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DOE OSTI2025

Blue Carbon Stocks Along the Pacific Coast of North America Are Mainly Driven by Local Rather Than Regional Factors

Coastal wetlands, including seagrass meadows, emergent marshes, mangroves, and temperate tidal swamps, can efficiently sequester and store large quantities of sediment organic carbon (SOC). However, SOC stocks may vary by ecosystem type and along environmental or climate gradients at different scales. Quantifying such variability is needed to improve blue carbon accounting, conservation effectiveness, and restoration planning. We analyzed SOC stocks in 1,284 sediment cores along >6,500 km of the Pacific coast of North America that included large environmental gradients and multiple ecosystem types. Tidal wetlands with woody vegetation (mangroves and swamps) had the highest mean stocks to 1 m depth (357 and 355 Mg ha −1 , respectively), 45% higher than marshes (245 Mg ha −1 ), and more than 500% higher than seagrass (68 Mg ha −1 ). Unvegetated tideflats, though not often considered a blue carbon ecosystem, had noteworthy stocks (148 Mg ha −1 ). Stocks increased with tidal elevation and with fine (<63 μm) sediment content in several ecosystems. Stocks also varied by dominant plant species within individual ecosystem types. At larger scales, marsh stocks were lowest in the Sonoran Desert region of Mexico, and swamp stocks differed among climate zones; otherwise stocks showed little correlation with ecoregion or latitude. More variability in SOC occurred among ecosystem types, and at smaller spatial scales (such as individual estuaries), than across regional climate gradients. These patterns can inform coastal conservation and restoration priorities across scales where preserving stored carbon and enhancing sequestration helps avert greenhouse gas emissions and maintains other vital ecosystem services.

emergent marsh↗

DOE OSTI2021

Simultaneous ambient pressure x-ray photoelectron spectroscopy and grazing incidence x-ray scattering in gas environments

We have developed an experimental system to simultaneously measure surface structure, morphology, composition, chemical state, and chemical activity for samples in gas phase environments. This is accomplished by simultaneously measuring x-ray photoelectron spectroscopy (XPS) and grazing incidence x-ray scattering in gas pressures as high as the multi-Torr regime while also recording mass spectrometry. Scattering patterns reflect near-surface sample structures from the nano-scale to the meso-scale, and the grazing incidence geometry provides tunable depth sensitivity of structural measurements. Scattered x rays are detected across a broad range of angles using a newly designed pivoting-UHV-manipulator for detector positioning. At the same time, XPS and mass spectrometry can be measured, all from the same sample spot and under ambient conditions. To demonstrate the capabilities of this system, we measured the chemical state, composition, and structure of Ag-behenate on a Si(001) wafer in vacuum and in O 2 atmosphere at various temperatures. These simultaneous structural, chemical, and gas phase product probes enable detailed insights into the interplay between the structure and chemical state for samples in gas phase environments. The compact size of our pivoting-UHV-manipulator makes it possible to retrofit this technique into existing spectroscopic instruments installed at synchrotron beamlines. Because many synchrotron facilities are planning or undergoing upgrades to diffraction limited storage rings with transversely coherent beams, a newly emerging set of coherent x-ray scattering experiments can greatly benefit from the concepts we present here.

47 OTHER INSTRUMENTATION↗

NASA NTRS2017

Environmental Barrier Coating Fracture, Fatigue and High-Heat-Flux Durability Modeling and Stochastic Progressive Damage Simulation

Advanced environmental barrier coatings will play an increasingly important role in future gas turbine engines because of their ability to protect emerging light-weight SiC/SiC ceramic matrix composite (CMC) engine components, further raising engine operating temperatures and performance. Because the environmental barrier coating systems are critical to the performance, reliability and durability of these hot-section ceramic engine components, a prime-reliant coating system along with established life design methodology are required for the hot-section ceramic component insertion into engine service. In this paper, we have first summarized some observations of high temperature, high-heat-flux environmental degradation and failure mechanisms of environmental barrier coating systems in laboratory simulated engine environment tests. In particular, the coating surface cracking morphologies and associated subsequent delamination mechanisms under the engine level high-heat-flux, combustion steam, and mechanical creep and fatigue loading conditions will be discussed. The EBC compostion and archtechture improvements based on advanced high heat flux environmental testing, and the modeling advances based on the integrated Finite Element Analysis Micromechanics Analysis Code/Ceramics Analysis and Reliability Evaluation of Structures (FEAMAC/CARES) program will also be highlighted. The stochastic progressive damage simulation successfully predicts mud flat damage pattern in EBCs on coated 3-D specimens, and a 2-D model of through-the-thickness cross-section. A 2-parameter Weibull distribution was assumed in characterizing the coating layer stochastic strength response and the formation of damage was therefore modeled. The damage initiation and coalescence into progressively smaller mudflat crack cells was demonstrated. A coating life prediction framework may be realized by examining the surface crack initiation and delamination propagation in conjunction with environmental degradation under high-heat-flux and environment load test conditions.

Mechanical creep and fatigue loading condition↗

NASA NTRS1992

Stability investigations of airfoil flow by global analysis

As the result of global, non-parallel flow stability analysis the single value of the disturbance growth-rate and respective frequency is obtained. This complex value characterizes the stability of the whole flow configuration and is not referred to any particular flow pattern. The global analysis assures that all the flow elements (wake, boundary and shear layer) are taken into account. The physical phenomena connected with the wake instability are properly reproduced by the global analysis. This enhances the investigations of instability of any 2-D flows, including ones in which the boundary layer instability effects are known to be of dominating importance. Assuming fully 2-D disturbance form, the global linear stability problem is formulated. The system of partial differential equations is solved for the eigenvalues and eigenvectors. The equations, written in the pure stream function formulation, are discretized via FDM using a curvilinear coordinate system. The complex eigenvalues and corresponding eigenvectors are evaluated by an iterative method. The investigations performed for various Reynolds numbers emphasize that the wake instability develops into the Karman vortex street. This phenomenon is shown to be connected with the first mode obtained from the non-parallel flow stability analysis. The higher modes are reflecting different physical phenomena as for example Tollmien-Schlichting waves, originating in the boundary layer and having the tendency to emerge as instabilities for the growing Reynolds number. The investigations are carried out for a circular cylinder, oblong ellipsis and airfoil. It is shown that the onset of the wake instability, the waves in the boundary layer, the shear layer instability are different solutions of the same eigenvalue problem, formulated using the non-parallel theory. The analysis offers large potential possibilities as the generalization of methods used till now for the stability analysis.

Morzynski, Marek↗

DOE OSTI2020

Ink-substrate interactions during 3D printing revealed by time-resolved coherent X-ray scattering

Additive printing techniques are regarded as revolutionary and versatile methods of advanced device manufacturing, stemming from the possibility to pattern materials on a custom-based approach and the potential to create novel microstructures and achieve new functionalities. Despite these advantages, the inherent anisotropy of the printing process is a source of property gradients within the printed materials, often associated with variable and/or poor performance. Up to date, the evolutionary pathways associated with printing have largely remained unaddressed, mainly owing to the difficulty to study the transformations induced in the material during processing. Time-resolved coherent X-ray scattering techniques, such as X-ray photon correlation spectroscopy, enable the in situ study of transient nanoscale and mesoscale states in a large variety of materials, including amorphous ones, by directly accessing the most relevant timescales and length scales of their nanoscale and mesoscale dynamics, self-assembly, and mesostructure evolution. In this work, we conduct in operando studies of continuous-flow direct writing with colloidal inks, focusing on how the ink formulation and ink-substrate interactions affect the processes that determine the macroscopic properties of the printed materials. We find fundamental differences in the ink structural relaxations emerging from the primary colloid properties (monodisperse versus aggregated colloids) and the substrates surface energy and mechanical properties. Our work helps to reveal and quantify the basic science governing the evolution of 3D-printed materials during processing, ultimately improving engineering criteria for the design of printable materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2020

Shape Validation and RF Performance of Inflatable Antennas

Inflatable aperture antennas are an emerging technology that is being investigated for potential use in science and exploration missions. In particular, for missions to Mars and beyond, large deployable aperture antennas can provide the antenna gain required for high data rate communications, where the necessary antenna diameter exceeds the available volume of typical launch vehicle platforms. As inflatable aperture antennas have not been proven fully qualified for space missions, the author's Master's Thesis assessed the Ruze equation in characterizing this antenna technology. Inflatable aperture antennas do not follow a parabolic shape, and so the Ruze equation is not applicable due to the macroscopic shape errors of this technology. Therefore, geometric evaluations of the surface profile cannot simply correlate antenna gain degradation with the root-mean-square shape error with a parabolic surface. Consequently, the focus of this work was to derive an accurate mathematical model of an inflatable aperture antenna in order to characterize its Radio Frequency (RF) performance. Calculus of Variations methodologies were used to derive the surface profile shape of the inflatable aperture antenna. Physical Optics techniques were used to generate the antenna pattern profile. Validation testing of the predicted inflatable antenna shape model was performed through use of Laser Radar metrology measurements on an inflatable test article. Assessments of the RF performance of the inflatable aperture antenna, compared with nominally shaped paraboloidal antennas, were obtained through simulations of both technologies using a common diameter, depth, and arc length. Assessments of the RF performance of the inflatable aperture antenna was also performed against itself for changes in distance of the antenna feed location in the axial direction. Whereas the Ruze equation is limited to assessing gain reduction, this effort will also assess beam spreading and first side lobe angle and magnitude. The ability to characterize the RF response of this antenna will provide for an improved understanding of this technology. The accurate representation of the shape of this type of antenna technology will help to identify the most appropriate ways in which this technology could be utilized in planning future communication architectures for NASA missions to Mars and beyond.

Welch, Bryan W.↗

DOE OSTI2021

Sequence Analysis of 20,453 Severe Acute Respiratory Syndrome Coronavirus 2 Genomes from the Houston Metropolitan Area Identifies the Emergence and Widespread Distribution of Multiple Isolates of All Major Variants of Concern

Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been international concern about the emergence of virus variants with mutations that increase transmissibility, enhance escape from the human immune response, or otherwise alter biologically important phenotypes. In late 2020, several variants of concern emerged globally, including the UK variant (B.1.1.7), the South Africa variant (B.1.351), Brazil variants (P.1 and P.2), and two related California variants of interest (B.1.429 and B.1.427). These variants are believed to have enhanced transmissibility. For the South Africa and Brazil variants, there is evidence that mutations in spike protein permit it to escape from some vaccines and therapeutic monoclonal antibodies. On the basis of our extensive genome sequencing program involving 20,453 coronavirus disease 2019 patient samples collected from March 2020 to February 2021, we report identification of all six of these SARS-CoV-2 variants among Houston Methodist Hospital (Houston, TX) patients residing in the greater metropolitan area. Although these variants are currently at relatively low frequency (aggregate of 1.1%) in the population, they are geographically widespread. Houston is the first city in the United States in which active circulation of all six current variants of concern has been documented by genome sequencing. Finally, as vaccine deployment accelerates, increased genomic surveillance of SARS-CoV-2 is essential to understanding the presence, frequency, and medical impact of consequential variants and their patterns and trajectory of dissemination.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2020

Shape Validation and RF Performance of Inflatable Antennas

Inflatable aperture antennas are an emerging technology that is being investigated for potential use in science and exploration missions. In particular, for missions to Mars and beyond, large deployable aperture antennas can provide the antenna gain required for high data rate communications, where the necessary antenna diameter exceeds the available volume of typical launch vehicle platforms. As inflatable aperture antennas have not been proven fully qualified for space missions, the author's Master's Thesis assessed the Ruze equation in characterizing this antenna technology. Inflatable aperture antennas do not follow a parabolic shape, and so the Ruze equation is not applicable due to the macroscopic shape errors of this technology. Therefore, geometric evaluations of the surface profile cannot simply correlate antenna gain degradation with the root-mean square shape error with a parabolic surface. Consequently, the focus of this work was to derive an accurate mathematical model of an inflatable aperture antenna to in order to characterize its Radio Frequency (RF) performance. Calculus of Variations methodologies were used to derive the surface profile shape of the inflatable aperture antenna. Physical Optics techniques were used to generate the antenna pattern profile. Validation testing of the predicted inflatable antenna shape model was performed through use of Laser Radar metrology measurements on an inflatable test article. Assessments of the RF performance of the inflatable aperture antenna, compared with nominally shaped paraboloidal antennas, were obtained through simulations of both technologies using a common diameter, depth, and arc length. Assessments of the RF performance of the inflatable aperture antenna was also performed against itself for changes in distance of the antenna feed location in the axial direction. Whereas the Ruze equation is limited to assessing gain reduction, this effort will also assess beam spreading and first side lobe angle and magnitude. The ability to characterize the RF response of this antenna will provide for an improved understanding of this technology. The accurate representation of the shape of this type of antenna technology will help to identify the most appropriate ways in which this technology could be utilized in planning future communication architectures for NASA missions to Mars and beyond.

Welch, Bryan↗

DOE OSTI2020

Wettability and capillary effects: Dynamics of pinch-off in unconstricted straight capillary tubes

We study the interfacial evolution of immiscible two-phase flow within a capillary tube in the partial wetting regime using direct numerical simulation. We investigate the flow patterns resulting from the displacement of a more viscous fluid by a less viscous one under a wide range of wettability conditions. We find that beyond a wettability dependent critical capillary number, a uniform displacement by a less viscous fluid can transition into a growing finger that eventually breaks up into discrete blobs by a series of pinch-off events for both wetting and non-wetting contact angles. This study validates previous experimental observations of pinch-off for wetting contact angles and extends those to non-wetting contact angles. We find that the blob length increases with the capillary number. We observe that the time between consecutive pinch-off events decreases with the capillary number and is greater for more wetting conditions in the displaced phase. Here, we further show that the blob separation distance as a function of the difference between the inlet velocity and the contact line speed collapses into two monotonically decreasing curves for wetting and non-wetting contact angles. For the phase separation in the form of pinch-off, this work provides a quantitative study of the emerging length and time scales and their dependence on the wettability conditions, capillary effects, and viscous forces.

42 ENGINEERING↗

NASA NTRS1995

Neurochemical phenotype of corticocortical connections in the macaque monkey: quantitative analysis of a subset of neurofilament protein-immunoreactive projection neurons in frontal, parietal, temporal, and cingulate cortices

The neurochemical characteristics of the neuronal subsets that furnish different types of corticocortical connections have been only partially determined. In recent years, several cytoskeletal proteins have emerged as reliable markers to distinguish subsets of pyramidal neurons in the cerebral cortex of primates. In particular, previous studies using an antibody to nonphosphorylated neurofilament protein (SMI-32) have revealed a consistent degree of regional and laminar specificity in the distribution of a subpopulation of pyramidal cells in the primate cerebral cortex. The density of neurofilament protein-immunoreactive neurons was shown to vary across corticocortical pathways in macaque monkeys. In the present study, we have used the antibody SMI-32 to examine further and to quantify the distribution of a subset of corticocortically projecting neurons in a series of long ipsilateral corticocortical pathways in comparison to short corticocortical, commissural, and limbic connections. The results demonstrate that the long association pathways interconnecting the frontal, parietal, and temporal neocortex have a high representation of neurofilament protein-enriched pyramidal neurons (45-90%), whereas short corticocortical, callosal, and limbic pathways are characterized by much lower numbers of such neurons (4-35%). These data suggest that different types of corticocortical connections have differential representation of highly specific neuronal subsets that share common neurochemical characteristics, thereby determining regional and laminar cortical patterns of morphological and molecular heterogeneity. These differences in neuronal neurochemical phenotype among corticocortical circuits may have considerable influence on cortical processing and may be directly related to the type of integrative function subserved by each cortical pathway. Finally, it is worth noting that neurofilament protein-immunoreactive neurons are dramatically affected in the course of Alzheimer's disease. The present results support the hypothesis that neurofilament protein may be crucially linked to the development of selective neuronal vulnerability and subsequent disruption of corticocortical pathways that lead to the severe impairment of cognitive function commonly observed in age-related dementing disorders.

Non-NASA Center↗

NASA NTRSDate not supplied

A Climate and History Case Study of 18th- and 19th-Century Multidecadal Droughts in East Africa Using a new Tree-Ring Drought Atlas

Historians and paleoclimatologists have both identified the decades spanning the late-18th and early-19th centuries in many East African regions as a period of prolonged and severe drought. A challenge that emerges from both the historical evidence and the paleolimnological data that have been primarily used to characterize this drought period, however, concerns the dating of events and the characterization of their spatial character; both oral traditions and lake-core proxies cannot typically offer hydroclimate estimates with seasonal or annual resolution, nor can they provide gridded reconstructions on the order of 1° latitude and longitude. The new East African Drought Atlas (EADA) incorporates far-field dendroclimatic records and several local East African tree-ring chronologies to provide estimates of the self-calibrating Palmer Drought Severity Index (scPDSI) on a 0.5° latitude-longitude grid. We present the Point-by-Point Regression technique that was used to create the EADA, the rationale for the employed input data network, and the calibration and validation skill of the derived hydroclimatic field reconstruction. We use the EADA to characterize the timing of two decadal-scale droughts, centered over the Uganda and Kenya regions, which occurred in the late 18th century and early in the 19th century and were separated by a shorter, but pronounced wet period. We characterize the spatial patterns of the associated events and the dynamical causes that that the patterns imply. The reconstructed droughts are also compared to historical and paleolimnological data to evaluate the robustness of the reconstruction and the degree of agreement across the different sources of information.

Droughts↗

NASA NTRSDate not supplied

A Climate and History Case Study of 18th- and 19th-Century Multidecadal Droughts in East Africa Using a new Tree-Ring Drought Atlas

Droughts↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS1990

The vestibular nerve of the chinchilla. III. Peripheral innervation patterns in the utricular macula

1. Nerve fibers supplying the utricular macula of the chinchilla were labeled by extracellular injection of horseradish peroxidase into the vestibular nerve. The peripheral terminations of individual fibers were reconstructed and related to the regions of the end organ they innervated and to the sizes of their parent axons. 2. The macula is divided into medial and lateral parts by the striola, a narrow zone that runs for almost the entire length of the sensory epithelium. The striola can be distinguished from the extrastriolar regions to either side of it by the wider spacing of its hair cells. Calyx endings in the striola have especially thick walls, and, unlike similar endings in the extrastriola, many of them innervate more than one hair cell. The striola occupies 10% of the sensory epithelium; the lateral extrastriola, 50%; and the medial extrastriola, 40%. 3. The utricular nerve penetrates the bony labyrinth anterior to the end organ. Axons reaching the anterior part of the sensory epithelium run directly through the connective tissue stroma. Those supplying more posterior regions first enter a fiber layer located at the bottom of the stroma. Approximately one-third of the axons bifurcate below the epithelium, usually within 5-20 microns of the basement membrane. Bifurcations are more common in fibers destined for the extrastriola than for the striola. 4. Both calyx and bouton endings were labeled. Calyces can be simple or complex. Simple calyces innervate individual hair cells, whereas complex calyces supply 2-4 adjacent hair cells. Complex endings are more heavily concentrated in the striola than in the extrastriola. Simple calyces and boutons are found in all parts of the epithelium. Calyces emerge from the parent axon or one of its thick branches. Boutons, whether en passant or terminal, are located on thin collaterals. 5. Fibers can be classified into calyx, bouton, or dimorphic categories. The first type only has calyx endings; the second, only bouton endings; and the third, both kinds of endings. Calyx units make up 6% of the labeled fibers, bouton units less than 2%, and dimorphic units greater than 92%. The three fiber types differ in the macular zones they supply and in the diameters of their parent axons. Calyx units were restricted to the striola. The few bouton units were found in the extrastriola.(ABSTRACT TRUNCATED AT 400 WORDS).

NASA Discipline Neuroscience↗

DOE OSTI2022

Imaging Phase Segregation in Nanoscale Li x CoO 2 Single Particles

Li x CoO 2 (LCO) is a common battery cathode material that has recently emerged as a promising material for other applications including electrocatalysis and as electrochemical random access memory (ECRAM). During charge– discharge cycling LCO exhibits phase transformations that are significantly complicated by electron correlation. While the bulk phase diagram for an ensemble of battery particles has been studied extensively, it remains unclear how these phases scale to nanometer dimensions and the effects of strain and diffusional anisotropy at the single-particle scale. Understanding these effects is critical to modeling battery performance and for predicting the scalability and performance of electrocatalysts and ECRAM. Here we investigate isolated, epitaxial LiCoO 2 islands grown by pulsed laser deposition. After electrochemical cycling of the islands, conductive atomic force microscopy (c-AFM) is used to image the spatial distribution of conductive and insulating phases. Above 20 nm island thicknesses, we observe a kinetically arrested state in which the phase boundary is perpendicular to the Li-planes; we propose a model and present image analysis results that show smaller LCO islands have a higher conductive fraction than larger area islands, and the overall conductive fraction is consistent with the lithiation state. Thinner islands (14 nm), with a larger surface to volume ratio, are found to exhibit a striping pattern, which suggests surface energy can dominate below a critical dimension. When increasing force is applied through the AFM tip to strain the LCO islands, significant shifts in current flow are observed, and underlying mechanisms for this behavior are discussed. The c-AFM images are compared with photoemission electron microscopy images, which are used to acquire statistics across hundreds of particles. Finally, the results indicate that strain and morphology become more critical to electrochemical performance as particles approach nanometer dimensions.

25 ENERGY STORAGE↗

DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗

NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗