Characterization of the Molecular Mechanisms of Noise Stress on Developmental Plasticity in Gryllus bimaculatus & Co-teaching and Peer-Led Team Learning in Undergraduate Introductory Biology Courses Toward Expanding Participation in STEM for All

As part of my training as a Discipline Based Education Researcher (DBER), I gained human subjects educational research experience as well as traditional biology research experience in the laboratory setting. This dissertation explores both my biology education research and traditional developmental biological research. The first and second projects focus on biology education research, examining how Peer-Led Team Learning (PLTL) and modifying introductory biology course instructional design can influence student outcomes in undergraduate Biology education. The third project examines how anthropogenic noise affects the developmental biology of the two-spotted field cricket Gryllus bimaculatus using a genetic approach. Peer-Led Team Learning (PLTL) is an active learning model that has been shown to be beneficial for student learning. Using a longitudinal approach, this study focuses on understanding the trajectory of students over the course of five years to examine the potential long-term effects of PLTL. We assessed the relationship between PLTL participation and student outcomes like earning a C+ or better in introductory biology and graduation rates in any major and STEM specific majors. PLTL participation was associated with a higher likelihood of passing introductory biology with a grade of C+ or better with URM and first-gen status being associated with lower odds. When considering the percentage of students who achieved a C+ or better, both URM and non-URM students who participated in PLTL had significantly higher rates of earning a C+ or better. A performance gap was observed between URM and non-URM students in the non-PLTL groups. A similar relationship was observed in first-generation students, with students who participated in PLTL outperforming their non-PLTL peers with a similar gap in the non-PLTL group. Analysis disaggregated by sex showed that PLTL benefitted both male and female students in a similar manner. Students who completed a STEM degree showed similar trends with performance gains across URM status, sex, and first-generation status. These results suggest that PLTL participation continues to promote positive student outcomes and mitigate structural barriers for success in STEM. We also examined how instructional strategies may influence student learning trajectories. Introductory biology is often seen as a gateway course in which students tend to compartmentalize information for performing well on exams without considering the connection between foundational content and the content of more advanced courses. There also exists a discrepancy between what advanced course instructors expect foundational courses to cover, and student retention of that knowledge. We investigated whether a co-teaching model in the introductory biology course can bridge the divide between these contexts. We involved the instructor for the genetics course in the instruction of genetics content in the introductory classroom, and measured student performance using a course assessment by implementing 20 items that typically appear on exams in the genetics course into the introductory course’s exam. The implemented items directly aligned with the exam objectives of the introductory course. Following a nonequivalent control-group design, one year of introductory biology served as our control group which had the typical instruction with one instructor, with the genetics items implemented. Our treatment group was a population of students in the following year with the genetics instructor teaching the introductory content and genetics items implemented. We used an open-ended survey to measure students’ impression of the experience, opinions on diversity of instructors, and whether they thought including upper division course instructors in introductory biology instruction was a good idea. The results indicated that students who were taught by the genetics instructor improved at a greater rate than their peers in the control group. Students reported positive impressions of the experience, and several mentioned a need for increased diversity in instructor identities. Anthropogenic noise levels are increasing as humans continue to expand their usage of land and natural resources. Noise pollution has been recognized as a source of environmental stress with associations with detrimental health impacts on humans and other organisms. Invertebrates in particular use sound for many biologically relevant processes like mate selection, therefore, increased noise levels may work to impede those processes. We investigated the developmental and genetic impact that roadside noise exposure has on the cricket species Gryllus bimaculatus. Crickets were exposed to 24-hour playbacks of roadside noise or silence in our trials. We compared the developmental timings, body size, and expression profiles of adult crickets from the two experimental treatments. There was a significant decrease in the time to adulthood of crickets exposed to chronic noise stress. We hypothesized that the adult physiological differences were due to differential gene expression patterns as a result of the environmental stimulus of chronic noise. Male and female cricket brains and gonads were dissected and examined to identify any significant differences in gene expression under the experimental conditions. Female brains and male gonads were found to have the highest numbers of differentially expressed genes that included genes like heat shock factor, ryanodine receptors, among other genes involved in cell signaling pathways. GO term enrichment analysis revealed significant enrichment of pathways linked to stress response, energy metabolism, and protein interactions suggesting that noise stress may alter both cellular stress responses but also metabolic processes in a tissue and sex specific manner. Though this dissertation describes three distinct projects, they work together to contribute to our understanding of both biological systems and the systems through which biology is taught and learned by undergraduates

An Introduction to Ecofeminist Thought for International Relations Students

This article introduces students of International Relations (IR) to ecofeminism, its diverse approaches, and critiques. Ecofeminism emerged in the 1970’s following the history of the intersection between the transnational women’s movement and the global environmental movement (Kelleher 2019)

Expectancies for Alcohol Analgesia and Alcohol Use among Veterans with Chronic Pain: The Moderating role of Discrimination in Medical Settings

Introduction. Chronic pain and alcohol use are highly prevalent and frequently co-occur among U.S. military veterans. Expectancies for alcohol analgesia (i.e., degree to which an individual believes that consuming alcohol can help reduce or manage their pain) may contribute to alcohol consumption, dependence, and related harms. Discrimination in medical settings (i.e., degree to which one experiences inequitable treatment while receiving healthcare due to a stigmatized identity) has been linked to a variety of deleterious health outcomes and may amplify associations between expectancies for alcohol analgesia and drinking behavior. Methods. Participants included 430 U.S. military veterans with chronic pain who endorsed past month alcohol consumption (24% female; 73% White; Mage = 57) and completed an online survey via Qualtrics Panels. Results. Hierarchical linear regression analyses revealed that expectancies for alcohol analgesia were positively associated with indices of alcohol consumption, dependence, and related harms. Experiences of discrimination in medical settings were found to moderate associations between expectancies for alcohol analgesia and indices of alcohol consumption and dependence (p’s \u3c .05). Discussion. Expectancies for alcohol analgesia were associated with greater alcohol consumption, dependence, and alcohol-related harms. Discrimination in medical settings moderated two of these associations, suggesting that healthcare-related stigma may amplify associations between expectations for alcohol analgesia and drinking trajectories. Future research is needed to establish temporal precedence and to examine the utility of alcohol interventions that address analgesia expectancies and medical discrimination among veterans with chronic pain

Dynamic Resource Allocation for Wireless Networks and Radar Systems via Deep Reinforcement Learning

The rapid advancement of wireless communication technologies and the proliferation of smart devices have led to increasingly complex and dynamic network environments. These developments have posed significant challenges to traditional radio resource management (RRM) techniques, which often struggle with scalability, adaptability, and real-time decision-making. In response to these limitations, this dissertation explores the application of advanced machine learning, particularly deep reinforcement learning (DRL), to develop intelligent, adaptive, and data-efficient solutions for resource management in wireless networks and radar systems. We begin by addressing joint channel access and power control in wireless interference networks using centralized, distributed, and federated multi-agent DRL frameworks. These methods demonstrate strong adaptability and performance with limited information exchange. To tackle the data inefficiency and generalization limitations of conventional DRL, we introduce meta-reinforcement learning strategies using Model-Agnostic Meta-Learning (MAML) for dynamic channel access, power control, and UAV trajectory design. These frameworks enable rapid adaptation to new environments with minimal retraining, making them well-suited for real-world deployments. Expanding into radar systems, we investigate adaptive radar resource management for multi-target tracking. We develop a constrained deep reinforcement learning (CDRL) approach that optimally allocates time under budget constraints. To address data scarcity in radar environments, we propose hybrid learning frameworks combining offline and online CDRL. This work is extended to track-while-scan radar systems and integrated sensing and communication (ISAC) platforms, highlighting the trade-offs between sensing and communication tasks. Multi-objective reinforcement learning techniques, implemented with soft actor-critic (SAC), are also employed to find Pareto-optimal policies in radar scanning and tracking tasks. Finally, recognizing the importance of interpretability in AI systems, we introduce a novel explainable artificial intelligence (XAI) framework—Deep learning assisted local interpretable modal-agnostic explanations (DL-LIME)—which enhances the traditional LIME method by incorporating deep learning into the sampling process. This approach improves both fidelity and task performance, providing greater transparency in neural network decision-making. Overall, this dissertation contributes a suite of innovative learning-based frameworks for efficient, robust, and interpretable resource management across wireless communication and radar systems. The proposed methodologies pave the way for the development of next-generation intelligent networks, capable of operating effectively in highly dynamic and data-constrained environments

Lattice and Continuum Anomalies : Bridging the Gap

In this thesis, we explore anomalies both in the continuum and on the lattice, and attempt to establish a connection between the two frameworks. In the continuum limit, we work with Kähler-Dirac fields, while in the discrete setting, we focus on staggered fermions. To facilitate insights relevant to the growing field of quantum computing, we consider a Hamiltonian formalism in addition to the path integral. Anomalies offer a concrete bridge between ultraviolet (UV) and infrared (IR) physics, understanding how anomalies manifest on the lattice versus in the continuum provides a powerful predictive framework. By numerically studying lattice gauge theories, we can impose non-perturbative constraints on the continuum theory. We show that massless Kähler-Dirac fermions exhibit a mixed gravitational anomaly involving a U (1) symmetry which is unique to Kähler-Dirac fields. Under this U (1) symmetry, the partition function transforms by a phase depending only on the Euler character of the background space. Compactifying flat space to a sphere, we find that the anomaly vanishes in odd dimensions but breaks the symmetry to Z4 in even dimensions. This Z4 prohibits bilinear terms in the fermionic effective action. Four-fermion terms are allowed but require multiples of two flavors of Kähler-Dirac fields. In four-dimensional flat space, each Kähler- Dirac field can be decomposed into four Dirac spinors, and hence these anomaly constraints ensure that eight Dirac fermions—or, for real representations, six- teen Majorana fermions—are needed for a consistent interacting theory. These constraints on fermion number agree with results for topological insulators and discrete anomalies rooted in the Dai-Freed theorem. Our work suggests that Kähler-Dirac fermions may offer an independent path to understanding these constraints. We point out that this anomaly survives intact under discretization and is therefore relevant to recent numerical results on lattice models possessing massive symmetric phases. We further show that the effective action resulting from integrating out mas- sive Kähler-Dirac fermions propagating on a curved three-dimensional space is a topological gravity theory of Chern-Simons type. In the presence of a domain wall, massless, two-dimensional Kähler-Dirac fermions appear, localized to the wall. Potential gravitational anomalies arising for these domain wall fermions are canceled via anomaly inflow from the bulk gravitational theory. We also study the invariance of the theory under large gauge transformations. The analysis and conclusions generalize straightforwardly to higher dimensions. Staggered fermions can be thought of as discretized Kähler-Dirac fermions, their shift symmetries satisfy an algebra and, in four Euclidean dimensions, can be re- lated to a discrete subgroup of an SU(4) flavor symmetry. This connection plays a crucial role in showing that staggered fermions lead to a theory of four degener- ate Dirac fermions in the continuum limit. These symmetries are associated with the appearance of certain Z2-valued global parameters. Lattice anomalies can be thought of as obstructions to gauging on the lattice. We propose a strategy to partially gauge these translation symmetries by allowing these parameters to vary locally on the lattice. To maintain invariance of the action under such local variations requires the introduction of Z2-valued higher-form lattice gauge fields. An analogous procedure can also be carried out for reduced staggered fermions, where the shifts correspond to a discrete subgroup of an SO(4) flavor symmetry. We then review the shift and time reversal symmetries of Hamiltonian staggered fermions and their connection to continuum symmetries, concentrating in par- ticular on the case of massless fermions in (3 + 1) dimensions. We construct operators using the staggered fields that implement these symmetries on the lat- tice. We show that the elementary shift symmetry of a single staggered field is tied to a Z4 subgroup of an additional U(1) phase symmetry and anti-commutes with time reversal. This latter property implies that time reversal symmetry will be broken if this phase symmetry is gauged—a mixed ’t Hooft anomaly. How- ever, this anomaly can be canceled for multiples of four staggered fields. Finally, we observe that the naive continuum limit of the minimal anomaly-free lattice model possesses the symmetries and matter representations characteristic of the Pati–Salam Grand Unified Theory (GUT)

Nanoscale Mapping of Charge Carrier Dynamics in Photovoltaic or Optoelectronic Devices

In recent years, solution-processable photovoltaic (PV) technologies such as organic solar cells (OSCs), perovskite solar cells (PSCs), and perovskite tandem cells have rapidly advanced, achieving certified power conversion efficiencies (PCEs) of approximately 20%, 27%, and 30.1%, respectively. Despite this progress, challenges remain in fully understanding and controlling performance-limiting factors in the photoactive layers of OSCs and PSCs, including nanoscale surface conductivity, topography, and morphological distortions. This work provides an in-depth exploration of nanoscale imaging and mapping techniques for OSCs and PSCs, highlighting how addressing nanoscale defects can enhance PV device performance. We examine the similarities and differences between these two technologies in key processes such as charge generation, separation, transport, collection, and recombination. Furthermore, we connect these mechanisms to the intrinsic material properties of organic and perovskite semiconductors, elucidating their impact on overall photovoltaic performance. Furthermore, some optoelectronic samples like perovskite nanocrystals (NCs), perovskite-based Light-emitting diodes (LEDs), and some other nanostructure materials have been successfully tested by this technique. To resolve the standard degrading issue in the semiconductor field, the experimental setup has been upgraded with a PID thermal management system and an adjustable pressure gas delivery system to minimize sample degradation caused by moisture and oxygen exposure. An improved fitting algorithm has been applied to measure special samples with short TPV and TPC