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Human Capital, Immigration, and Growth: A State-Level Dynamic Panel Study
No full textThis study examines whether who immigrates, rather than how many, matters for state economic growth in the United States. It integrates a policy-relevant proxy for skill (H-1B approvals) into an augmented Solow framework that separates immigration\u27s quantity channel from its human capital channel and estimates dynamic effects in a balanced quarterly panel of 50 states (2010 to 2023 ). The empirical strategy estimates a two-step difference GMM Arellano-Bond model that reinforces identification using a double/debiased machine learning (DML) variant that orthogonalizes high-dimensional nuisance components via cross-fitting. This design targets the distinct roles of immigrant headcount versus skill in per capita income dynamics. Three findings emerge from this analysis. First, skill composition matters. Increases in the skilled share, proxied by H-1B approvals, are positively associated with subsequent growth in real GDP per capita. Quarterly effects are modest but accumulate to economically meaningful gains over multi-year horizons. Second, headcount alone is not sufficient. Immigration measured as labor force quantity alone is, on average, neutral to slightly negative in per capita terms after accounting for dynamics and common shocks; consistent with capital dilution in a Solow framework. Third, heterogeneity across states is economically meaningful. States with deeper innovation ecosystems translate skilled inflows into productivity growth more readily. Policy implications follow from these findings. At the federal level, expanding and smoothing high-skill pathways can raise aggregate productivity and diffuse gains across regions. At the state level, policies that attract, retain, and integrate skilled immigrants enhance the payoff to talent inflows, particularly outside traditional tech hubs. For example, states that invest in domestic human capital may realize larger gains from skilled inflows. Policies that emphasize openness to skilled immigration and robust education or training systems may complement one another. Limitations in the study point to avenues for future work. For instance, H-1B approvals are not a perfect proxy for skill and location. Here, state assignments can involve minor measurement error since workers may change location after their initial application is approved, and visa flows may respond to economic conditions despite the panel design. State-level analysis also aggregates meaningful variation that occurs at a more local level. Future studies could strengthen causal identification using shocks, such as visa lotteries or cap changes, leverage worker data, such as H-1B salaries, to trace channels from talent inflows to productivity, and consider distributional outcomes. Because the data end in 2023, the analysis largely predates the diffusion of generative AI. Future work could also test whether AI adoption amplifies the productivity return to skilled immigration
Third-Party Equipment Repair at a Local Container Terminal: Economic, Operational, and Community Impacts
No full textThis thesis investigates the economic, operational, and community impacts of third-party equipment repair at a local container terminal in Mobile, Alabama. The terminal handles containers, chassis, reefers, and gensets, and it has implemented a 15% markup policy on modernized parts to replace outdated components. The research explores how third-party repair functions as a value-added service, supporting operational reliability, enhancing local employment opportunities, and sustaining supply chain continuity.
Data were collected through port performance metrics, repair logs, semistructured interviews with terminal staff, local business owners, and consumers, and public economic indicators. Findings indicate that modernization and repair initiatives improve equipment uptime by approximately 12% and reduce emergency repairs by 20%, while marginally affecting downstream consumer prices by an average of 0.3%. The study highlights both the benefits and challenges of third-party repair. It provides evidence-based recommendations for policy and operational strategies that optimize terminal efficiency and minimize negative impacts on local consumers
Designing an Unconscious Bias Triggering System by Virtual Patients
No full textA growing number of patients in the US come from diverse backgrounds, but healthcare professionals may not always reflect this diversity. Because of this, healthcare professionals may develop unintentional cognitive biases resulting from cultural stereotypes. This can perpetuate health inequities, affecting interactions between patients and clinicians, hiring, and promotion. Unconscious bias training can help healthcare professionals mitigate this issue by enabling them to identify, acknowledge, and minimize their biases. Ultimately, this can create a more respectful, safe, and diverse workplace. Simulation-based training using virtual patients has become increasingly popular among medical professionals. This type of training allows students to practice scenarios, make mistakes, reflect, receive feedback, and develop clinical skills without compromising patient safety. In addition to promoting ethical decision-making, virtual patients can increase learners\u27 enthusiasm and engagement in their educational pursuits. Using virtual patients, this study aims to identify or trigger unconscious biases among final-year medical students based on race, age, biological sex, socioeconomic status, and weight, utilizing a system framework. A unique feature of this study is the use of virtual patients to trigger unconscious biases
The Search for Slow Particles and Magnetic Monopoles with NOvA
Full text linkSingular magnetic poles, north or south, have been theorized to exist for hundreds of years. In the modern day, the elusive singular magnetic pole still remains undiscovered. The appearance of this particle would help confirm many Grand Unified Theories, GUTs, and revolutionize our understanding of some of the fundamental forces of the universe. Fermilab\u27s NOvA collaboration is working on ways to screen and detect magnetic monopoles and other slow-moving particles coming from outer space alongside their main mission to study neutrinos. The aim of this work is to determine and improve the Far Detector’s efficiency at identifying slow moving particles. It was determined that using a slicer time window of 25 μs and an energy cut of 100 ADC units is optimal for extending the detectable range of slow-moving Monte Carlo simulated magnetic monopoles from a lower end of β = 3 × 10−4 to β = 2 × 10−4 with an improvement or no sacrifice to efficiency. With some sacrifice and change to these settings, simulated particles as slow as β = 5 × 10−5 can be detected
Comparison and Evaluation of Overlay and Leveling Binder in Traffic Circle Construction
Full text linkTraffic circles play a vital role in managing traffic flow and ensuring safety, yet their unique traffic conditions make them prone to early cracking and deformation. This study evaluated the performance of overlay and leveling binder asphalt mixtures for a reconstructed traffic circle at the University of South Alabama, using plant-mixed laboratory-compacted (PMLC) specimens. Volumetric analyses were performed to determine maximum specific gravity (Gmm), bulk specific gravity (Gmb), and air voids content. Laboratory tests were conducted on both the overlay and leveling binder layers using indirect tensile cracking test (IDEAL-CT) and indirect tensile rutting test (IDEAL-RT) to assess cracking tolerance and rutting resistance, along with cantabro mass loss and moisture susceptibility tests to evaluate durability. Mechanistic-empirical pavement design was executed using PerRoad 4.4 simulation software to predict fatigue and rutting life. The results indicated that the overlay exhibited superior cracking resistance and moisture susceptibility, along with a longer predicted fatigue life compared to the leveling binder. Field monitoring conducted over eight months revealed good ride quality with no vibrations, and the pavement condition index (PCI) showed that the road remained in good to satisfactory condition
Detecting Sensor Data Manipulation
Full text linkThe integration of Information Technology (IT) and Operational Technology (OT) have made OT devices vulnerable to threats that have been successfully exploited with devastating results. Many modern techniques for hardening and securing enterprise IT systems are either incompatible with OT components in an Industrial Control System (ICS), reduce the efficiency of processes, or are prohibitively expensive to implement. Research in the area of ICS security focuses on a top-down approach, such as intrusion prevention by securing the perimeter of the network and hardening computer systems. This approach is useful in business IT systems, but full compatibility with OT components in an ICS or the processes that have been configured on an existing ICS remains a problem. There is, therefore, a need to evaluate methods for reducing vulnerabilities that could affect ICS components. This research will present a bottom-up approach to detecting data manipulation that focuses on OT equipment in an ICS. Evaluating real-time data from multiple sensors in a related process has the potential to detect data manipulation, but a threat actor can manipulate the devices that collect the data for all of the sensors if they are all on the same network. Adding additional sensors to a separate network could make manipulation of sensor data detectable by providing multiple streams of sensor data coming from the same device. Vibration sensors are used on electric motors to analyze vibration data to detect physical problems such as wear on shaft bearings. Experiments will be designed to identify a correlation between a motor’s rotational speed sensor and the frequencies from a separate vibration sensor on the same motor, then use the data from both sensors to detect anomalies in rotational speed in real time. Each sensor will be connected to separate networks to prevent undetectable manipulation of both sensors at the same time.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1027/thumbnail.jp
Using Image-Based Representation for Network Intrusion Detection
Full text linkThe primary focus of our research is to evaluate the effectiveness of converting network traffic data, PCAPs, into image-based representations for anomaly-based network intrusion detection. We aim to analyze PCAPs to detect malware, or malicious software in hopes of creating a useful approach for anomaly detection against cyber threats including Advanced Persistent Threats (APTs). With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology, by protecting and defending against threat agents. Our research will apply novel machine learning (ML) techniques to detect potential malware transmitted over a network effectively. The overall approach involves evaluating conversion of packet-based data into image form and deriving features that can be used to train traditional classifiers such as Random Forest, Decision Trees, and others. We are interested in whether these methods are as effective as other methods such as Deep Learning models and Convolutional Neural Networks (CNN). Our methodology will involve selection of appropriate datasets in PCAP format, derivation of packet-based information, transformation of packets into RGB images, and then application of machine learning techniques. Our research questions are aimed to identify the best image mapping schemes, the most effective classifiers, and identification of best image features.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1005/thumbnail.jp
Preserving Privacy in Senior Care at Home Monitoring Systems
Full text linkMany seniors prefer to live at home which necessitates research into the application of technologies to provide a safer environment with less caregiver resources. However, the application of home health care (HHC) monitoring for seniors is still in an evolutionary stage. Present HHC systems are produced by private companies with general regulatory guidelines lacking specific care of the elderly. As such, each company that produces such a system claims to have better safety, privacy, and security that their competitors. A pressing issues is devising and applying a general framework for the application of technologies that delivers safety while preserving privacy. Previous research concerning the privacy of such systems focused on the acceptance by seniors for these products with much emphasis on information privacy and security and little emphasis on the physical privacy aspect. This research focuses on the physical privacy aspect of HHC monitoring in determining if physical privacy represents an acceptance hurdle, with a focus on seniors with osteoporosis. Additionally, the safety requirements will be determined by surveying elderly care providers. Furthermore, unlike most studies into home health care which gives users a binary choice, a sample of the senior population will be educated on and questioned on their attitudes on individual components of HHC monitoring using a range scale. The resultant analysis will extract a combination of components that maximizes the mixture safety and physical privacy accepted by seniors. Finally, a HHC monitoring framework for seniors with osteoporosis will be developed based upon the results.https://jagworks.southalabama.edu/southalabama-shgrf-posters/1030/thumbnail.jp
Improving the Viability of Continuous Fiber Reinforced Composite Materials in Extreme Temperature Applications and Additive Manufacturing
Full text linkPresentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama
Human YRNA Associations with Viral Infections
No full textY RNAs are a poorly-studied class of small non-coding RNAs (sncRNAs) which have previously been implicated in the pathogenesis of different human diseases, including cardiac and autoimmune conditions, as well as certain cancers. In recent years, however, multiple studies have reported correlations between Y RNA expressions and disease outcomes in viral infections (e.g., influenza virus, HIV, HPV, and SARS-CoV-2) as well as potential mechanistic roles that Y RNAs may play in host anti-viral defense. These studies suggest that Y RNAs may be associated with upregulation of viral defense proteins as well as altered cell-cell communication during viral infections. To systematically evaluate the emerging role YRNAs play, we conducted a comprehensive literature review spanning 2000-2025, focusing on peer-reviewed studies that investigate Y RNA functions in viral infections. Our analysis revealed several key mechanistic insights: during influenza A virus infection, the ysRNA miR-1975 (derived from HY5) shows significant upregulation and gets packaged into exosomes where it enhances IFN-β production in neighboring cells. In HIV-1 infection, HY4 (RNY4) undergoes 5\u27-triphosphorylation due to viral suppression of DUSP11, enabling its binding to RIG-I and subsequent amplification of interferon responses. Perhaps most clinically relevant, plasma levels of HY4 demonstrate an inverse correlation with disease severity in COVID-19 patients, suggesting a protective function. These molecular findings are complemented by important clinical correlations, including the enrichment of Y RNA fragments in extracellular vesicles during viral infections (potentially serving as early biomarkers) and the association between HY1 expression and improved survival in HPV-positive head and neck cancers. The therapeutic potential of Y RNAs is further highlighted by several promising developments: synthetic Y RNA fragments have shown efficacy in blocking RSV entry by interfering with nucleolin binding, while the dysregulation of Y RNA profiles in severe COVID-19 cases provides important insights into viral immune evasion strategies. Despite these advances, significant knowledge gaps remain regarding the precise structural interactions between Y RNAs and immune sensors, as well as the need for standardized detection methods for clinical applications. Future research should prioritize high-resolution structural studies to elucidate Y RNA-immune sensor interactions, develop robust detection protocols for clinical settings, and validate therapeutic approaches in animal models. As our understanding of these versatile RNA molecules continues to grow, Y RNA research stands at the forefront of innovative approaches to combat viral infections, offering exciting possibilities for both diagnostic and therapeutic advancements that could significantly improve patient outcomes in infectious disease management