Applied Research (United States)

Lithium-sulfur hexafluoride (Li-SF6) stored chemical energy propulsion systems (SCEPS) have been employed as self-contained, high-specific-energy, power systems for undersea vehicles in defense applications. Recent investigations have studied the potential to adapt this technology for spacecraft power and heating. The reaction between lithium and SF6 produces 4049 Wh/kg of heat, which can enable long duration space missions operating in cold, dark, and resource-constrained environments. However, there are knowledge gaps related to the spatial distribution of corrosive intermediate products in such reactors and heat flux distribution on the reactor surface. The present study develops a multiphase reacting flow simulation approach for wick-type SCEPS reactors to predict such factors and guide system engineering for long-duration spacecraft operation. Results from this multiphase simulation are compared with a previously developed ideal gas formulation. Minimal deviations (<1%) are observed in many parameters such as the reacting oxidizer jet velocity field. Moderate deviations are identified in other fields (up to ~10%), such as the mixture density field. Overall, findings suggest that multiphase phenomena in the main combustion zone are minor and that the peak condensed phase volume fraction is < 1E-5. A second analysis is performed to assess impacts of two-phase SF6 oxidizer injection into the reactor. This could occur due to operation in cold environments and under reduced gravity, which could make phase separation more challenging in the oxidizer tank. An analytic state-point model is employed to predict phase distribution along the SF6 nozzle and jet flow. From this analysis, cases where liquid SF6 is present in the nozzle flow demonstrated higher velocities in the flow when compared to scenarios where solid SF6 is present. This aspect of the flow would be favorable as it could potentially push the corrosive species further downstream of the injector and hence reducing the possibility of corrosion.

The comprehensive identification of environmental and endogenous chemicals in human biospecimens is a critical bottleneck for realizing the Human Exposome Project. Untargeted metabolomics, particularly liquid chromatography–high-resolution mass spectrometry (LC–HRMS), offers unparalleled coverage of small molecules, but most detected features remain unidentified due to limited spectral libraries and structural ambiguity. Retention time (RT) prediction—based on quantitative structure–retention relationships (QSRR) and enhanced by artificial intelligence (AI)—is an underutilized orthogonal parameter that can substantially improve metabolite annotation confidence. This review synthesizes advances in machine learning–based RT prediction, probabilistic calibration, and cross-platform harmonization for liquid chromatography and gas chromatography, including deep learning, graph neural networks, and transfer learning approaches. We evaluate workflows integrating RT prediction with mass-based searches and network-based annotation tools, highlighting their potential to refine candidate ranking and reduce false positives in environmental exposure assessment. The use of endogenous compounds as internal calibrants is discussed as a practical strategy for improving RT transferability across laboratories. We further outline how RT-aware annotation supports non-targeted screening of emerging contaminants, transformation products, and exposure biomarkers, thereby enhancing the interpretability and reproducibility of exposomics data. By integrating RT prediction, QSRR modeling, and AI into untargeted metabolomics pipelines, researchers can move from qualitative detection toward quantitative, inference-driven mapping of environmental influences on human health, strengthening the scientific foundation for environmental health policy and preventive public health strategies.

Coral reefs are undergoing unprecedented degradation due to rising ocean temperatures, acidification, overfishing, and coastal pollution. Despite conservation efforts, including marine protected areas and sustainable fishing practices, the magnitude of these challenges calls for innovative approaches to repair and restore coral reefs. In this study, we explore the application of bioinspired materials to address the challenge of algal competition, a key bottleneck for effective restoration approaches. We develop and optimize slippery liquid-infused porous surfaces (SLIPS), as a fouling-prevention coating tailored for coral reef restoration and engineering. Through aquarium experiments and in situ trials on O'ahu, Hawai'i, we assess the effectiveness of these coatings in mitigating algal competition and facilitating coral growth. Our results demonstrate that PDMS-based SLIPS coatings significantly reduce algal coverage compared to commercial aragonite-based surfaces, with up to 70% reduction observed over a 12-week deployment period in situ. We also develop coral-guards, which are slippery substrates customized for coral fragment outplanting. Coral-guards facilitate tissue growth of Stylophora pistillata fragments, without competitive turf algal growth. These approaches hold promise for advancing restoration efforts, including the engineering of hybrid reefs and targeted coral gardening approaches.

We present observational data from the mouth of the Connecticut River, a shallow salt-wedge estuary characterised by intense interfacial mixing. The motivation is to better understand, and ultimately predict, density-stratified turbulent mixing driven by shear instabilities at high Reynolds numbers (Re &gt; 10^5). Such processes span an immense turbulent energy cascade across eight orders of magnitude, from coherent instabilities at kilometre scales to the smallest mixing eddies at micrometre scales. Using multi-beam echo-sounding imagery, we reveal the spatial structure and temporal evolution of turbulent mixing with unprecedented detail. During the flood tide, large-scale topography and hydraulics cause the pycnocline to slope, which triggers, through baroclinic forcing, primary shear instabilities in the form of long trains of Kelvin-Helmholtz billows. Our data demonstrate that at Re ~ 5x10^5, mixing occurs primarily by turbulence in the braids connecting the cores of the billows rather than within the cores themselves. This secondary 'braid turbulence' is continuously forced by the secondary baroclinic generation of shear within the sloping braids. This finding challenges the prevailing paradigm built upon direct numerical simulations (DNS) at lower Reynolds numbers (Re ~ 10^3-10^4), where mixing is thought to occur primarily by overturning in the billow cores. This distinction is a significant shift in understanding the high-Re turbulent cascade in mixing hotspots, with potential implications for mixing parameterisations in the coastal ocean.

Artificial intelligence has left the auditorium and entered daily rounds.The US Food and Drug Administration lists about 1016 AI or machine learning medical devices as of March 25, 2025, and roughly 9 more join the roster each month.Students and early-career clinicians must now decide when to trust, revise, or reject machine output during every step of patient care. How This Book Is Organized Part I: FoundationsYou will diagram transformer layers, trace self-attention scores, and show classmates how a single prompt token changes an answer.Master these skills and you can explain any "black-box" result during bedside teaching. Part II: Clinical IntegrationYou will design a pilot project, secure stakeholder support, insert legal and ethical checkpoints, and present results that speak to safety and equity.By the final page, you can defend or decline an AI tool with hard evidence, not hype. Part III: Capstone PracticeGuided rubrics lead you through an AI journal club you can run on rotation.You will refine prompts with Plan-Do-Study-Act cycles and track accuracy, speed, or cost so the team sees measurable value.viii Part IV: Professional GrowthClear roadmaps connect micro-credentials, grant opportunities, and SMART goals to your residency timeline.A closing worksheet turns today's curiosity into tomorrow's expertise. How You Will Learn Quick-look questions start each chapter and set the frame. Mid-chapter quizzes reveal gaps before they harden into habits. Reflection prompts tie new concepts to last week's patients. Practical drills-prompt tuning, bias audits, and PDSA cycles-convert theory into bedside routine. Online notebooks, datasets, and a faculty guide extend the experience beyond the printed page.Every chapter follows the same reliable rhythm: objectives, concise theory, hands-on practice, takeaway tables, and a "Ready for Rounds" checklist.Sidebars flag regulatory changes, ethical dilemmas, and cautionary case reports.A glossary keeps jargon in check. Your Guiding QuestionCarry this question through every exercise: How will this tool improve patientcentered care where I practice?The answer will change as data shift, software updates, and policies evolve.Work through each quiz, prompt, and drill, and you will build the habit of continuous evaluation that defines responsible stewardship of clinical AI.Thank you for investing your time and curiosity.May the skills you develop here deepen your clinical reasoning, strengthen teamwork, and, above all, improve the lives of the people who trust you.

This final chapter summarizes key strategies for successful digital marketing in healthcare, from local SEO to online reputation management. It reinforces ethical principles and provides a clear call to action, urging providers to assess their current efforts and adopt forward-thinking, patient-centered marketing strategies.

The interactions between Earth’s surface and atmosphere are crucial to understanding their impact on surface layer optical turbulence, specifically the temperature structure function (C_T²) and refractive index structure function (C_n²). The Energy Balance Bowen Ratio (EBBR) – the ratio of sensible heat flux to latent heat flux – has shown promising capabilities to calculate sensible heat flux, a key component for computing C_T² and C_n². Sensible heat as calculated via the Bowen Ratio inherently accounts for moisture content and evaporation as it apportions the balance of sensible heat to latent heat in the ratio. Thus it better permits the calculation of C_T² and C_n² via a single equation only dependent on temperature and sensible heat in any stability condition as compared to “ground truth” sonic anemometer turbulence values during daylight and nighttime hours at various land sites. The Bulk Aerodynamic method relies on standard meteorological observations but requires stability corrections based on underlying assumptions with this approach. Researchers have shown success of Bulk Aerodynamic methods and similarity theory to predict C_n² in the maritime surface layer, but many adjustments for weakness in stable conditions (air warmer than the water) are necessary. In this study, field data from a marine wave boundary layer test site allow for assessments of both the EBBR and Aerodynamic methods to quantify maritime surface layer turbulence, and the results compared to sonic anemometer and DELTA C_n² values.