scenarios were categorized into AV-first, AV-second, and non-AV scenarios. AVs' crossing conflict resolution behavior was then categorized into high, medium, and low risk driving categories based on driving volatility measures using a hierarchical clustering algorithm. Approximately 29% of the conflict events in the AV-first scenario (in which HDV was the following vehicle in the conflict region) exhibited a high crossing conflict risk. All AV-second scenario conflict events were classified as low-risk or medium-risk. The conflict model results indicated AVs had safer interactions with other roadway users (i.e., HDVs, pedestrians, and cyclists) while maintaining higher speeds and uniform driving profiles. The interaction of AVs with vulnerable road users (i.e., pedestrians and cyclists) demonstrated a lower crash risk compared to HDVs, indicating AVs\u2019 safer driving behavior. Additionally, AVs exhibited safer conflict resolution behavior in performing unprotected left turns at the intersection than HDVs. AVs and HDVs merging event data showed that, contrary to CF and crossing conflict, the merging behavior of AVs and HDVs was quite similar in a mixed traffic environment, in terms of gap time (GT). Higher GT reduced the following vehicle\u2019s speed variation at the target lane for AV and HDV merging events. A Weibull random parameter hazard-based duration model was developed to examine the effect of different driving volatility and traffic measures (i.e., relative speed, velocity standard deviation, and merging location) on merging GT, representing the aggressiveness of the merging events. Similar to the CF and crossing conflicts, merging crash risk was estimated using the EVT approach. The hazard model and the EVT model crash risk analysis revealed similar merging crash risks irrespective of the presence of AVs in merging events. The driving behavior analysis showed that current AVs need to adopt a more human-like driving style for future large-scale deployment. Standardized vehicle-to-vehicle (V2V) communication can warn conflicting HDVs equipped with low-level autonomous driving systems (ADS) technology and connected vehicle (CV) features about potential safety hazards to avoid unsafe interactions with AVs.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
An Act of Communion : Unearthing Black Girls' Memories to Define Self
No full textThesis (Ph.D.)--Michigan State University. K-12 Educational Administration - Doctor of Philosophy, 2025As Black girls are discovering who they are in a world that violently retaliates against them, recalling, reflecting, and meditations are how Black girls become \u201cexperts of their lives\u201d (Alexander, 2005). Thus, this study examines the stories of Black girls that situate their realities about what being Black and a girl is like in society through the amplification of their memories. The methodological approach is a fusion of Black feminist womanist storytelling, kitchen table talk/sista circle methodology, podcasting as methodology, and arts-based qualitative inquiry to explore Black girls\u2019 memories. An arts-based curriculum and podcasting were used as pedagogical approaches to: (1) build a learning community with Black women, femmes, and girls; (2) provide space for the girls to construct their own narratives; and (3) create a podcast that served as a vessel for the girls\u2019 stories. I employ Black girlhood studies and Black and Hip Hop feminism as theoretical frameworks to guide my study. My research asks: (1) How does being in community with Black women inspire Black girls in telling their truths? and (2) What is amplified when Black girls are able to produce a podcast that unearths memories of their racialized and gendered experiences? The research questions were explored through a thematic analysis of the facilitated sessions, kitchen table talks/sista circles, my audio and video reflections, the research partners\u2019 journal reflections, and transcripts from the podcast episodes. Most importantly, this dissertation unapologetically centers the voices of Black girls to re-imagine the possibilities of equitable educational futures.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Novel Designs and Photoemission Physics to Enhance Brightness of RF Photoinjectors
Full text linkThesis (Ph.D.)--Michigan State University. Electrical and Computer Engineering - Doctor of Philosophy, 2025High-brightness injectors are key to improvements in UED, XFELs, and Laser Compton Back Scattering technologies as they increase their resolution, efficiency, and performance when used. Current advancements in cathode technologies and emittance compensation have provided substantial gains in brightness in recent years but additional approaches will be necessary to continue pushing to higher levels of brightness and resulting light source luminosity.This dissertation discusses novel practical approaches and designs that can be implemented on various accelerators to improve their brightness. Chapter 2 focused on Space charge emittance and RF emittance management exampled using a canonical injector. Chapter 3 discusses implementing cathode retraction for in-situ intrinsic emittance measurement with the goal of decreasing emittance as well as ensuring desired cathode performance. Chapter 4 explores a novel multimode cavity design that focuses on bunch compression to increase the current of the bunch and thus the brightness.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references