德州大学阿灵顿分校

Background Angina with no obstructive coronary arteries is often due to coronary vascular dysfunction, which is increasingly described as a contributor and predictor of heart failure with preserved ejection fraction. We tested the hypothesis that coronary vascular dysfunction contributes to cardiac dysfunction. Methods Sixty-eight participants with suspected coronary vascular dysfunction underwent LV pressure-volume assessment at rest and during isometric handgrip stress. Coronary function testing was performed by infusing standard doses of adenosine, acetylcholine, and nitroglycerine through a guiding catheter placed in the left main coronary artery, per established protocols. Results Of the participants studied, 12 had normal coronary vascular function while 56 had coronary vascular dysfunction. At rest, there were no differences between groups in any invasive pressure or volumetric measurement. With isometric handgrip, peak filling rate increased significantly in participants with normal coronary vascular function (0.56 ± 0.31 vs. 0.85 ± 0.68 1/s/mmHg) but remained unchanged in participants with coronary vascular dysfunction (0.51 ± 0.22 vs. 0.55 ± 0.43 1/s/mmHg; group x time interaction, p = 0.04). Moreover, left ventricular end-diastolic pressure decreased significantly with isometric handgrip in participants with normal coronary vascular function (12 ± 3 vs. 9 ± 4 mmHg) but increased in participants with coronary vascular dysfunction (14 ± 4 vs. 15 ± 6 mmHg; group x time interaction, p = 0.03). Conclusions Coronary vascular dysfunction was associated with an impaired ability of the LV to adapt its filling during stress. Further work is needed to assess potential clinical significance.

Armed conflict can disrupt food systems simultaneously through reduced production, damaged infrastructure, insecure transport corridors, export restrictions, fertilizer scarcity, energy-price inflation, and diminished purchasing power. This chapter examines the organizational consequences of these interacting shocks through grain and fertilizer supply chains affected by the Russia–Ukraine war. It develops a source–corridor–input vulnerability framework connecting origin concentration, route concentration, input criticality, substitution time, and buffering capacity. Scenario-based analysis shows that alternative maritime, river, rail, and road corridors can preserve physical availability but frequently increase handling, freight, insurance, storage, and financing costs. Fertilizer and energy shocks can also outlast the immediate interruption of grain exports by reducing subsequent harvests. Resilience therefore requires simultaneous protection of supply continuity, commercial affordability, agricultural production, and access for vulnerable consumers.

Abstract Bridges are critical to the US transportation network and are essential components of our infrastructure systems. Coastal floods can disrupt these structures, isolating communities and highlighting the urgent need for effective maintenance and prioritization strategies. This study introduces a digital twin framework that evaluates the impacts of coastal floods on bridge substructures by integrating multisource data and employing spatial algorithms like inverse distance weighting and k - d tree methods to model flood severity. Using a generative adversarial network (GAN) with focal loss, the framework simulates preflood and postflood scenarios, generating synthetic data sets that reflect realistic damage patterns and evolving maintenance priorities. The GAN-enhanced model improved F1 scores (the harmonic mean of precision and recall, providing a balance between the two metrics), particularly for Condition 4 (bridges with advanced section loss, deterioration, spalling, or scour), where the score increased from 0.63 to 0.87 preflood and from 0.63 to 0.88 postflood, effectively addressing class imbalances. It also enhanced macro and weighted average F1 scores, demonstrating robust predictive capabilities. Confusion matrices showed increased identification of bridges in Conditions 4 and 5, consistent with the model’s emphasis on vulnerable infrastructure. The study’s findings reveal shifts in maintenance priorities when flood severity is incorporated, emphasizing bridges with higher susceptibility to combined flooding and socioeconomic impacts. While the framework primarily focused on physical infrastructure impacts, it also highlighted potential disparities in resource allocation, suggesting the need for further research into equitable distribution strategies. The digital twin provides actionable insights for local and federal agencies, offering a valuable tool for managing and prioritizing infrastructure maintenance in response to extreme weather events while laying the groundwork for more inclusive resource planning.

One of the key challenges in shallow geothermal technology is improving the thermal performance of ground heat exchangers (GHEs) while reducing operational costs. Existing enhancement techniques are typically applied uniformly along the GHE depth, despite field data showing non-uniform thermal performance. This paper presents a spatiotemporal characterization of a single u-pipe GHE, consolidating theoretical framework, laboratory characterization, and numerical modeling to identify where and when coupled heat and moisture transfer becomes significant within the GHE geometry. The coupled model is validated against a laboratory column test under ambient, and non-insulated boundary conditions and applied to characterize the three-dimensional axial and radial extent of soil drying and wetting around a GHE operating in heat rejection mode. Results reveal an asymmetric temperature and moisture distribution, with greater desaturation near the inlet leg, concentrated in the upper 40-50% of the GHE depth and extending 1.21 m - 1.52 m (4 - 5 ft) radially from the pipe. This desaturation is self-limiting, stabilizing within one to two months of operation, and corresponds to an approximately fivefold increase in matric suction near the pipe, from approximately 348 kPa (7268.13 psf) to approximately 1851 kPa (38,658.94 psf) over four months. The associated reduction in thermal conductivity and heat capacity substantially alters the heat exchange capacity of the surrounding soil. These findings establish the spatiotemporal basis for future design and operational strategies, such as targeted use of high-conductivity materials near the GHE inlet and periodic flow reversal, to improve the cost-effectiveness of shallow geothermal systems.

Accurate characterization of nanoscale pore geometry and pore size distributions (PSDs) in hydrocarbon-bearing unconventional reservoirs (e.g., shale) is crucial for evaluating hydrocarbon storage and fluid mobility. However, a reliable inversion of PSDs from small-angle scattering (SAS) analyses of such dense porous geomaterials remains challenging. We extend empirical-Bayes/Maximum Entropy (MaxEnt) inversion in two directions: joint nonparametric PSD-S(q; theta) inference, and a Poisson-consistent hierarchical inference strategy with profiled regularization. Specifically, structure-factor parameters, which characterize effective inter-pore spatial correlations in dense geological media, are estimated in the outer layer by Laplace-profile evidence, while PSDs are reconstructed in positivity-preserving log space in the inner layer through a Poisson-MaxEnt maximum a posteriori solve. This Poisson formulation models count statistics directly through a Poisson likelihood rather than a Gaussian approximation. Across synthetic tests and shale samples with small-and very-small-angle neutron scattering (SANS/VSANS) applications, the joint treatment reduces correlation-induced false large-size PSD features relative to the baseline of a unity structure-factor and yields more stable PSD derivation. The framework supports more reliable nanoscale pore-structure characterization for unconventional reservoir evaluation, hydrocarbon storage and mobility assessment, and subsurface energy-storage applications.