Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs

Effective knowledge management is critical for preserving institutional expertise and improving the efficiency of workforce training in state transportation agencies. Traditional approaches, such as static documentation, classroom-based instruction, and informal mentorship, often lead to fragmented knowledge transfer, inefficiencies, and the gradual loss of expertise as senior engineers retire. Moreover, given the enormous volume of technical manuals, guidelines, and research reports maintained by these agencies, it is increasingly challenging for engineers to locate relevant information quickly and accurately when solving field problems or preparing for training tasks. These limitations hinder timely decision-making and create steep learning curves for new personnel in maintenance and construction operations. To address these challenges, this paper proposes a Retrieval-Augmented Generation (RAG) framework with a multi-agent architecture to support knowledge management and decision-making. The system integrates structured document retrieval with real-time, context-aware response generation powered by a large language model (LLM). Unlike conventional single-pass RAG systems, the proposed framework employs multiple specialized agents for retrieval, answer generation, evaluation, and query refinement, which enables iterative improvement and quality control. In addition, the system incorporates an open-weight vision-language model to convert technical figures into semantic textual representations, which allows figure-based knowledge to be indexed and retrieved alongside text. Retrieved text and figure-based context are then provided to an open-weight large language model, which generates the final responses grounded in the retrieved evidence. Moreover, a case study was conducted using over 500 technical and research documents from multiple State Departments of Transportation (DOTs) to assess system performance. The multi-agent RAG system was tested with 100 domain-specific queries covering pavement maintenance and management topics. The results demonstrated Recall@3 of 94.4%. These results demonstrate the effectiveness of the system in supporting document-based response generation for DOT knowledge management tasks

SCGclust: Single-Cell Graph Clustering Using Graph Autoencoders That Integrate SNVs and CNAs

Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognoses and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and the responses to drugs and treatments. While low-coverage scDNA-seq technologies typically provide a large number of cells, accurate cell clustering is essential for effectively characterizing the ITH. The existing cell clustering methods are typically based on either single-nucleotide variations (SNV) or copy number alterations (CNA), without leveraging both signals together. Since both SNVs and CNAs are indicative of cell subclonality, in this paper, we designed a robust cell-clustering tool that integrates both signals using a graph autoencoder. Our model co-trains the graph autoencoder and a graph convolutional network (GCN) to guarantee meaningful clustering results and to prevent all cells from collapsing into a single cluster. Given the low-dimensional embedding generated by the autoencoder, we adopted a Gaussian mixture model (GMM) to further cluster the cells. We evaluated our method on eight simulated datasets and a real cancer sample. Our results demonstrate that our method consistently achieved higher V-measure scores compared to SBMClone, an SNV-based method, and a K-means method that relies solely on CNA signals. These findings highlight the advantage of integrating both SNV and CNA signals within a graph autoencoder framework for accurate cell clustering

The Responsible Health AI Readiness and Maturity Index (RHAMI): Applications for a Global Narrative Review of Leading AI Use Cases in Public Health Nutrition

Poor diet is the leading preventable risk factor for death worldwide, associated with over 10 million premature deaths and USD 8 trillion related costs every year. Artificial intelligence or AI is rapidly emerging as the most historically disruptive, innovatively dynamic, rapidly scaled, cost-efficient, and economically productive technology (which is increasingly providing transformative countermeasures to these negative health trends, especially in low- and middle-income countries (LMICs) and underserved communities which bear the greatest burden from them). Yet widespread confusion persists among healthcare systems and policymakers on how to best identify, integrate, and evolve the safe, trusted, effective, affordable, and equitable AI solutions that are right for their communities, especially in public health nutrition. We therefore provide here the first known global, comprehensive, and actionable narrative review of the state of the art of AI-accelerated nutrition assessment and healthy eating for healthcare systems, generated by the first automated end-to-end empirical index for responsible health AI readiness and maturity: the Responsible Health AI readiness and Maturity Index (RHAMI). The index is built and the analysis and review conducted by a multi-national team spanning the Global North and South, consisting of front-line clinicians, ethicists, engineers, executives, administrators, public health practitioners, and policymakers. RHAMI analysis identified the top-performing healthcare systems and their nutrition AI, along with leading use cases including multimodal edge AI nutrition assessments as ambient intelligence, the strategic scaling of practical embedded precision nutrition platforms, and sovereign swarm agentic AI social networks for sustainable healthy diets. This index-based review is meant to facilitate standardized, continuous, automated, and real-time multi-disciplinary and multi-dimensional strategic planning, implementation, and optimization of AI capabilities and functionalities worldwide, aligned with healthcare systems’ strategic objectives, practical constraints, and local cultural values. The ultimate strategic objectives of the RHAMI’s application for AI-accelerated public health nutrition are to improve population health, financial efficiency, and societal equity through the global cooperation of the public and private sectors stretching across the Global North and South

The Productivity–Safety Nexus: The Impact of Human Factors on Operational Efficiency in Construction Projects

This paper explores the relationship between human factors in construction safety and their effects on operational efficiency. It investigates how safety incidents resulting from human errors influence productivity, project timelines, and overall costs, while examining how strategic safety management can improve organizational performance. The study employs a mixed-methods design that combines quantitative statistical modeling with qualitative case analysis to ensure both empirical rigor and contextual depth (R<sup>2</sup> = 0.87, <i>p</i> < 0.001). Data were drawn from four construction firms, encompassing a sample of 120 employees across residential, commercial, and infrastructure projects. Variables such as training hours, fatigue levels, safety compliance, and technology adoption were analyzed against key operational performance indicators, including rework hours, schedule adherence, and productivity scores. Statistical analyses were performed using SPSS 29 and AMOS 28, incorporating descriptive statistics, regression analysis, and mediation testing to examine the pathways linking human factors, safety performance, and operational productivity. Reliability and validity were confirmed through Cronbach’s alpha and variance inflation factor (VIF) diagnostics. Results demonstrate that safety compliance acts as a mediating variable connecting training, fatigue, and technology adoption to measurable business outcomes. By providing a quantitative framework that links human factor management to operational efficiency, this research contributes to both construction management theory and practice, emphasizing safety as a strategic driver of performance and competitiveness

Intertwined Electron–Electron Interactions and Disorder in the Metal–Insulator Phase Transition

Quantum materials exhibit a rich dynamic of physical parameters, which, when combined, can lead to entirely different behaviors. These parameters constantly compete with each other, with the most influential parameters determining the state of the system. For example, in the case of metal–insulator transitions, electron–electron interactions compete with the kinetic energy of the electrons and disorder. Understanding these complex dynamics is crucial for both fundamental physics and the development of novel technological applications, particularly given the role of disorder in tuning critical temperatures, a property with significant potential benefit in the fabrication of new devices where temperature requirements are still the bottleneck. In this article, properties of the Mott metal–insulator transition within disordered electron systems are explored using the disordered Hubbard model, the simplest Hamiltonian for capturing the metal–insulator transition. The model solutions are obtained using the self-consistent statistical dynamical mean-field theory (statDMFT). statDMFT incorporates local electronic correlation effects while allowing for Anderson localization due to disorder

From Experience to Practice: The Effects of Study Abroad Experiences on Faculty and Staff’s Internationalization Practices

Background: This study examines the role of faculty and staff preparedness in fostering global competencies among students within the context of internationalization in higher education. Despite the proven benefits of study abroad experiences for students, including enhanced global competency and professional skills, there remains a significant gap in research addressing how these experiences affect faculty and staff’s work in internationalization and their pedagogical practices. Purpose: The purpose of this study is to explore how faculty and staff integrate intercultural learning and pedagogical skills that are developed through studying abroad into their professional role at an urban university. Methods: This research utilizes a case study method grounded in Kolb’s Experiential Learning Theory to explore how faculty and staff experiences during study abroad programs influence their understanding of global competency and their professional goals moving forward. The research questions focus on the changes in faculty and staff perceptions regarding global competency, their understanding of its relevance to student development, and the adjustments they make in their daily practices post-experience. Results: The results found that after a study abroad program to China, participants returned with expanded views of global competency and internationalization that impacted their own careers and their interactions with their student populations. Faculty and staff had expanded views in global humility, awareness and respect and found that self-reflection was critical to this development. Additionally, through community building and mentorship, faculty and staff created more opportunities for internationalization at their institution through teaching global courses, leading study abroad programs, or developing international research initatives. Conclusion: The findings highlight the necessity of intentional faculty development initiatives that empower educators to better prepare students for global citizenship and to become effective advocates for internationalization, ensuring that global competency development is prioritized in the educational landscape

Evaluation of Patterns in Pharmacy Labor Modeling and Hospital Revenue, Expense, and Volume Indicators

Background. Pharmacy departments are often considered expense centers rather than potentially improving hospital revenue with increased personnel. However, contemporary pharmacy practice models can translate to improved hospital financial health. The aim of this study was to evaluate pharmacy staffing level in relation to hospital and pharmacy financial metrics. Methods. This national study was conducted at CommonSpirit Health, a large, national, not-for-profit health-system with over 140 acute-care hospitals across 24 states. Centralized databases provided organizational and financial indicator data. Correlation between pharmacy staffing and financial metrics were compared using univariate analyses and multivariable linear regression. Results. Ninety-one to 97 hospitals contributed data annually to the database from 2023-25. Data from 2025 was normalized for the entire calendar year. Most hospitals were non-critical access hospitals (76%) with annualized visits between 2,000-10,000 per year (38%). Pharmacy staffing hours demonstrated a statistically significant positive correlation with hospital contribution margin, pharmacy non-separately reimbursable drug spend, and total drug spend (p < 0.001, all). Using multivariable linear regression, pharmacy staffing hours demonstrated a statistically significant positive association with all three dependent variables adjusting for health system region, critical access hospital, number of visits, and fiscal year. Conclusion. This study demonstrated a positive correlation between inpatient pharmacy staffing hours and hospital total contribution margin despite a concurrent increase in drug expenditures. Further investigations are warranted to evaluate the impact of clinical and operational workflows, pharmacist-technician ratios, and pharmacist-patient ratios on hospital financial performance

The MERIT Mammography Cohort to Develop Improved Screening for Breast Cancer Through Integration of AI and Blood Based Biomarkers

Breast cancer is the most common cancer among women and the second leading cause of cancer death. While mammography screening reduces mortality, it faces criticism for overscreening, false positives, and overdiagnosis of non-threatening cancers. These issues cause significant patient anxiety and strain the healthcare system. Improved personalized risk assessment methods are needed to refine screening and preventive strategies. The MERIT (Mammography, Early Detection, Risk Assessment, and Imaging Technologies) cohort addresses this need. By integrating multi-modal data-including patient characteristics, biomarkers, radiomic data, and radiologist interpretations-MERIT aims to develop advanced predictive models. This approach seeks to enhance risk assessment precision, optimize screening protocols, reduce unnecessary procedures, and improve patient outcomes. [This project was completed with contributions from Ehsan Irajizad, Samir Hanash, Olena Weaver, Jessica Leung, and Jennifer Dennison from UT MD Anderson Cancer Center.]Honors Colleg

Plasma-Assisted Atomic Layer Etching of Si in Cl and Br-Containing Plasma

Atomic-scale precision in silicon plasma etching is indispensable for the fabrication of next-generation three-dimensional (3D) semiconductor devices. Yet plasma-assisted atomic layer etching (PA-ALE) continues to be limited by low throughput, poor self-limiting behavior, and an incomplete understanding of surface kinetics. My research tries to address these challenges through a systematic study performed in a modified continuous-wave (CW) inductively coupled plasma (ICP) reactor. Time-resolved, in-situ optical emission spectroscopy (OES) is established as a quantitative tool of surface reactions during ALE cycles employing Cl₂, HBr, and Br₂ chemistries. The measurements show that SiCl₂ and SiCl constitute the primary products in Cl₂-based ALE. Two process sequences—gas dosing and plasma gas dosing—are explored and compared: pseudo-self-limiting behavior emerges in HBr plasma gas dosing cycles, whereas Br₂ have greater Br surface coverage and higher etch rates under gas-dosing conditions compared to HBr. Because both Br₂ and HBr have high sticking coefficients, gas residence time experiments reveal a two-stage purge consisting of a volume-limited decay followed by wall retention limited desorption; wall passivation via temperature control, Ar/SF₆/O₂ conditioning, and increased total flow substantially shortens the wall retention time. Moreover, fast-pulsed substrate bias with continuous gas flow effectively decouples the dose and etch steps, eliminating mechanical gas-pulsing hardware and markedly increasing throughput. Simultaneously tracking the ALE percentage and the bias-on integrated intensity of dominant OES lines enables evaluation of both self-limiting fidelity and etch rate. Collectively, this work (i) elucidates the primary reaction products and pathways in Si ALE for multiple halogen chemistries, (ii) delivers robust, high-throughput recipes that achieve sub-nm precision with cycle times below 2 s, and (iii) provides diagnostic and hardware guidelines transferable to other ICP tools. These advances expedite the transition of PA-ALE from lab to high-volume manufacturing, making a further step to achieve damage-free patterning of sub-10 nm features for future logic and memory devices

Fatal Restrictions: Exploring State-Level Abortion Laws, Sociodemographic Contexts, and Maternal Mortality in the United States

The United States reports one of the highest maternal mortality rates (MMR) among high-income nations, reflecting a persistent public health crisis. Pregnancy-related deaths disproportionately affect individuals across racial and socioeconomic lines, illustrating long-standing systems of disadvantage. In the wake of the 2022 Dobbs v. Jackson Women’s Health Organization decision, which overturned Roe v. Wade, understanding the policy and structural drivers of maternal mortality is increasingly urgent. This study independently examines how state-level abortion laws and key sociodemographic factors, including race, immigration status, poverty, health insurance coverage, public assistance, unemployment, and income, shape variation in MMR across U.S. states from 2018 to 2022. Using a state-level analysis, I classify abortion laws along a restrictive-protective continuum and use multiple linear regression models to assess the independent effects of sociodemographic factors. Results show that restrictive abortion laws consistently emerge as a significant predictor of higher maternal mortality rates. Additionally, states with larger Black populations, higher poverty and unemployment rates, greater proportions of uninsured residents, and increased participation in Supplemental Nutrition Assistance Program (SNAP) experience elevated MMR, each exhibiting a statistically significant association within their respective models. Interaction effects further reveal that socioeconomic resources, such as income and education, moderate the impact of abortion restrictions. These findings demonstrate that maternal mortality is influenced by both restrictive policies and entrenched structural inequality. This research provides critical insight into the potential consequences for maternal health in a post-Dobbs landscape, where legal restrictions on reproductive care intersect with broader social disparities