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Economic Indicators of the College Station-Bryan MSA, February 2024
No full textEconomicGrowth_Development_TechnicalChangeThe Business-Cycle Index increased from 226 in May 2023 to 228 in June 2023. The local unemployment rate decreased from 3.4% in May 2023 to 3.3% in June 2023. Local nonfarm employment increased by 0.3% from May to June. June���s inflation-adjusted taxable sales were down by 1.2% from May. By using a modified poverty measure that adjusts for college students, the poverty rate in Brazos County drops from 25% to 18%
Labelled Fungicides for Use in Wheat in Texas
Get PDFChart of fuingicides by brand name and active ingredient
Housing Collective Thresholds
No full textArchitecture is about relationships. The inception of this project began by questioning how the current attitude of architectural density and lack of architectural diversity in the East Austin neighborhoods creates a generic neighborhood model. This current approach, among many other issues, eliminates the diversity of people, architecture, biodiversity, activities, and neighborhood relationships. As a way to engage in an open dialogue about the urgent need to imagine new ways of embracing density and architectural diversity with a deep care for the natural environment and existing ecology, how can we learn new ways of living together
Development and Demonstration of Airfoil Performance Enhancement Techniques in a 7-ft by 10-ft Wind Tunnel
No full textThis dissertation presents the evaluation of two airfoil performance enhancement techniques by conducting scientific experiments in a large-scale, production-type wind tunnel. The research bridges the gap between small-scale, exploratory wind tunnel testing, computational fluid dynamics (CFD) simulations, and more expensive testing in larger wind tunnels or in flight experiments.
The first experiment investigates mitigation of roughness-induced transition caused by discrete roughness elements (DREs), such as insect strikes, using two-dimensional shielding strips. The performance of wings designed for laminar flow can be spoiled by premature transition due to roughness accumulation near the leading edge, which can compromise the benefits of laminar flow. This work investigates the shielding performance in the leading edge region of a NACA 63(3)-418 airfoil using infrared thermography to assess the boundary layer state behind multiple DREs, with and without shielding strips, placed upstream and downstream of potential DRE accumulation sites. Optimal shielding configurations and performance improvements of combined strategies are determined.
The second experiment explores lift enhancement through aerodynamic flow control on a two-element NLR7301 airfoil. A microjet injects air normal to the pressure side of the Fowler flap, near its trailing edge along its entire span. Particle Image Velocimetry (PIV) measurements are used to quantify the momentum injection and calculate the relationship between momentum injection and lift enhancement. These findings are compared to results obtained using CFD
Enhancing Cybersecurity with Large Language Models Using Automated Detection and Remediation of Security Vulnerabilities
No full textThe rapid evolution of cyber threats necessitates advanced techniques for the analysis and understanding of malware. Traditional methods, such as static and dynamic analysis, have proven valuable but face inherent limitations. This research investigates the accuracy of Language Models in the context of reverse engineering malware. The primary objective is to assess the extent to which Large Language Models can contribute to the efficacy of malware analysis. Building on a foundation of existing research in malware analysis and the application of Language Models in cybersecurity, this study employs a carefully curated dataset of malware samples. A rigorous experimental setup, utilizing state-of-the-art tools for reverse engineering and Large Language Models, is designed to evaluate the performance of these models. The results reveal insights into the capabilities and limitations of Large Language Models in reverse engineering malware. Comparative analysis with traditional methods highlights the potential strengths and weaknesses of Large Language Models in this domain. Interpretation of the findings, encompassing unexpected outcomes and potential influencing factors, adds depth to the understanding of the research. Despite notable contributions, the study acknowledges its limitations, including dataset constraints and model biases. The research concludes with a discussion of the broader implications of the results, proposing avenues for future research and improvements in Large Language Models for malware analysis. As the cybersecurity landscape continues to evolve, this research contributes to the ongoing discourse on leveraging advanced language models in the fight against malicious software