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At least 217 records · Page 12

Verification of TROPOMI NO2 Product Using OMI NO2 algorithm

We evaluated S5P TROPOspheric Monitoring Instrument (TROPOMI) operational nitrogen dioxide (NO2) product by comparing with the NO2 retrievals from Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite. We compared spatially matched NO2 vertical column density (VCD) data from OMI and TROPOMI to identify any discrepancies between the two operational products. We also applied OMI Air-Mass Factor (AMF) algorithm to TROPOMI Slant Column Density (SCD) data (v02.03.01) aiming to create long-term merged NO2 Earth Science Data Record (https://disc.gsfc.nasa.gov/datasets/TROPOMI_MINDS_NO2_1.1/summary ). The algorithm (version 4.0) uses new Geometry-dependent Lambertian Equivalent surface Reflectivity (GLER) product available for each TROPOMI pixel. GLER is pre-calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance based on MODIS gap-filled in-water chlorophyll-a data. The GLER data, combined with consistently retrieved cloud parameters, provide improved information for the calculation of the scattering weight profiles. Additional AMF improvements result from using high resolution (0.25o latitude x 0.25o longitude) a priori NO2 profile shapes and other auxiliary information from the Global Modeling Initiative (GMI) Replay simulation sampled using S5P orbital simulator. Our TROPOMI_MINDS_NO2 algorithm employs the stratosphere-troposphere separation scheme, de-striping, and surface snow/ice treatment consistently with the latest OMI NO2 re-processing (version 4). We evaluate the TROPOMI_MINDS_NO2 product by comparing with the S5P operational NO2 product as well as with independent NO2 observations from ground-based Pandora and aircraft observations. Our results indicate that the new TROPOMI_MINDS_NO2 retrievals are generally higher than the S5P operational NO2 product over polluted regions and show improved agreement with independent validation data.

TROPOMI↗

Heterogeneity and Chemical Reactivity of the Remote Troposphere Defined By Aircraft Measurements - Corrected

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10 s (2 km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146 494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80 %–90 % of the total reactivity lies in the top 50 % of the parcels and 25 %–35 % in the top 10 %, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2 km ATom 10 s data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find generally good agreement with the reactivity rates for O3 and CH4. Models distinctly underestimate O3 production below 2 km relative to the mid-troposphere, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation.

Tropospheric Chemistry↗

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗

Leveraging ­ CPF Spectral Information for Effective Angular Corrections in Sensor Inter-calibration Studies

The Climate Absolute Radiance and Refractivity Observatory Pathfinder (CPF) mission will provide a high-accuracy (0.3% radiometric uncertainty at k=1) SI-traceable on-orbit calibration reference for sensor intercalibration in reflective solar spectral region. CPF inter-calibration algorithms have been developed to address various sampling differences between CPF and target sensors, particularly in spectral, angular, spatial and temporal domains. CPF provides unique hyper-spectral measurements that allow the utilization of rich spectral information for critical inter-calibration procedures including angular correction and spectral-gap filling. The angular correction scheme of CPF mission has been constructed based on the spectral correlation relationship between CPF observed spectra and the spectra to be measured at the viewing/sun geometry angles of target sensors. The angular correction relationship is both angular and scene dependent. The scene variability issue can impose undesired uncertainties that complicates the correction for those errors. We have demonstrated that hyper-spectral radiance information can be well utilized to identify different scene types associated with CPF observations. Using spectral radiances-based scene clustering analysis can greatly reduce the uncertainty associated with the angular correction algorithm. Additionally, a reliable quality control scheme has been established, utilizing spectral similarity and regression-prediction error analyses, to ensure the successful and efficient implementation of the angular correction method.

Wan Wu↗

Comment on "Observation of Large and All-Season Ozone Losses Over the Tropics" [AIP Advances 12, 075066 (2022)]

As discussed above, and supported by extensive literature, there is no robust, credible observational evidence for substantial ozone depletion (i.e., an “ozone hole”) in the tropics. It is well known that climatological total ozone in the tropics is much lower than that in the mid-latitudes (e.g., Sahai et al., 2000; Weber et al., 2022). Satellite and ozonesonde measurements indicate a 3%–5% per decade decline of tropical lower stratosphere ozone prior to 2000, far smaller than that reported by L2022. The stronger decline reported by L2022 is caused by inappropriate use of the gap-filled version of the TOST ozone dataset, which is based on sparse tropical ozone sondes before the 1990s. This misuse of data (TOST and total column ozone) shows the importance of collaboratively engaging with groups who obtain the measurements and create climatological datasets before performing such analyses. Furthermore, the study by L2022 has multiple flaws in its discussion of atmospheric chemistry and dynamics, particularly in the proposed, and previously refuted (see Sec. III A), cosmicray- driven electron induced (CRE) mechanism. Evidence for the occurrence of tropical stratospheric clouds, as needed for the tropical CRE mechanism, is lacking, nor do CFC-12 observations show signatures of depletion in the tropical lower stratosphere, which could be associated with dissociative electron attachment-induced loss of CFC-12 on particulate matter (i.e., the CRE mechanism). Finally, it is worth reiterating that the CRE mechanism is also not responsible for polar LS ozone depletion. Polar ozone loss can be well explained by the gas phase and heterogeneous chemistry, based on extensive observations and modeling studies documented in many thousands of scientific papers on the topic [e.g., see WMO (2018) and references therein], which is not acknowledged by L2022. L2022’s research paper is a severely flawed one. There is no tropical ozone hole, and the CRE mechanism does not explain observed changes in stratospheric ozone either in the polar regions or in the tropics.

Quality assurance↗

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗

Detection of CH 4 Hotspots from NASA’s GEOS Composition Analysis System

Recent research indicates that 8 to 12% of the global oil and gas production methane emissions could be attributed to ultra-emitters, which result in high concentration ‘hotspots’ near point sources. Identifying these emissions in near real time provides useful information to the policy makers and private industry, who are working to reduce their impact. To meet this need, scientists are increasingly analyzing satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) instrument aboard ESA’s Sentinel 5-Precursor mission. While direct analysis of TROPOMI level 2 swath data has been successful in identifying some large emission events, identifying hotpots is challenging because of the imaging noise due to a variety of artifacts and limits in daily coverage. Here we explore possible methodologies to detect methane hotspots using a new, gap-filled, and temporally continuous methane product from NASA’s Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), which assimilates column averaged methane mole fractions from the TROPOMI with capabilities to assimilate other remote sensing measurements. The CoDAS has been expanded from a heritage of stratospheric composition and carbon dioxide assimilation allowing for the support of regional modeling, validation with non-coincident operations, and merging variety of datasets. The current work mainly explores Observing System Simulation Experiments with methane GEOS simulations without assimilation to prepare the groundwork for further experiments with assimilated TROPOMI. First, known hotspots based on the known inventory are identified to demonstrate the capability of the system to point out emission hotspots. In the next step, a variety of machine learning techniques such as Self-Organizing Maps and Deep Learning are explored to automate detection of the plumes. Finally, a few approaches to quantify emissions from the identified hotspots are presented and are evaluated against the inventory. The effort is directed toward a future evaluation of the CoDAS based methane monitoring system’s ability to successfully detect and quantify hotspots.

Nikolay Balashov↗

Harnessing Synthetic Communities and Microbial Recycling of Space Waste Streams for Biomanufacturing Applications

The long-term habitation of extraterrestrial environments such as the Moon or Mars presents significant challenges including supplying materials to sustain life. Off-world recycling of waste materials into biomanufacturer products may ameliorate this. Current literature highlights the need for efficient waste recycling systems to support the bioproduction of essential materials including foods, pharmaceuticals, and biomaterials. The study herein concerns itself with the investigation of three key aspects: 1) formulating an optimal wastewater media on which to grow recombinant microbes for bioproduction in space, 2) examining the potential for constructing stable and metabolically synergistic synthetic microbial communities for largescale and multi-tiered biomanufacturing, and 3) testing the efficacy of one such bioengineered enzyme, cutinase, on the degradation of PET plastics characteristic of those found in ISS waste as a model for recombinant recycling-based biomanufacturing of mission-critical substrates. Formulation of an optimal wastewater media involved growing several microorganisms on mixtures of synthetic planetary wastes representative of those found in space waste systems, combined with simple carbon sources derived from a physio-chemical CO conversion system to determine their growth potential. Potential synthetic microbial communities were conceptually designed, and their stability and metabolic synergism was evaluated within the context of co-cultures. Cutinase activity assays were utilized to determine the efficacy of bioengineered cutinase on PET plastic degradation. Findings will contribute to optimization of wastewater-based media formulations, data on stable synthetic microbial communities for bioproduction, and effective methods for measuring cutinase-based PET plastic degradation. These outcomes support the development of sustainable waste recycling systems for space habitation and aim to fill gaps in the current literature and proposing innovative solutions for waste recycling in space environments. By leveraging synthetic biology this study seeks to enhance the feasibility of long-term extraterrestrial habitation through sustainable resource management.

Bioprocessing↗

Coupling of Pronghorn and RELAP-7 for a Pebble Bed Reactor

High temperature gas cooled reactors (HTGR) are a candidate for timely Gen-IV reactor technology deployment because of high technology readiness and walk-away safety. Among HTGRs, pebble bed reactors (PBRs) have attractive features such as low excess reactivity and online refueling. Pebble bed reactors pose unique challenges to analysts and reactor designers such as continuous burnup distribution depending on pebble motion and recirculation, radiative heat transfer across a variety of gas-filled gaps, and long design basis transients such as pressurized and depressurized loss of forced circulation. Modeling and simulation is essential for both the PBR’s safety case and design process. In order to verify and validate the new generation codes the Nuclear Energy Agency (NEA) Data bank provide a set of benchmarks data together with solutions calculated by the participants using the state of the art codes of that time. An important milestone to test the new PBR simulation codes is the OECD NEA PBMR-400 benchmark which includes thermal hydraulic and neutron kinetic standalone exercises as well as coupled exercises and transients scenarios. In this work, the reactor multiphysics code MAMMOTH and the thermal hydraulics code Pronghorn, both developed by the Idaho National Laboratory (INL) within the multiphysics object-oriented simulation environment (MOOSE), have been used to solve Phase 1 exercises 1 and 2 of the PBMR-400 benchmark. The steady state results are in agreement with the other participants’ solutions demonstrating the adequacy of MAMMOTH and Pronghorn for simulating PBRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Scaling Equitable Finance

Driven by dramatic declines in up-front cost, the U.S. solar photovoltaics (PV) industry has taken off over the past decade, growing from 1 gigawatt of installed capacity in 2009 to 89 gigawatts in 2020—or enough capacity to power roughly 19 million homes. The industry is expected to double in size over just the next 5 years.1 Much of the growth has been driven by large, utility-scale projects that can produce 5 mega- watts or more of power—enough to power at least 1,000 homes. The cost of electricity produced by these projects has decreased by more than 70 percent since 2010. As of Q3 2020, development costs of large, util- ity-scale solar PV power plants were under $1 per watt, down by more than 70 percent from 2010.2 A robust array of investors has come forward to efficiently deliver capital to these kinds of utility-scale projects including large banks, insurance companies, pension funds, and others. But low- and moderate-income communities, including communities of color, are at risk of being left behind in the transition to clean energy. Mission- driven solar project developers and financial institu- tions have been working alongside energy justice advocates to open up solar access for these communi- ties, using strategies ranging from community solar, to solar installations on affordable multifamily housing, to distributed solar and storage programs, and more. Their goals go beyond simply generating more green energy to advancing social equity by: • empowering communities to control their energy future • stabilizing energy prices, saving money, and build- ing wealth for low-income families • creating quality jobs • improving health by reducing pollution • providing energy resilience for vulnerable communities Mission-driven actors are successfully deploying a wide variety of strategies to meet these goals, from helping low-income homeowners get solar—and some- times battery storage, to developing solar projects serv- ing affordable rental housing and community facilities, to building larger “shared solar” projects to which households from across the community can subscribe. However, the financing ecosystem does not work nearly as well for these “mission driven” solar proj- ects as it does for utility-scale projects. For home rooftop solar, even if low-income consumers have a home and suitable roof, they may fail to qualify for federal tax incentives, lack adequate credit to qualify for a loan—or the mission-driven lenders seeking to serve them may not be adequately capitalized to make long-term loans. For mission-driven commercial or community-scale projects, assembling nearly every component of the project capital stack—whether bridging early-stage costs, attracting tax credit equity investors, securing long-term debt, or coming up with sponsor equity and filling gaps—can present challenges. A variety of obstacles contribute to the scarcity of financing for low-income solar, including small project sizes, lack of developer balance sheet capacity, both real and perceived issues with credit risk, elevated technical assistance needs, and greater subsidy requirements to pursue goals such as deep energy affordability, climate resilience, or job creation. Still other obstacles are regulatory: for example, not all states allow community solar projects or Power Purchase Agreements, common strategies used for providing low-income solar—and the potential for regulations to shift over time creates risks that mission-driven projects can ill afford. This report synthesizes information garnered from 47 key informant interviews, four focus group discus- sions involving 60 stakeholders, and a review of the substantial existing literature on low-income solar finance to assess the current landscape of mission- driven solar development in the United States, examine the roles that community-based financial institutions could play, and recommend public invest- ments and policy changes that could help to scale the provision of equitable solar finance. Key recommen- dations for policymakers and funders in the renew- able energy and community development fields that emerge from this process include the following: • Help to capitalize and support community-based lenders to provide flexible, low-cost, and long- term financing to mission-driven solar projects— including providing guarantees or other forms of credit enhancement. • Provide federal support for equitable solar, including a grant-in-lieu-of-credits option for the Investment Tax Credit to improve access to this critical government subsidy. • Develop pools of government and philanthropic support that can complement financing from community-based lenders to complete the capi- tal stack for mission-driven projects, as well as to support education and technical assistance to both consumers and potential project sponsors. • Create a national Renewable Energy Credits pro- gram that includes social equity targets to provide a baseline of support for clean energy generation. • Change utility regulations to remove barriers to low-income solar projects; lower permitting costs; provide greater certainty for developers, consumers and owners; and measure progress toward equity in renewable energy policy implementation.

14 SOLAR ENERGY↗

Recent Improvements in the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) has significantly evolved since the first release of the point source database in 1993. The NSRDB has been widely used by the solar energy industry to provide long-term time-series solar resource data for various project phases. The NSRDB represents the state of the art in the satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that allows improvements in accuracy with the deployment of the next-generation geostationary satellites. This poster provides an overview of (1) the improved spatiotemporal resolution; (2) on-demand services and their applications; (3) future Improvements, such as a new direct normal irradiance model and new methods to gap-fill missing data using physics-guided machine learning; (4) data quality; and (5) data dissemination.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗

System and method to fuse multiple sources of optical data to generate a high-resolution, frequent and cloud-/gap-free surface reflectance product

Aspects of the subject disclosure may include, for example, performing, by a processing system, image fusion using two or more groups of images to generate predicted images, wherein each group of the two or more groups has one of a different resolution, a different frequency temporal pattern or a combination thereof than another of the two or more groups. Gap filling can be performed by the processing system to correct images of the two or more groups. Additional embodiments are disclosed.

Guan, Kaiyu↗

Toroidal-Core Microinductors Biased by Permanent Magnets

The designs of microscopic toroidal-core inductors in integrated circuits of DC-to-DC voltage converters would be modified, according to a proposal, by filling the gaps in the cores with permanent magnets that would apply bias fluxes (see figure). The magnitudes and polarities of the bias fluxes would be tailored to counteract the DC fluxes generated by the DC components of the currents in the inductor windings, such that it would be possible to either reduce the sizes of the cores or increase the AC components of the currents in the cores without incurring adverse effects. Reducing the sizes of the cores could save significant amounts of space on integrated circuits because relative to other integrated-circuit components, microinductors occupy large areas - of the order of a square millimeter each. An important consideration in the design of such an inductor is preventing magnetic saturation of the core at current levels up to the maximum anticipated operating current. The requirement to prevent saturation, as well as other requirements and constraints upon the design of the core are expressed by several equations based on the traditional magnetic-circuit approximation. The equations involve the core and gap dimensions and the magnetic-property parameters of the core and magnet materials. The equations show that, other things remaining equal, as the maximum current is increased, one must increase the size of the core to prevent the flux density from rising to the saturation level. By using a permanent bias flux to oppose the flux generated by the DC component of the current, one would reduce the net DC component of flux in the core, making it possible to reduce the core size needed to prevent the total flux density (sum of DC and AC components) from rising to the saturation level. Alternatively, one could take advantage of the reduction of the net DC component of flux by increasing the allowable AC component of flux and the corresponding AC component of current. In either case, permanent-magnet material and the slant (if any) and thickness of the gap must be chosen according to the equations to obtain the required bias flux. In modifying the design of the inductor, one must ensure that the inductance is not altered. The simplest way to preserve the original value of inductance would be to leave the gap dimensions unchanged and fill the gap with a permanent- magnet material that, fortuitously, would produce just the required bias flux. A more generally applicable alternative would be to partly fill either the original gap or a slightly enlarged gap with a suitable permanent-magnet material (thereby leaving a small residual gap) so that the reluctance of the resulting magnetic circuit would yield the desired inductance.

Lieneweg, Udo↗

Filling data analysis gaps in time-resolved crystallography by machine learning

There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.

Trujillo, Justin (ORCID:0000000285505360)↗

High-rate strength response of tantalum from dynamic hole closure experiments

The science and engineering communities have significant interest in experimental platforms to evaluate and improve models for dynamic material deformation. While well-developed platforms exist, there are still gaps to fill for strain and strain rate conditions accessed during impact and other high-rate loading scenarios. To fill one such gap for strength measurements, a platform was recently developed that accesses high strain rate (≥ 10 5 /s) and large strain (≥ 50%) conditions by measuring the transient closure of a cylindrical hole using in situ x-ray imaging. In the work reported here, further refinement of the platform is performed to reduce the potential effects of porosity and anelasticity on the measurement. This helps us to isolate the strength effects that are the focus of the experiment. The updated experimental configuration employs a two-layer flyer design and elongated target to reduce the magnitude of the tensile excursions associated with rarefaction wave interactions. This allows for a more direct assessment of strength models commonly used for dynamic simulations of metals. Here we apply the new technique to well-characterized tantalum material, allowing for a robust connection to other experimental techniques. Deformation localization can be a concern in large strain experiments, and to help inform future use of the experimental platform, we use simulations with a sub-zone treatment of shear banding to explore potential localization behavior. Here we develop and utilize an experimental configuration with improved isolation of strength effects that can be applied to an expanded range of materials.

42 ENGINEERING↗

From soil to sequence: filling the critical gap in genome-resolved metagenomics is essential to the future of soil microbial ecology

Abstract Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.

59 BASIC BIOLOGICAL SCIENCES↗

Filling the expertise gap

Aeroelasticity, structures, and stability-and-control specialists can work in concerted fashion during the early design phases of future aircraft to achieve active control of naturally unstable configurations. In order to take full advantage of active control, attention must be given by designers to control-law synthesis, and to tools for the efficient synthesis and analysis of complex flexible-aircraft control systems. Analysts must consider interfaces among unsteady aerodynamics, structures, and control theory, as explored by the theory of analytic continuation for unsteady aerodynamics.

Abel, Irving↗

Filling the Assurance Gap on Complex Electronics

Many of the methods used to develop software bare a close resemblance to Complex Electronics (CE) development. CE are now programmed to perform tasks that were previously handled by software, such as communication protocols. For example, the James Webb Space Telescope will use Field Programmable Gate Arrays (FPGAs), which can have over a million logic gates, to send telemetry. System-on-chip (SoC) devices, another type of complex electronics, can combine a microprocessor, input and output channels, and sometimes an FPGA for programmability. With this increased intricacy, the possibility of software-like bugs such as incorrect design, logic, and unexpected interactions within the logic is great. Since CE devices are obscuring the hardware/software boundary, mature software methodologies have been proposed, with slight modifications, to develop these devices. By using standardized S/W Engineering methods such as checklists, missing requirements and bugs can be detected earlier in the development cycle, thus creating a development process for CE that can be easily maintained and configurable based on the device used.

Plastow, Richard A.↗