13251 research outputs found
Sort by
Translating the Ocean
No full textBy engaging key thematic concerns in African diasporic literature such as identity, cultural negotiation, nostalgia, grief, and spirituality, this thesis—an anthology of poems titled Translating the Ocean—positions itself within a “third space” where home and diaspora interact to reflect the multi-spatiality of diasporic bodies. Alongside a diasporic framework, I incorporate other theories and techniques, including the “Portrait” and “Rest and Silence” modes of universality in lyric poetry, ambiguous loss, creolization, and transliteration, to enable interstitial discourse within the collection. I argue that diasporic writers—such as Chinua Achebe, Chimamanda Adichie, Buchi Emecheta, Romeo Oriogun, and myself—continue to embrace multiple sites of meaning-making that complicate simplistic notions of the African diasporic experiences. By juxtaposing multiple cultural worlds of the homeland and host land, Translating the Ocean interrogates the ocean as a metaphor for transit—as a journey and of time
Characterization of the Overexpression of RECA Homologs RAD51 and DMC1 in Tetrahymena Thermophila
No full textRecA homologs, Dmc1 and Rad51, work to repair DNA double-strand breaks (DSBs) within the cell through the recombination of homologous sections of DNA. Dmc1 works to repair programmed DSBs through meiotic recombination, while Rad51 functions to repair both meiotic and non-meiotic DSBs, the latter repaired through the process of homologous recombination repair (HHR). Chemotherapeutics, exogenous agents, work to form DSBs in cancer cells, attempting to inhibit the cell’s growth. A hyper recombinant phenotype is often seen in cancer cells due to the overexpression of RAD51, leading to drug resistance, the persistence of cancers, and an overall poor patient outcome. In the model organism Tetrahymena thermophila, an amacronuclear phenotype and increase in cell diameter phenotype is observed at elevated growth temperatures when RAD51 is overexpressed. A complication in elongation of the macronucleus occurs, but DNA synthesis is not halted, resulting in a macronucleus containing up to 5 times the normal genetic content. When DMC1 is overexpressed, an amacronuclear phenotype is observed at a reduced level; though, cell diameter does not increase. Further study between the two RecA homologs will help elucidate how RAD51 overexpression leads to genomic instability in the cell
Braiding Culture Through Ceramics & Mixed Media Sculpture
No full textStories are embedded in each strand of Nigerian hair, serving as a bridge across generations and embodying the legacy of cultural expression. Hair-making, a skill passed down through generations in my family, began as a personal practice and has evolved into a powerful means of exploring and showcasing the cultural significance of hair braiding through creative works. The sculptures included in this thesis, Braiding Culture Through Ceramics & Mixed Media Sculpture, feature hair sculpture, afro combs, stools, adire batik made from materials such as clay, wood, glaze and cotton fabric. These materials are carefully selected for their cultural relevance and are used to emphasize and highlight these narratives. By celebrating the stories woven into each strand of Nigerian hair, this work underscores its role in connecting the past with the present, bridging generational divides, and preserving cultural heritage. Through these art forms, the aim is to honor and sustain the personal and collective legacy that continues to bind generations together
Developing Scholarship of Teaching and Learning (SoTL) on a Regional Campus
No full textHow can academic affairs administrators foster robust Scholarship of Teaching and Learning practices on a regional baccalaureate campus? Fulfilling tenure-system faculty requirements for scholarly research at a teaching institution alongside 4-4 teaching loads is a challenge, but supporting faculty in SoTL engagement shows them how it draws on their expertise to yield meaningful benefits for themselves as teacher-scholars and for their students. To lay a solid foundation for a new programmatic emphasis on SoTL, academic leadership at the Crookston campus of the University of Minnesota harnessed system resources available through the Center for Educational Innovation to a supportive cohort model. The 15-participant 2024-25 pilot program drew on incentives for participation, clear deliverables, and campus recognition to generate enthusiasm and reward results
Exploring Student Success at Fresno State – What is working? What did not work? And, What is a Work in Progress?
No full textOver the past decade, Fresno State has engaged in numerous efforts in support of Student Success. Some of these efforts are a result of systemwide initiatives (e.g., Graduation Initiative 2025 and Equity Priorities) while others are developed in response to grants received (e.g., SSEI, PALS, and APLES). We closely examine our extensive data on areas of concern to develop campus pilots with the hope of closing equity gaps and improving student outcomes. This presentation will explore these various efforts and provide an overview of what is working, what did not work, and what remains a work in progress
Towards Automated Sensor Actuator Mapping for Dynamic Smart Spaces
No full textWith the advancements of smart assistants in smart spaces, such as smart homes and smart offices, actuators are controlled by voice commands or preconfigured instructions that respond to sensor events triggered by occupant activities requiring human intervention. Complete automation of these predefined instructions, eliminating manual control, hinges on identifying the dependencies between sensors and actuators. Moreover, as the number of sensors and actuators increases, the volume of their events grows significantly, generating large and diverse time series data that must be analyzed to identify actuator dependencies on sensors. This identification task becomes complicated as user preferences and behaviors change over time, such as morning versus evening routines or seasonal variations. This dynamic nature of preferences necessitates adaptive, context-aware rule-setting for actuator control. Additionally, manually configuring actuation rules becomes increasingly complicated since most end users lack expertise in sensor-actuator operations. Hence, in this thesis, I propose a novel approach called Sensor Actuator Mapping, aimed at determining sensor and actuator dependency. This method analyzes time series data consisting of sensor and actuator events that represent the activities of occupants. The proposed approach has two phases: in the first phase, it identifies contextually related sensors; in the second phase, it extracts features from time series data and applies an unsupervised clustering technique to group related sensors and actuators based on temporal correlations that reflect their interaction patterns. By leveraging unsupervised learning, this approach can operate without labeled data or human intervention, thereby enhancing user convenience by eliminating the need for manual rule changes. Experimental evaluations on real-world multiple dynamic smart spaces deployments demonstrate the efficacy of my proposed approach across various deployments with diverse sensor-actuator configurations, underscoring its adaptability and accuracy in autonomous sensor-to-actuator mapping
Advancing Mental Disorder Detection: Evaluating Transformer and LSTM-Based Models on Social Media
No full textMental disorders such as suicidal ideation, bipolar disorder, and depression are common, and early detection is crucial for effective intervention. Social media has emerged as a bountiful source of mental illness indicators since many individuals express their psychological state over the internet. This thesis explores social media text usage for identifying mental disorders based on advanced Natural Language Processing (NLP) techniques. The scope spans four principal tasks: early prediction of suicidal ideation; bipolar disorder-related language classification; binary classification distinguishing between individuals with mental disorders and healthy controls; and multi-class diagnosis of six individual conditions (Attention-deficit/hyperactivity disorder (ADHD), anxiety, bipolar disorder, depression, Complex Post-Traumatic Stress Disorder (CPTSD), and schizophrenia) versus a control group. The performance of recent Transformer-based models (BERT, RoBERTa, DistilBERT, ALBERT, ELECTRA) and Long Short-Term Memory (LSTM) networks augmented with attentional and contextual embedding is investigated, evaluating their efficacy across these tasks. Experimental results show that Transformer models consistently display excellent classification performance, whereas LSTM-based models offer competitive accuracy with reduced computational expense when integrated with contextual embedding. Above all, light Transformer versions (e.g., DistilBERT) achieve near that of the large models’ accuracy while being significantly more efficient, indicating their feasibility for deployment in real-time or resource-limited settings. Model robustness regarding identifying general mental disorder is further assessed, and it is concluded that top-performing models exhibit robust performance under various data conditions. The main contributions of this work include the construction of annotated social media corpora, an exhaustive benchmark of state-of-the-art NLP models on mental health prediction, and new findings on models’ efficiency-performance-generalizability trade-offs. These findings advance the development of feasible NLP-based early intervention monitoring tools for mental health
Generational Exposures to Cerium Oxide Nanoparticles and Perfluorooctanesulfonic Acid Affect the Performance of Daughter Plants
No full textThe effects of parental stress on the performance of next generation exposed to another contaminant were investigated. Wheat was exposed to cerium oxide nanoparticles (CeO2-NPs) in first and second generations and exposed to perfluorooctanesulfonic acid (PFOS) in the third generation. Phenotypic or metabolic responses were assessed at 21-day (short-term exposure) or 90-day (long-term exposure) exposure periods. Biomass production, chlorophyll content, enzyme activity, and membrane damage were measured at short-term exposure, while elemental and PFOS concentrations, and grain metabolites were analyzed in long-term exposure. Results showed that continued exposures to CeO2-NPs and PFOS enhanced chlorophyll content but reduced concentrations of important macro- and micro-elements in the grains of daughter plants. PFOS was accumulated in the grains of wheat while metabolomic analysis revealed that grain metabolite composition was significantly altered. Continued exposure to CeO2-NPs and PFOS decreased the abundances of most metabolites (22 out of 34). Consistent and repeated previous exposures to CeO2-NPs also had progressively decreased the concentrations of sucrose-6 phosphate, adenine, and other organic acid metabolites. The findings suggest that prior generation’s exposure could still influence succeeding progeny generations via invisible changes in metabolite and elemental compositions of grains
The Influence of Flower-Microbe Interactions on Bumble Bee Behavior and Gut Microbiome Composition
No full textThis thesis examines how microbial interactions in floral nectar and environmental variation influence bumble bee (Bombus spp.) foraging behavior and gut microbiome composition. Pollen germination experiments and behavioral assays in the lab tested whether Metschnikowia reukaufii (yeast) and Acinetobacter nectaris (bacteria) induce pollen germination and bursting in nectar and affect bee behavior subsequently. Both microbes reduced intact pollen, but bees did not alter flower preference. Furthermore, the study also assessed how floral and bee community composition shaped gut microbiome composition across six Missouri prairies. Using amplicon-sequenced fungal ITS and bacterial 16S rRNA genes, I found that fungal microbiome composition varied significantly with floral and bee abundance, becoming more similar across bees when abundance declined-suggesting increased transmission via shared floral use. In contrast, bacterial composition was primarily structured by bee species identity, indicating host filtering or social transmission. By linking microbial dynamics in both floral resources and the bee gut, this work reveals how flower-microbe interactions shape pollinator behavior and microbiome composition, deepening our understanding of microbial roles in pollinator ecology
Sparse Transformer for Anomaly Detection in Mobile Crowdsensing
No full textMobile Crowdsensing (MCS) is a sensing paradigm that leverages mobile devices to conduct a large-scale data collection. However, due to its openness and mobility nature, it is highly vulnerable to security issues such as injection attacks of malicious workers and fake tasks that can severely affect the platform’s normal functioning. To address this problem, the arrival of workers and task submission process is represented as a multivariate time series, and a two-stage framework is proposed. In the first step, we propose a novel transformer-based model, DozerAnomaly, that can efficiently detect anomalies in multivariate time series. We integrated a sparse attention mechanism, Dozer self-attention, with local and seasonal adaptation that captures the locality and seasonality of data patterns. It addresses the quadratic complexity of the standard self-attention mechanism by focusing only on relevant time steps, thereby reducing computational overhead. We employ the association discrepancy between prior and series associations to distinguish normal and abnormal time series patterns. The detection of malicious attackers contributes to improving the resilience and reliability of the sensing platform. In the second step, we conduct experiments by transforming task assignments into a bipartite graph. Experimental results in five real-world datasets show that our model achieves state-of-the-art performance in terms of accuracy and efficiency, with a 42% reduction in floating-point operations (FLOPs) compared to the baseline, significantly improving computational efficiency without compromising accuracy. Additionally, we perform task assignment experiments that show around 36% improvement in work and task assignment accuracy, demonstrating the effectiveness of the proposed framework in the MCS domain