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At least 1,117 records · Page 62

Market Survey 2020: Commercial Clinical Decision Support Systems and Wellness Tools

For long-duration, deep space exploration missions, current methods for managing and supporting crew health and medical conditions will be unsuitable. Communication and data transmission lags will necessitate the use of a sophisticated clinical decision support system (CDSS) that will tailor diagnosis and treatment guidance that is context-sensitive for anticipated astronaut health, wellness, and medical conditions. A variety of clinical decision support (CDS) and wellness tools (WT) are currently available in the commercial market and a broad-brush survey of this market can provide an initial impression of the current state of the art which, in turn, can inform the roadmap of NASA deep space CDSS development and associated requirements. Such a survey was undertaken during the first six months of 2020 using directed convenience sampling to obtain information provided by vendors on their websites; both commercially available CDS and WT (such as those used to track and monitor nutrition, exercise, and sleep) were included. Areas assessed were item type (e.g., software/application, device); primary purpose of the item (e.g., diagnostic support, nutrition tracking); additional purposes (if any); reported features, capabilities, and functionality; setting of use (e.g., inpatient, outpatient); intended user (e.g., clinician, patient); location and sources of data/information used or produced by the item; integration with patient electronic health record (EHR); compliance with interoperability ontologies and standards (e.g., Health Level 7 [HL7], Systematized Nomenclature of Medicine – Clinical Terminology [SNOMED-CT]); and whether the item is knowledge-based (derived from research findings) or non-knowledge-based (derived through artificial intelligence, machine learning, advanced probability and statistics), among others. Ninety-seven (97) vendor websites describing 196 CDS and 73 WT (269 total) were reviewed and coded. The primary purpose of the majority of CDS reviewed is diagnosis or diagnosis/treatment/drug decision support—targeted for clinician use— and the primary purpose of the majority of WT reviewed is the monitoring of different health metrics, most often through the use of a biosensor device (e.g., blood pressure)—targeted for patient use. Very few CDS or WT appear to comply with major international interoperability standards or can be integrated with a patient’s EHR data. None consider contextual factors, such as conditions of the physical environment (e.g., CO2 levels). The majority of CDS and WT reviewed are non-knowledge, cloud- or web-based applications or software. Forty-three (43) major findings were identified and the implications those findings have for NASA will be discussed. Example major findings include: CDS-WT capabilities range from diagnosis to treatment applications, CDS-WT may be wearable or non-wearable and are technologically advanced and only a few CDS-WT tools referenced compliance to ensure interoperability, among other findings. Recommendations will also be offered that will help to address ExMC Gap, Medical-701: Enhance medical capabilities within an exploration medical system.

market survey↗

A Catalog of Emission-line Galaxies from the Faint Infrared Grism Survey: Studying Environmental Influence on Star Formation

We present a catalog of 208 0.3 < z < 2.1 emission-line galaxies (ELGs) selected from 1D slitless spectroscopy obtained using Hubble's WFC3 G102 grism, as part of the Faint Infrared Grism Survey. We identify ELG candidates by searching for significant peaks in all continuum-subtracted G102 spectra and, where possible, confirm candidates by identifying consistent emission lines in other available spectra or with published spectroscopic redshifts. We provide derived emission-line fluxes and errors, redshifts, and equivalent widths for Hα λ6563, [O iii] λλ4959, 5007, and [O ii] λ3727 emission lines, for ELGs down to AB(F105W) >28 and >10^(−17) erg/ sq. cms line flux. We use the resulting line catalog to investigate a possible relationship between line emission and a galaxy's environment. We use seventh-nearest-neighbor distances to investigate the typical surroundings of ELGs compared to non-ELGs, and we find that [O iii] emitters are preferentially found at intermediate galaxy densities near galaxy groups. We characterize these ELGs in terms of the galaxy specific star formation rate (SFR) versus stellar mass and find no significant influence of environment on that relation. We calculate SFRs and find no dependence of SFR on local galaxy surface density for 0.3 < z < 0.8 Hα emitters and for 0.8 < z < 1.3 [O iii] emitters. We find similar rates of close-pair interaction between ELGs and non-ELGs. For galaxy surface densities Σ ≤ 30 Mp/sq.c, we find no consistent effect of environment on star formation.

John Pharo↗

Adaptive Stress Testing of Collision Avoidance Systems for Small UASs with Deep Reinforcement Learning

The next-generation Airborne Collision Avoidance System for smaller UASs (ACAS sXu) is currently being developed and tested by the Federal Aviation Administration (FAA) to provide detect-and-avoid capability for small unmanned aircraft operating beyond line-of-sight. Due to the complexity and safety-critical nature of the system, safety validation is important not only for the certification of the final system, but also for informing changes during the iterative development process. In this paper, we analyze a prototype of ACAS sXu in simulated aircraft encounters to discover scenarios of small near mid-air collisions (sNMACs), an important safety event in which two aircraft come closer than 50 feet horizontally and 15 feet vertically. Due to the size and complexity of the system as well as rarity of sNMAC events, traditional methods such as Monte Carlo testing often require informed setup and targeting to elicit failures. However, such a dependence on domain knowledge can be incompatible with the independent verification and validation (IV&V) process, the aim of which is to discover unforeseen issues. To address these challenges, we apply an accelerated validation method called adaptive stress testing (AST) to find the most likely sNMAC scenarios without reliance on system introspection. AST uses reinforcement learning to adapt the search towards the most promising areas of the search space as it progresses. We use a state-of-the-art deep reinforcement learning algorithm, proximate policy optimization, to more efficiently search the large and continuous state space. We find that this approach significantly improves the performance of AST compared to a prior approach based on Monte Carlo tree search. We perform experiments using AST to find sNMAC events under various encounter configurations, varying parameters pertaining to dynamics and coordination. Our experiments show AST to be very effective at finding sNMAC scenarios. We summarize our findings, presenting high-level categories of discovered sNMACs and specific examples of encounters in each category.

aircraft collision avoidance↗

One-Carbon Metabolism and SANS 2022 Update

Spaceflight Associated Neuro-ocular Syndrome, or SANS, affects a subset of astronauts (1, 2), and biochemical evidence has documented differences in those astronauts (3). Specifically, they had higher circulating concentrations of metabolites of the one-carbon metabolic pathway (1C), including homocysteine, and these concentrations were higher before flight (3). After ruling out many potential confounding factors in these otherwise healthy individuals (e.g., sex, kidney function, vitamin status, coffee consumption), a study of genetics was warranted. In an initial pilot effort, we documented a genetic predisposition to develop ophthalmic changes after long-duration space flight (4). That is, from a limited study of 5 single-nucleotide polymorphisms (SNPs), we found that the G allele for the MTRR A66G SNP was associated with a greater risk of choroidal folds and cotton-wool spots after flight, and the C allele for SHMT1 C1420T was protective against optic disc edema (4). These data provide a potential pathway for understanding why some individuals develop SANS, while others do not. The initial pilot study of 5 SNPs yielded striking findings, but the 1C pathway is far more complex. An effort was undertaken to examine more than 500 1C SNPs to see if a broader examination could help illuminate this association. That work is ongoing. The astronaut findings led us to advocate for the inclusion of 1C pathway genetic and biochemistry testing on other SANS-related projects, noting that genetics might help identify responders, non-responders, or outliers. The first such effort yielded evidence of an association of specific forms of the MTRR and SHMT-1 SNPs and vitamin B12 status with end-tidal CO2 after acute carbon dioxide exposure (5). The second such effort led to the identification that individuals exposed to strict head-down tilt and CO2 for 30-d who developed optic disc edema also had risk alleles for the two SNPs described above (6). Additionally, we identified a clinical population with many characteristics either attributed or purported to be involved in the ocular changes seen in affected astronauts: women with polycystic ovary syndrome (PCOS). PCOS is a condition of androgen excess and anovulatory menstrual cycles. The shared characteristics and clinical findings between SANS and PCOS generally include higher circulating homocysteine concentrations, increased retinal nerve fiber layer thickness, increased androgen concentrations (or responses), and altered carbohydrate metabolism. To our knowledge, no study has examined whether women with PCOS have asymptomatic ophthalmic anomalies observed in astronauts with SANS. While researchers have evaluated the one-carbon metabolism pathway polymorphisms of PCOS patients, and initial studies show an association with certain one-carbon polymorphisms, none have looked at the set of SNPs identified in our studies that are associated with ophthalmic changes in astronauts. Accordingly, we designed a study to evaluate the association of one-carbon pathway SNPs and ophthalmic findings in patients with PCOS and/or IIH compared to controls. Subjects provided blood samples for vitamin and one carbon biochemistry analyses, an extensive analysis of >500 SNPs associated with one carbon metabolism and had eye examinations and ocular imaging. Data analysis are underway. The data collected to date have shown associations between one carbon pathway biochemistry and genetics and incidence of SANS. The mechanisms for SANS has yet to be identified, although many hypotheses exist. Based on our data, we have developed (8, 9) and expanded (6) a multi-hit hypothesis for how these seemingly disparate findings could be linked. While intriguing, the hypothesis represents the starting point for further research. We aim to clarify the relationship between B-vitamin status and genetics with regard to the risk of SANS. Ultimately, understanding the mechanism(s) behind this will provide a means to predict, prevent, or treat these ophthalmologic pathologies in astronauts, and terrestrial populations.

S M Smith↗

Gravity Wave Observations by the Mars Science Laboratory REMS Pressure Sensor and Comparison with Mesoscale Atmospheric Modeling with MarsWRF

Surface pressure measurements on Mars have revealed a wide variety of atmospheric phenomena. The Mars Science Laboratory Rover Environmental Monitoring Station pressure sensor dataset is now the longest duration record of surface pressure on Mars. We use the first 2580 martian sols, nearly 4 Mars years,of measurements to identify atmospheric pressure waves with periods of tens of minutes to hours using wavelet analysis on residual pressure after the tidal harmonics are removed. We find these waves have a clear diurnal cycle with strongest activity in the early morning and late evening and a seasonal cycle with the strongest waves in the second half of the martian year (Ls= 180-360°). The strongest such waves of the entire mission occurred during the Mars Year 34 global dust storm. Comparable atmospheric waves are identified using atmospheric modeling with the MarsWRF general circulation model in a “nested” high spatial resolution mode. With the support of the modeling, we find these waves best fit the expected properties of inertia-gravity waves with horizontal wavelengths of O(100s) of km. PLAIN LANGUAGE SUMMARY Measuring air pressure from the surface of Mars has revealed a wide variety of atmospheric phenomena. The Curiosity rover's record or surface air pressure is now the longest yet made on Mars.We use the first ~8 years of Curiosity's pressure observations to look for atmospheric waves with periods of tens of minutes to hours. We find these waves have a clear pattern in their daily behavior with the strongest activity in the early morning and late evening and a seasonal cycle with the strongest waves in the second half of the martian year (Northern hemisphere fall and winter). The strongest such waves occurred in 2018 during a global dust storm. We find comparable waves in atmospheric modeling. With the support of modeling, we find these waves best fit the expected properties of buoyancy waves forced by airflow over topography with horizontal wavelengths of 100-1000 km.

Scott D Guzewich↗

Tectonic tremor and LFEs on a reverse fault in Taiwan

In this paper, we compare low-frequency earthquakes (LFEs) from triggered and ambient tremor under the southern Central Range, Taiwan. We apply the PageRank algorithm used by Aguiar and Beroza (2014) that exploits the repetitive nature of the LFEs to find repeating LFEs in both ambient and triggered tremor. We use these repeaters to create LFE templates and find that the templates created from both tremor types are very similar. To test their similarity, we use both interchangeably and find that most of both the ambient and triggered tremor match the LFE templates created from either data set, suggesting that LFEs for both events have a common origin. Finally, we locate the LFEs by using local earthquake P wave and S wave information and find that LFEs from triggered and ambient tremor locate to between 20 and 35 km on what we interpret as the deep extension of the Chaochou-Lishan Fault.

58 GEOSCIENCES↗

Contrasts in compliant fault zone properties inferred from geodetic measurements in the San Francisco Bay area

In crustal fault zones, regions of damaged rock characterized by reduced elastic shear modulus can influence patterns of near-field interseismic deformation. In order to study these compliant fault zones (CFZs) and how they might develop over the lifetimes of faults, we compare two fault segments with contrasting fault age and lithology along the San Andreas Fault in the San Francisco Bay Area. New geodetic measurements of the interseismic velocity fields at each location are used to constrain fault zone parameters through a Markov chain Monte Carlo method. At Black Mountain, in the Santa Cruz Mountains of the San Francisco Peninsula, we do not find evidence for a compliant fault zone; instead, we find that the geodetic data are more consistent with a model of a single fault in a homogeneous elastic half-space. At Lake San Andreas, a younger fault segment 35 km farther north, we find evidence for a compliant fault zone about 3.4 +1.1/-1.4 km wide, containing a shear modulus of about 40% of the shear modulus of the surrounding rock. We also find that the best fitting CFZ model at this location, unlike the best fitting homogeneous half-space model, has a locking depth that agrees well with the observed depth of microseismicity. Based on differences in fault age, cumulative displacement, and lithology between Black Mountain and Lake San Andreas, we infer that lithology plays an important and, in this case, perhaps a dominant role in the accumulation of fault zone damage structures and the development of CFZs over the lifetime of a fault.

58 GEOSCIENCES↗

Quantification of marine aerosol subgrid variability and its correlation with clouds based on high‐resolution regional modeling

Abstract One limitation of most global climate models (GCMs) is that with the horizontal resolutions they typically employ, they cannot resolve the subgrid variability (SGV) of clouds and aerosols, adding extra uncertainties to the aerosol radiative forcing estimation. To inform the development of an aerosol subgrid variability parameterization, here we analyze the aerosol SGV over the southern Pacific Ocean simulated by the high‐resolution Weather Research and Forecasting model coupled to Chemistry. We find that within a typical GCM grid, the aerosol mass subgrid standard deviation is 15% of the grid‐box mean mass near the surface on a 1 month mean basis. The fraction can increase to 50% in the free troposphere. The relationships between the sea‐salt mass concentration, meteorological variables, and sea‐salt emission rate are investigated in both the clear and cloudy portion. Under clear‐sky conditions, marine aerosol subgrid standard deviation is highly correlated with the standard deviations of vertical velocity, cloud water mixing ratio, and sea‐salt emission rates near the surface. It is also strongly connected to the grid box mean aerosol in the free troposphere (between 2 km and 4 km). In the cloudy area, interstitial sea‐salt aerosol mass concentrations are smaller, but higher correlation is found between the subgrid standard deviations of aerosol mass and vertical velocity. Additionally, we find that decreasing the model grid resolution can reduce the marine aerosol SGV but strengthen the correlations between the aerosol SGV and the total water mixing ratio (sum of water vapor, cloud liquid, and cloud ice mixing ratios).

Lin, Guangxing↗

Uncovering the Size-dependent Thermal Solid Transformation of Akaganéite

Investigating the structural evolution and phase transformation of iron oxides is crucial for gaining a deeper understanding of geological changes on diverse planets and preparing oxide materials suitable for industrial applications. In this study, we employed in-situ heating techniques in conjunction with transmission electron microscopy (TEM) observations and ex-situ characterization to thoroughly analyze the thermal solid-phase transformation of akaganéite one-dimensional (1D) nanostructures with varying diameters. Our findings offer compelling evidence for a size-dependent morphology evolution in akaganéite 1D nanostructures, which can be attributed to the transformation from akaganéite to maghemite (?-Fe2O3) and subsequent crystal growth. Specifically, we observed that akaganéite nanorods with a diameter of around 50 nm transformed into hollow polycrystalline maghemite nanorods, which demonstrated remarkable stability without arresting crystal growth under continuous heating. In contrast, smaller akaganéite nanoneedles or nanowires with a diameter ranging from 20 to 8 nm displayed a propensity for forming single-crystal nanoneedles or nanowires through phase transformation and densification. By manipulating the size of the precursors, we have developed a straightforward method for the synthesis of single-crystal and polycrystalline maghemite nanowires through solid-phase transformation. These significant findings provide new insights into the size-dependent structural evolution and phase transformation of iron oxides at the nanoscale.

Wang, Xiang↗

An independent analysis of bias sources and variability in wind plant pre-construction energy yield estimation methods

The wind resource assessment community has long had the goal of reducing the bias between wind plant pre-construction energy yield assessment (EYA) and the observed annual energy production (AEP). This comparison is typically made between the 50% probability of exceedance (P50) value of the EYA and the long-term corrected operational AEP (hereafter OA P50), and is known as the P50 bias. The industry has critically lacked an independent analysis of bias reduction investigated across multiple consultants to identify the greatest sources of uncertainty and variance in the EYA process and the best opportunities for uncertainty reduction. The present study addresses this gap by benchmarking consultant methodologies against each other and against operational data at a scale not seen before in industry collaborations. We consider data from 10 wind plants and evaluate discrepancies between eight consultancies in the steps taken from estimates of gross to net energy. Consultants tend to overestimate the gross energy produced at the turbines and then compensate by further overestimating downstream losses, leading to a mean P50 bias near zero, still with significant variability among the individual wind plants. Within our data sample, we find that consultant estimates of all loss categories, except environmental losses, tend to reduce the project-to-project variability of the P50 bias. The disagreement between consultants, however, remains flat throughout the addition of losses. Finally, we find that differences in consultants’ estimates of project performance can lead to differences up to $10/MWh in the levelized cost of energy for a wind plant.

Todd, Austin C.↗

Time-dependent observables in heavy ion collisions. Part II. In search of pressure isotropization in the φ 4 theory

To understand the dynamics of thermalization in heavy ion collisions in the perturbative framework it is essential to first find corrections to the free-streaming classical gluon fields of the McLerran-Venugopalan model. The corrections that lead to deviations from free streaming (and that dominate at late proper time) would provide evidence for the onset of isotropization (and, possibly, thermalization) of the produced medium. To find such corrections we calculate the late-time two-point Green function and the energy-momentum tensor due to a single 2 → 2 scattering process involving two classical fields. To make the calculation tractable we employ the scalar φ 4 theory instead of QCD. We compare our exact diagrammatic results for these quantities to those in kinetic theory and find disagreement between the two. The disagreement is in the dependence on the proper time τ and, for the case of the two-point function, is also in the dependence on the space-time rapidity η: the exact diagrammatic calculation is, in fact, consistent with the free streaming scenario. Kinetic theory predicts a build-up of longitudinal pressure, which, however, is not observed in the exact calculation. We conclude that we find no evidence for the beginning of the transition from the free-streaming classical fields to the kinetic theory description of the produced matter after a single 2 → 2 rescattering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Island for gravitationally prepared state and pseudo entanglement wedge

We consider spacetime initiated by a finite-sized initial boundary as a generalization of the Hartle-Hawking no-boundary state. We study entanglement entropy of matter state prepared by such spacetime. We find that the entanglement entropy for large subregion is given either by the initial state entanglement or the entanglement island, preventing the entropy to grow arbitrarily large. Consequently, the entanglement entropy is always bounded from above by the boundary area of the island, leading to an entropy bound in terms of the island. The island I is located in the analytically continued spacetime, either at the bra or the ket part of the spacetime in Schwinger-Keldysh formalism. The entanglement entropy is given by an average of complex pseudo generalized entropy for each entanglement island. We find a necessary condition of the initial state to be consistent with the strong sub-additivity, which requires that any probe degrees of freedom are thermally entangled with the rest of the system. We then find a large parameter region where the spacetime with finite-sized initial boundary, which does not have the factorization puzzle at leading order, dominates over the Hartle-Hawking no-boundary state or the bra ket wormhole. Due to the absence of a moment of time reflection symmetry, the island in our setup is a generalization of the entanglement wedge, called pseudo entanglement wedge. In pseudo entanglement wedge reconstruction, we consider reconstructing the bulk matter transition matrix on A ∪ I, from a fine-grained state on A. The bulk transition matrix is given by a thermofield double state with a projection by the initial state. We also provide an AdS/BCFT model by considering EOW branes with corners. We also find the exponential hardness of such reconstruction task using a generalization of Python’s lunch conjecture to pseudo generalized entropy.

2D gravity↗

Diagnostic performance of magnetic resonance cholangiopancreatography (MRCP) versus endoscopic retrograde cholangiopancreatography (ERCP) in the pediatric population: a clinical effectiveness study

Purpose: To determine the diagnostic performance of magnetic resonance cholangiopancreatography (MRCP) for detecting biliary and pancreatic abnormalities in a pediatric population, using endoscopic retrograde cholangiopancreatography (ERCP) as the reference standard. Materials and methods: Institutional review board approval with a waiver of informed consent was obtained for this retrospective investigation. Records from the Cincinnati Children’s Hospital Medical Center, Division of Gastroenterology and Department of Radiology were used to identify patients aged ≤ 18 years who had undergone both ERCP and MRCP within a 4-week interval between January 2013 and May 2017. Biliary and pancreatic duct findings were documented for each modality to determine the diagnostic performance of MRCP (with 95% confidence intervals), using ERCP as the reference standard. Results: 54 patients met inclusion criteria. Mean patient age at time of ERCP was 10.4 ± 4.9 years, and 25 (46%) were male. Mean interval between ERCP and MRCP was 11.2 ± 9.7 days. For detection of any abnormality (n = 99 ERCP findings), MRCP had a sensitivity of 76.8% (67.5–84.0%) and a positive predictive value (PPV) of 81.7% (72.7–88.3%). MRCP was 75.7% (59.9–86.6%) sensitive, with a PPV of 84.9% (69.1–93.4%) for biliary findings (n = 37) and 73.5% (59.7–83.8%) sensitive, with a PPV of 78.3% (64.4–87.7%) for pancreatic findings (n = 49). For pancreatobiliary abnormalities (n = 13), MRCP had a sensitivity of 92.3% (66.7–99.6%) and a PPV of 85.7% (60.1–97.5%). Conclusion: In clinical practice, MRCP is moderately sensitive for biliary and pancreatic abnormalities, with false-negative and false-positive examinations being relatively common.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗

Assessing Documentation of Critical Imaging Result Follow-up Recommendations in Emergency Department Discharge Instructions

To facilitate follow-up of critical test results across transitions in patient care settings, we implemented an electronic discharge module that enabled care providers to include follow-up recommendations in the discharge instructions. We assessed the impact of this module on documentation of follow-up recommendations for critical imaging findings in Emergency Department (ED) discharge instructions. We studied 240 patients with critical imaging findings discharged from the ED before (n = 80) and after (n = 160) implementation of the module. We manually reviewed hand-written forms and electronic discharge instructions to determine if follow-up recommendations were documented. Follow-up recommendations in ED discharge instructions increased from 60.0% (48/80) to 73.8% (118/160) post-module implementation (p = 0.03), a relative increase of 23%. There was no significant change in the rate of documented critical imaging findings in the discharge instructions (77.5% [62/80] before the intervention and 76.9% [123/160] after the intervention; p = 0.91). Implementation of a discharge module was associated with increased documentation of critical imaging finding follow-up recommendations in ED discharge instructions. However, one in four patients still did not receive adequate follow-up recommendations, suggesting further opportunities for performance improvement exist.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Bioenergy Technologies in Long-Run Climate Change Mitigation: Results from the EMF-33 Study

Bioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

bioenergy↗

Implicit and Implicit–Explicit Strong Stability Preserving Runge–Kutta Methods with High Linear Order

Strong stability preserving (SSP) time discretizations preserve the monotonicity properties satisfied by the spatial discretization when coupled with the first order forward Euler, under a certain time-step restriction. The search for high order strong stability preserving time-stepping methods with high order and large allowable time-step has been an active area of research. It is known that implicit SSP Runge–Kutta methods exist only up to sixth order; however, if we restrict ourselves to solving only linear autonomous problems, the order conditions simplify and we can find implicit SSP Runge–Kutta methods of any linear order. In the current work we find implicit SSP Runge–Kutta methods with high linear order p lin ≤ 9 and nonlinear orders p = 2,3,4, that are optimal in terms of allowable SSP time-step. Next, we formulate a novel optimization problem for implicit–explicit (IMEX) SSP Runge–Kutta methods and find optimized IMEX SSP Runge–Kutta pairs that have high linear order p lin ≤ 7 and nonlinear orders up to p = 4. Here, we also find implicit methods with large linear stability regions that pair with known explicit SSP Runge–Kutta methods. These methods are then tested on sample problems to demonstrate the sharpness of the SSP coefficient and the typical behavior of these methods on test problems.

97 MATHEMATICS AND COMPUTING↗

Chapter 4 - Recent Advances in Identification of Differential Equations from Noisy Data: IDENT Review

Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the underlying differential equation from a single observation of time-dependent data. If we assume that the differential equation is a linear combination of various linear and nonlinear differential terms, then the identification problem can be formulated as solving a linear system. The goal then reduces to finding the optimal coefficient vector that best represents the time derivative of the given data. We review some recent works on the identification of differential equations. We find some common themes for the improved accuracy: (i) The formulation of linear system with proper denoising is important, (ii) how to utilize sparsity and model selection to find the correct coefficient support needs careful attention, and (iii) there are ways to improve the coefficient recovery. We present an overview and analysis of recent developments on the topic.

97 MATHEMATICS AND COMPUTING↗