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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A coupled ordinates method for solution acceleration of rarefied gas dynamics simulations

Non-equilibrium rarefied flows are frequently encountered in a wide range of applications, including atmospheric re-entry vehicles, vacuum technology, and microscale devices. Rarefied flows at the microscale can be effectively modeled using the ellipsoidal statistical Bhatnagar–Gross–Krook (ESBGK) form of the Boltzmann kinetic equation. Numerical solutions of these equations are often based on the finite volume method (FVM) in physical space and the discrete ordinates method in velocity space. However, existing solvers use a sequential solution procedure wherein the velocity distribution functions are implicitly coupled in physical space, but are solved sequentially in velocity space. This leads to explicit coupling of the distribution function values in velocity space and slows down convergence in systems with low Knudsen numbers. Furthermore, this also makes it difficult to solve multiscale problems or problems in which there is a large range of Knudsen numbers. In this paper, we extend the coupled ordinates method (COMET), previously developed to study participating radiative heat transfer, to solve the ESBGK equations. In this method, at each cell in the physical domain, distribution function values for all velocity ordinates are solved simultaneously. This coupled solution is used as a relaxation sweep in a geometric multigrid method in the spatial domain. Enhancements to COMET to account for the non-linearity of the ESBGK equations, as well as the coupled implementation of boundary conditions, are presented. The methodology works well with arbitrary convex polyhedral meshes, and is shown to give significantly faster solutions than the conventional sequential solution procedure. Acceleration factors of 5–9 are obtained for low to moderate Knudsen numbers on single processor platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Effect of respiratory motion on internal radiation dosimetry

Purpose: Estimation of the radiation dose to internal organs is essential for the assessment of radiation risks and benefits to patients undergoing diagnostic and therapeutic nuclear medicine procedures including PET. Respiratory motion induces notable internal organ displacement, which influences the absorbed dose for external exposure to radiation. However, to their knowledge, the effect of respiratory motion on internal radiation dosimetry has never been reported before. Methods: Thirteen computational models representing the adult male at different respiratory phases corresponding to the normal respiratory cycle were generated from the 4D dynamic XCAT phantom. Monte Carlo calculations were performed using the MCNP transport code to estimate the specific absorbed fractions (SAFs) of monoenergetic photons/electrons, the S-values of common positron-emitting radionuclides (C-11, N-13, O-15, F-18, Cu-64, Ga-68, Rb-82, Y-86, and I-124), and the absorbed dose of {sup 18}F-fluorodeoxyglucose ({sup 18}F-FDG) in 28 target regions for both the static (average of dynamic frames) and dynamic phantoms. Results: The self-absorbed dose for most organs/tissues is only slightly influenced by respiratory motion. However, for the lung, the self-absorbed SAF is about 11.5% higher at the peak exhale phase than the peak inhale phase for photon energies above 50 keV. The cross-absorbed dose is obviously affected by respiratory motion for many combinations of source-target pairs. The cross-absorbed S-values for the heart contents irradiating the lung are about 7.5% higher in the peak exhale phase than the peak inhale phase for different positron-emitting radionuclides. For {sup 18}F-FDG, organ absorbed doses are less influenced by respiratory motion. Conclusions: Respiration-induced volume variations of the lungs and the repositioning of internal organs affect the self-absorbed dose of the lungs and cross-absorbed dose between organs in internal radiation dosimetry. The dynamic anatomical model provides more accurate internal radiation dosimetry estimates for the lungs and abdominal organs based on realistic modeling of respiratory motion. This work also contributes to a better understanding of model-induced uncertainties in internal radiation dosimetry.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Methodological approach to the sensitivity analysis of failure effects in modern digital I and C systems - 136

Modern I and C systems of nuclear power plants increasingly use digital equipment, software-based applications for I and C functions and different types and topologies of communication networks as well. The structure, operation and communication of digital safety-related I and C systems are strongly influenced by the implemented automatic fault detection and fault treatment procedures as barriers against failure propagation. Thus, these I and C systems can change their operating states automatically during the operation time, e.g. due to the detection and correction of certain failure types. Reasonably because of these dynamic properties of digital I and C systems the use of classical fault tree and event tree methods solely for the reliability analysis of modern I and C systems is less suitable. These methods basically apply static models and do not consider the temporal changes of the operating state of the I and C system or equipment appropriately. In this paper, a recently developed approach for the sensitivity analysis of digital I and C systems will be introduced that allows to investigate the dynamic behavior of such systems in cases of internal faults and to quantify the effects on the system reliability. The development and testing of the analysis methodology is carried out using models of generic I and C systems with different typical architectures of stepwise increased complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

A decomposition approach based on meta-heuristics and exact methods for solving a two-stage stochastic biofuel hub-and-spoke network problem

An alternative to the production of fossil transportation fuels is the production of biofuels, particularly, bioethanol. One of the main opportunity areas is the reduction in the overall cost of biofuel. An approach to reduce this cost is to design and implement a supply chain (SC) that considers the quality-related properties of the biomass as well as the economies of scale to minimize the logistics and quality-related costs. This problem is formulated as a hub location problem, which has been classified as an NP-hard problem, thus, meta-heuristics are a suitable approach to solve this problem. We propose a hybrid meta-heuristic solution procedure to solve large-scale instances of a two-stage stochastic biomass-to-biorefinery hub-and-spoke network problem. This solution procedure is proposed to support the large-scale production and distribution of bioethanol by considering the variability in its moisture and ash contents. The hybrid method utilizes a simulated annealing-simplex method to find an initial solution and a tabu search-simplex method to improve the solution. Numerical experimentation was performed on a realistic case study in Texas. The findings demonstrate that the hybrid procedure outperforms the standard L-shaped (LS) method. The meta-heuristic combining simulated annealing and tabu search with the simplex method (SATS-SM) achieved 2.48% lower costs and required 96.57% less time, on average, when compared to the results from using the LS.

09 BIOMASS FUELS↗
DOE OSTI2016

Comparison of 3-dimensional dose reconstruction system between fluence-based system and dose measurement-guided system

COMPASS system (IBA Dosimetry, Schwarzenbruck, Germany) and ArcCHECK with 3DVH software (Sun Nuclear Corp., Melbourne, FL) are commercial quasi-3-dimensional (3D) dosimetry arrays. Cross-validation to compare them under the same conditions, such as a treatment plan, allows for clear evaluation of such measurement devices. In this study, we evaluated the accuracy of reconstructed dose distributions from the COMPASS system and ArcCHECK with 3DVH software using Monte Carlo simulation (MC) for multi-leaf collimator (MLC) test patterns and clinical VMAT plans. In a phantom study, ArcCHECK 3DVH showed clear differences from COMPASS, measurement and MC due to the detector resolution and the dose reconstruction method. Especially, ArcCHECK 3DVH showed 7% difference from MC for the heterogeneous phantom. ArcCHECK 3DVH only corrects the 3D dose distribution of treatment planning system (TPS) using ArcCHECK measurement, and therefore the accuracy of ArcCHECK 3DVH depends on TPS. In contrast, COMPASS showed good agreement with MC for all cases. However, the COMPASS system requires many complicated installation procedures such as beam modeling, and appropriate commissioning is needed. In terms of clinical cases, there were no large differences for each QA device. The accuracy of the compass and ArcCHECK 3DVH systems for phantoms and clinical cases was compared. Both systems have advantages and disadvantages for clinical use, and consideration of the operating environment is important. The QA system selection is depending on the purpose and workflow in each hospital.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

WE-AB-BRA-05: PET-Guided Delivery Quality Evaluation of Yttrium-90 Microsphere Radioembolizaton for Hepatocellular Carcinoma Patients: The Optimal Sequence of Radioembolizaton and Chemoembolization Treatments

Purpose: Selective-internal-radiation-therapy (SIRT) and transarterial-chemoembolization (TACE) are commonly used for treatment of liver tumors. The use of TACE, which is macroembolic, prior to SIRT may cause hemodynamic changes in tumor vasculature that impair yttrium-90 (90Y) microsphere delivery to the targeted lesions. This work aims to quantify dosimetric tumor coverage using 90Y positron emission tomography (PET) dosimetry after SIRT alone compared to TACE followed by SIRT. Methods: A total of 40 consecutive hepatocellular carcinoma (HCC) SIRT patients who had a post-SIRT 90Y PET/CT scan were evaluated. The patient-specific-3D-dose was reconstructed from the PET images. Patients were categorized into two groups: patients received TACE prior SIRT procedure (n=18) and patient received SIRT alone (n=22). The lesions and liver were delineated by a senior radiologist. We evaluated both the lesion-specific dose-volume-histogram (DVH) and the selectivity index (SI) defined as the ratio of the average dose inside the total lesion(s) and the average dose of the normal liver. The SI values of patients were compared based on whether TACE was previously used. Results: A wide spectrum was observed in the lesion-specific DVH-evaluation and SI appeared to be suitable of evaluating the quality of each SIRT infusion. The average SI of the entire patient group was 3.0, i.e. targeted lesion receiving three times higher dose than normal liver. The average SI was 1.8 for patients who had prior TACE and 3.9 for patients who did not have prior TACE (p=0.008). 85% of the patients with prior TACE demonstrated poor 90Y-microsphere delivery (SI <2) while none demonstrated excellent delivery (SI >4). On the other hand, the incidence SI >4 among patients with no prior TACE was 37%. Conclusion: 3D dose evaluation using post-SIRT PET suggests that 90Y microsphere delivery to liver tumors is impaired among patients who received prior TACE compared to those who receive SIRT alone.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spectrally Stabilized Interface Capturing Formulation and Implementation in Nek5000/NekRS

This report documents the formulation of a novel level-set method for incompressible two-phase flows in the continuous Galerkin (CG) high order spectral element framework. The overall method hinges on a novel implementation of the spectral vanishing viscosity (SVV) operator for the stabilization of linear/non-linear hyperbolic problems. The multidimensional SVV convolution kernels, which in essence, have a similar effect as a high pass filter applied to the derivatives, are formulated by exploiting the tensor product form, analogous to the construction of the usual stiffness matrix system. The resulting kernels are directionally decoupled and ensure a linear, symmetric positive definite, elliptic matrix operator. The SVV formulation is demonstrated to provide a robust stabilizing mechanism through challenging linear and non-linear hyperbolic problems, including problems pertinent to the level-set formulation. The two-phase framework conceptualized herein is based on the conservative level-set (CLS) method which represents the interface between the fluids by the 0.5 iso-contour of the smoothed Heaviside function. The CLS method is augmented with a preconditioning procedure for interface normals using the signed distance function which precludes the manifestation of spurious oscillations in the vicinty of the interface. Further, the existing mixed explicit-implicit approach for the solution of Navier-Stokes equations in Nek5000, as described in Tomboulides et al, is augmented with a pressure coefficient splitting approach for the Poisson equation, which greatly accelerated the convergence of pressure solver for two-phase systems with large density ratio. The robustness and accuracy of the overall two-phase method is demonstrated through canonical challenging problems involving high density and viscosity ratios, with and without surface tension. The two-phase formulation is wholly implemented in Nek5000 and the SVV stabilization method is implemented in NekRS, which is the essential precursor to the two-phase framework, undergoing active development.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Comparison of Fuel Loading Pattern Optimization Results using Exhaustive Search for Fresh Fuels and Local Search for Burned Fuels

Fuel loading pattern optimization is a combinatorial optimization problem from the mathematical point of view. However, exhaustive search is impractical because enormous computational cost is necessary due to huge search space. For this reason, various optimization methods have been developed so far, but no methods are able to guarantee to reach the optimum solution. In the present study, search space is divided into subspaces using key parameters that well represent characteristics of a loading pattern and then promising subspaces are extensively searched. In the present method, balance between division of a search space into subspaces and a search method used in a subspace is essential. For example, when the number of division is set to be one, it is equivalent to the exhaustive search thus is impractical. Similarly, when the division to subspaces is too coarse, an optimum (or near optimum) solution cannot be found through local search in each subspace since design space of a subspace is still too large. Contrary, if division to subspaces is too fine, number of subspaces becomes to large thus promising subspaces cannot be found. In the present study, search space is divided focusing on the location of fresh fuel assemblies with gadolinia since it has significant impact on core characteristics. Number of possible loading patterns of gadolinia bearing fresh fuel assemblies is not very large thus exhaustive search is possible. Local fine search is executed by considering location of burned fuels for each loading pattern of gadolinia bearing fresh fuel assembly. If the same solution is obtained by multiple local searches starting from different initial burnt fuel locations (but the same loading patterns for gadolinia bearing fresh fuel assemblies), the obtained solution is highly likely the optimum in the subspace. In more general, the present method is based on the 'divide and conquer' approach, which is familiar in the optimization field. Namely, after division of search space into subspaces and then promising subspace is searched. Once the promising subspace is found, the subspace is further subdivided into subspaces and repeat the above procedures. This method would be efficient when appropriate balance between search space division method and performance of local search is achieved. The previous works utilizes the similar approach. In reference [1], exhaustive search in small region in a core is carried out focusing on loading patterns of gadolinia bearing fresh fuel assemblies. In reference [2], rough exhaustive search based on the 'batch-wise' loading pattern is performed with the assist of the linear programming. By contrast, in the present study, exhaustive search is carried out for loading patterns of gadolinia bearing fresh fuel assemblies. The random Multi-start Local Search (MSLS) with the direct search or the exhaustive binary swap is used to find out optimal solutions in a subspace, which consists of loading patterns having the same arrangement of gadolinia bearing fresh fuel assemblies. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Corrected Score Function Framework for Modelling Circadian Gene Expression

Many biological processes display oscillatory behaviour based on an approximately 24 h internal timing system specific to each individual. One process of particular interest is gene expression, for which several circadian transcriptomic studies have identified associations between gene expression during a 24 h period and an individual's health. A challenge with analysing data from these studies is that each individual's internal timing system is offset relative to the 24 h day-night cycle, where day–night cycle time is recorded for each collected sample. Laboratory procedures can accurately determine each individual's offset and determine the internal time of sample collection. However, these laboratory procedures are labour-intensive and expensive. Here, in this paper, we propose a corrected score function framework to obtain a regression model of gene expression given internal time when the offset of each individual is too burdensome to determine. A feature of this framework is that it does not require the probability distribution generating offsets to be symmetric with a mean of zero. Simulation studies validate the use of this corrected score function framework for cosinor regression, which is prevalent in circadian transcriptomic studies. Illustrations with data from three circadian transcriptomic studies further demonstrate that the proposed framework consistently mitigates bias relative to using a score function that does not account for this offset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

OPERA models for predicting physicochemical properties and environmental fate endpoints

The collection of chemical structure information and associated experimental data for quantitative structure–activity/ property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

EDGE COVID-19: a web platform to generate submission-ready genomes from SARS-CoV-2 sequencing efforts

Abstract Summary Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences. Availability and implementation https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Toward ‘Omic Scale Metabolite Profiling: A Dual Separation–Mass Spectrometry Approach for Coverage of Lipid and Central Carbon Metabolism

Although the objective of any ‘omic science is broad measurement of its constituents, such coverage has been challenging in metabolomics because the metabolome is comprised of a chemically diverse set of small molecules with variable physical properties. While extensive studies have been performed to identify metabolite isolation and separation methods, these strategies introduce bias toward lipophilic or water-soluble metabolites depending on whether reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) is used, respectively. Here we extend our consideration of metabolome isolation and separation procedures to integrate RPLC/MS and HILIC/MS profiling. An aminopropyl-based HILIC/MS method was optimized on the basis of mobile-phase additives and pH, followed by evaluation of reproducibility. When applied to the untargeted study of perturbed bacterial metabolomes, the HILIC method enabled the accurate assessment of key, dysregulated metabolites in central carbon pathways (e.g., amino acids, organic acids, phosphorylated sugars, energy currency metabolites), which could not be retained by RPLC. To demonstrate the value of the integrative approach, bacterial cells, human plasma, and cancer cells were analyzed by combined RPLC/HILIC separation coupled to ESI positive/negative MS detection. In this work, the combined approach resulted in the observation of metabolites associated with lipid and central carbon metabolism from a single biological extract, using 80% organic solvent (ACN:MeOH:H2O 2:2:1). It enabled the detection of more than 30,000 features from each sample type, with the highest number of uniquely detected features by RPLC in ESI positive mode and by HILIC in ESI negative mode. Therefore, we conclude that when time and sample are limited, the maximum amount of biological information related to lipid and central carbon metabolism can be acquired by combining RPLC ESI positive and HILIC ESI negative mode analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Numerical and Experimental Investigations of Polyurethane Foam for Use as Cask Impact Limiter in Accidental Drop Scenarios - 12099

Rigid, closed-cell polyurethane foams are frequently used as cask impact limiters in nuclear materials and hazardous waste transport due to their high energy-absorption potential. When assessing the cask integrity in accidental scenarios based on numerical simulations, a description of the foam damping properties is required for different strain rates and for a wide temperature range with respect to waste heat generation in conjunction with critical operating and environmental conditions. Implementation and adaption of a respective finite element material model strongly relies on an appropriate experimental data base. Even though extensive impact experiments were conducted e.g. in Sandia National Laboratories, Savannah River National Laboratory and by Rolls Royce plc, not all relevant factors were taken into account. Hence, BAM who is in charge of the mechanical evaluation of such packages within the approval procedure in Germany, incorporated systematic test series into a comprehensive research project aimed to develop numerical methods for a couple of damping materials. In a first step, displacement driven compression tests have been performed on confined, cubic specimens at five loading rates ranging from 0.02 mm/s to 3 m/s at temperatures between +90 deg. C and -40 deg. C. Materials include two different polyurethane foam types called FR3718 and FR3730 having densities of 280 kg/m{sup 3} and 488 kg/m{sup 3} from the product line-up of General Plastics Manufacturing Company. Their data was used to adapt an advanced plasticity model allowing for reliably simulating cellular materials under multi-axial compression states. Therefore, an automated parameter identification procedure had been established by combining an artificial neural network with local optimization techniques. Currently, the selected numerical material input values are validated and optimized by means of more complex loading configurations with the prospect of establishing methods applicable to impact limiters under severe accidental conditions. The reference data base is provided by experiments, where weights between 212 kg and 1200 kg have been dropped from heights between 1.25 m and 7 m on confined 10 cm cubic foam specimens. By presenting the deviations between experimental values and the corresponding output of finite element simulations, the potentials and restrictions of the resulting models are highlighted. Systematic compression tests on polyurethane foams had been performed at BAM test site within the framework of a research project on impact limiters for handling casks for radioactive waste. The experimental results had been used to adapt numerical models for simulating the behaviour of different foam types at different temperatures. The loading speed, however, turned out to have a major influence on their flow curves that can not be captured by simple strain-rate dependent multipliers. Especially for guided drop tests that come close to real accidental scenarios there is a significant gap between experimental and numerical results even when applying such advanced material models. Hence, the extensive data base is currently deployed for expanding the standard algorithms to include adequate dynamic hardening factors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

{sup 18}F-Choline Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging for the Detection of Early Local Recurrence of Prostate Cancer Initially Treated by Radiation Therapy: Comparison With Systematic 3-Dimensional Transperineal Mapping Biopsy

Purpose: To compare the diagnostic performance of {sup 18}F-fluorocholine positron emission tomography/computed tomography (FCH-PET/CT), multiparametric prostate magnetic resonance imaging (mpMRI), and a combination of both techniques for the detection of local recurrence of prostate cancer initially treated by radiation therapy. Methods and Materials: This was a retrospective, single-institution study of 32 patients with suspected prostate cancer recurrence who underwent both FCH-PET/CT and 3T mpMRI within 3 months of one another for the detection of recurrence. All included patients had to be cleared for metastatic recurrence. The reference procedure was systematic 3-dimensional (3D)-transperineal prostate biopsy for the final assessment of local recurrence. Both imaging modalities were analyzed by 2 experienced readers blinded to clinical data. The analysis was made per-patient and per-segment using a 4-segment model. Results: The median prostate-specific antigen value at the time of imaging was 2.92 ng/mL. The mean prostate-specific antigen doubling time was 14 months. Of the 32 patients, 31 had a positive 3D-transperineal mapping biopsy for a local relapse. On a patient-based analysis, the detection rate was 71% (22 of 31) for mpMRI and 74% (23 of 31) for FCH-PET/CT. On a segment-based analysis, the sensitivity and specificity were, respectively, 32% and 87% for mpMRI, 34% and 87% for FCH-PET/CT, and 43% and 83% for the combined analysis of both techniques. Accuracy was 64%, 65%, and 66%, respectively. The interobserver agreement was κ = 0.92 for FCH-PET/CT and κ = 0.74 for mpMRI. Conclusions: Both mpMRI and FCH-PET/CT show limited sensitivity but good specificity for the detection of local cancer recurrence after radiation therapy, when compared with 3D-transperineal mapping biopsy. Prostate biopsy still seems to be mandatory to diagnose local relapse and select patients who could benefit from local salvage therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Modernising ATLAS Software Build Infrastructure

In the last year ATLAS has radically updated its software development infrastructure hugely reducing the complexity of building releases and greatly improving build speed, flexibility and code testing. The first step in this transition was the adoption of CMake as the software build system over the older CMT. This required the development of an automated translation from the old system to the new, followed by extensive testing and improvements. This resulted in a far more standard build process that was married to the method of building ATLAS software as a series of 12 separate projects from Subversion. We then proceeded with a migration of the code base from Subversion to Git. As the Subversion repository had been structured to manage each package more or less independently there was no simple mapping that could be used to manage the migration into Git. Instead a specialist set of scripts that captured the software changes across official software releases was developed. With some clean up of the repository and the policy of only migrating packages in production releases, we managed to reduce the repository size from 62 GiB to 220 MiB. After moving to Git we took the opportunity to introduce continuous integration so that now each code change from developers is built and tested before being approved. With both CMake and Git in place we also dramatically simplified the build management of ATLAS software. Many heavyweight homegrown tools were dropped and the build procedure was reduced to a single bootstrap of some external packages, followed by a full build of the rest of the stack. This has reduced the time for a build by a factor of 2. It is now easy to build ATLAS software, freeing developers to test compile intrusive changes or new platform ports with ease. We have also developed a system to build lightweight ATLAS releases, for simulation, analysis or physics derivations which can be built from the same branch.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2021

Integrating static PRA information with risk informed safety margin characterization (RISMC) simulation methods

The overall objective of the project was to develop a computationally feasible and user-friendly mechanized process to integrate traditional probabilistic risk assessment (PRA) and dynamic PRA (DPRA) results. Starting with the systematic identification of items in an existing PRA that need dynamic augmentation, the project used a generic 4-loop pressurized reactor (PWR) and 3-loop PWR as example plants. Station blackout (SBO) and large break loss of coolant accident SBLOCA) were selected as the example initiating events. Using the traditional event-tree (ET)/fault-tree (FT) methodology augmented by dynamic evet tree approach, the potential consequences of the initiating events were simulated with RELAP-3D and MELCOR/RASCAL codes to cover Level 1 through Level 3 of PRA. RAVEN and ADAPT software were used to generate Level 1 simulations with RELAP-3D and Level 2/3 simulations with MELCOR (Level 2)/RASCAL (Level 3), respectively. Example branching conditions (BCs) for SBO included AC power recovery time, valve repair failure time, reactor coolant pump leak time/break size and emergency power supply duration to a total of 9. Example BCs for LOCA included off-site power recovery time, diesel generator power recovery time, auxiliary feed water system operation time, safety relief valve failure to open upon demand, reactor coolant pump seal break time and size to a total of 21. Each RELAP-3D simulation (9,587 scenarios) was labelled OK or Core Damage based on the maximum allowed peak clad temperature (2,100oF). Each MELCOR simulation (4610 scenarios) was labeled as Bin over 10rem or Bin 0-10rem based on the dose at the site boundary. The scenarios were clustered based on the criteria above using the mean shift methodology. Classical PRA (CPRA) and DPRA results were compared to identify the ET sequences that need DPRA augmentation. Several approaches were proposed for the incorporation of these sequences into CPRA using clustering with the mean shift methodology, restructuring the CPRA ETs by adding new BCs/sequences, and using the concept of a limit surface. Procedures for decision making regarding the possible consequences of an initiating event (e.g. core damage or not, site evacuation or not) were developed using a convolutional neural network (CNN), a recurrent neural network (RNN) and a transformer neural network (TNN). The project has led to two PhD degrees, three archival journal papers and five refereed conference proceedings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗