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NASA NTRSDate not supplied

The Collection, Usage, and Preliminary Examination of the Apollo Sample Suite: Lessons for Artemis

Apollo Sample Collection and Usage: From 1969 to 1972 there were six Apollo missions to the surface of the Moon during which the astronauts collected 382 kg of rock and regolith (~2200 samples). The samples collected fall into these general categories: rocks (~66% by mass), rake samples (~4%), bulk regolith (~24%), and specialty regolith (deep drill cores, drive tubes, sealed bulk regolith) samples (~6%). In each category there are a variety of different subtypes available for study, e.g., among the bulk regolith samples there are also skim, trench, and (partially) shaded regolith samples each sampling unique types or depths of regolith. This variety of subsamples has enabled a multitude of different studies over the past 55 years (>3400 individual requests). We are still averaging ~50 unique requests and have allocated >500 individual Apollo samples annually for the past 10 years (2020 excepted). Looking at the 4,675 non-ANGSA (Apollo Next Generation Sample Analysis) samples allocated over the past 10 years, the proportions of allocated samples do not precisely align with the abundance (by mass) of those samples withing the collection: Rock (69.4 %); Rake (10.8 %); Bulk Regolith (15.5 %); Drive Tube (3.3 %); Core/Specialty (1.0 %). Apollo Preliminary Examination (PE): The PE process differs significantly for the various sample types enumerated above; we focus on regolith and rock samples here. During the Apollo mission era, the PE process evolved over the course of the missions; below is what was done for the Apollo 17 mission. For regolith samples, the PE process was: (1) documented bags containing regolith are opened, photographed, and described; (2) large rocks are removed and treated separately; (3) 25% to 33% of the bulk soil is scooped out, weighed, and stored in reserve; (4) the remaining sample is sieved to produce the size fractions <1, 1- 2, 2-4, and 4-10 mm, all of which are weighed. For rock samples, the process is: (1) removing rocks from the container(s) it came back from the Moon in; (2) rematching any materials that spalled off the rock to their original location; (3) numbering, weighing, and basic photographic documentation; (4) dusting with a gentle N2 gas jet; (5) Orthogonal photography; (6) detailed description of the textures and features of the rock; (7) rock modelling and measurement; (8) stereophotography; (9) determination of the orientation of the rock on the lunar surface. Drive tubes and deep drill cores were not characterized during PE beyond an initial weight and a sketch of the interior tube materials derived from 2D medical X-ray images. Catalogs: The ongoing utility of the Apollo samples is enabled by the robust cataloguing process for the samples [3-5], which allows the scientific community to accurately request samples uniquely suited to their proposed studies. A common misconception, however, is the amount of detail that goes into the initial catalog (e.g., [6]) for a collection from the preliminary examination (PE) period, versus what goes into the catalogs that come later in the life cycle of the samples from that mission (e.g., [7]). The only required data for a PE catalog is a weight, a basic photograph, and a description of the nature of the sample. Artemis PE: Over the past few years, the ongoing ANGSA project studied previously unopened Apollo 17 double drive tube samples 73001/2 [1], and a PE of the drive tubes was done. The PE took the existing core dissection process (developed during PE of Apollo cores in the 1970s, 1980s), and modernized it [7]. The main lesson from the ANGSA PE relative to future missions was that the physical work done during PE of lunar samples has not changed much over the past 5 decades. The use of “modern” technology during PE (e.g., XCT; multispectral analyses) resulted in an enhanced initial understanding of the 73001 and 73002 drive tubes, but greatly increased the time required. The lessons learned from recent astromaterial PEs (e.g., ANGSA and OREx) are important to consider when planning for Artemis, but the unique nature of the Artemis Campaign means many lessons learned from these mission will not be applicable. Given the time constraints (6 months) and likely number of samples that will be returned by Artemis (>200), the Artemis PE catalog will necessarily look much more like [4] than [6].

J Gross↗

NASA NTRS2022

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Katie Fankhauser↗

NASA NTRSDate not supplied

Requesting Antarctic Meteorite Samples for Research

The U.S. Antarctic meteorite program began in the 1970’s and has provided more than 24,000 samples. The program is based on a three agency agreement between NASA, the National Science Foundation, and the Smithsonian Institution. The collection, stored at the Johnson Space Center and the Smithsonian, is one of the largest collections of meteorites in the world and features samples from the moon, Mars, asteroids, and material from the early solar system. A brief overview of the collection shows it contains 92.2% ordinary chondrites (7205 H, 9126 L, 3890 LL, 146 enstatite, 30 R chondrites, 3.2% (973) carbonaceous chondrites, 3.7% (560) achondrites (1.7% HED), 118 irons, 27 pallasites, 41 mesosiderites, as well as many puzzling, ungrouped meteorites. JSC has sent splits of over 20,000 meteorite samples to more than 500 scientists around the world since 1977. After the meteorites are collected in Antarctica, they are shipped frozen to JSC in Houston, TX, arriving in April following the field season. The Astromaterials Curation Office at JSC is responsible for: - providing supplies and tools for the field team. - receiving the frozen meteorites. - staging: repackaging and changing the samples’ field identification numbers with official names. - submitting the names to the Nomenclature Committee of the Meteoritical Society for approval as new meteorites. - providing storage and handling of the meteorites in a class 10,000 clean room. - initial processing: weighing, measuring, describing, and photographing the sample and providing a chip for classification to the Smithsonian Institution staff. - the issuing of two newsletters per year, announcing hundreds of new meteorites. - the handling of requests from the scientific community and the allocation of those requests that are approved. - making petrographic thin and thick sections for the JSC library and scientific investigators. - maintaining the meteorite database with more than 76,000 sample splits.

C.E. Satterwhite↗

NASA NTRS1999

Space Weathering in the Fine Size Fractions of Lunar Soils: Soil Maturity Effects

The effects of space weathering on the optical properties of lunar materials have been well documented. These effects include a reddened continuum slope, lowered albedo, and attenuated absorption features in reflectance spectra of lunar soils as compared to finely comminuted rocks from the same Apollo sites. However, the regolith processes that cause these effects are not well known, nor is the petrographic setting of the products of these processes fully understood. A Lunar Soil Characterization Consortium has been formed with the purpose of systematically integrating chemical and mineralogical data with the optical properties of lunar soils. Understanding space-weathering effects is critical in order to fully integrate the lunar sample collection with remotely-sensed data from recent robotic missions (e.g., Lunar Prospector, Clementine, and Galileo) We have shown that depositional processes (condensation of impact-derived vapors, sputter deposits, accreted impact material, e.g., splash glass, spherules, etc.) are a major factor in the modification of the optical surfaces of lunar regolith materials. In mature soils, it is the size and distribution of the nanophase metal in the soil grains that has the major effect on optical properties. In this report, we compare and contrast the space-weathering effects in an immature and a mature soil with similar elemental compositions. For this study, we analyzed <10 micron sieve fractions of two Apollo 17 soils, 79221 (mature, Is/FeO = 81) and 71061 (immature, Is/FeO = 14). Details of the sieving procedures and allocation scheme are given else where. The results of other detailed chemical, mineralogical, and spectroscopic analyses of these soil samples are reported elsewhere. A representative sample of each soil was embedded in low-viscosity epoxy, and thin sections (about 70nm thick) were obtained through ultra microtomy. The thin sections used for these analyses typically contained cross sections of up to 500 individual grains. The thin sections were studied using a JEOL 2010 transmission electron microscope (TEM) equipped with a thin window energy-dispersive X-ray (EDX) spectrometer. An individual thin section was selected from each soil, and for each grain in the section we determined (1) the elemental composition by EDX; (2) whether the grain was crystalline or glassy using electron diffraction and darkfield imaging; (3) the presence or absence of rims and accreted material; and (4) the distribution of nanophase Fe where present. Most of the categories are self-evident; however, we divide the agglutinate derived material into agglutinitic glass (glass with approximately the same composition as the bulk soil that contains nanophase Fe with or without vesicles) and agglutinate fragments, which are composed of crystalline grains and agglutinitic glass. Lithic fragments are defined as polymineralic grains with no glass. Pyroxene grains have been divided into high- and low-Ca groups. As expected, there are a number of differences in the petrography of the <10-microns fractions of 79221 and 71061 given the great difference in their respective maturities, but we focus here on two major distinctions: agglutinate content and the number of grains with micropatina. Slightly over 50% of the particles in 79221 consist of agglutinitic glass and agglutinate fragments, while the remainder are predominantly crystalline mineral grains. The agglutinic glass particles contain abundant nanophase Fe and vesicles. Angular particles are rare, with most showing smooth, rounded exteriors, Of the mineral grains analyzed thus far, over 90% of the grains have amorphous rims that contain nanophase Fe (these rims are believed to have formed by vapor deposition and irradiation effects). The nanophase Fe in these rims probably accounts for a significant fraction of the increase in Is/FeO measured in these size fractions. In addition to the rims, the majority of particles also show abundant accreted material in the form of glass splashes and spherules that also contain nanophase Fe. In stark contrast, the surfaces of the mineral grains in the 71061 sample are relatively prisitine, as only about 14% of the mineral grains in the sample exhibited amorphous rims. Furthermore, the mineral particles are more angular and show greater surface roughness than in the mature sample. Accreted material on particle surfaces is rare. Agglutinitic material is a major component of the 71061 sample; however, nanophase Fe and vesicles are not as well developed as in the 79221 sample. It is now recognized that nanophase Fe is probably the main agent in modifying the optical properties of lunar soil grains. The most important result of this study is the observation that in the fine size fractions of mature soils, nearly every grain has nanophase Fe within 100 run of the particle surface. (Additional Information contained in original)

Keller, L. P.↗

DOE OSTI2022

Whole plant traits of coastal dune vegetation and implications for interactions with dune dynamics

Abstract Coastal dunes are important protective features against sea level rise and coastal storms. Interactions between dune plant aboveground structures and sediment trapping that allow for dune building and maintenance are well established. More recently, studies documenting belowground biomass for promoting erosion resistance in dominant dune species have been conducted, yet a knowledge gap remains regarding species‐specific characterization of whole plants, specifically with respect to roots, rhizomes, and belowground stems. Our objective was to quantify above‐ and belowground traits of four dominant dune grasses to document the potential for species‐specific effects on dune growth, maintenance, and erosion resistance. We examined above‐ and belowground traits among four prominent dune grasses of the Atlantic and Gulf Coasts of North America: Ammophila breviligulata , Panicum amarum , Spartina patens , and Uniola paniculata . Whole plant samples of each species were collected from the foredune at the US Army Engineer Research and Development Center's Field Research Facility in Duck, North Carolina, USA, and quantified for several above‐ and belowground traits (e.g., stem height, rhizome number and length, root surface area by diameter class, root tensile strength, and mycorrhizal percent infection). Belowground factors known to impact important dune processes, such as rhizome length, mycorrhizal percent infection, and root traits, differed substantially among species. When visualized in multivariate space, all species significantly differed in suites of above‐ and belowground traits. When considering belowground only, Ammophila and Spartina were similar, despite differences in biomass allocation. Species separated along axes related to mycorrhizal association, biomass allocation, and root construction. The four co‐occurring dune grass species were dissimilar in suites of plant traits. Belowground trait differences were driven by those describing root construction, biomass allocation, and mycorrhizal infection. Dissimilarity in above‐ and belowground suites of traits may demonstrate different approaches for surviving the dune environment. Incorporating belowground traits into modeling will enhance predictions of dune response to climate change through interactions between vegetation and dune dynamics that facilitate coastal resistance and resilience.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗

NASA NTRS2002

Behavioral consequences of radiation exposure to simulated space radiation in the C57BL/6 mouse: open field, rotorod, and acoustic startle

Two experiments were carried out to investigate the consequences of exposure to proton radiation, such as might occur for astronauts during space flight. C57BL/6 mice were exposed, either with or without 15-g/cm2 aluminum shielding, to 0-, 3-, or 4-Gy proton irradiation mimicking features of a solar particle event. Irradiation produced transient direct deficits in open-field exploratory behavior and acoustic startle habituation. Rotorod performance at 18 rpm was impaired by exposure to proton radiation and was impaired at 26 rpm, but only for mice irradiated with shielding and at the 4-Gy dose. Long-term (>2 weeks) indirect deficits in open-field activity appeared as a result of impaired experiential encoding immediately following exposure. A 2-week recovery prior to testing decreased most of the direct effects of exposure, with only rotorod performance at 26 rpm being impaired. These results suggest that the performance deficits may have been mediated by radiation damage to hippocampal, cerebellar, and possibly, forebrain dopaminergic function.

Non-NASA Center↗

DOE OSTI2023

3P Program: Phenotyping X Prediction = Productivity (Final Scientific/Technical Report)

The goal of the 3P Program was to establish integrated, real-time phenotyping and to analyze above- and below-ground plant architecture and total carbon partitioning and allocation to predict heterosis and develop superior crop hybrids by fully leveraging the Sorghum gene pool. There were two overarching themes: 1) the development of a new crop improvement approach utilizing advances in high-throughput phenotyping (HTP), computing, and genomics for public dissemination and 2) leveraging this platform for sorghum crop improvement and commercialization. The Clemson team worked on creating genomic resources and using both statistical learning and high-throughput phenotyping in genomics-assisted breeding. Research was broadly interested in the genetics of carbon partitioning, with the aim of improving crop performance and achieving sustainability. The technology and resources created can be readily found in the public domain and serve to advance scientific understanding of crop genomics and breeding. Genomic prediction was able to identify top crosses to be made, and a hybrid prediction pipeline is in place to drive year-over-year genetic gain. Roots have long been ignored by plant breeders and agronomists, not because they are unimportant but because they are hard to measure. This is an untapped white space of potential insight and innovation. To address this, Hi Fidelity Genetics developed the RootTracker to measure roots in the field on a continuous basis. A database system called RootTracker Tracker was developed to handle data coming from the RootTrackers. In using this device, valuable data was observed for plant breeding, hydrochemical development, and other agricultural biology applications. Carnegie Mellon’s goal was developing new techniques to generate high-resolution 3D models of plants from data collected in the field. The idea was that more useful and more informative phenotypes could be extracted by resolving small features, such as seeds and flowers, and that by modeling in 3D, the spatial structure of plants could be examined. To achieve this, multiple images collected by a new small format structured light stereo imager were fused together. A sorghum panicle modeling pipeline was developed to allow the collection and processing of data. Carolina Seed Systems is an agricultural technology company focused on decarbonizing the agricultural system. Their technology pipeline serves to drive fundamental progress towards creation and distribution of carbon negative crops. The genomic and the engineering technology developed through the 3P Program was leveraged to deliver both value and sustainability from the grower to the consumer. Promising sorghum hybrids were scaled up and commercialized. The overall goal of our research was to integrate, create, and deploy genetic and engineering concepts and technologies to enhance crop productivity in a sustainable fashion. The combination of public and private partners allowed the basic research and hypothesis testing to be quickly accelerated for commercial application by the companies yet maintained that the core framework and academic insights remain in the public domain for continued market disruption, competition, and innovation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

The effects of protease, xylanase, and xylo-oligosaccharides on growth performance, nutrient utilization, short-chain fatty acids, and microbiota in Eimeria -challenged broiler chickens fed low-protein diet

A total of 392 Cobb 500 off-sex male broiler chicks were used in a 21-day experiment to study the effect of protease, xylanase, and xylo-oligosaccharides (XOS) on improving growth performance, nutrient utilization (ileal digestibility and total tract retention), gene expression of nutrient transporters, cecal short-chain fatty acids (SCFAs), and microbiota profile of broilers challenged with Eimeria spp. Chicks at 0-day old were allocated to 8 treatments in a 4 × 2 factorial arrangement: 1) corn-soybean meal diet with no enzyme (Con); 2) Con plus 0.2 g/kg protease alone (PRO); 3) Con plus 0.2 g/kg protease combined with 0.1 g/kg xylanase (PRO + XYL); or 4) Con plus 0.5 g/kg xylo-oligosaccharides (XOS); with or without Eimeria challenge. The 4 diets were formulated to be marginally low in crude protein (183 g/kg). Challenged groups were inoculated with a solution containing E. maxima, E. acervulina, and E. tenella oocysts on d 15. Eimeria depressed (P < 0.01) growth performance and nutrient utilization. Supplemental protease improved (P < 0.05) body weight gain and feed intake in the prechallenge phase (d 0-15) but had no effect during the infection period (d 15-21). There was no interaction between infection and feed supplementation for nutrient utilization. The supplementations of either PRO or XOS alone increased (P < 0.01) total tract retention of Ca and tended (P < 0.1) to improve total tract retention of N, P, AME, and AMEn. Eimeria decreased (P < 0.05) expressions of GLUT2, GLUT5, PepT1, ATP2B1, CaSR, Calbidin D28K, NPT2, and ZnT1 but increased (P < 0.01) expression of GLUT1. XOS supplementation increased (P < 0.05) ATP2B1 expression. Protease decreased (P < 0.05) isobutyrate concentration in unchallenged treatments but not in challenged treatments. Eimeria decreased (P < 0.01) cecal saccharolytic SCFAs acetate and propionate but increased (P < 0.01) branched-chain fatty acid isovalerate. The supplementation of PRO + XYL or XOS increased (P < 0.05) cecal butyrate or decreased cecal isobutyrate concentrations, respectively. PRO + XYL and XOS decreased cecal protein levels in unchallenged birds but not challenged ones. Eimeria challenge significantly (P < 0.05) decreased the microbial richness (Observed features) and diversity (Shannon index and phylogenetic diversity) and changed the microbial composition by reducing the abundance of certain bacteria, such as Ruminococcus torques, and increasing the abundance of others, such as Anaerostipes. In contrast, none of the additives had any significant effect on the cecal microbial composition. In conclusion, PRO or XOS supplementation individually improved nutrient utilization. All the additives decreased the cecal content of branched-chain fatty acids, consistent with decreased cecal N concentration, although the effects were more pronounced in unchallenged birds. In addition, none of the feed additives impacted the Eimeria-induced microbial perturbation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Implications of an emission trading scheme for India’s net-zero strategy: a modelling-based assessment

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India’s long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India’s near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India’s ETS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2009

Real-Time Detection of Dust Devils from Pressure Readings

A method for real-time detection of dust devils at a given location is based on identifying the abrupt, temporary decreases in atmospheric pressure that are characteristic of dust devils as they travel through that location. The method was conceived for use in a study of dust devils on the Martian surface, where bandwidth limitations encourage the transmission of only those blocks of data that are most likely to contain information about features of interest, such as dust devils. The method, which is a form of intelligent data compression, could readily be adapted to use for the same purpose in scientific investigation of dust devils on Earth. In this method, the readings of an atmospheric- pressure sensor are repeatedly digitized, recorded, and processed by an algorithm that looks for extreme deviations from a continually updated model of the current pressure environment. The question in formulating the algorithm is how to model current normal observations and what minimum magnitude deviation can be considered sufficiently anomalous as to indicate the presence of a dust devil. There is no single, simple answer to this question: any answer necessarily entails a compromise between false detections and misses. For the original Mars application, the answer was sought through analysis of sliding time windows of digitized pressure readings. Windows of 5-, 10-, and 15-minute durations were considered. The windows were advanced in increments of 30 seconds. Increments of other sizes can also be used, but computational cost increases as the increment decreases and analysis is performed more frequently. Pressure models were defined using a polynomial fit to the data within the windows. For example, the figure depicts pressure readings from a 10-minute window wherein the model was defined by a third-degree polynomial fit to the readings and dust devils were identified as negative deviations larger than both 3 standard deviations (from the mean) and 0.05 mbar in magnitude. An algorithm embodying the detection scheme of this example was found to yield a miss rate of just 8 percent and a false-detection rate of 57 percent when evaluated on historical pressure-sensor data collected by the Mars Pathfinder lander. Since dust devils occur infrequently over the course of a mission, prioritizing observations that contain successful detections could greatly conserve bandwidth allocated to a given mission. This technique can be used on future Mars landers and rovers, such as Mars Phoenix and the Mars Science Laboratory.

Wagstaff, Kiri↗

NASA NTRSDate not supplied

Using X-Ray Computed Tomography to Catalog Rock Fragments in Apollo Drive Tube 73002

Overview: The Apollo missions collected 382 kg of rock, regolith, and core samples from six locations on the nearside of the Moon. Approximately 84% by mass of the Apollo collection remains in pristine condition within the curation facility at Johnson Space Center (i.e., never allocated, continuously stored in dry-N2 purged cabinets, exposure history restricted to Teflon, stainless steel, and Al-metal). Although most Apollo samples have been well characterized, there are several types of samples that have remained wholly or largely unstudied since their return, and/or that have been cu-rated under special conditions, e.g., frozen samples, samples stored in He-purged environment, and previously unopened drive tubes. NASA solicited proposals for the Apollo Next Generation Sample Analysis Pro-gram (ANGSA), and 9 teams were selected to study a subset of the unopened and frozen samples [1]. The first sample opened as part of the ANGSA pro-gram was drive tube 73002. This was originally a ~30 cm long, 4 cm diameter drive tube collected on a land-slide deposit near Lara Crater at the Apollo 17 landing site. It was part of a ~60 cm long double drive tube collected, and the bottom half of the tube (73001) was sealed under vacuum on the Moon [2]. Prior to opening sample 73002, the sample was imaged with a high resolution X-ray Computed Tomography (XCT) scan of the entire tube [3], which provided invaluable information during the dissection process [4]. In addition to the pre-dissection XCT scans, individual >4 mm particles were separated from the 73002 regolith during processing and scanned at high resolution by XCT. Here we pre-sent the initial lithologic classification of 134 individual >4 mm rock fragments separated from the 73002 core during the dissection process. Methodology: Drive tube 73002 was manually dissected in 0.5 cm depth intervals in three passes (Fig. 1) [4,5]. Each interval from pass 1 and 2 was sieved to <1 mm and >1 mm size fractions, and >1 mm particles were further manually subdivided into 1-2, 2-4, 4-10, and >10 mm size fractions. Pass 3 was not sieved, but >10 mm clasts were separated manually. Each 4-10 mm and >10 mm fragment was individually weighed, triply bagged in Teflon, and scanned by XCT. There are 60 rock fragments in Pass 1, 64 rock fragments in Pass 2 (from the 4-10 mm and >10 mm size fractions), and 8 rock fragments from Pass 3 (>10 mm size fraction). Each individually bagged rock fragment was scanned using the 180 kV nano-focus transmission source on the Nikon XTH 320 XCT system at NASA Johnson Space Center [6]. Scanning conditions varied considerably for individual particles, in large part be-cause of the large variation in size (0.008-19.623 g). All scans fell within the following range of scan conditions: 2.8-20.6 um voxel size; 90-155 kV voltage; 18-39 uA current; 1891-3141 projections; and 902-2000 slices. Results and Discussion: The 132 rock fragments from sample 73002 fall into the following general cat-gories: agglutinates (n = 6); basalts (13); impact melts (5); impact melt breccias (IMB; 42); regolith breccias (62); and soil breccias (4); see Figure 2 for representative examples of each lithology. Within most of these broad lithologic groups are recognizable subgroups. For example, a significant portion of regolith breccia fragments contain some agglutinate-like glass (n = 9) or are dilithologic (7) because they contain a single large clast (~50% or more by volume). Subgroups can be based on similarities to previously identified lunar lithologies, such as high-Ti basalts (9) and VLT basalts (4; Fig. 3), or based on commonly seen features within the fragments, such as poikilitic ilmenite IMB (10), ilmenite-lath IMB (12), or vesicular IMB (11) “groups”. Particles in the same “group” are not necessarily intended to be genetically related, but rather identify particles that are similar and that follow-up studies can classify in more detail [7]. Conclusion: Identification of lithologies based on XCT is a powerful tool, but a more absolute classification will sometimes require additional textural information from thin sections (e.g., glassy-matrix regolith vs. impact-melt vs. granulitic breccia) or quantitative mineral compositions (e.g., basalt vs. monomict breccia).

R A Zeigler↗