University of Tennessee Institute of Agriculture
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“NO LONGER AT EASE”: EXPLORING SUB-SAHARAN AFRICAN INTERNATIONAL STUDENTS’ SENSE OF BELONGING IN A PREDOMINANTLY WHITE INSTITUTION.
Get PDFSub-Saharan African international students in U.S. predominantly White institutions (PWIs) face unique disadvantages from intersecting racial, cultural, and linguistic identities (Mandishona 2018; Wafula 2023). This research examines (West) African international graduate students’ experience. Utilizing raciolinguistics reveals how exclusionary practices reinforce linguistic racism and impact mental health, academic performance, and belonging. Through interviews with ten West African graduate students, the research explores critical questions: (1) What factors affect the integration of Black African students in a PWI in southern America? (2) How does sense of belonging affect the mental health of Black African international students?
Utilizing a cultural rhetorics methodology, this research aims to enrich the academic discourse within the field of Rhetoric and Writing studies, which has increasingly called for more inclusive and equitable approaches for diverse and international students. Emphasizing anti-racist pedagogy, curriculum design, and institutional support, the findings provide valuable insights for administrative personnel, professors, and other professionals who support African and other minoritized students. It adds to the discourse on raciolinguistics and the politics of belonging in higher education, advocating for systemic support to advance equity.
Keywords: Raciolinguistics, Sense of Belonging, Anti-Racist Pedagogy, West African International Students, U.S. Higher Education, Linguistic Racism, Cultural rhetoric
INCREASING COGNITIVE TASK COMPLEXITY IN A RUN TO CUT MANEUVER
Get PDFAnterior cruciate ligament (ACL) injuries are prevalent in sports, particularly among female athletes, and are often associated with non-contact mechanisms such as rapid directional changes and poor neuromuscular control. Traditional injury prevention and rehabilitation programs focus primarily on neuromuscular training; however, emerging evidence suggests that neurocognitive challenges play a critical role in injury risk and recovery. This study examines the impact of increasing neurocognitive task complexity on knee biomechanics during a run-to-cut maneuver, with specific attention to lower limb kinetics and kinematics. Using the Flanker Task to introduce varying cognitive loads, this research evaluates changes in knee abduction angles, knee abduction moments, and center of mass displacement in response to visual stimuli. The study aims to determine whether increased cognitive demands exacerbate biomechanical risk factors for ACL injury and to assess differences in movement patterns between dominant and non-dominant limbs. Findings from this research will contribute to the development of comprehensive injury prevention and rehabilitation programs that integrate neurocognitive training with traditional neuromuscular control exercises, ultimately enhancing athlete safety and performance
THE USE OF ULTRASONICS FOR THE DETECTION OF SOLID HOLDUP IN GASEOUS PIPE SYSTEMS
Get PDFUltrasonic detection methods are often used for non-destructive monitoring and visualization. These applications range from weld inspection to thickness measuring to structural monitoring. One of these applications is the monitoring of multi-phase pipe systems, such as a liquid with solid particulates or gaseous voids, the purpose of which is to monitor any holdup or blockages that may occur within the pipe system. The purpose of this research is to explore the usage of ultrasonic detection methods for monitoring solid holdup generation within a gaseous pipe system. Gaseous systems differ from the more common liquidus and multi-phase systems in that due to the low density of gaseous systems, the travelling gases cannot be characterized with standard ultrasonic monitoring techniques. Gaseous systems also deposit holdup along the walls of its pipe system, whereas liquidus and multi-phase systems are more likely to carry their holdup along through the pipe. This research found that when surrogate holdup with a volume of 0.2 cm3 and a height of 0.1 cm is present in a gaseous system, it can cause ultrasonic waves traveling through the pipe to drop in amplitude by as much as 21%. This is a large enough change in amplitude to be detected by automated computer systems. This work lays the foundation for more specific use cases for non-destructive assay of gaseous systems, such as uranium hexafluoride (UF6) systems
Embedding-Enhanced Probabilistic Modeling of Ferroelectric Field Effect Transistors (FeFETs)
No full textFerroelectric Field-Effect Transistors (FeFETs) are promising candidates for next-generation non-volatile memory and in-memory computing systems due to their fast switching, low power consumption, and CMOS compatibility. However, their practical deployment is challenged by significant stochastic behavior arising from both cycle-to-cycle fluctuations and device-to-device process variations. To explore and better understand this variability, we began by applying a simple machine learning model. While this deterministic approach captured general trends, it lacked the ability to represent the full spread of observed I–V characteristics and thus fell short in reflecting the true stochastic behavior of real devices. We then adopted a probabilistic modeling framework using Mixture Density Networks (MDNs), which improved variability capture by learning to predict distributions rather than point estimates. In contrast to prior work, our approach uniquely integrates C∞ continuous activation functions for smooth, stable learning and device-specific embedding layers to capture intrinsic variability. This allows accurate prediction of both median and distributional behavior, enabling simulation of nominal and worst-case scenarios. Furthermore, by sampling the learned embedding space, the framework can generate synthetic yet realistic device behaviors. Our embedding enhanced probablistic model has an R² value of 0.92, demonstrating its ability to represents a meaningful extent of the variability. Altogether, our approach moves beyond traditional modeling and offers a flexible, data-driven way to understand and simulate the natural randomness found in FeFETs