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At least 19 records

Influence Network: Network visualization of influence between stories for Earth Science data and information exploration

Using storytelling to present data has been demonstrated as an effective way to help data users gain deeper insight of information. For this purpose, the “Data in Action” story concept has been adopted by NASA Commercial Smallsat Data Acquisition (CSDA) Program to encourage creating and sharing data information in story form. To aid researchers in exploring similar stories and data in their fields of interest, we created an “Influence Network” within the “Data in Action” framework. The Influence Network is a data visualization system component which gathers information of story relationships using a concept called “influence,” which we define based on the number of visits and keyword similarity between stories. The visualization then provides insights into the influence flows between different stories within the system. The visualization of this “Influence Network” focuses on one story at a time, introducing a time series of neighbor stories that have the most influence to the currently focused story. By focusing on views over time and visualizing influence flows between stories, we aim to assist authors in understanding how their readers perceive their stories as well as advancing their methods for delivering more meaningful stories to expand and lower the barrier to use of CSDA datasets.

Dan Pham↗

The influence of tillage and fertilizer on the flux and source of nitrous oxide with reference to atmospheric variation using laser spectroscopy

Nitrous oxide (N 2 O) is the third most important long-lived greenhouse gas and agriculture is the largest source of N 2 O emissions. Curbing N 2 O emissions requires understanding influences on the flux and sources of N 2 O. We measured flux and evaluated microbial sources of N 2 O using site preference ( S P ; the intramolecular distribution of 15 N in N 2 O) in flux chambers from a grassland tilling and agricultural fertilization experiments and atmosphere. We identified values greater than that of the average atmosphere to reflect nitrification and/or fungal denitrification and those lower than atmosphere as increased denitrification. Our spectroscopic approach was based on an extensive calibration with 18 standards that yielded S P accuracy and reproducibility of 0.7 ‰ and 1.0 ‰, respectively, without preconcentration. Chamber samples from the tilling experiment taken ~ monthly over a year showed a wide range in N 2 O flux (0–1.9 g N 2 O-N ha -1 d -1 ) and S P (- 1.8 to 25.1 ‰). Flux and S P were not influenced by tilling but responded to sampling date. Large fluxes occurred in October and May in no-till when soils were warm and moist and during a spring thaw, an event likely representing release of N 2 O accumulated under snow cover. These high fluxes could not be ascribed to a single microbial process as S P differed among chambers. However, the year-long S P and flux data for no-till showed a slight direct relationship suggesting that nitrification increased with flux. The comparative data in till showed an inverse relationship indicating that high flux events are driven by denitrification. Corn ( Zea mays ) showed high fluxes and S P values indicative of nitrification ~ 4 wk after fertilization with subsequent declines in S P indicating denitrification. Although there was no effect of fertilizer treatment on flux or S P in switchgrass ( Panicum virgatum) , high fluxes occurred ~1 month after fertilization. In both treatments, S P was indicative of denitrification in many instances, but evidence of nitrification/fungal denitrification also prevailed. At 2 m atmospheric N 2 O S P had a range of 31.1 ‰ and 14.6 ‰ in the grassland tilling and agricultural fertilization experiments, respectively. These data suggest the influence of soil microbial processes on atmospheric N 2 O and argue against the use of the global average atmospheric S P in isotopic modeling approaches.

Environmental Sciences & Ecology↗

Data for Propagation Method and Planting Density Influence Canopy Developmental Transition and Biomass Productivity in Miscanthus × giganteus

Understanding how establishment practices influence the mechanisms underlying Miscanthus × giganteus (miscanthus) productivity and canopy development is critical for optimizing management. Data was collected during the juvenile (2011–2013) and mature (2024) phases of a long-term field experiment established in Urbana, Illinois, to evaluate the effects of propagation method (plug propagation [PP] and rhizome propagation [RP]), planting density (1.0, 0.75, and 0.25 plants m⁻²), and nitrogen application (0 and 67 kg N ha⁻¹) on end-of-season biomass yield, tiller mass, tiller density, and tiller height. Linear regression models identified the dominant predictors of yield across stand ages and management regimes. Planting density, nitrogen (N) application, and propagation method significantly influenced early yield and canopy development. During the juvenile phase, biomass yield was driven by tiller density due to canopy expansion; in the mature phase, yield became driven by tiller mass. The PP plots produced higher tiller density than the RP plots, resulting in faster canopy closure and higher juvenile-phase yields. Rhizome-propagated (RP) plots produced lower tiller density, but individual tillers were 3.3–6.4 g tiller−1 heavier than PP tillers. After the canopy reached equilibrium, the PP and RP yields were similar because greater RP tiller mass compensated for its lower tiller density. Higher planting density resulted in greater yield and tiller density during the second year (2012), but this effect was absent from the third year (2013) onward. In the juvenile phase, N fertilization enhanced yield by 1.6–3.4 Mg ha−1. Initiating fertilization in 2013 on unfertilized plots produced biomass similar to that in fertilized plots, suggesting yield recovery in the mature phase. These findings revealed that establishment strategies, including propagation method and planting density, influence juvenile miscanthus canopy development and productivity, transitioning from tiller-density- to mass-dominated yields, but not mature phase productivity.

Miscanthus↗

Exploration of Factors That Influence Willingness to Consider Pooled Rideshare

Ridesharing has become an increasingly prevalent form of transportation. Although transportation network companies such as Uber and Lyft initially started as a personal rideshare service where individuals ride alone or with people they know, rideshare services have been expanded to pooled rideshare—a dynamic rideshare system where an individual rides with passengers they do not know. Despite the growth in rideshare services worldwide, the use of pooled rideshare in the U.S.A. is relatively low compared to other forms of transportation. A national U.S. survey (N = 5385) was conducted to investigate reasons why individuals are willing or unwilling to consider pooled rideshare. Exploratory and confirmatory factor analyses were performed, where the exploratory factor analysis suggests five factors, specifically,service experience,time/cost,traffic/environment,privacy, andsafety. Model fit indices of the confirmatory factor analysis verified that these five factors can represent the factors behind riders’ willingness to consider pooled rideshare. Furthermore, a binomial logistic regression was conducted to explore how the five factors influence riders’ willingness to consider pooled rideshare. The three factors that influence riders’ willingness to consider pooled rideshare wereservice experience(B = 1.05),traffic/environment(B = .38), andtime/cost(B = .26), while a lack ofprivacy(B = −1.46) can be a deterrent for pooled rideshare.Safetyis important for those who are both willing and unwilling to consider the use of pooled rideshare. Understanding these factors is important for the future of pooled rideshare services in the U.S.A.

Engineering↗

Predicting High Energy Arcing Fault Zones of Influence for Aluminum Using an Arc Flash Modeling Approach: Evaluation of a model bias, uncertainty, parameter sensitivity and zone of influence estimation

This report documents the development of an arc flash hazard model to calculate the incident energy and zone of influence from high energy arcing faults involving aluminum. The NRC has identified the potential for (HEAFs) involving aluminum to increase the damage zone beyond what is currently postulated in fire probabilistic risk assessment (PRA) methodologies. To estimate the hazard from HEAFs involving aluminum an arc flash model was developed. Differences between the initial model and nuclear power plant (NPP) fire PRA scenarios were identified. Modification of the initial model established from existing literature and test data was used to minimize these differences. The developed model was evaluated against NRC datasets to understand the model prediction and relative uncertainties. Finally, a range of fire PRA zone of influences (ZOI) were developed based on the developed model, target fragility estimates and update HEAF PRA methodology. The results were developed to support an NRC LIC-504 evaluation in tandem with other modeling efforts. The report documents the effort and provides a reference for any future advancements in arc flash modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Mutations in the Pectin Methyltransferase QUASIMODO2 Influence Cellulose Biosynthesis and Wall Integrity in Arabidopsis

Abstract Pectins are abundant in the cell walls of dicotyledonous plants, but how they interact with other wall polymers and influence wall integrity and cell growth has remained mysterious. Here, we verified that QUASIMODO2 (QUA2) is a pectin methyltransferase and determined that QUA2 is required for normal pectin biosynthesis. To gain further insight into how pectin affects wall assembly and integrity maintenance, we investigated cellulose biosynthesis, cellulose organization, cortical microtubules, and wall integrity signaling in two mutant alleles of Arabidopsis (Arabidopsis thaliana) QUA2, qua2 and tsd2. In both mutants, crystalline cellulose content is reduced, cellulose synthase particles move more slowly, and cellulose organization is aberrant. NMR analysis shows higher mobility of cellulose and matrix polysaccharides in the mutants. Microtubules in mutant hypocotyls have aberrant organization and depolymerize more readily upon treatment with oryzalin or external force. The expression of genes related to wall integrity, wall biosynthesis, and microtubule stability is dysregulated in both mutants. These data provide insights into how homogalacturonan is methylesterified upon its synthesis, the mechanisms by which pectin functionally interacts with cellulose, and how these interactions are translated into intracellular regulation to maintain the structural integrity of the cell wall during plant growth and development.

Biochemistry & Molecular Biology↗

Factors Influencing Adoption of Pooled Rideshare An Explorative Study on User-Centered Design and Services

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals ride alone or with people they know) initially dominated the market, the popularity of pooled ridesharing (individuals share rides with strangers) has grown globally. However, pooled rideshare remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. A national U.S. survey ( N = 5,385) used exploratory and confirmatory factor analyses to identify four key factors influencing riders’ willingness to consider pooled rideshare: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Understanding and implementing these user-centered design principles and service-related factors may be critical for increasing the future use of pooled rideshare services

Gangadharaiah, Rakesh↗

Data for Genetic Variation in Zea mays Influences Microbial Nitrification and DeNitrification in Conventional Agroecosystems

Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds ( Z. mays ssp. mays ), ex-PVP hybrids ( Z. mays ssp. may s), and teosinte ( Z. mays ssp. mexicana and Z. mays ssp. parviglumis ). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Nitrogen↗

Mechanisms influencing and prediction of tool influence function spots during hemispherical sub-aperture tool polishing on fused silica

Sub-aperture tool polishing of precision optics requires a detailed understanding of the local material removal [tool influence function (TIF)] at the contact spot between the workpiece and tool to achieve high removal determinism and hence precision of the optic relative to the desired/design surface figure. In this study, the mechanisms influencing and the quantitative prediction of the removal rate and shape of TIF spots during polishing of fused silica glass with cerium oxide slurry using a rotating hemispherical pad-foam tool for a wide variety of process conditions (including tool properties, kinematics, and applied displacements) are investigated. The TIF volumetric removal rate can be estimated utilizing the average relative velocity and contact area using a simple analytical model. In addition, stability of the volumetric removal rate for fixed process conditions is shown to be greatly dependent on the pad preparation and amount of tool use (affecting both pad topography and slurry buildup), whose general behavior shows an increase in removal rate followed by stabilization with polishing time. The determination of the TIF removal shape is more complex. An extended version of the Preston removal model is developed to explain a comprehensive set of measured TIF removal shapes to within ∼ <#comment/> 22 % <#comment/> . This model incorporates a number of phenomena impacting the TIF removal shape including: (a) temporal and spatial dependent relative velocity between the workpiece and tool; (b) an elastic mechanics based, as well as hydrodynamic, pressure distribution; (c) a spatially dependent friction coefficient possibly caused by both reduced slurry replenishment in low velocity regions and pad slurry islands (100 µm scale) and porosity (millimeter scale); and (d) a shear-based removal mechanism on the periphery of the contact spot.

Suratwala, T. (ORCID:0000000190861039)↗

Calculated spanwise lift distributions, influence functions, and influence coefficients for unswept wings in subsonic flow

Spanwise lift distributions have been calculated for nineteen unswept wings with various aspect ratios and taper ratios and with a variety of angle-of-attack or twist distributions, including flap and aileron deflections, by means of the Weissinger method with eight control points on the semispan. Also calculated were aerodynamic influence coefficients which pertain to a certain definite set of stations along the span, and several methods are presented for calculating aerodynamic influence functions and coefficients for stations other than those stipulated. The information presented in this report can be used in the analysis of untwisted wings or wings with known twist distributions, as well as in aeroelastic calculations involving initially unknown twist distributions.

Diederich, Franklin W↗

Influence of Hydrogen Isotopes on Vacancy Formation and Antisite Defect Diffusion in Palladium and Vanadium Metals

Density functional theory calculations are performed to study the interaction between hydrogen isotopes, vacancy, and antisite defects in Pd and V. Various defect configurations and defect-defect distances are systematically explored. Binding energies and migration energy barriers are calculated and tabulated. The study provides atomistic data for subsequent mesoscale simulations of tritium, vacancy, and antisite diffusion. In Pd, a strong binding is found between a vacancy and tritium (0.16 eV). This tritium binding increases the thermal concentration of vacancies by a factor of ~10 at 500 ?C. The increase in vacancy concentration enhances V antisite diffusion in Pd by a factor of ~6 at 500 ?C. The influence of tritium is even stronger in V, with a tritium and vacancy binding energy of 0.38 eV. Such a strong binding increases the thermal concentration of vacancies by a factor of ~300 at 500 ?C. The increase in vacancy concentration enhances Pd antisite diffusion in V by a factor of ~640 at 500 ?C. Vanadium and Pd exhibit a strong driving force to intermix with a formation energy of -1.57 eV for V antisite in Pd and -1.05 eV for Pd antisite in V. The results suggest vanadium diffusion into Pd is energetically stronger than the reverse. Zero-point-energy corrections are taken into account and calculations for hydrogen and tritium are presented.

Setyawan, Wahyu↗

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.

Cohen, Stuart↗