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In this perspective, we highlight the need to integrate data management infrastructure across scales to best take advantage of advancements in automated and autonomous experimental methods in materials science.
The Laue neutron macromolecular crystallography beamline, IMAGINE, at the High Flux Isotope Reactor (HFIR) is undergoing a major upgrade to incorporate the dynamic nuclear polarization (DNP) technique. Upon completion, the new IMAGINE-X instrument is expected to enhance the signal-to-noise ratio of the diffraction data by an order of magnitude. To take full advantage of the benefits of DNP, both a highly polarized neutron beam and a high-efficiency neutron spin flipper are required. We have developed a compact cryogenic spin flipper utilizing the Meissner effect in high-Tc yttrium–barium–copper–oxide superconducting films. The flipper has been successfully tested at two HFIR neutron development beamlines, Larmor and Poplar, with neutron wavelengths of 5.3 and 2.4 Å respectively. At Larmor, a flipping efficiency of 0.995 ± 0.004 was achieved for 5.3 Å neutrons, and at Poplar, efficiency of up to 0.9987 ± 0.0004 was obtained for 2.4 Å neutrons. Both tests demonstrated the flipper’s excellent performance and suitability for integration into IMAGINE-X.
While there are numerous performance metrics that have been developed for the evaluation of distracted driving prevention programs, there is little information on how to select them depending on the requirements and/or objectives of the study. Here, this paper describes a systematic literature review that was conducted on the metrics for evaluating distracted driving countermeasures in order to bridge this research gap. A summary of the evaluation metrics used for the existing distracted driving countermeasures was provided. Guidance for choosing an evaluation measure was provided by analyzing the metrics from the perspectives of functionality, spatial-temporal dimension, and equity. Three examples of distracted driving countermeasure evaluation processes were thoroughly reviewed in order to offer insight into metric selection and measurement. This paper contributes to the body of knowledge by discussing the implications for policy on how to enhance the thoroughness and accuracy of the evaluation of distracted driving countermeasures. Analysis from multiple angles, the development of data collection tools and direct behavior indicators, taking into account temporal dimensions, and equity considerations, are proposed.
Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.
The net CO 2 assimilation (A) response to intercellular CO 2 concentration (C i ) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/C i protocols rely on steady-state measurements and take 15–40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO 2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO 2 protocols in steady-state measurements, and tested whether different CO 2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO 2 protocols, with slight differences in Rubisco carboxylation limitation. The A/C i curves were not affected by the direction of the change of CO 2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (V cmax ), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.
Industrial Cyber-Physical Systems (ICPS) are an essential backbone of national critical infrastructures. They help monitor and control crucial cyber-enabled services such as energy generation. Commonly ICPS monitors the physical process through Supervisory Control and Data Acquisition (SCADA) systems. The SCADA ecosystem takes critical real-time and future system operational decisions based on the runtime state behavior of field sensors. Traditional SCADA systems use legacy and insecure communication protocols such as the Modbus protocol that lack adequate security mechanisms to provide robust runtime state behavior assurance of constrained field sensors. Therefore, constrained field sensors are commonly vulnerable to standard semantic attacks that gradually change the behavior state of infected devices. This paper discusses process integrity assurance techniques necessary to enhance the security of behavior-based protocols such as the Modbus protocol. The Runtime State Verification (RSV) protocol proposed in this paper aims to address semantic attacks in the SCADA ecosystem by integrating behavior-based Mandatory Results Automata (MRA) and a Hyperledger Fabric (HLF) network. The RSV protocol provides high process integrity assurance through enhanced behavior-based MRA suitable for the constrained field devices. A proof of concept of the RSV protocol has been evaluated in an emulated water-tube boiler. Preliminary evaluations of the RSV protocol aimed to measure the efficiency of the proposed protocol by monitoring an Combustion Efficiency (CE) process necessary to preserve optimal combustion, thus minimizing costs and future maintenance of water-tube boilers. We analyze the overall network overhead and latency of the proposed RSV protocol by evaluating the HLF network performance and comparing the proposed RSV protocol with the state-ofart BloSPAI protocol. Through the preliminary evaluations of the proposed RSV protocol, this paper demonstrates that the proposed RSV protocol overcomes the shortcomings and network overhead of the BloSPAI protocol by integrating behavior-based authentication through novel MRAs and HLF networks.
Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g., data assimilation), and some are single purpose and customized (e.g., for model validation). We review current methods and best practices for integrating models and observations. We highlight how future developments can enable advanced heterogeneous observation networks and models to improve predictions of the Earth system (including atmosphere, land surface, oceans, cryosphere, and chemistry) across scales from weather to climate. As the community pushes to develop the next generation of models and data systems, there is a need to take a more holistic, integrated, and coordinated approach to models, observations, and their uncertainties to maximize the benefit for Earth system prediction and impacts on society.
Tomo2Mesh is an open-source project targeted towards real-time reconstruction, segmentation, and visualization of computed tomography (CT) data in mesh format. The CT reconstruction scheme is based on filtered back-projection of voxel subsets. The segmentation scheme uses a 3D convolutional neural network. To allow for fast, real-time reconstruction, voxel subsets are first identified by coarse reconstruction. The detail in specific regions of interest is improved through full reconstruction of voxel subsets in that region. Data structures are implemented to store and process voxel subsets. Finally, voxel data is labeled using connected components to detect disconnected regions such as voids whose morphological attributes (e.g., Feret diameter, principal axis orientation, local number density, size, etc.) can be measured also in real-time. Finally, a fast marching cubes implementation processes labeled voxel data into a triangular face mesh (vertices and faces) in .ply format for visualization in Paraview or other mesh visualization tools. The code provides a simple programming interface for detecting, classifying, and visualizing regions of interest based on morphology. For example, detected voids can be classified as round pores or extended cracks. Highly porous neighborhoods can be identified based on local number density. The mesh texture (or color) is assigned based these morphological attributes to allow smart visualization scenarios in real-time (e.g., show only long cracks). At the time of first release (July 2022), extraction of face mesh for visualization for raw CT data from a 2 megapixel camera would take between 1-5 minutes for most scenarios.
This code is designed to take an existing STIX bundle with vulnerability data and enrich it with additional potential vulnerabilities to provide further insight during threat analysis. It also acts as a launch platform for other enrichments tools. The additional tools include, WAVgraph and STIXEnforcer.
Accelerated neutron irradiation testing is an component of accelerated qualification of new nuclear fuels for light water reactor (LWRs), microreactors, and other special purpose reactors. The qualification and licensing of nuclear fuel is a lengthy process that can take 20-25 years to bring a new fuel into service. Accelerated fuel qualification combines both experimental and modeling work to expedite the total qualification time to 5-10 years timeframe. The experimental aspect of this is accelerated irradiation aims to reduce the total time needed for neutron irradiation to achieve targeted burnup, which can take years using conventional irradiation profiles. The data that results from this irradiation testing can then be entered into BISON models to develop robust and reliable performance simulations to ensure safe operation under normal and off normal conditions. This milestone focused on the fabrication of test articles for accelerated irradiation testing at the Advance Test Reactor (ATR).
The goal of the Advanced Materials and Manufacturing Technologies (AMMT) program is to accelerate the incorporation of new materials and manufacturing technologies into advanced nuclear-related systems. Although 316H stainless steel fabricated by laser powder bed fusion (LPBF) has already been identified as an alloy that could have a significant effect on various reactor technologies, many other materials and manufacturing techniques are being evaluated. Nickel-based alloys typically offer higher-temperature capabilities compared with advanced stainless steels, and previous reports looked at three Ni-based alloy categories: low-Co alloys with a potential use close to the reactor core; high-temperature, high-strength alloys; and molten salt–compatible alloys. In the first category, alloy 718 was studied in 2023, and creep testing at 600°C and 650°C revealed that the alloy exhibited great creep strength after the appropriate annealing but had low ductility. Advanced characterization was recently conducted to highlight the presence of strengthening γ' and γ" precipitates after creep testing and to show that brittle phases at grain boundaries might explain the low ductility of LPBF 718 compared with wrought 718. For the high-temperature, high-strength alloys, previously purchased powders of alloys 617, 230, and 625 were used to assess the printability of these three solution-strengthened alloys. Hot cracking could not be suppressed for alloy 617 and 230, and it was shown that these cracks, which were elongated along the build direction (BD), had a drastic effect on the ductility of alloy 230 at room temperature when specimens were machined perpendicular to the BD. On the contrary, LPBF printing of crack-free alloy 625 was achieved using similar printing parameters, and the alloy looked like a promising candidate for various reactor technologies. The fabrication of alloy 282 by LPBF, a γ'-strengthened alloy with great creep strength up to 800°C, was performed in 2023, and x-ray computed tomography (XCT) scans of the alloy before and after creep testing at 750°C were carried out to assess the effect of flaws on the alloy’s creep behavior. Correlation between the flaws’ volume fraction, creep ductility, and creep lifetime could be established, and future work on LPBF 625 will take full advantage of in situ printing data and ex situ XCT scans to accelerate the alloy qualification. Finally, single track experiments were performed on the two alloys previously identified as good molten salt–resistant, Ni-based candidates: Hastelloy N and 244. Various laser parameters were considered, and cracking was not observed for either of the two alloys. Wrought 244 offers better creep strength and molten salt compatibility than alloy 625, and future work will aim to establish the alloy LPBF processing window.
The High Luminosity Large Hadron Collider is a planned upgrade to the LHC that will allow it to take up to 7 times as much data. Upgrades to all detector systems, including the Outer Tracker, are necessary to allow them to handle the increased radiation and pileup. Fermilab has been tasked with assembling modules for the Outer Tracker system. This presentation discusses the QA tests performed on these modules and the integration procedure required to ensure all modules work in sync. The QA tests include sensor IV curves, MaPSA testing, visual inspection, skeleton testing, and module testing. There are additional tests that can only be performed on integrated modules that are working in sync, and developing an understanding of the timing and latency of the modules is necessary to perform this synchronization.
The High Luminosity Large Hadron Collider is a planned upgrade to the LHC that will allow it to take up to 7 times as much data. Upgrades to all detector systems, including the Outer Tracker, are necessary to allow them to handle the increased radiation and pileup. Fermilab has been tasked with assembling modules for the Outer Tracker system. This presentation discusses the QA tests performed on these modules and the integration procedure required to ensure all modules work in sync. The QA tests include sensor IV curves, MaPSA testing, visual inspection, skeleton testing, and module testing. There are additional tests that can only be performed on integrated modules that are working in sync, and developing an understanding of the timing and latency of the modules is necessary to perform this synchronization.
The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment aimed at determining the neutrino mass hierarchy and the CP-violating phase. The DUNE physics program also includes the detection of astrophysical neutrinos and the search for signatures beyond the Standard Model, such as nucleon decays. DUNE consists of a near detector complex located at Fermilab and four 17 kton Liquid Argon Time Projection Chamber (LArTPC) far detector modules to be built 1.5 km underground at SURF, approximately 1300 km away. The detectors are exposed to a wideband neutrino beam generated by a 1.2 MW proton beam with a planned upgrade to $>$ 2 MW. Two 770 ton LArTPCs (ProtoDUNEs) have been operated at CERN for over 2 years as a testbed for DUNE far detectors and have been optimized to take new cosmic and test-beam data in 2024-2025. The DUNE and ProtoDUNE experiments and physics goals, as well as recent progress and results, are presented.
In this paper, we present an experimental and theoretical study of excitation processes for the heaviest stable helium-like ion, that is, He-like uranium occurring in relativistic collisions with hydrogen and argon targets. In particular, we concentrate on angular distributions of the characteristic Kα radiation following the K → L excitation of He-like uranium. We pay special attention to the magnetic sub-level population of the excited 1s2lj states, which is directly related to the angular distribution of the characteristic Kα radiation. We show that the experimental data can be well described by calculations taking into account the excitation by the target nucleus as well as by the target electrons. Moreover, we demonstrate for the first time an important influence of the electron-impact excitation process on the angular distributions of the Kα radiation produced by excitation of He-like uranium in collisions with different targets.
The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. Here, we utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star's 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ~5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.
The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 minute per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis.
To assess DC fast charging station network required for electrified road trips by 2030 in California, a new charging infrastructure simulation tool/model, EVI-Pro (Electric Vehicle Infrastructure Projection) RoadTrip, has been developed. In contrast to the existing EVI-Pro model that is primarily for short-distance travels, EVI-Pro RoadTrip is exclusively focused on road trips (long-distance travels, 100 or miles per day per vehicle). Also, the charging paradigm or strategy is different. EVI-Pro RoadTrip is built upon waypoint charging, in which vehicles are forced to stop to charge or replenish the on-board batteries, along the routes between origins and destinations. On the other hand, EVI-Pro is based on destination charging, in which charging is conducted when vehicles are parked in destinations (e.g., work, home). EVI-Pro RoadTrip takes coordinate-level origin and destination data for road trips (intra-state as well as domestic or international out-of-state) and estimates energy consumption and charging needs along the routes between origins and destinations on a minute-by-minute resolution. Based on charging demands for electrified road trips across the state, the optimal locations of charging stations are determined accounting for preferred land use types (e.g., commercial areas) and station service area (e.g., 5 or less miles). Based on station-by-station charging load profiles, the required number of plugs/connectors is estimated for each station and entire state. By comparing hosting capacity of the electric grid (circuit-level) and the charging load output from EVI-Pro RoadTrip, capacity deficit is also evaluated.