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The paper describes the architectual details of a fine-grained Programmable Transistor Array (PTA) architecture and illustrates its use in evolutionary experiments on the synthesis of both analog and digital circuits.
High-altitude balloons (HABs) are platforms for collecting astrophysical and planetary science data that offer a number of advantages compared to conventional groundbased or space-based systems. However, they also pose a set of new environmental challenges that must be addressed in order to offer a viable alternative to ground- or space- based assets. In particular, maintaining science-quality pointing stability is a critical challenge for HAB platforms. For these missions, dynamic errors must be limited to a fraction of the observation wavelength, so as the wavelength becomes smaller, it becomes more difficult to meet the needed performance. As a result, there are very few existing pointing stabilization solutions that use visible-spectrum guide stars, despite their relatively wide distribution across the sky. This paper describes the results achieved with the STABLE (Subarcsecond Telescope And BaLloon Experiment) project whose goal is to provide the fine pointing stage for a balloonborne platform observing in the visible wavelength.
Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.
Fine-grained Ca-Al-rich inclusions (FGIs) in carbonaceous chondrites are interpreted as aggregates of nebular gas-solid condensates that escaped significant melting.Thus far, a small number of FGIs from CV3 chondrites have been analyzed, showing a range of (26Al/27Al)0=~5.4×10−5to ~3.4×10−5.These values are similar to (26Al/27Al)0inferred from coarse-grained, igneous CAIs (CGIs) from CV3 chondrites.This observed spread in (26Al/27Al)0values indicates the CAI formation over an extended time period. However, more analyses are needed to determine if the FGIs formed continuously over a significant time period or by multiple episodic events of nebular condensation. In this study, we present high-precision Al-Mg isotopic data of FGIs from reduced CV3 chondrites to better constrain their formation timescale relative to CGIs in CV3 chondrites, as well as to smaller FGIs in CO3 chondrites. This isotopic study is coordinated with a FIB/TEM study to elucidate the origin and nature of FGIs in the petrologic and mineralogical context.
The temporary decrease of fine particulate matter (PM(sub 2.5)) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM(sub 2.5) concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM(sub 2.5) surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM(sub 2.5) concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM(sub 2.5) were heterogeneous over space. PM(sub 2.5) concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.
Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.
JAXA Hayabusa2 spacecraft brought back 5.4 g of samples from the near-Earth C-type asteroid (162173) Ryugu to Earth on December6th, 2020 [1]. The returned samples give us a unique opportunity to investigate material from the surface of a C-type asteroid that is expected to record space weathering. Here, we report the mineralogy of fine fraction of the Ryugu grains and a variation of their surface features related to space weathering.
NASA is investigating the use of the Plasma Pyrolysis Assembly (PPA), which is a methane post-processing technology with the goal to recover hydrogen from the Carbon dioxide Reduction Assembly (CRA) currently onboard the International Space Station (ISS). The PPA can theoretically recover 75% of hydrogen from methane produced by the CRA. During methane processing the PPA creates roughly 40 mg/hr of carbon dust when operating at 4 crew member levels. The unwanted fines need to be removed from the stream to prevent any clogging of downstream components. The Regenerable Carbon Filter (RCF) was designed by Umpqua Research Company to address the carbon particulate created in the PPA. The RCF includes two stages. The first is an electrostatic precipitator designed to collect ultrafine particles. The second is a low pressure drop physical filter. Both filters are made of heat tolerant materials to allow regeneration of the filtration capacity by O2 oxidation of captured carbon. This paper will discuss the RCF hardware and test plans.
Grossite (CaAl4O7) is predicted to condense as the third major phase, after corundum and hibonite, from a cooling gas of solar composition and likely preserves an important record of physico-chemical conditions in the early Solar System. However, this mineral is relatively rare in CAIs from most carbonaceous chondrite groups, except for CH chondrites. In particular, from CV3 chondrites where CAIs are large and abundant, only six grossitebearing CAIs have been reported and studied, and most of them were coarse-grained Type A inclusions. Here, we report the occurrence of grossite-bearing, fine-grained inclusions (FGIs) from reduced CV3 chondrites. We present preliminary results of a FIB/TEM study of one grossite-bearing FGI that provide clues to constrain its formation and alteration history in the solar nebula and on the parent body and to address the overall rarity of grossite in CAIs.
Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health(1–9) . A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-19(10–20). Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.
Fine-grained Ca-Al-rich inclusions (FGIs) in carbonaceous chondrites are interpreted as aggregates of direct condensates from the solar nebula that escaped extensive melting. Thus far, a few high-precision SIMS Al-Mg chronology studies of FGIs from CV3 chondrites have been reported. Argued that (26Al/27Al)0 of all FGIs are close to the bulk refractory inclusion value of ~5.2×10−5 which may define a time zero for the FGI condensation. A similar (26Al/27Al)0 value was also inferred from an amoeboid olivine aggregate (AOA), which also represents a direct condensate from the solar nebula. In contrast, reported significant variations in (26Al/27Al)0 of FGIs, ranging from ~5.2×10−5 to ~3.4×10−5, and concluded that their formation continued over at least ~0.4 Ma after the canonical value. These contrasting results suggest that the chronological significance for the formation age spread of FGIs in CV3 chondrites is still poorly constrained.
Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.
The sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types. SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (ε c ) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (ε f ). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius (“inflection point”) across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarching dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a “linearly inverse” fashion (slope and intercept of approximately 1−ε c and ε c ) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of ε c over ε f in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis.
Fine coal is cleaned of its mineral matter impurities and dewatered by mixing the aqueous slurry containing both with a hydrophobic liquid, subjecting the mixture to a phase separation. The resulting hydrophobic liquid phase contains coal particles free of surface moisture and droplets of water stabilized by coal particles, while the aqueous phase contains the mineral matter. By separating the entrained water droplets from the coal particles mechanically, a clean coal product of substantially reduced mineral matter and moisture contents is obtained. The spent hydrophobic liquid is separated from the clean coal product and recycled. The process can also be used to separate one type of hydrophilic particles from another by selectively hydrophobizing one.
This paper presents a reconfigurable, low-cost ultra-wideband microwave radar front-end and radar demonstrator designed for nadir-looking measurements of layered media (e.g. snow or vegetation) from either a fast-moving ground-based platform or a C-3 class unmanned aerial system (UAS). The radar demonstrator uses a frequency modulated continuous-wave (FMCW) architecture and can be configured to operate in either the 2-8 GHz or in the 12-18 GHz range, providing fine vertical resolution while offering a significantly reduced weight, size, and power consumption with respect to prior implementations. We have demonstrated the operation of the system in the 2-8 GHz and 12-18 GHz bands in the laboratory; in the 2-8 GHz band using a surface-based setup in Antarctica; and in the 2.6-7 GHz band on local flight tests onboard an Aurelia X6 Pro UAS equipped with 3-D printed antennas. This paper provides an overview of the radar front-end and demonstrator design and implementation and a summary of the different tests completed to document their performance.
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Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.