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The Responsible Health AI Readiness and Maturity Index (RHAMI): Applications for a Global Narrative Review of Leading AI Use Cases in Public Health Nutrition
No full textPoor 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
No full textThis 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> &lt; 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&rsquo;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
SCGclust: Single-Cell Graph Clustering Using Graph Autoencoders That Integrate SNVs and CNAs
No full textIntra-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
Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs
No full textEffective 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
Intertwined Electron&ndash;Electron Interactions and Disorder in the Metal&ndash;Insulator Phase Transition
No full textQuantum 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&ndash;insulator transitions, electron&ndash;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&ndash;insulator transition within disordered electron systems are explored using the disordered Hubbard model, the simplest Hamiltonian for capturing the metal&ndash;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
Tectonics, Crustal Types, Structural Style, and Hydrocarbon Potential of the Northern Red Sea Rift
No full textPrevious GPS studies have shown that present-day rifting in the northern Red Sea is oblique to its basin axis and occurs at a lower opening rate while rifting in the southern Red Sea is orthogonal to its axis and occurs at a faster rate. Previous studies over the past 50 years have used potential fields data and seismic reflection data along the 1900-km-long Red Sea rift to reach different conclusions on the crustal types that underlie the rift. Some previous workers propose that the entire area of the Red Sea is underlain by oceanic crust. In contrast, others propose that oceanic crust underlies the southern half of the rift and that continental crust underlies its northern half. In order to address these unresolved questions, I integrate 10,920 line-km of industry seismic reflection and ship-based marine gravity data with wells from the Egyptian sector of the northwestern Red Sea. Using these data, I created six, geological-geophysical transects that merge the gravity and seismic reflection data and reveal the following crustal and structural features of the Red Sea: 1) the crust underlying the northwestern Red Sea is rifted, continental crust with the rifts overlying the necked zone of continental crust with an Oligocene syn-rift section overlain by a Late Miocene salt section; 2) elongate, gravity highs correlate with footwall uplifts on basinward-dipping, domino-style normal faults observed on seismic reflection data; 4) these elongate, gravity highs are oblique to the northern Red Sea basinal axis and reflect the oblique opening direction known from GPS data; 5) the Zabargad fracture zone offsets the Red Sea basin axis and marks the boundary between highly- oblique continental rifting to the north and slightly-oblique, zone of oceanic spreading that extends 755 km to the southern end of the Red Sea; 6) this narrow band of young, oceanic crust is splitting the Late Miocene salt body into two halves because the salt deposition pre-dates the onset of oceanic spreading; and 7) I propose sub-salt hydrocarbon plays for the Red Sea that are based on similar, discoveries in the billion-barrel oil fields of the Gulf of Suez to the north of the Red Sea
Evaluation of Source Rock and Crude Oil for Petroleum Exploration and Paleoenvironmental Studies using UEP Modeling and Chemical Analysis of Carbazoles
No full textThis dissertation presents a geochemical evaluation of petroleum source rocks from a molecular scale in Chapter 2, to a localized scale in Chapter 3 and, finally, a regional scale in Chapter 4. In Chapter 2, oil samples are analyzed for carbazoles and other components in their whole-petroleum and fractionated forms by both GC-MS Selective Ion Monitoring (SIM) and GC-MS Multiple Reaction Monitoring (MRM) methods, to compare the differences. Then, an updated analytical method for the measurement of carbazoles using GC-MS/MS is defined. This chapter explores (1) the benefits of a hybrid MRM-SIM method for the analysis of carbazoles and other molecular markers in a single run, (2) surrogate standards to accurately quantify up to 21 species of carbazole molecules, and (3) evidence for the advantage of GC-MS/MS over previous methods for evaluations of thermal condensates and heavy crude oil. Chapter 3, focused on Albian and Upper Cretaceous stratigraphic units in Venezuela, Trinidad, and Guyana, outlines a novel method for the back-calculation of original, depositional geochemical parameters. This back-calculation, based on the relationship between total organic carbon and inert carbon in organically undepleted source rock units, allows for the modeling of Ultimate Expellable Potential (UEP) for both the undepleted and depleted datasets. The outlined method also identifies trends, such as the high source rock potentials at 87 and 89 Ma, that exist across the region. In addition, potential error in UEP trends due to variation in the outlined mathematical equations is evaluated and deemed negligible. Finally, Chapter 4 uses fundamental concepts of organic preservation, primary productivity, and sedimentation rates to evaluate the UEP trends identified in Chapter 3. The calculated UEP for individual dataset locations is mapped throughout northern South America. Additional inputs, such as the paleoenvironmental shelf break, UEP from the West African conjugate margin, locales of tectonic deformation, and the continental-oceanic boundary, are used to constrain the source rock potential maps. The chronostratigraphic and lateral trends of source rock potential are evaluated in relation to the interpreted paleoenvironments to identify both localized and regional drivers behind source rock potential
Exploration Science of the Moon, Mars, and a Terrestrial Analog Site
No full textThis dissertation explores planetary science through the investigation of the Moon, Mars, and a terrestrial analog site in Iceland. The research is divided into three main studies, each contributing to the broader field of exploration science and our understanding of planetary processes. Chapter 2 focuses on the NASA Artemis program’s exploration of the lunar south pole, a region of significant scientific interest due to its diverse geological features and potential for harboring vast quantities of natural resources. This study analyzes six potential landing sites, each with distinct rock types determined using orbital data from JAXA’s Kaguya Spectral Profiler, and evaluates the chronologic opportunities samples returned from them present. The findings of our study underscore the potential of these sites to address unresolved questions about the Moon's thermal evolution and volcanic history, providing a framework for selecting landing sites and designing a sample return program that will maximize scientific return. Chapter 3 presents a framework to study thirteen silica-rich deposits on the Martian surface, utilizing thermal infrared orbital datasets to apply a spectral linear unmixing technique. The framework offered here will provide insight into the size, shape, provenance, and geologic context of these deposits. This work lays the groundwork for future research into Martian silica-rich materials, which may preserve evidence of evolved volcanism and past aqueous environments. Chapter 4 examines Prestahnúkur Volcano in Iceland as a terrestrial analog for Gale Crater on Mars. A source-to-sink sampling campaign was conducted within two sediment transport pathways to understand the generation, incorporation, and preservation of siliceous sediments within basaltic terrain. Using a combination of X-ray diffraction, X-ray fluorescence, and size fraction analyses, the study provides new insights into sedimentary processes that created the silica-rich Buckskin mudstone unit within Gale crater on Mars. Collectively, this dissertation advances the understanding of planetary geology by addressing critical questions about lunar chronology, Martian silica-rich deposits, and sediment transport and mixing relationships within a Martian analog site. The findings have significant implications for future space exploration missions, particularly in guiding the selection of landing sites, refining exploration strategies, and enhancing our understanding of planetary environments
Cognitive Predictors of Internalizing Symptoms in Clinically Anxious Youth
No full textInternalizing disorders are the most common form of psychopathology in youth, linked with a host of deleterious sequelae such as substance use, interpersonal difficulties, academic underachievement, and even suicide. Therefore, there is critical need for more research regarding the risk factors that contribute to youth internalizing psychopathology. The present study investigated the unique and shared roles of two prominent cognitive biases—anxiety sensitivity and interpretation biases—as predictors1 of internalizing symptom severity in clinically anxious youth, above and beyond the effects of negative emotionality. It was hypothesized that both anxiety sensitivity and interpretation biases would account for a statistically significant amount of variance in internalizing symptom severity, above and beyond the effects of negative emotionality and after accounting for sociodemographic covariates. A diverse sample of clinically anxious youth (N = 105; Mage = 10.09 years, SD = 1.22; 56.7% female; 49% ethnic minority) completed a diagnostic interview and self-report measures of interpretation biases, anxiety sensitivity, and internalizing symptom severity. Hierarchical regression analyses revealed that both anxiety sensitivity (b = 0.79, 95% CI [0.55, 1.02], sr2 = 0.11) and interpretation biases (b = 0.22, 95% CI [0.12, 0.31], sr2 = 0.05) accounted for an additional 22.7% of unique variance in internalizing symptom severity (p < .001), above and beyond the effects of negative emotionality. Post hoc exploratory analyses identified the disease and social concerns facets of anxiety sensitivity, and the overgeneralization facet of interpretation biases, as predictors of internalizing symptoms. These findings suggest that treatments targeting cognitive biases may potentially be beneficial among temperamentally labile clinically anxious youth. 1 This term and its variations (e.g., predicting) are used throughout the manuscript in a statistical sense, without suggesting a causal relationship between variables or constructs
Advanced Forecasting and System Integration Impact Analysis of Renewable Energy and Electric Vehicles on Power Distribution Networks
No full textThe rapid evolution of modern power systems, driven by renewable energy integration and transportation electrification, has introduced new challenges in grid reliability, efficiency, and sustainability. This thesis explores advanced modeling techniques to address these challenges, focusing on three interconnected themes: load forecasting, renewable energy integration, and the impact of electric vehicle (EV) adoption on distribution grids. First, a comparative analysis of machine learning (ML) models, including feedforward neural networks, recurrent neural networks, long short-term memory networks (LSTM), gated recurrent units, and attention temporal graph convolutional networks, is conducted for short-term load forecasting. Using real-world data from Houston’s Energy Corridor distribution system, the study identifies the most effective ML models for accurate load prediction, offering insights for optimizing grid operations. Second, the thesis develops an LSTM-based deep learning framework for net load forecasting in microgrids equipped with solar and wind power. Leveraging typical meteorological year datasets, the model accurately predicts net load dynamics, enabling improved energy management in renewable-based microgrids and addressing the variability inherent in renewable energy sources. Lastly, the impact of widespread EV charging on power distribution networks is assessed using a detailed simulation of a 240-bus system with 1120 customers. By evaluating ampacity violations, line loading, and voltage stability under various EV penetration scenarios, the research identifies critical grid infrastructure challenges and proposes strategies for grid reinforcement and voltage-level adjustments to ensure reliable operation. Together, these studies provide a comprehensive framework for advancing power and energy systems through predictive modeling, renewable energy forecasting, and infrastructure planning. By addressing the challenges of grid modernization, this work contributes to the development of resilient, efficient, and sustainable power systems capable of meeting the demands of a decarbonized future