North Carolina Agricultural and Technical State University
North Carolina Agricultural and Technical State University: NC A&T SU Bluford Library's Aggie Digital Collections and ScholarshipNot a member yet
9765 research outputs found
Sort by
Exploring the Role of Pleiotropy in the Adaptive Mechanisms of Silver-Resistant E. Coli
No full textTwo-component response systems (TCRS) are key mechanisms by which prokaryotes acclimate to changing environments. While their role in acclimation is well-documented, their contribution to long-term environmental adaptation remains poorly understood. Understanding how Gene x Environment (GxE) interactions, epistasis, and pleiotropy drive these processes is crucial and has implications for clinical treatment strategies. This study investigated the role of pleiotropy in adaptive mechanisms of silver resistant E. coli strains. Six single cusS mutant strains, identified through experimental evolution and resequencing, and six silver-adapted populations were tested in the presence and absence of silver nitrate. Biolog Gen III 96-well phenotypic microarray plates were used to evaluate phenotypes under silver-selective and non-selective conditions. cusS mutations influence multiple traits, including pH tolerance, salt tolerance, antibiotic resistance, and carbon source utilization. All mutants exhibited reduced tolerance to extreme pH and saline conditions, suggesting shared fitness costs. Additionally, mutations conferred resistance to specific antibiotics and altered carbon metabolism, with enhanced growth on certain substrates but reduced versatility overall. This study highlights how adaptive cusS mutations reshape fitness through GxE interactions, epistasis, and pleiotropy, emphasizing the interconnected roles of genetic mutations, environmental pressures, and their impacts.https://digital.library.ncat.edu/gradresearchsymposium25/1163/thumbnail.jp
Reliability of Hardware/ Edge ML Accelerators.
No full textThis study evaluates the reliability of hardware accelerators for edge machine learning applications by examining how computing architectures and model optimizations affect system robustness under fault conditions. It begins with a detailed review of the literature surrounding the vulnerability of current acccelerator architectures namely; GPU, DSP, NPU. It then investigates whether platform-specific vulnerabilities lead to differences in user- visible errors during fault injection experiments by focusing on two distinct platforms — a Raspberry Pi 4 augmented with a Coral Edge TPU and a Jetson Xavier utilizing GPU acceleration. Additionally, the study compares the impact of optimization frameworks (TensorFlow Lite versus TensorRT) and assesses the relative resilience of specialized computational units (GPU, DSP, and NPU) in common ML operations such as matrix multiplication and convolution. Using controlled fault injection techniques with tools like Tensor-FI and BFA, ML applications are deployed on both platforms to analyze error propagation and performance degradation. The resulting comparative analysis aims to identify the most reliable hardware accelerator and computing model configuration for edge ML deployments, providing valuable insights for designing robust and resilient edge computing systems.https://digital.library.ncat.edu/gradresearchsymposium25/1167/thumbnail.jp
TinyML and Reliability: Does Quantization affect the Reliability of Machine Learning Models?
No full textMachine Learning (ML) ability to handle and automate complicated tasks allows for its wide application (facial recognition, predictive text, ChatGPT, etc.). TinyML is a field that aids in ML’s shortcomings (ML is complex and memory expensive) finding ways to compress these large models, which widens the scope of application that ML can aid. One such scope involves mission-critical tasks (self-navigation, healthcare, manufacturing, etc.) which have catastrophic consequences for failure in these tasks. Ensuring we can safely implement these machine learning applications such that they run consistently is important.https://digital.library.ncat.edu/gradresearchsymposium25/1171/thumbnail.jp
A Tool for Reverse Analysis and Classification of Executables (T.R.A.C.E) Family of Algorithms
No full textNational security faces increasing risks due to the evolving capabilities of enemy drones. In reverse engineering, analyzing executable files remains a challenge, as minor code modifications can bypass traditional signature based detection methods. Dynamic analysis, while effective, is often time consuming. Many static analysis approaches require expert interpretation, limiting their accessibility. Existing static methods, such as variable name prediction and sequence-based techniques, suffer from low accuracy due to compiler variations. Reverse engineering tools also fail to provide meaningful variable names for analysis, further complicating the process. Graph Neural Networks (GNNs) offer a promising solution for executable code analysis without requiring extensive expertise or external domain knowledge. While GNNs have shown success in static analysis using control flow graphs (CFGs) and function call graphs, limited research has explored their application in data flow graphs (DFGs) for algorithm identification. Current research primarily focuses on detecting malicious behavior, but there is a gap in classifying executable files based on their algorithmic families. This study aims to develop a system capable of analyzing Windows executable files and predicting their algorithm classification with confidence levels ranging from 0% to 100%. By leveraging data flow graphs and GNNs, this approach seeks to enhance executable file analysis, improving accuracy and efficiency.https://digital.library.ncat.edu/gradresearchsymposium25/1191/thumbnail.jp
SUAB Tiny Desk Concert Performance: Jasmine Clowney (2025)
No full textThis performance by Kai Johnson was recorded live at the 2025 SUAB Tiny Desk Concert, held at F.D. Bluford Library on April 15, 2025. Sponsored by the Student University Activities Board (SUAB), the concert featured student performers sharing original music and creative works in a cozy library setting
New Farmers Of America Association
No full textCurtis Mitchell presents honorary superior farmer degrees.https://digital.library.ncat.edu/photos/2105/thumbnail.jp
New Farmers of America Association
No full textTwo young men standing with a bull.https://digital.library.ncat.edu/photos/2097/thumbnail.jp
New Farmers of America Association
No full textYoung men cleaning fishhttps://digital.library.ncat.edu/photos/2096/thumbnail.jp
New Farmers of America Association
No full textGroup of men observing hogs.https://digital.library.ncat.edu/photos/2095/thumbnail.jp
New Farmers of America Association
No full textChild feeding chickens. Labeled \u27C. J. Belhaven.\u27https://digital.library.ncat.edu/photos/2145/thumbnail.jp