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At least 253 records · Page 14

Structure Factors for Hot Neutron Matter from Ab Initio Lattice Simulations with High-Fidelity Chiral Interactions

We present the first ab initio lattice calculations of spin and density correlations in hot neutron matter using high-fidelity interactions at next-to-next-to-next-to-leading order in chiral effective field theory. These correlations have a large impact on neutrino heating and shock revival in core-collapse supernovae and are encapsulated in functions called structure factors. Unfortunately, calculations of structure factors using high-fidelity chiral interactions were well out of reach using existing computational methods. In this Letter, we solve the problem using a computational approach called the rank-one operator (RO) method. The RO method is a general technique with broad applications to simulations of fermionic many-body systems. It solves the problem of exponential scaling of computational effort when using perturbation theory for higher-body operators and higher-order corrections. Using the RO method, we compute the vector and axial static structure factors for hot neutron matter as a function of temperature and density. Here, the ab initio lattice results are in good agreement with virial expansion calculations at low densities but are more reliable at higher densities. Random phase approximation codes used to estimate neutrino opacity in core-collapse supernovae simulations can now be calibrated with ab initio lattice calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Global Data-Driven Determination of Baryon Transition Form Factors

Hadronic resonances emerge from strong interactions encoding the dynamics of quarks and gluons. The structure of these resonances can be probed by virtual photons parametrized in transition form factors. Here, in this study, twelve N* and Δ transition form factors at the pole are extracted from data with the center-of-mass energy from πN threshold to 1.8 GeV, and the photon virtuality 0 ≤ Q 2 /GeV 2 ≤ 8. For the first time, these results are determined from a simultaneous analysis of more than one state, i.e., ~10 5 π⁢N, η⁢N, and K⁢Λ electroproduction data. In addition, about 5×10 4 data in the hadronic sector as well as photoproduction serve as boundary conditions. For the Δ⁡(1232) and N⁡(1440) states our results are in qualitative agreement with previous studies, while the transition form factors at the poles of some higher excited states are estimated for the first time. Realistic uncertainties are determined by further exploring the parameter space.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Efficient structure-factor modeling for crystals with multiple components

Diffraction intensities from a crystallographic experiment include contributions from the entire unit cell of the crystal: the macromolecule, the solvent around it and eventually other compounds. These contributions cannot typically be well described by an atomic model alone, i.e . using point scatterers. Indeed, entities such as disordered (bulk) solvent, semi-ordered solvent ( e.g. lipid belts in membrane proteins, ligands, ion channels) and disordered polymer loops require other types of modeling than a collection of individual atoms. This results in the model structure factors containing multiple contributions. Most macromolecular applications assume two-component structure factors: one component arising from the atomic model and the second one describing the bulk solvent. A more accurate and detailed modeling of the disordered regions of the crystal will naturally require more than two components in the structure factors, which presents algorithmic and computational challenges. Here an efficient solution of this problem is proposed. All algorithms described in this work have been implemented in the computational crystallography toolbox ( CCTBX ) and are also available within Phenix software. These algorithms are rather general and do not use any assumptions about molecule type or size nor about those of its components.

59 BASIC BIOLOGICAL SCIENCES↗

Large Scale Tensor Factorization via Parallel Sketches

Tensor factorization methods have recently gained increased popularity. A key feature that renders tensors attractive is the ability to directly model multi-relational data. In this work, we propose ParaSketch, a parallel tensor factorization algorithm that enables massive parallelism, to deal with large tensors. The idea is to compress the large tensor into multiple small tensors, decompose each small tensor in parallel, and combine the results to reconstruct the desired latent factors. Prior art in this direction entails potentially very high complexity in the (Gaussian) compression and final combining stages. Adopting sketching matrices for compression, the proposed method enjoys a dramatic reduction in compression complexity, and features a much lighter combining step. Moreover, theoretical analysis shows that the compressed tensors inherit latent identifiability under mild conditions, hence establishing correctness of the overall approach. Numerical experiments corroborate the theory and demonstrate the effectiveness of the proposed algorithm.

block term decomposition↗

Key environmental and production factors for understanding variation in switchgrass chemical attributes

Abstract Switchgrass ( Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using variance estimation, location/cultivar, nitrogen application rate, and year explained 65%–96% of the variation for switchgrass chemical attributes. Location/cultivar × year interaction was a significant factor for all chemical attributes indicating environmental‐based influences. Nitrogen rate was less influential. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. Upland switchgrass results highlight the complexity in plant responses to growing conditions because all production and environmental variables had strong relationships with one or more chemical attributes. Lowland switchgrass was limited to observations of year‐to‐year environmental variability and nitrogen application rate. All explanatory variable categories were important for lowland switchgrass models but stand age and precipitation relationships were particularly strong. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry.

09 BIOMASS FUELS↗

Plant GATA Factors: Their Biology, Phylogeny, and Phylogenomics

GATA factors are evolutionarily conserved transcription factors that are found in animals, fungi, and plants. Compared to that of animals, the size of the plant GATA family is increased. In angiosperms, four main GATA classes and seven structural subfamilies can be defined. In recent years, knowledge about the biological role and regulation of plant GATAs has substantially improved. Individual family members have been implicated in the regulation of photomorphogenic growth, chlorophyll biosynthesis, chloroplast development, photosynthesis, and stomata formation, as well as root, leaf, and flower development. In this review, we summarize the current knowledge of plant GATA factors. Using phylogenomic analysis, we trace the evolutionary origin of the GATA classes in the green lineage and examine their relationship to animal and fungal GATAs. Lastly, we speculate about a possible conservation of GATA-regulated functions across the animal, fungal, and plant kingdoms.

59 BASIC BIOLOGICAL SCIENCES↗

Flavor Fragmentation Function Factorization

A definition of partonic jet flavor that is both theoretically well-defined and experimentally robust would have profound implications for measurements and predictions especially for heavy flavor applications. Recently, a definition of jet flavor was introduced as the net flavor flowing along the direction of the Winner-Take-All axis of a jet which is soft safe to all orders, but not collinear safe. Here, we exploit the lack of collinear safety and propose a factorization theorem of perturbative flavor fragmentation functions that resum collinear divergences and describe the evolution of flavor from the short distance of jet production to the long distance at which hadronization occurs. Collinear flavor evolution is governed by a small modification of the DGLAP equations. We present a detailed all-orders analysis and identify exact relations that must hold amongst the various anomalous dimensions by probability conservation and the existence of fixed points of the renormalization group flow. We explicitly validate the factorization theorem at one-loop order, and demonstrate its consistency at two loops in particular flavor channels. Starting at two-loops, constraints on phase space imposed by flavor measurements potentially allow for non-trivial soft contributions, but we demonstrate that they are scaleless and so explicitly vanish, ensuring that soft particles are summed inclusively and all divergences are exclusively collinear in nature. This factorization theorem opens the door to precision calculations with identified flavor in the infrared.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Nucleon Isovector Axial Form Factors

We present results for the isovector axial vector form factors obtained using thirteen 2 + 1 + 1-flavor highly improved staggered quark (HISQ) ensembles generated by the MILC collaboration. The calculation of nucleon two- and three-point correlation functions has been done using Wilson-clover fermions. In the analysis of these data, we quantify the sensitivity of the results to strategies used for removing excited state contamination and invoke the partially conserved axial current relation between the form factors to choose between them. Our data driven analysis includes removing contributions from multihadron $Nπ$ states that make significant contributions. Our final results are $g_A$ = 1.292(53) stat (24) sys for the axial charge; $g_S$ = 1.085(50) stat (103) sys and $g_T$ = 0.991(21) stat (10) sys for the scalar and tensor charges; $\langle{r^2_A}\rangle$ = 0.439(56) sta t(34) sys fm 2 for the mean squared axial charge radius, $g^*_P$ = 9.03(47) stat (42) sys for the induced pseudoscalar charge; and $g_{πNN}$ = 14.14(81) stat (85) sys for the pion-nucleon coupling. We also provide a parametrization of the axial form factor $G_A(Q^2)$ over the range 0 ≤ $Q^2$ ≤ 1 GeV 2 for use in phenomenology and a comparison with other lattice determinations. We find that the various lattice data agree within 10% but are significantly different from the extraction of $G_A(Q^2)$ from the $ν$-deuterium scattering data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A toolkit for plant lipid engineering: surveying the efficacies of lipogenic factors for accumulating specialty lipids

Plants produce energy-dense lipids from carbohydrates using energy acquired via photosynthesis, making plant oils an economically and sustainably attractive feedstock for conversion to biofuels and value-added bioproducts. A growing number of strategies have been developed and optimized in model plants, oilseed crops and high-biomass crops to enhance the accumulation of storage lipids (mostly triacylglycerols, TAGs) for bioenergy applications and to produce specialty lipids with increased uses and value for chemical feedstock and nutritional applications. Most successful metabolic engineering strategies involve heterologous expression of lipogenic factors that outperform those from other sources or exhibit specialized functionality. In this review, we summarize recent progress in engineering the accumulation of triacylglycerols containing - specialized fatty acids in various plant species and tissues. We also provide an inventory of specific lipogenic factors derived from a wide variety of organisms, along with their reported efficacy in supporting the accumulation of desired lipids. A review of previously obtained results serves as a foundation to guide future efforts to optimize combinations of factors to achieve further enhancements to the production and accumulation of desired lipids in a variety of plant tissues and species.

59 BASIC BIOLOGICAL SCIENCES↗

Chromatin Changes in Phytochrome Interacting Factor-Regulated Genes Parallel Their Rapid Transcriptional Response to Light

As sessile organisms, plants must adapt to a changing environment, sensing variations in resource availability and modifying their development in response. Light is one of the most important resources for plants, and its perception by sensory photoreceptors (e.g., phytochromes) and subsequent transduction into long-term transcriptional reprogramming have been well characterized. Chromatin changes have been shown to be involved in photomorphogenesis. However, the initial short-term transcriptional changes produced by light and what factors enable these rapid changes are not well studied. Here, we define rapidly light-responsive, Phytochrome Interacting Factor (PIF) direct-target genes (LRP-DTGs). We found that a majority of these genes also show rapid changes in Histone 3 Lysine-9 acetylation (H3K9ac) in response to the light signal. Detailed time-course analysis of transcript and chromatin changes showed that, for light-repressed genes, H3K9 deacetylation parallels light-triggered transcriptional repression, while for light-induced genes, H3K9 acetylation appeared to somewhat precede light-activated transcript accumulation. However, direct, real-time imaging of transcript elongation in the nucleus revealed that, in fact, transcriptional induction actually parallels H3K9 acetylation. Collectively, the data raise the possibility that light-induced transcriptional and chromatin-remodeling processes are mechanistically intertwined. Histone modifying proteins involved in long term light responses do not seem to have a role in this fast response, indicating that different factors might act at different stages of the light response. This work not only advances our understanding of plant responses to light, but also unveils a system in which rapid chromatin changes in reaction to an external signal can be studied under natural conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Opportunities for human factors in machine learning

Introduction The field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these difficulties. Methods Using semi-structured interviews with data scientists at a National Laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges. Results Results of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described. Discussion Recommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.

97 MATHEMATICS AND COMPUTING↗

The Eco-Bio-Social Factors That Modulate Aedes aegypti Abundance in South Texas Border Communities

Aedes aegypti control requires dedicated resources that are usually scarce, limiting the reach and sustainability of vector control programs. This generates a need to focus on areas at risk of disease transmission and also understand the factors that might modulate local mosquito abundance. We evaluated the eco-bio-social factors that modulate indoor and outdoor relative abundance of female Ae. aegypti in communities of South Texas. We conducted housing quality and Knowledge Attitudes and Practices surveys in households that were part of a weekly mosquito surveillance program in November of 2017 and 2018. Our results showed widespread knowledge of mosquitoes and Zika virus by our participants. However, less than 35% considered them as serious problems in this region. The presence of window-mounted air conditioning units increased the risk of female mosquito relative abundance indoors. An increase in outdoor relative abundance was associated with larger properties and a higher number of children between 6 to 17 years of age. Interestingly, we observed that an increasing number of children <5 years of age modulated both indoor and outdoor relative abundance, with a 52% increase indoors and 30% decrease outdoors. The low perception of mosquito and disease risk highlights engagement needs for vector-borne disease prevention in this region. The identified risk factors can help guide public health officials in their efforts to reduce human and vector contact.

54 ENVIRONMENTAL SCIENCES↗

Using Qualified On-Site Nuclear Power Plant Simulators in Human Factors Validations of Control Room Upgrades

Existing commercial nuclear power plants (NPPs) are valuable assets in the infrastructure portfolio of the United States (U.S.) because they safely and reliably generate about 1/5th of all the electricity used. The instrumentation and control (I&C) systems in commercial NPPs are the ‘eyes and ears' of the operator, allowing operators to maintain situation awareness, thereby allowing the plant to operate safely and efficiently for all phases of operation. Because the I&C systems in NPPs are still mostly based on analog technologies, which are reliable but not especially cost-effective, upgrading the existing I&C in com-mercial NPPs to new digital I&C is very important. Furthermore, simulators and simulation are critical human factors engineering tools for this I&C modernization work. NPP control room simulators are essential test beds to simulate normal, abnormal, and emergency operations that allow human factors researchers to safely and realistically evaluate early design phase prototypes of the upgraded digital I&C, and validate final as-built digital I&C systems that have been modernized deployed in NPPs. This paper describes research performed using qualified on-site NPP simulators to perform human factors validations of digital I&C control room upgrades.

99 GENERAL AND MISCELLANEOUS↗

ENABLING BUSINESS-DRIVEN INNOVATION THROUGH HUMAN FACTORS ENGINEERING IN NUCLEAR POWER PLANTS

For existing United States nuclear power plant fleet to remain economically viable, the nuclear industry needs to fundamentally change the way in which these plants are operated, maintained, and supported. A digital transformation is a key strategy to address this challenge. Though, guidance in this area is a continued effort. One framework to support innovation in the nuclear industry has taken a broader perspective by focusing on how technology can be used to meet specific business needs and work for the people and processes at hand. This work discusses the role and value of human factors engineering within this nuclear innovation framework. Human factors methods are presented here regarding how they address the phases of nuclear innovation. This work seeks to describe how human factors can be applied in nuclear innovation by strengthening the alignment of technology, people, processes, and regulations such that the needs of the business is addressed.

99 GENERAL AND MISCELLANEOUS↗

General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior, without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. Non-negative tensor factorization, on the other hand, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Herein, our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.

97 MATHEMATICS AND COMPUTING↗

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).

15 GEOTHERMAL ENERGY↗

Reproductive Factors and Endometrial Cancer Risk Among Women

Despite evidence of an association between reproductive factors and endometrial cancer risk, prospective studies have been conducted mainly in non-Asian countries. To assess the association between reproductive factors, such as number of deliveries, age at menarche, or menopause, and endometrial cancer risk. This cohort study used pooled individual data from 13 prospective cohort studies conducted between 1963 and 2014 in the Asia Cohort Consortium. Participants were Asian women. Data analysis was conducted from September 2019 to April 2023. Reproductive factors were assessed using a questionnaire in each cohort. The main outcome was time to incidence of endometrial cancer. A Cox proportional hazards model was used to calculate hazard ratios (HRs) and 95% CIs.

60 APPLIED LIFE SCIENCES↗

Toward Ultra‐High‐Quality‐Factor Wireless Masing Magnetic Resonance Sensing

It has recently been shown that a bolus of hyperpolarized nuclear spins can yield stimulated emission signals similar in nature to maser signals, potentially enabling new ways of sensing hyperpolarized contrast media, including most notably [1- 13 C]pyruvate that is under evaluation in over 50 clinical trials for metabolic imaging of cancer. The stimulated NMR signal emissions lasting for minutes do not require radio-frequency excitation, offering unprecedented advantages compared to conventional MR sensing. However, creating nuclear spin maser emission is challenging in practice due to stringent fundamental requirements, making practical in vivo applications hardly possible using conventional passive MR detectors. Here, in this study, we demonstrate the utility of a wireless NMR maser detector, the quality factor of which was enhanced 22-fold (to 1,670) via parametric pumping. This active-feedback technique breaks the intrinsic fundamental limit of NMR detector circuit quality factor. We show the use of parametric pumping to reduce the threshold requirement for inducing nuclear spin masing at 300 MHz resonance frequency in a preclinical MRI scanner. Indeed, stimulated emission from hyperpolarized protons was obtained under highly unfavorable conditions of low magnetic field homogeneity (T 2 * of 3 ms). Greater gains of the quality factor of the MR detector (up to 1 million) were also demonstrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗