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The Influence of Scale in Modeling Social Vulnerability and Disaster Assistance
No full textUnderstanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy-misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social vulnerability and federal disaster assistance after Hurricane Harvey. Using spatial econometric models at both household and census tract levels, we assessed the strength of key vulnerability indicators in explaining disaster assistance. Results show that disability, housing tenure, household size, and income predict assistance at the household level, but their influence shifts across scales. Income and disability remain strong predictors at the tract level, whereas housing factors weaken or reverse. These findings suggest that using aggregated data to model household-level relationships between social vulnerability and disaster assistance can distort understanding of vulnerability processes, potentially leading to misinformed disaster policies and inequitable outcomes. Our findings have implications for disaster management, but the primary contribution of this study is methodological, in providing a critical evaluation of how spatial scale and data aggregation shape the statistical interpretation of social vulnerability and aid distribution
Kristen Renee Miller
No full textPublicity photo submitted by author/presenter for ODU\u27s Annual Literary Festival 2025.
Photo credit Kertis Creativehttps://digitalcommons.odu.edu/litfest_images/1008/thumbnail.jp
Erika Howsare
No full textPublicity photo submitted by author/presenter for ODU\u27s Annual Literary Festival 2025.https://digitalcommons.odu.edu/litfest_images/1019/thumbnail.jp
SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics
No full textDespite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based topic modeling with classifier-based importance scoring to identify highly spatially informative genes, and fuses a local expression graph with a topic-driven non-local graph via consensus refinement and contrastive graph representation learning to jointly learn spatial and functional embeddings. Evaluated on 34 real-world datasets, SAGE not only outperforms existing methods in clustering accuracy but also reveals functionally coherent regions and interpretable gene expression patterns. In case studies, SAGE reveals spatial heterogeneity associated with a pre-malignant activation state in human breast cancer. Moreover, in zebrafish melanoma, it refines the tumor-muscle interface into transcriptionally distinct subdomains and uncovers shared vascular signatures between anatomically separate tissues. Together, these results demonstrate that SAGE can be used not only for accurate spatial domain delineation across diverse ST platforms, but also for dissecting microenvironmental niches and long-range tissue interactions underlying disease progression
Health and Nutrition in the First 1000 Days of Life
No full textMalnutrition and health issues remain persistent global crises, with developing regions bearing the brunt of the burden. According to the World Health Organization, nearly half of under-five deaths in low-and middle-income countries are linked to malnutrition, while stunting, wasting, and micronutrient deficiencies impair the development of millions of children annually. The studies in this special issue focus on countries facing distinct yet interconnected challenges: Sub-Saharan African nations grapple with high rates of stunting and maternal high-risk fertility behaviors; China confronts rural-urban disparities in dietary diversity and grandparenting practices; Colombia struggles with intersectional inequalities driving infant mortality; and Zambia faces unique nutritional barriers in resource-constrained mining communities, etc. By centering these contexts, the special issue fills critical gaps in region-specific evidence, as many global health frameworks often lack granular data on how local cultures, economies, and infrastructures shape early-life nutrition outcomes. The motivation is clear: to consolidate rigorous, context-sensitive research that moves beyond one-size-fits-all solutions and empowers policymakers to design interventions tailored to local realities. The articles in this special issue cluster around four interconnected themes, each shedding light on critical dimensions of early-life health and nutrition. This special issue reinforces that investing in the first 1000 days requires context-sensitive, multi-sectoral action. The studies collectively show that while the consequences of early-life nutrition gaps are severe-from cognitive disability to increased mortality-the barriers are addressable through targeted interventions: scaling micronutrient supplementation, improving dietary diversity, strengthening maternal health services, equipping caregivers with knowledge, and addressing structural inequalities. For policymakers, the evidence underscores the need to prioritize early-life nutrition in national health agendas, integrate nutrition counseling into routine care, and allocate resources to marginalized communities. For researchers, the special issue highlights gaps in long-term intervention impacts and the need for more studies on urban-rural and intersectional disparities. Ultimately, the first 1000 days offer an unparalleled opportunity to break cycles of malnutrition and poverty-one that demands urgent, evidence-driven action to ensure every child has the chance to thrive
Explainable Physics-Based Constraints on Reinforcement Learning for Accelerator Optimization
No full textWe present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments
Beyond Discrete Indicators: Modeling Intersectional Flood Vulnerability
No full textSocial vulnerability to flooding is shaped by intersectional social marginalization, yet most quantitative assessments employ indicators of single populations. This study applies spatial machine learning to examine how the intersectional social vulnerability indicators of poverty-race, poverty-housing tenure, and race-housing tenure compare with traditional discrete indicators of single populations in predicting flood exposure in California. Using geographically weighted random forests and partial dependence plots, we model spatial heterogeneity and non-linear relationships between social vulnerability and exposure. We quantified flood exposure using a population-adjusted measure derived from building footprints and modeled 500-year fluvial and pluvial flood hazard. The results reveal distinct explanatory power of discrete and intersectional indicators. Variable importance analysis shows that intersectional indicators, such as Poor Renters and Non-white Renters, have stronger predictive importance than their discrete counterparts, particularly in urban regions, with mean local IncMSE values of 15.6–16.9 % compared to 12.3–14.8 %. Partial dependence analysis revealed threshold effects of non-linear indicator influence, with predicted exposure increasing sharply once intersectional populations exceed ~60 % of tract-level representation. Our findings highlight limitations of assuming uniform indicator effects, and the need for non-linear, spatially adaptive models that increase conceptual alignment between social vulnerability theory and indicator modeling by integrating intersectional dimensions
Flexible Cu Nanostructured Laser-Induced Graphene Electrodes for Highly Sensitive and Non-Invasive Lactate Detection in Saliva
No full textA scalable and facile fabrication strategy is presented for developing a flexible, nanostructured, non-enzymatic electrochemical sensor for lactate detection based on copper-modified laser-induced graphene (CuNPs/LIG). A one-step electrodeposition process was employed to uniformly decorate the porous LIG framework with copper nanostructures, offering a cost-effective and reproducible approach for constructing enzyme-free sensing platforms. Scanning electron microscopy and energy-dispersive X-ray spectroscopy confirmed dense Cu nanostructure loading and efficient interfacial integration across the conductive LIG surface. The resulting CuNPs/LIG electrode exhibited excellent electrocatalytic performance, achieving a sensitivity of 8.56 μA µM−¹ cm−² with a low detection limit of 42.65 μM and a linear response toward lactate concentrations ranging from 100 to 1100 μM in artificial saliva under physiological conditions. The sensor maintained high selectivity in the presence of physiologically relevant interferents. Practical applicability was demonstrated through recovery studies, where recovery rates exceeding 104% showcase the sensor’s analytical reliability in complex biological matrices. Overall, this work establishes a robust, sensitive, and cost-efficient Cu-nanostructured LIG sensing platform, offering strong potential for non-invasive lactate monitoring in real-world biomedical and wearable applications