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DOE OSTI2012

The use of nested sampling in the extraction of polarisation observables at CLAS

The extraction of polarisation observables from photoproduction experiments provides an insight into the spectrum of nucleon resonances and the 'missing resonance' problem. Experiments carried out at JLab, Mainz and Bonn cover a wide range of reactions, which will soon result in the first 'complete measurement' in pseudoscalar meson photoproduction. Traditionally, these measurements have been analysed using frequentist statistics, where parameters are extracted by fitting distributions. An alternative method is the application of Bayesian statistics, where any existing knowledge about the results can be used in the initial conditions. One such application of this is nested sampling. This work discusses nested sampling and how it can be applied to the extraction of spin observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Automated annotation of scientific texts for ML-based keyphrase extraction and validation

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for researchers to find, curate, and search them effectively. The lack of metadata poses a significant challenge in the utilization of these data sets. Machine learning (ML)–based metadata extraction techniques have emerged as a potentially viable approach to automatically annotating scientific data sets with the metadata necessary for enabling effective search. Text labeling, usually performed manually, plays a crucial role in validating machine-extracted metadata. However, manual labeling is time-consuming and not always feasible; thus, there is a need to develop automated text labeling techniques in order to accelerate the process of scientific innovation. This need is particularly urgent in fields such as environmental genomics and microbiome science, which have historically received less attention in terms of metadata curation and creation of gold-standard text mining data sets. In this paper, we present two novel automated text labeling approaches for the validation of ML-generated metadata for unlabeled texts, with specific applications in environmental genomics. Our techniques show the potential of two new ways to leverage existing information that is only available for select documents within a corpus to validate ML models, which can then be used to describe the remaining documents in the corpus. The first technique exploits relationships between different types of data sources related to the same research study, such as publications and proposals. The second technique takes advantage of domain-specific controlled vocabularies or ontologies. In this paper, we detail applying these approaches in the context of environmental genomics research for ML-generated metadata validation. Our results show that the proposed label assignment approaches can generate both generic and highly specific text labels for the unlabeled texts, with up to 44% of the labels matching with those suggested by a ML keyword extraction algorithm.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2017

Extraction of geometrical structure of ethylene molecules by laser-induced electron diffraction combined with ab initio scattering calculations

In this paper, we measured angle-resolved high-energy electron spectra emitted from ${\mathrm{C}}_{2}{\mathrm{H}}_{4}$ in an intense laser field, extracted field-free electron-ion elastic scattering differential cross sections (DCSs) according to quantitative rescattering theory, and obtained molecular contrast factors (MCFs) subtracting the incoherent sum of DCSs of all the atoms in the molecule. Comparing the results with ab initio scattering calculations and employing least-squares fitting, we have extracted the C-C and C-H bond lengths of the molecule with ${\sim}5$ % uncertainty. Finally, this approach opens the way to retrieve the structure of hydrocarbon molecules, potentially at high temporal resolution, employing low collision energies where electron scattering is sensitive to the hydrogen atoms; and where the independent atom model calculations may fail to reproduce the experimentally extracted MCF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Benchmarking the extraction of statistical neutron capture cross sections on short-lived nuclei for applications using the β -Oslo method

Numerous scientific fields including astrophysics, nuclear power, and nuclear forensics require a knowledge of basic nuclear properties for large numbers of short-lived, radioactive isotopes far removed from stable nuclei. Neutron-capture cross sections are one such piece of nuclear data where direct measurements are not possible and theoretical predictions can vary by orders of magnitude. A recently developed indirect technique for inferring neutron capture rates, the β-Oslo method, has been introduced but not compared against a known, directly measured neutron capture cross section. To provide this benchmark, two indirect methods based on β decay and charged-particle reactions were used to extract the nuclear level density and γ-ray strength function of 51 Ti. The nuclear level density and γ-ray strength function from the two data sets were found to be equivalent and were used to extract the neutron capture cross section of 50 Ti which agrees with previous direct measurements at high neutron energies. Here, the results demonstrate the validity of the β-Oslo method for extracting neutron capture cross sections of short-lived nuclei and provide a sufficiently small uncertainty to be used in various applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2015

Precise determination of the deuteron spin structure at low to moderate Q 2 with CLAS and extraction of the neutron contribution

In this study, we present the final results for the deuteron spin structure functions obtained from the full data set collected with Jefferson Lab's CLAS in 2000-2001. Polarized electrons with energies of 1.6, 2.5, 4.2 and 5.8 GeV were scattered from deuteron ( 15 ND 3 ) targets, dynamically polarized along the beam direction, and detected with CLAS. From the measured double spin asymmetry, the virtual photon absorption asymmetry A d 1 and the polarized structure function g d 1 were extracted over a wide kinematic range (0.05 GeV2 < Q2 < 5 GeV2 and 0.9 GeV < W < 3 GeV). We use an unfolding procedure and a parametrization of the corresponding proton results to extract from these data the polarized structure functions A n 1 and g 1 n of the (bound) neutron, which are so far unknown in the resonance region, W < 2 GeV. We compare our final results, including several moments of the deuteron and neutron spin structure functions, with various theoretical models and expectations as well as parametrizations of the world data. The unprecedented precision and dense kinematic coverage of these data can aid in future extractions of polarized parton distributions, tests of perturbative QCD predictions for the quark polarization at large x, a better understanding of quark-hadron duality, and more precise values for higher-twist matrix elements in the framework of the Operator Product Expansion.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Sub-Coulomb He 3 transfer and its use to extract three-particle asymptotic normalization coefficients

In this work, data for the 13 C( 6 Li,t) 16 O reaction, obtained in inverse kinematics at a 13 C incident energy of 7.72 MeV, are presented. A distorted wave Born approximation (DWBA) analysis was used to extract spectroscopic factors and asymptotic normalization coefficients (ANCs) for the < 16 O | 13 C + 3 He> overlaps, subject to the assumption of a fixed < 6 Li | 3 He + 3 H> overlap. The variation of the extracted spectroscopic factors and ANCs as a function of various inputs to the DWBA calculations was explored. The extracted ANCs were found to vary as a cubic function of the radius of the potential well binding the transferred 3 He to the 13 C core while the spectroscopic factors varied as a quartic function of the radius. Finally, the ANC values could be determined to within a factor of two for this system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Extraction of the neutron F 2 structure function from inclusive proton and deuteron deep-inelastic scattering data

The available world deep-inelastic scattering (DIS) data on proton and deuteron structure functions F 2 p , F 2 d , and their ratios are leveraged to extract the free neutron F 2 n structure function, the F 2 n / F 2 p ratio, and associated uncertainties using the latest nuclear effect calculations in the deuteron. Special attention is devoted to the normalization of the proton and deuteron experimental datasets and to the treatment of correlated systematic errors, as well as the quantification of procedural and theoretical uncertainties. The extracted F 2 n dataset is utilized to evaluate the Q 2 dependence of the Gottfried sum rule and the nonsinglet F 2 p − F 2 n moments. To facilitate replication of our study, as well as for general applications, we provide a comprehensive DIS database including all recent Jefferson Lab 6 GeV measurements, the extracted F n 2 , a modified CTEQ-JLab global parton distribution function fit named CJ15nlo_mod, and grids with calculated proton, neutron, and deuteron DIS structure functions. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports

Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification m acro F -score o ver the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.

Alawad, Mohammed↗

DOE OSTI2023

The Recent Improvements of the SNS Extraction Kicker Power Supplies

A total of 14 extraction kickers., with one as the hot spare., are in service to extract protons out of the storage ring at the SNS. The jitter issue and the short lifetime of the switches were resolved after the thyratrons were replaced with solid state switches in 2018. This paper discusses the recent improvements. Two thyristor switches suffered overheating damage in separate incidents. One was due to the oil pump failure and the other was the result of a disconnected oil hose. An ultrasonic flow meter and a fiber optic temperature monitoring system have been installed for each extraction kicker power supply. The flow meter continuously monitors the entire tank oil flow. The temperature monitor detects the thyristor switch real-time temperatures in three locations. Fault thresholds are selected so that the thyristor switches are protected from overheating damage. Alarms are configured to alert staff to take actions before faults occur. In addition., the cause of an intermittent misfire issue was identified, and the solution was implemented. Lastly., a future oscilloscope upgrade and an oil level sensor are discussed.

Tan, Yugang↗

DOE OSTI2016

The Voltage Boost Enabled by Luminescence Extraction in Solar Cells

A new physical principle has emerged to produce record voltages and efficiencies in photovoltaic cells, 'luminescence extraction.' This is exemplified by the mantra 'a good solar cell should also be a good LED.' Luminescence extraction is the escape of internal photons out of the front surface of a solar cell. Basic thermodynamics says that the voltage boost should be related to concentration ratio, C, of a resource by ..delta..V=(kT/q)ln{C}. In light trapping, (i.e. when the solar cell is textured and has a perfect back mirror) the concentration ratio of photons C={4n2}, so one would expect a voltage boost of ..delta..V=kT ln{4n2} over a solar cell with no texture and zero back reflectivity, where n is the refractive index. Nevertheless, there has been ambiguity over the voltage benefit to be expected from perfect luminescence extraction. Do we gain an open circuit voltage boost of ..delta..V=(kT/q)ln{n2}, ..delta..V=(kT/q)ln{2n2}, or ..delta..V=(kT/q)ln{4n2}? What is responsible for this voltage ambiguity ..delta..V=(kT/q)ln{4}=36mVolts? We show that different results come about, depending on whether the photovoltaic cell is optically thin or thick to its internal luminescence. In realistic intermediate cases of optical thickness the voltage boost falls in between; ln{n2}q..delta..V/kT)<;ln{4n2}.

14 SOLAR ENERGY↗

DOE OSTI2021

Segmentation of Deposition Periods: An Opportunity to Improve PV Soiling Extraction

Soiling profiles are commonly assumed to have sawtooth shapes, made of alternating cleaning events and soiling deposition periods. The rates at which soiling deposit on the PV modules are considered to be constant for each period. In reality, events such as changes in climatic conditions can lead to a sudden variation in soiling deposition rate. These changes cannot be reproduced if cleanings are the only events modelled to extract soiling profiles directly from PV performance data. For this reason, in this work, the use of change points and segmented regression is proposed to improve the extraction of soiling profiles through the model of up to two deposition rates per period in between cleanings. The results show that the quality of soiling extraction can be enhanced compared to a cleanings-only identification approach if both cleanings and change points are considered.

monitoring↗

DOE OSTI2018

Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression

Abstract The United States Food and Drug Administration (FDA) ruled that partially hydrogenated oils (PHO), the major dietary source of industrially produced trans fat (TF), were no longer “generally recognized as safe (GRAS)” for any use in human food. Consequently, the objective of this study was to develop a rapid screening procedure using attenuated total reflection Fourier‐transform infrared (ATR‐FTIR) spectroscopy in conjunction with partial least squares regression (PLSR) for the quantitative and accurate prediction of low concentrations of trans fatty acid (TFAs) (<1% of total fatty acids (FAs)). Broad‐based calibration models were developed for a combined set of samples consisting of edible oils and fast food lipid extracts. Predicted concentrations of TFAs in the two matrices showed good correlation with the primary reference data generated by gas chromatography (GC) ( R 2 > 0.99) and high accuracy as evidenced by low root‐mean‐square error of cross‐validation (RMSECV) values. The lowest TFA concentration, determined by GC to be 0.13% of total FAs, was accurately predicted by ATR‐FTIR/PLSR as 0.18% of total FAs. This simple, rapid ATR‐FTIR/PLSR methodology has the potential for use as a screening alternative to conventional gas chromatographic methods for predicting the TFA content of edible oils and food lipid extracts for regulatory purposes and quality control of raw material and processed food. Practical Applications F DA ruled that partially hydrogenated oils were no longer “generally recognized as safe (GRAS)” for any use in human food. Consequently, we have proposed a rapid screening procedure, based on infrared spectroscopy and chemometrics, to rapidly and accurately predict low concentrations of trans fatty acids (<1% of total fatty acids) in edible oils and food lipid extracts.

Karunathilaka, Sanjeewa R.↗

DOE OSTI2013

Enhanced phyto-extraction of cadmium and zinc using rapeseed

In a green house pot experiment, the effects of three amendments, sulphur (S), ammonium sulphate ((NH{sub 4}){sub 2}SO{sub 4}) and ethylenediaminetetracetic acid (EDTA) were tested for phyto-extraction of Cd and Zn by rapeseed (Brassica napus L.). Elemental sulphur was applied as 20.00, 60.00, and 120.00 mg.kg{sup -1} soil. EDTA was tested at a dose of 585.00 mg.kg{sup -1} soil, and (NH{sub 4}){sub 2}SO{sub 4}) at a rate of 0.23 mg.kg{sup -1} soil. All treatments received a base fertilization (Hogland) before sowing. Plants were harvested after 51 days of growth and shoot dry matter and soil samples were analysed for metal contents. All amendments caused a significant increase in Cd and Zn contents in plant shoots of all treatments than control treatment. Further, EDTA was most effective for extraction metals concentrations in shoot biomass but the plants showed significant signs of toxicity and yield were severely depressed. The addition of sulfur favorably influenced plant biomass production. The fertilized ammonium sulfate treatment resulted in the highest phyto-extraction of Cd and Zn and the amounts of these metals accumulated in plant shoot exceeded by a factor of 4 and 3 respectively. Finally, Brassica napus could be used for soil remediation keeping its other uses which will make the contaminated site income generating source for the farmers. (authors)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents

Purpose: Mammography is the most widely accepted and utilized screening modality for early breast cancer detection. Providing high quality mammography education to radiology trainees is essential, since excellent interpretation skills are needed to ensure the highest benefit of screening mammography for patients. The authors have previously proposed a computer-aided education system based on trainee models. Those models relate human-assessed image characteristics to trainee error. In this study, the authors propose to build trainee models that utilize features automatically extracted from images using computer vision algorithms to predict likelihood of missing each mass by the trainee. This computer vision-based approach to trainee modeling will allow for automatically searching large databases of mammograms in order to identify challenging cases for each trainee. Methods: The authors’ algorithm for predicting the likelihood of missing a mass consists of three steps. First, a mammogram is segmented into air, pectoral muscle, fatty tissue, dense tissue, and mass using automated segmentation algorithms. Second, 43 features are extracted using computer vision algorithms for each abnormality identified by experts. Third, error-making models (classifiers) are applied to predict the likelihood of trainees missing the abnormality based on the extracted features. The models are developed individually for each trainee using his/her previous reading data. The authors evaluated the predictive performance of the proposed algorithm using data from a reader study in which 10 subjects (7 residents and 3 novices) and 3 experts read 100 mammographic cases. Receiver operating characteristic (ROC) methodology was applied for the evaluation. Results: The average area under the ROC curve (AUC) of the error-making models for the task of predicting which masses will be detected and which will be missed was 0.607 (95% CI,0.564-0.650). This value was statistically significantly different from 0.5 (p < 0.0001). For the 7 residents only, the AUC performance of the models was 0.590 (95% CI,0.537-0.642) and was also significantly higher than 0.5 (p = 0.0009). Therefore, generally the authors’ models were able to predict which masses were detected and which were missed better than chance. Conclusions: The authors proposed an algorithm that was able to predict which masses will be detected and which will be missed by each individual trainee. This confirms existence of error-making patterns in the detection of masses among radiology trainees. Furthermore, the proposed methodology will allow for the optimized selection of difficult cases for the trainees in an automatic and efficient manner.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-150: Four-Dimensional Cone-Beam CT Algorithm by Extraction of Physical and Motion Parameter of Mobile Targets Retrospective to Image Reconstruction with Motion Modeling

Purpose: To develop 4D-cone-beam CT (CBCT) algorithm by motion modeling that extracts actual length, CT numbers level and motion amplitude of a mobile target retrospective to image reconstruction by motion modeling. Methods: The algorithm used three measurable parameters: apparent length and blurred CT number distribution of a mobile target obtained from CBCT images to determine actual length, CT-number value of the stationary target, and motion amplitude. The predictions of this algorithm were tested with mobile targets that with different well-known sizes made from tissue-equivalent gel which was inserted into a thorax phantom. The phantom moved sinusoidally in one-direction to simulate respiratory motion using eight amplitudes ranging 0–20mm. Results: Using this 4D-CBCT algorithm, three unknown parameters were extracted that include: length of the target, CT number level, speed or motion amplitude for the mobile targets retrospective to image reconstruction. The motion algorithms solved for the three unknown parameters using measurable apparent length, CT number level and gradient for a well-defined mobile target obtained from CBCT images. The motion model agreed with measured apparent lengths which were dependent on the actual target length and motion amplitude. The gradient of the CT number distribution of the mobile target is dependent on the stationary CT number level, actual target length and motion amplitude. Motion frequency and phase did not affect the elongation and CT number distribution of the mobile target and could not be determined. Conclusion: A 4D-CBCT motion algorithm was developed to extract three parameters that include actual length, CT number level and motion amplitude or speed of mobile targets directly from reconstructed CBCT images without prior knowledge of the stationary target parameters. This algorithm provides alternative to 4D-CBCT without requirement to motion tracking and sorting of the images into different breathing phases which has potential applications in diagnostic CT imaging and radiotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Foundations of automatic feature extraction at LHC–point clouds and graphs

Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Bhardwaj, Akanksha↗

DOE OSTI2011

Extraction of the Compton Form Factor H from recent DVCS measurements at Jefferson Lab

Working at twist 2 accuracy and assuming the dominance of the Generalized Parton Distribution H we study the helicity-dependent and independent cross sections measured in Hall A and the beam spin asymmetries measured in Hall B at Jefferson Laboratory. We extract the real and imaginary parts of the Compton Form Factor H, the latter being obtained with a 20–50 % uncertainty. We discuss our results and compare to other extractions as well as to the popular VGG model. We pay extra attention to the estimation of systematic errors on the extraction of H.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Modeling the Moisture Content and Dry Matter Loss in Dynamic Woody Biomass Storage Piles with Variable Extraction

The urgent need to mitigate climate change has spurred significant interest in renewable energy sources. This paper explores the storage and processing of woody biomass for biofuel production, considering the dynamic nature of biomass piles in real-world scenarios. A model has been developed to analyze moisture content changes and dry matter loss in woody biomass stored in piles prior to processing, taking into account varying extraction methods and environmental conditions. Case studies that examine the effects of different unpiling methods (FIFO, LIFO, and homogeneous) on moisture content and dry matter loss under various feedstock arrival rates and weather conditions are presented. Results indicate that unpiling methods significantly impact moisture content, with LIFO typically resulting in higher moisture content due to the utilization of fresher feedstock. Dry matter loss increases with pile size and time, emphasizing the importance of accurate modeling for assessing carbon emissions and feedstock quality. Furthermore, the model highlights the importance of process loading order and extraction methods in biomass storage, suggesting potential cost benefits associated with decreased moisture content. The difference between different extraction methods could vary the moisture content in the feedstock reaching the biofuel process by as much as 37.6%, however dry matter loss varies minimally for realistic pile changes. Overall, this research contributes to a better understanding of biomass storage dynamics and informs sustainable biofuel production practices.

Niska, Janel↗