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At least 559 records · Page 31

Testing for the Genomic Footprint of Conflict Between Life Stages in an Angiosperm and Moss Species

Abstract The maintenance of genetic variation by balancing selection is of considerable interest to evolutionary biologists. An important but understudied potential driver of balancing selection is antagonistic pleiotropy between diploid and haploid stages of the plant life cycle. Despite sharing a common genome, sporophytes (2n) and gametophytes (n) may undergo differential or even opposing selection. Theoretical work suggests antagonistic pleiotropy between life stages can generate balancing selection and maintain genetic variation. Despite the potential for far-reaching consequences of gametophytic selection, empirical tests of its pleiotropic effects (neutral, synergistic, or antagonistic) on sporophytes are generally lacking. Here, we examined the population genomic signals of selection across life stages in the angiosperm Rumex hastatulus and the moss Ceratodon purpureus. We compared gene expression between life stages and sexes, combined with neutral diversity statistics and the analysis of the distribution of fitness effects. In contrast to what would be predicted under balancing selection due to antagonistic pleiotropy, we found that unbiased genes between life stages were under stronger purifying selection, likely explained by a predominance of synergistic pleiotropy between life stages and strong purifying selection on broadly expressed genes. In addition, we found that 30% of candidate genes under balancing selection in R. hastatulus were located within inversion polymorphisms. Our findings provide novel insights into the genome-wide characteristics and consequences of plant gametophytic selection.

Evolutionary Biology↗

Band structure and Fermi surface nesting in LaSb 2

Here we use high-resolution angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) to investigate the electronic structure of the charge density wave (CDW) system LaSb 2 . This compound is among an interesting group of materials that manifests both a CDW transition and lower temperature superconductivity. We find the DFT calculations to be in good agreement with our ARPES data. The Fermi surface of LaSb 2 consists of two small hole pockets close to Γ and four larger pockets near the Brillouin zone boundary. The key features of the Fermi surface do not vary significantly with temperature. A saddle point is present at -0.19 eV below the Fermi level at Γ. Saddle points in the band structure have more pronounced effects on materials properties when they are located closer to the Fermi level, making doped LaSb 2 compounds a potential interesting subject of future research. Multiple peaks are present in the generalized, electronic susceptibility calculations, indicating the presence of three possible nesting vectors. We were not able to detect any signatures of the CDW transition at 355 K down to the lowest temperature of 7 K, pointing to the subtle nature of this transition. This is unusual, given that such a high transition temperature is expected to be associated with the presence of a large CDW gap. This is confirmed through investigation of the Fermi surface and through analysis of momentum distribution curves. It is possible that changes are subtle and occur below current sensitivity of our measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Measurement of the branching fraction of the decay B − → D 0 ρ ( 770 ) − at Belle II

We measure the branching fraction of the decay B − → D 0 ρ ( 770 ) − using data collected with the Belle II detector. The data contain 387 million B B ¯ pairs produced in e + e − collisions at the ϒ ( 4 S ) resonance. We reconstruct 8360 ± 180 decays from an analysis of the distributions of the B − energy and the ρ ( 770 ) − helicity angle. We determine the branching fraction to be ( 0.939 ± 0.021 ( stat ) ± 0.050 ( syst ) ) % , in agreement with previous results. Our measurement improves the relative precision of the world average by more than a factor of two. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

Nematic ordering of hard rods under strong confinement in a dense array of nanoposts

The effect of confinement on the behavior of liquid crystals is interesting from a fundamental and practical standpoint. In this work, we report Monte Carlo simulations of hard rods in an array of hard nanoposts, where the surface-to-surface separations between nanoposts are comparable to or less than the length of hard rods. This particular system shows promise as a means of generating large-scale organization of the nematic liquid by introducing an entropic external field set by the alignment of nanoposts. The simulations show that nematic ordering of hard rods is enhanced in the nanopost arrays compared with that in bulk, in the sense that the nematic order is significant even at low concentrations at which hard rods remain isotropic in bulk, and the enhancement becomes more significant as the passage width between two nearest nanoposts decreases. An analysis of local distribution of hard-rod orientations at low concentrations with weak nematic ordering reveals that hard rods are preferentially aligned along nanoposts in the narrowing regions between two curved surfaces of nearest nanoposts; hard rods are less ordered in the passages and in the centers of interpost spaces. Finally, it is concluded that at low concentrations the confinement in a dense array of nanoposts induces the localized nematic order first in the narrowing regions and, as the concentration further increases, the nematic order spreads over the whole region. The formation of a well-ordered phase at low concentrations of hard rods in a dense array of nanoposts can provide a new route to the low-concentration preparation of nematic liquid crystals that can be used as anisotropic dispersion media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Making sense of vacancy correlations with single-crystal diffuse scattering data

Local ordering of atoms within a crystalline structure is increasingly recognized as essential to understanding the properties of a variety of interesting materials. This is shown by the success of pair distribution function (PDF) analysis of scattering data from powder samples to investigate departures from perfectly crystalline order (Billinge & Kanatzidis, 2004; Egami & Billinge, 2012). Diffuse scattering from single crystals can also be a powerful probe of local order, potentially providing key information that would be difficult to obtain from PDF methods; however, difficulties in both measurement and interpretation of data have too often restricted their use by a larger community. In this issue of IUCrJ, Rothet al.(2020) demonstrate how simple concepts can be applied to better understand single-crystal diffuse scattering from half-Heusler systems.

36 MATERIALS SCIENCE↗

Methods—Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition

The mesostructure of porous electrodes used in lithium-ion batteries strongly influences cell performance. Accurate imaging of the distribution of phases in these electrodes would allow this relationship to be better understood through simulation. However, imaging the nanoscale features in these components is challenging. While scanning electron microscopy is able to achieve the required resolution, it has well established difficulties imaging porous media. This is because the flat imaging planes prepared using focused ion beam milling will intersect with the pores, which makes the images hard to interpret as the inside walls of the pores are observed. It is common to infiltrate porous media with resin prior to imaging to help resolve this issue, but both the nanoscale porosity and the chemical similarity of the resins to the battery materials undermine the utility of this approach for most electrodes. In this study, a technique is demonstrated which uses in situ infiltration of platinum to fill the pores and thus enhance their contrast during imaging. Reminiscent of the Japanese art of repairing cracked ceramics with precious metals, this technique is referred to as the kintsugi method. The images resulting from applying this technique to a conventional porous cathode are presented and then segmented using a multi-channel convolutional method. We show that while some cracks in active material particles were empty, others appear to be filled (perhaps with the carbon binder phase), which will have implications for the rate performance of the cell. Energy dispersive X-ray spectroscopy was used to validate the distribution of phases resulting from image analysis, which also suggested a graded distribution of the binder relative to the carbon additive. The equipment required to use the kintsugi method is commonly available in major research facilities and so we hope that this method will be rapidly adopted to improve the imaging of electrode materials and porous media in general.

25 ENERGY STORAGE↗

fife-utils

This is a collection of scripts related to the FIFE project at Fermilab, including utiltities for performing bulk oprations with our SAM and MetaCat (github)data handling systems. The most heavily used is the fife_launch/fife_wrap script pair, which is used to convert physics analysis executables into distributed grid jobs.

Mengel, Marc [Fermi National Accelerator Laborator↗

Parametric and Nonparametric Models of U.S. Cost Overruns for Nuclear Power Plants

This study presents new data-driven models to estimate the effect of capacity on the percentage of cost overruns in the United States for nuclear power plant construction projects before and after the Three Mile Island accident. Parametric and nonparametric models have been developed that describe the significant shifts in nuclear energy costs during the dynamic environment. Employing a contemporary descriptive methodology and a quantitative analysis, we furnish a comprehensive overview of the alterations in cost overrun distribution and show the changes observed in other pivotal metrics alongside cost overruns. Our emphasis lies in documenting the fluctuations in cost overruns alongside nuclear reactor capacity levels and the increase of the overnight capital costs to build nuclear reactors. Our results show that increasing the size of nuclear reactors is not a factor statistically significant to decrease the percentage of cost overruns, and the probit model results provide evidence that an increase in size increases the probability of having cost overruns larger than 100% (double the estimated cost). We also compare our findings to two other regions: Asia and Europe.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Overview of Hydraulic Fracturing Test Site 2 in the Permian Delaware Basin (HFTS-2)

Here, the Hydraulic Fracturing Test Site 2 (HFTS-2) is a large collaborative field-based R&D program in the Permian Delaware Basin, funded by the US Department of Energy through the National Energy Technology Laboratory (NETL) and the E&P industry, with support from academia. The projects' main objective is to improve the understating of the hydraulic fracturing process through utilization of advanced diagnostics and collection of through-fracture cores to provide undisputable evidence and attributes of the created hydraulic fractures. At the HFTS-2, in excess of $30 million was used to perform hydraulic fracturing research focusing on the Wolfcamp formation at a field site hosted and operated by Occidental. In addition to the research data collected by the project, Occidental provided a significant amount of background data for about a dozen existing wells in the test area as well as access to previously collected core. Additional technical and laboratory support was provided by the program members. Building on learnings and unanswered questions from HFTS-1 in the Permian Midland basin, the HFTS-2 used eight new producing wells and two existing (parent) wells to perform hydraulic fracturing research. Multiple science wells were drilled to sample and characterize the subsurface, including the collection of 540 feet of core in a vertical pilot hole and 948 feet of high-angle through-fracture core. The project installed permanent fiber optic cables in 3 wells to monitor near wellbore signals during fracturing and to collect cross-well strain measurements. Additional advanced diagnostics included a significant formation evaluation program on the vertical whole core, multi array moment tensor inversion capable microseismic survey, multi-well time-lapse geochemistry analysis, analysis of proppant distribution in producing child and slant core well, and others. We will provide an overview of the HFTS-2 project, including list of the consortium members, details of the test site, experiments performed, and technologies tested.

58 GEOSCIENCES↗

CJ15 global PDF analysis with new electroweak data from the STAR and SeaQuest experiments

We present updates to a recent CTEQ-Jefferson Lab (CJ) global analysis of parton distribution functions with a new set of electroweak data that provide unique access to quark flavor separation in the proton. In particular, recent W W and Z Z boson measurements from the STAR experiment at RHIC put additional constraints on light quarks and antiquarks near the valence regime. The new measurement of the Drell-Yan lepton pair production ratio in p+p p + p and p+d p + d collisions by the SeaQuest experiment at Fermilab extends the large- x x coverage of the previous E866 experiment and sheds new light on the light antiquarks distribution. In this report, the impact of these new data sets on parton distribution functions will be presented with emphasis given to the flavor asymmetry of the light antiquark sea at large values of the parton momentum x x .

Park, Sanghwa↗

Enhanced Feedstock Characterization and Modeling to Facilitate Optimal Preprocessing and Deconstruction of Corn Stover (Final Report)

This project addresses the challenge of processing corn stover by fractionating this biomass feedstock to both streamline processing and generate new potential co-products. Additionally, the project developed new field-deployable analytical tools that can be coupled with empirical models that were used to predict feedstock properties and processing performance. The overall scope of this project was: (1) identify conditions for optimal corn stover fractionation using a two- stage physical fractionation, (2) assess how physical fractionation impacts properties, partitioning of biomass, and response to processing, (3) further adapt, develop, and validate several advanced characterization tools for assessing biomass properties that can be linked to processing behavior, and (4) develop and validate predictive models based on measurements that can be performed “in the field” or “at the biorefinery gate” to predict feedstock processing behavior (preprocessing and deconstruction). The first objective employed pre-separation processing (size reduction) which was next subjected to enhanced separations to yield fractions enriched or depleted in select compositional components or properties. For the second objective, fractions were screened for their response to post-separation processing (pretreatment and enzymatic hydrolysis). Detailed characterization profiles were developed and dynamic image analysis to assess distribution of particle size and morphology. For the final objective, we utilized these tools to develop empirical models to assess the relative abundance of tissue type in order to assess fractionation efficacy and to predict fraction performance during pretreatment and enzymatic hydrolysis.

09 BIOMASS FUELS↗

Sample IEEE123 Bus system for OEDI SI

Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder), but some modifications were made to it to add some solar power modules and measurements on the system. The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation. The readme file included in the S3 bucket provides information about the directory structure and how to use the algorithms. The sensors.json file is used to define the measurement locations.

123 bus↗

CJ15 global PDF analysis with new electroweak data from the STAR and SeaQuest experiments

We present updates to a recent CTEQ-Jefferson Lab (CJ) global analysis of parton distribution functions with a new set of electroweak data that provide unique access to quark flavor separation in the proton. In particular, recent W and Z boson measurements from the STAR experiment at RHIC put additional constraints on light quarks and antiquarks near the valence regime. The new measurement of the Drell-Yan lepton pair production ratio in p+p and p+d collisions by the SeaQuest experiment at Fermilab extends the large-x coverage of the previous E866 experiment and sheds new light on the light antiquarks distribution. In this report, the impact of these new data sets on parton distribution functions will be presented with emphasis given to the flavor asymmetry of the light antiquark sea at large values of the parton momentum x.

Park, Sanghwa↗

Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans.

97 MATHEMATICS AND COMPUTING↗

Production of Fischer-Tropsch Synfuels at Nuclear Plants

A case study analysis was performed to evaluate nuclear-powered synthetic fuel production in the midwestern United States (U.S.). A Fischer-Tropsch (FT) fuel synthesis plant design was used as the basis for the analysis. The FT plant design was configured to produce a product slate consisting of diesel fuel, jet fuel, and motor gasoline blend stocks from carbon dioxide (CO 2 ) and hydrogen (H 2 ) feedstocks. The CO 2 feedstock for the FT plant was assumed to be sourced from biorefineries in the region around a Midwest light water reactor (LWR) nuclear power plant (NPP). The analysis specifies that power from the LWR is used to produce H 2 via high-temperature steam electrolysis and to operate the FT synfuel production plant. Capital costs were estimated for the FT plant while capital costs for the electrolysis plant were based on previous Idaho National Laboratory (INL) studies. In addition to labor and maintenance costs for the FT and electrolysis plants, operating costs also include the costs for CO 2 feedstock transport. An analysis was performed to determine the cost of transporting CO 2 from the distributed biorefinery sources to the centralized fuel synthesis plant as a function of the synfuel plant capacity and corresponding CO 2 demand. The primary revenue streams are associated with sales of the synthetic fuel products. The synthetic fuel products will likely follow the same market trends as the conventional fuel products. The synfuel price data was thus based on projections made by the U.S. Energy Information Administration (EIA) 2021 Annual Energy Outlook (AEO) for conventional fuel products minus federal and state taxes, as well as marketing and distribution costs. The economic analysis also considered cases that included and excluded revenues from the 2022 Inflation Reduction Act (IRA) clean hydrogen production tax credit (PTC) of $\$ $3.00/kg for the first ten years of operation. The economic analysis calculated the net present value (NPV) for cases involving steady-state synfuel production for comparison with the NPV for a business-as-usual case in which NPP continues to sell only electric power to the grid. A synfuel production “Reference Case” was considered in addition to sensitivity cases in which the plant capacity, electricity price, and synthetic fuel product prices were perturbed. The synfuel production Reference Case considered a scenario in which the electrolysis and synfuel plants utilized a combined electrical load of 1000 megawatt electrical (MWe) from the LWR with the balance of the LWR power output being sold to the electric grid. The economic analysis suggests that the synfuel production Reference Case evaluated in this analysis would lead to considerable economic potential for near-term deployment of a nuclear-based synfuel production plant. Specifically, the economic analysis suggests that the deployment of a 1000 megawatt (MW) nuclear-powered synfuel plant could result in a NPV increase of approximately $\$ $1.7 billion for a case with no clean synfuel price premium relative to conventional petroleum fuels when accounting for the additional revenues from the 2022 IRA clean hydrogen PTCs of $\$ $3/kg. Sensitivity analysis was performed to evaluate the effect of perturbation of selected model input parameters on the NPV for the synfuel production Reference Case. The sensitivity analysis indicates that the plant capacity has the largest impact on the differential NPV, with a smaller synfuel production capacity resulting in a decrease in revenue when a larger fraction of the power from the NPP is sold to the grid and a smaller fraction of the power is used to produce synthetic fuel products. The synfuel product pricing has the next largest impact on the differential NPV, with lower synfuel prices resulting in decreased NPV from decreased synfuel sales revenue while higher synfuel prices result in increased NPV from increased synfuel sales revenue. Electricity pricing has a smaller effect on the NPV than the fuel sales price since, in the Reference Case, most of the energy from the NPP is used for synfuel production and a smaller amount of the system revenues are associated with electrical power sales. However, the electricity price sensitivity does indicate that the Synfuel Integrated Energy System (IES) would have a greater NPV than the business-as-usual case (e.g., grid power sales only) when electricity market prices are low, suggesting that synfuel production could provide a strategy for decreasing the economic risks to NPPs posed by a loss of revenues attributed to falling electricity market prices.

10 SYNTHETIC FUELS↗

The Pantheon+ Analysis: Forward Modeling the Dust and Intrinsic Color Distributions of Type Ia Supernovae, and Quantifying Their Impact on Cosmological Inferences

Abstract Recent studies have shown that the observed color distributions of Type Ia supernovae (SNe Ia) can be well described by a combination of a dust distribution and an intrinsic color distribution. Using the Pantheon+ sample of 1701 SN Ia, we apply a new forward-modeling fitting method (Dust2Dust) to measure the parent dust and color distributions, including their dependence on host-galaxy mass. At each fit step, the SN Ia selection efficiency is determined from a large simulated sample that is reweighted to reflect the proposed distributions. We use five separate metrics to describe the goodness of fit: distribution of fitted light-curve color c , cosmological residual trends with c , cosmological residual scatter with c , fitted color–luminosity relationship β SALT2 , and intrinsic scatter σ int . We present the results and the uncertainty in 12-dimensional space. Furthermore, we measure that the uncertainty on this modeling propagates to an upper threshold uncertainty in the equation of state of dark energy w of 0.014(1) for the Pantheon+ cosmology analysis and contributes negligible uncertainty to the Hubble constant H 0 . The Dust2Dust code is made publicly available at https://github.com/djbrout/dustdriver .

79 ASTRONOMY AND ASTROPHYSICS↗

Solving disorder in (3D) real space: a comparative study of the three-dimensional difference pair distribution function and atomic resolution holography reconstructions

The quantitative analysis of local ordering principles in disordered crystalline systems has gained much attention over the past few years, as it is often considered crucial for optimizing material functionality. This development has been driven by significant advancements in computational and experimental methods, which have led to the establishment and widespread use of various analytical techniques. In this study, we perform model calculations to compare the effectiveness of atomic resolution holography and three-dimensional difference pair distribution function analysis (3D-ΔPDF). Using Cu 3 Au as a model system, we demonstrate an approach to derive local order parameters quantitatively and show that both techniques are well suited to quantifying chemical short-range order correlations and local bond-distance variations. By evaluating the strengths and limitations of both techniques, we advocate for their combined use to solve complex short-range order problems accurately.

3D-ΔPDF↗

Modeling the hydrodynamic impact on the tool influence function during hemispherical subaperture optical polishing

To fabricate high-precision and accurate optics relative to the optical design surface, a high level of deterministic control of material removal (i.e., the tool influence function, TIF) during subaperture tool polishing is required. In this study, a detailed analysis of the pressure distribution, which is a key component of the TIF, has been performed using finite element analysis to couple together solid mechanics and fluid dynamics. Modeling experimental parameters of recently published work reveals that, when considering tool deformation, which in turn influences the fluid film thickness between the tool and workpiece, the effective pressure profile has a flat-top distribution. This flat-top pressure profile differs from the parabolic pressure distributions predicted by Hertzian mechanics. Furthermore, the shear contribution is shown here to be a key contributor to material removal, inducing the removal at the periphery of the contact edge and even outside the generally accepted contact area. Finally, the simulated fluid velocities provide evidence of mixed-mode contact polishing, supporting recent experimental findings that also suggest that onset of hydroplaning contributions lead to material removal drop-off.

36 MATERIALS SCIENCE↗