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DOE OSTI2026

Michel Electron Selection with SPINE for DUNE Far Detector Simulation

Michel electrons are a valuable input for particle detector calibration due to their consistent kinetic energy distribution. This report details the evaluation of a Michel electron identification method's application to simulated data from the DUNE (Deep Underground Neutrino Experiment) far detector. This method, which relies on the neural network-based particle classification software SPINE (Scalable Particle Imaging with Neural Embeddings), was developed and calibrated using simulated data for the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated DUNE data from a 1x2x6 subset of far detector modules.

Wilson, Dante [Colorado State U.]↗

DOE OSTI2017

Investigation of instrumentation containing an embedded digital device - 257

The U.S. nuclear power industry primarily relies on mature, well-proven analog technology as the basis for most instrumentation and control (I and C) equipment employed in its nuclear power plants (NPPs). However, the market for this legacy instrumentation is very small and availability of such equipment for spare parts or replacement is increasingly limited. Frequently, modern I and C equipment contains embedded digital devices (EDDs), such as microprocessors and programmable logic devices. While the enhanced functional capabilities provided by the digital components offer improved performance, reliability, and maintainability, the corresponding complexity of sophisticated digital implementations also exacerbate concerns about an increased potential for undetected systematic faults, which can lead to common-cause failure (CCF). As part of a research project sponsored by the U.S. Department of Energy, an investigation has been conducted of commercially available instrumentation marketed for nuclear or industrial application. The objective was to identify the usage of EDDs and to determine the types of functional roles allocated to the devices. Examples of the equipment surveyed include smart sensors, sequencers for emergency diesel generators, pumps, valve actuators, motor control centers, breakers, priority logic modules, and uninterruptible power sources. Where sufficient detail was found, an evaluation was performed to ascertain the nature and role of the digital technology implemented in the instrumentation. The information gained through this investigation serves as technical input for defining a classification approach for EDDs based on the functional impact of the device. For example, the role of an EDD can involve any of the following functions: performance monitoring and diagnostics, information extraction and communication, instrument condition monitoring and protection, or execution of core instrument functions. Classifying digital devices according to their role in the operation of the instrumentation in which they are embedded can enable determination of the potential functional impact of failure of that device and contribute to determination of the potential safety significance of a CCF event disabling all instances of that device. This paper will report on the findings of the investigation of instrumentation with EDDs and indicate considerations for the emerging classification approach. The higher-level goal of this effort is to determine whether a graded approach to qualification testing may be possible based on classification. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Using Eye Tracking to Elucidate the Mechanisms Underlying Stimulation-Enhanced Visual Target Detection

Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.

attention↗

DOE OSTI2016

Understanding the optical properties of ambient sub- and supermicron particulate matter: results from the CARES 2010 field study in northern California

Abstract. Measurements of the optical properties (absorption, scattering and extinction) of PM1, PM2.5 and PM10 made at two sites around Sacramento, CA, during the June 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are reported. These observations are used to establish relationships between various intensive optical properties and to derive information about the dependence of the optical properties on photochemical aging and sources. Supermicron particles contributed substantially to the total light scattering at both sites, about 50 % on average. A strong, linear relationship is observed between the scattering Ångström exponent for PM10 and the fraction of the scattering that is contributed by submicron particles (fsca, PM1) at both sites and with similar slopes and intercepts (for a given pair of wavelengths), suggesting that the derived relationship may be generally applicable for understanding variations in particle size distributions from remote sensing measurements. At the more urban T0 site, the fsca, PM1 increased with photochemical age, whereas at the downwind, more rural T1 site the fsca, PM1 decreased slightly with photochemical age. This difference in behavior reflects differences in transport, local production and local emission of supermicron particles between the sites. Light absorption is dominated by submicron particles, but there is some absorption by supermicron particles ( ∼ 15 % of the total). The supermicron absorption derives from a combination of black carbon that has penetrated into the supermicron mode and from dust, and there is a clear increase in the mass absorption coefficient of just the supermicron particles with increasing average particle size. The mass scattering coefficient (MSC) for the supermicron particles was directly observed to vary inversely with the average particle size, demonstrating that MSC cannot always be treated as a constant in estimating mass concentrations from scattering measurements, or vice versa. The total particle backscatter fraction exhibited some dependence upon the relative abundance of sub- versus supermicron particles; however this was modulated by variations in the median size of particles within a given size range; variations in the submicron size distribution had a particularly large influence on the observed backscatter efficiency and an approximate method to account for this variability is introduced. The relationship between the absorption and scattering Ångström exponents is examined and used to update a previously suggested particle classification scheme. Differences in composition led to differences in the sensitivity of PM2.5 to heating in a thermodenuder to the average particle size, with more extensive evaporation (observed as a larger decrease in the PM2.5 extinction coefficient) corresponding to smaller particles; i.e., submicron particles were generally more susceptible to heating than the supermicron particles. The influence of heating on the particle hygroscopicity varied with the effective particle size, with larger changes observed when the PM2.5 distribution was dominated by smaller particles.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

GEDet: Detecting Erroneous Nodes with A Few Examples

Detecting nodes with erroneous values in real-world graphs re- mains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet, an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1) Few-shot detection. Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet. GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2) Diverse error scenarios. GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily “plug in” new error types or examples. (3) User-centric detection. GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.

Guan, Sheng↗

DOE OSTI2025

Uncovering Sequence and Structural Characteristics of Fungal Expansin‐Related Proteins With Potential to Drive Substrate Targeting

Expansins loosen plant cell wall networks through disrupting non-covalent bonds between cellulose microfibrils and matrix polysaccharides. Whereas expansins were first discovered in plants, expansin-related proteins have since been identified in bacteria and fungi. The biological function of microbial expansins remains unclear; however, several studies have shown distinct binding preferences toward different structural polysaccharides. Earlier studies of bacterial expansin-related proteins uncovered sequence and structural features that correlate to substrate binding. Herein, 20 fungal expansin-related sequences were recombinantly produced in Komagataella phaffii, and the purified proteins were compared in terms of substrate binding to cellulosic and chitinous substrates. The impact of pH on the zeta potential of prioritized substrates was also measured, and Principal Component Analysis was performed to uncover correlations between protein characteristics (e.g., pI, hydrophobicity, surface charge distribution) and measured substrate binding preferences. Whereas acidic proteins with a predicted pI less than 5.0 preferentially bound to chitin, basic proteins with pI greater than 8.0 preferentially bound to xylan and xylan-containing fiber. Similar to many cellulases, binding to cellulose was correlated to relatively high aromatic amino acid content in the protein sequence and presence of a carbohydrate binding module (CBM), which in the case of expansins is a C-terminal CBM63. Whereas overall sequence characteristics could be correlated to substrate binding preference, the identity of amino acids occupying conserved positions that impact protein activity was better correlated with loosenin versus expansin classifications.

chitin↗

DOE OSTI2014

1FGL J0523.5–2529: A NEW PROBABLE GAMMA-RAY PULSAR BINARY

We report optical photometric and Southern Astrophysical Research spectroscopic observations of an X-ray source found within the localization error of the Fermi Large Area Telescope unidentified γ-ray source 1FGL J0523.5–2529. The optical data show periodic flux modulation and radial velocity variations indicative of a binary with a 16.5 hr period. The data suggest a massive non-degenerate secondary (≳ 0.8 M {sub ☉}), and we argue the source is likely a pulsar binary. The radial velocities have good phase coverage and show evidence for a measurable eccentricity (e = 0.04). There is no clear sign of irradiation of the secondary in either photometry or spectroscopy. The spatial location out of the Galactic plane and γ-ray luminosity of the source are more consistent with classification as a recycled millisecond pulsar than as a young pulsar. Future radio timing observations can confirm the identity of the primary and further characterize this interesting system.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.

Astronomy & Astrophysics↗

DOE OSTI2021

Novel nucleocytoplasmic protein O -fucosylation by SPINDLY regulates diverse developmental processes in plants

Here, in metazoans, protein O-fucosylation of Ser/Thr residues was only found in secreted or cell surface proteins, and this post-translational modification is catalyzed by ER-localized protein O-fucosyltransferases (POFUTs) in the GT65 family. Recently, a novel nucleocytoplasmic POFUT, SPINDLY (SPY), was identified in the reference plant Arabidopsis thaliana to modify nuclear transcription regulators DELLAs, revealing a new regulatory mechanism for gene expression. The paralog of AtSPY, SECRET AGENT (SEC), is an O-link-N-acetylglucosamine (GlcNAc) transferase (OGT), which O-GlcNAcylates Ser/Thr residues of target proteins. Both AtSPY and AtSEC are tetratricopeptide repeat-domain-containing glycosyltransferases in the GT41 family. The discovery that AtSPY is a POFUT clarified decades of miss-classification of AtSPY as an OGT. SPY and SEC play pleiotropic roles in plant development, and the interactions between SPY and SEC are complex. SPY-like genes are conserved in diverse organisms, except in fungi and metazoans, suggesting that O-fucosylation is a common mechanism in modulating intracellular protein functions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Developing a Framework for Reference Cell Standards for PV Resource Applications

Quantifying and predicting electricity production from photovoltaic (PV) systems is based on measured or modeled irradiance data. These solar resource data consist of global horizontal irradiance, global tilted irradiance, direct normal irradiance, and diffuse horizontal irradiance, which are either derived from satellite observations validated against ground-based measurements or directly obtained from ground-based measurements. The ground-based measurements are made using thermopile or photodiode radiometers. For a PV plant, the efficiency of the energy production is verified by comparing measured output with the modeled production, which is computed using either modeled or measured irradiance data. Thus, plant performance assessments typically include the uncertainty of the transposition and, in some cases, decomposition of a given irradiance to convert to the appropriate plane-of-array (POA) irradiance corresponding to the orientation of the PV installation. Additionally, the various types of uncertainty in radiometric measurements or the modeled irradiance and the PV module specifications influence the model results and thereby further increase the uncertainty. An alternative method of assessing the PV system performance has been to use reference cells to measure the "PV resource." When used to calculate PV performance ratios, there are inherent systematic differences between radiometers and reference cells, such as spectral, directional, temperature, time responses, nonstability, and nonlinearity differences. Reference cells tend to mimic the performance and characteristics of a PV module more closely. In this report, a framework is proposed to develop standards that will better quantify and characterize the use of reference cells for PV resource measurements. The measurement from an appropriate reference cell in the POA correlates closely with the plant performance, reduces the number of modeling steps needed to simulate PV performance, and hence reduces the uncertainties of the comparisons. Because technologically matched reference cells and PV modules respond similarly to each wavelength of light that composes the incident solar radiation, the uncertainty associated with the changing spectral distribution of incident radiation during the day and year can be greatly reduced. This will reduce the overall uncertainty in estimated PV performance. The same can be said for the angle-of-incidence effects because the reference cells are deployed in the same POA as the PV module. At that point, the main sources of uncertainty are in modeling the temperature effect differences between the reference cells and the PV module and accounting for the difference between the short-circuit current monitored by the reference cells and max power point current and voltage at which the PV module operates. These sources of uncertainty are also associated with typical irradiance measurements made by pyranometers and pyrheliometers; however, typical irradiance measurements also include uncertainties associated with the spectral mismatch between thermopile or photodiode pyranometers and the PV module, which are minimized with the use of reference cells. To enhance the use of reference cells for resource assessment, we identify the necessary data, characteristics, and calibration methodologies of reference cells and how to standardize the use of these data and methods. Further, as we develop this framework, the classification of reference cells will be essential to provide guidance for selecting PV reference cells appropriate for a specific application. As we address the calibration and sources of uncertainties of reference cells, classification schemes and expected limits of performance with respect to certain parameters become important.

14 SOLAR ENERGY↗

DOE OSTI2021

A Heterogeneous System for Eagle Detection, Deterrent, and Wildlife Collision Detection for Wind Turbines (Final Technical Report)

This report summarizes the design, implementation, and test of an integrated system for automated detection and deterrence of eagles, with included wind turbine blade strike detection and imaging functionality. A machine learning approach was used in conjunction with a 360° camera system for automated detection and classification of golden eagles. This was developed using footage obtained from trained golden eagles and other raptors, in collaboration with wildlife biologists and professional bird handlers. Oregon State University developed a visual deterrent system, which uses inflatable anthropomorphic sculptures with random, kinetic motion to deter eagles, and conducted limited field testing on live eagles; the deterrent can be triggered by the visual detection of eagles using the vision system. Finally, a multi-sensor module was developed that is mounted at the turbine blade root. This module measures vibration and other motions to detect blade strikes, and an integrated on-blade camera captures an image of any impacting objects. Long-term, this blade strike detection system is intended to support an automatic monitoring and certification system for the eagle detection and deterent system. Independent field testing of each system component is described. Testing of the integrated system on an operational wind turbine was conducted across three separate field tests. This includes multi-day fields tests on a General Electric 1.5MW wind turbine at the National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC) in Boulder, CO in October 2018 and July 2019; installation procedures, test procedures, and a summary of collected data are presented. A third multi-day on-turbine field test is also presented, which was performed using a General Electric 1.5MW wind turbine at the North American Wind Research and Training Center (NAWRTC) at Mesalands Community College, Tucumcari, NM in April 2019. Across these field tests, the vision system was demonstrated using unmanned aerial vehicles (UAV), and the eagle classification algorithm was not tested; the visual deterrent system was demonstrated, including automatic, remote deployment following surrogate visual detections; and, multi-sensor on-blade data was recorded across multiple wind turbine operational conditions and through more than 100 surrogate blade strikes using soft projectiles, including the successful demonstration of automatic image capture of striking objects. This data set was also used for offline development and validation of enhanced collision detection algorithms. As summarized in this report, the development and field validation of an integrated detection, deterrent, and blade collision detection system represents a critical proof of concept for future technology development of related detection and deterrent technologies, where both deterrent as well as collision detection recording devices are needed for future siting, monitoring, and operation of wind turbine installations, both onshore and offshore.

17 WIND ENERGY↗

DOE OSTI2013

Long-term Survival and Toxicity in Patients Treated With High-Dose Intensity Modulated Radiation Therapy for Localized Prostate Cancer

Purpose: To report long-term survival and toxicity outcomes with the use of high-dose intensity modulated radiation therapy (IMRT) to 86.4 Gy for patients with localized prostate cancer. Methods and Materials: Between August 1997 and December 2008, 1002 patients were treated to a dose of 86.4 Gy using a 5-7 field IMRT technique. Patients were stratified by prognostic risk group based on National Comprehensive Cancer Network risk classification criteria. A total of 587 patients (59%) were treated with neoadjuvant and concurrent androgen deprivation therapy. The median follow-up for the entire cohort was 5.5 years (range, 1-14 years). Results: For low-, intermediate-, and high-risk groups, 7-year biochemical relapse-free survival outcomes were 98.8%, 85.6%, and 67.9%, respectively (P<.001), and distant metastasis-free survival rates were 99.4%, 94.1%, and 82.0% (P<.001), respectively. On multivariate analysis, T stage (P<.001), Gleason score (P<.001), and >50% of initial biopsy positive core (P=.001) were predictive for distant mestastases. No prostate cancer-related deaths were observed in the low-risk group. The 7-year prostate cancer-specific mortality (PCSM) rates, using competing risk analysis for intermediate- and high-risk groups, were 3.3% and 8.1%, respectively (P=.008). On multivariate analysis, Gleason score (P=.004), percentage of biopsy core positivity (P=.003), and T-stage (P=.033) were predictive for PCSM. Actuarial 7-year grade 2 or higher late gastrointestinal and genitourinary toxicities were 4.4% and 21.1%, respectively. Late grade 3 gastrointestinal and genitourinary toxicity was experienced by 7 patients (0.7%) and 22 patients (2.2%), respectively. Of the 427 men with full potency at baseline, 317 men (74%) retained sexual function at time of last follow-up. Conclusions: This study represents the largest cohort of patients treated with high-dose radiation to 86.4 Gy, using IMRT for localized prostate cancer, with the longest follow-up to date. Our findings indicate that this treatment results in excellent clinical outcomes with acceptable toxicity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Non-Intrusive Appliance Identification with Appliance-Specific Networks

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. Here, we adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

42 ENGINEERING↗

DOE OSTI2014

SU-C-17A-01: MRI-Based Radiotherapy Treatment Planning In Pelvis

Purpose: To support radiotherapy dose calculation, synthetic CT (MRCT) image volumes need to represent the electron density of tissues with sufficient accuracy. This study compares CT and MRCT for pelvic radiotherapy. Methods: CT and multi-contrast MRI acquired using T1- based Dixon, T2 TSE, and PETRA sequences were acquired on an IRBapproved protocol patient. A previously published method was used to create a MRCT image volume by applying fuzzy classification on T1- weighted and calculated water image volumes (air and fluid voxels were excluded using thresholds applied to PETRA and T2-weighted images). The correlation of pelvic bone intensity between CT and MRCT was investigated. Two treatment plans, based on CT and MRCT, were performed to mimic treatment for: (a) pelvic bone metastasis with a 16MV parallel beam arrangement, and (b) gynecological cancer with 6MV volumetric modulated arc therapy (VMAT) using two full arcs. The CT-calculated fluence maps were used to recalculate doses using the MRCT-derived density grid. The dose-volume histograms and dose distributions were compared. Results: Bone intensities in the MRCT volume correlated linearly with CT intensities up to 800 HU (containing 96% of the bone volume), and then decreased with CT intensity increase (4% volume). There was no significant difference in dose distributions between CT- and MRCTbased plans, except for the rectum and bladder, for which the V45 differed by 15% and 9%, respectively. These differences may be attributed to normal and visualized organ movement and volume variations between CT and MR scans. Conclusion: While MRCT had lower bone intensity in highly-dense bone, this did not cause significant dose deviations from CT due to its small percentage of volume. These results indicate that treatment planning using MRCT could generate comparable dose distributions to that using CT, and further demonstrate the feasibility of using MRI-alone to support Radiation Oncology workflow. NIH R01EB016079.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Autonomous Sensor System for Wind Turbine Blade Collision Detection

This paper presents an automated blade collision detection system for use on wind turbines, toward the goal of supporting monitoring and quantitative assessment of wind energy impacts on wildlife. A wireless, multisensor module mounted at the blade root measures surface vibrations, and a blade-mounted camera provides image capture of colliding objects. Using sensor data recorded during field testing of the system on an operational wind turbine, we present the development, training, and testing of automated detection algorithms for collision detection using machine-learning approaches. In particular, we compare the use of a new two-step, anomaly-based classification algorithm with conventional adaptive boosting and amplitude-based detection techniques, where the two-step approach improves average precision for the experimental data set. This integrated sensor and classification systems demonstrates a new approach for automated, on-blade collision detection for wind turbines, with broad utility across structural health monitoring applications.

17 WIND ENERGY↗

DOE OSTI2014

THE CATALINA SURVEYS PERIODIC VARIABLE STAR CATALOG

We present ∼47,000 periodic variables found during the analysis of 5.4 million variable star candidates within a 20,000 deg{sup 2} region covered by the Catalina Surveys Data Release-1 (CSDR1). Combining these variables with type ab RR Lyrae from our previous work, we produce an online catalog containing periods, amplitudes, and classifications for ∼61,000 periodic variables. By cross-matching these variables with those from prior surveys, we find that >90% of the ∼8000 known periodic variables in the survey region are recovered. For these sources, we find excellent agreement between our catalog and prior values of luminosity, period, and amplitude as well as classification. We investigate the rate of confusion between objects classified as contact binaries and type c RR Lyrae (RRc's) based on periods, colors, amplitudes, metallicities, radial velocities, and surface gravities. We find that no more than a few percent of the variables in these classes are misidentified. By deriving distances for this clean sample of ∼5500 RRc's, we trace the path of the Sagittarius tidal streams within the Galactic halo. Selecting 146 outer-halo RRc's with SDSS radial velocities, we confirm the presence of a coherent halo structure that is inconsistent with current N-body simulations of the Sagittarius tidal stream. We also find numerous long-period variables that are very likely associated within the Sagittarius tidal stream system. Based on the examination of 31,000 contact binary light curves we find evidence for two subgroups exhibiting irregular light curves. One subgroup presents significant variations in mean brightness that are likely due to chromospheric activity. The other subgroup shows stable modulations over more than a thousand days and thereby provides evidence that the O'Connell effect is not due to stellar spots.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Chern numbers of topological phonon band crossing determined with inelastic neutron scattering

Topological invariants in the band structure, such as Chern numbers, are important for the understanding and classification of the topological properties of matter and dictate the occurrence of exotic behaviors, yet their direct spectroscopic determination has been largely limited to electronic bands. Here, we use inelastic neutron scattering in conjunction with ab initio calculations to identify a variety of topological phonon band crossings in MnSi and CoSi single crystals. We find a distinct relation between the Chern numbers of a band-crossing node and the scattering intensity modulation in momentum space around the node. Given sufficiently high resolution, our method can be used to determine arbitrarily large Chern numbers of topological phonon band-crossing nodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗