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A summary of nuclear data evaluation for the 39,41 K + γ reaction is given, which focuses on the production of radio-active isotopes. The statistical model calculation is performed with the CoH 3 code, and the calculated 39 K( γ,n) 38m K cross section is compared with the available experimental data.
There are many (n,γ) cross sections of great interest to radiochemical diagnostics and to nuclear astrophysics which are beyond the reach of current measurement techniques, and likely to remain so for the foreseeable future. In contrast, total neutron cross sections currently are feasible for many of these nuclides and provide almost all the information needed to accurately calculate the (n,γ) cross sections via the nuclear statistical model (NSM). I demonstrate this for the case of 151 Sm; NSM calculations constrained using average resonance parameters obtained from total cross section measurements made in 1975, are in excellent agreement with recent 151 Sm (n,γ) measurements across a wide range of energy. Furthermore, I demonstrate through simulations that total cross section measurements can be made at the Manuel Lujan Jr. Neutron Scattering Center at the Los Alamos Neutron Science Center for samples as small as 10μg. Samples of this size should be attainable for many nuclides of interest. Finally, I estimate that over half of the radionuclides identified ~20 years ago as having (n,γ) cross sections of importance to s-process nucleosynthesis studies (24/43) and radiochemical diagnostics (11/19), almost none of which have been measured, can be constrained using this technique.
This report summarizes the procedure to compare the robust UC approach with the deterministic counterpart. The procedure involves solving the robust and deterministic UC problems under various scenario configurations, and then performing system dispatch simulations under each UC solution to generate economic and operational performance measures. The report describes the parallelization process for solving UC problems as well as for running system dispatch simulations. The report also summarizes the statistical model used for generating Monte Carlo simulation samples and forming forecast ranges used in the robust UC approach. Some key findings are discussed as well. In particular, the economic and operational performance of the robust and deterministic approaches is compared under different 1) conservatism levels or the sizes of forecast range, 2) the wind penetration levels, and 3) the mixture of load and wind behaviors.
How much energy could be generated if PV modules were installed on all of the suitable roof area in the nation? To answer this question, we first use GIS methods to process a lidar dataset and determine the amount of roof area that is suitable for PV deployment in 128 cities nationwide, containing 23% of U.S. buildings, and provide PV-generation results for a subset of those cities. We then extend the insights from that analysis to the entire continental United States. We develop two statistical models--one for small buildings and one for medium and large buildings--and populate them with geographic variables that correlate with rooftop's suitability for PV. We simulate the productivity of PV installed on the suitable roof area, and present the technical potential of PV on both small buildings and medium/large buildings for every state in the continental US. Within the 128 cities covered by lidar data, 83% of small buildings have a location suitable for a PV installation, but only 26% of the total rooftop area of small buildings is suitable for development. The sheer number of buildings in this class, however, gives small buildings the greatest technical potential. Small building rooftops could accommodate 731 GW of PV capacity and generate 926 TWh/year of PV energy, approximately 65% of rooftop PV's total technical potential. We conclude by summing the PV-generation results for all building sizes and therefore answering our original question, estimating that the total national technical potential of rooftop PV is 1,118 GW of installed capacity and 1,432 TWh of annual energy generation. This equates to 39% of total national electric-sector sales.
This research project is part of the U.S. Nuclear Regulatory Commission’s (NRC’s) Probabilistic Flood Hazard Assessment (PFHA) Research plan in support of developing a risk-informed licensing framework for flood hazards and design standards at proposed new facilities and significance determination tools for evaluating potential deficiencies related to flood protection at operating facilities. The PFHA plan aims to build upon recent advances in deterministic, probabilistic, and statistical modeling of extreme precipitation events to develop regulatory tools and guidance for NRC staff with regard to PFHA for nuclear facilities. The tools and guidance developed under the PFHA plan will support and enhance NRC’s capacity to perform thorough and efficient reviews of license applications and license amendment requests. They will also support risk-informed significance determination of inspection findings, unusual events, and other oversight activities.
No abstract provided.
Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.
The Sandia-developed Transport of Radioactive Materials (STORM) code suite is used as part of the Radioisotope Power System Launch Safety (RPSLS) program to perform statistical modeling of the consequences due to release of radioactive material given a launch accident. As part of this modeling, STORM samples input parameters from probability distributions with some parameters treated as constants. This report described the work done to convert four of these constant inputs (Consumption Rate, Average Crop Yield, Cropland to Landuse Database Ratio, and Crop Uptake Factor) to sampled values. Consumption rate changed from a constant value of 557.68 kg / yr to a normal distribution with a mean of 102.96 kg / yr and a standard deviation of 2.65 kg / yr. Meanwhile, Average Crop Yield changed from a constant value of 3.783 kg edible / m 2 to a normal distribution with a mean of 3.23 kg edible / m 2 and a standard deviation of 0.442 kg edible / m 2 . The Cropland to Landuse Database ratio changed from a constant value of 0.0996 (9.96%) to a normal distribution with a mean value of 0.0312 (3.12%) and a standard deviation of 0.00292 (0.29%). Finally the crop uptake factor changed from a constant value of 6.37e -4 (Bq crop /kg)/(Bq soil /kg) to a lognormal distribution with a geometric mean value of 3.38e -4 (Bq crop /kg)/(Bq soil /kg) and a standard deviation value of 3.33 (Bq crop /kg)/(Bq soil /kg)
The 3-year accelerated aging study of the SAVY-4000 O-ring detailed in our previous Lifetime Extension Report showed very little evidence of significant degradation to samples subjected to aggressive elevated temperature and radiation conditions. Whole container thermal aging studies followed by helium leakage testing and compression set measurements were used to establish a failure criterion estimate for O-ring compression set of ≥ 65 %, which led to an O-ring lifetime estimate of greater than 65 years at 80°C. Herein, we describe a statistical model of the 3-year accelerated aging study and present additional results gathered in FY17 that further support the lifetime extension of the O-ring to 40 years at 80°C. Because we are choosing an extremely conservative lifetime value (40 years) compared to the aging study results (>65 years), the O-ring lifetime could be extended further, especially if supported by surveillance results collected over the years to come.
In recognition of their mission and in response to continuously evolving cyber threats against nuclear facilities, Department of Energy - Nuclear Energy (DOE-NE) is building the Nuclear Energy Cyber security Research, Development, and Demonstration (RD&D) Program, which includes a cyber risk management thrust. This report supports the cyber risk management thrust objective which is to deliver "Standardized methodologies for credible risk-based identification, evaluation and prioritization of digital components." In a previous task, the Sandia National Laboratories (SNL) team presented evaluation criteria and a survey to review methods to determine the most suitable techniques. In this task we will identify and evaluate a series of candidate methodologies. In this report, 10 distinct methodologies are evaluated. The overall goal of this effort was to identify the current range of risk analysis techniques that were currently available, and how they could be applied, with an focus on industrial control systems (ICS). Overall, most of the techniques identified did fall into accepted risk analysis practices, though they generally addressed only one step of the multi-step risk management process. A few addressed multiple steps, but generally their treatment was superficial. This study revealed that the current state of security risk analysis in digital control systems was not comprehensive and did not support a science-based evaluation. The papers surveyed did use mathematical formulation to describe the addressed problems, and tied the models to some kind of experimental or experiential evidence as support. Most of the papers, however, did not use a rigorous approach to experimentally support the proposed models, nor did they have enough evidence supporting the efficacy of the models to statistically analyze model impact. Both of these issues stem from the difficulty and expense associated with collecting experimental data in this domain.
Multivariate time-series datasets are intrinsic to the study of dynamic, naturalistic behavior, such as in the applications of finance and motion video analysis. Statistical models provide the ability to identify event patterns in these data under conditions of uncertainty, but researchers must be able to evaluate how well a model uses available information in a dataset for clustering decisions and for uncertainty information. The Hidden Markov Model (HMM) is an established method for clustering time-series data, where the hidden states of the HMM are the clusters. We develop novel methods for quantifying the uncertainty of the performance of and for visualizing the clustering performance and uncertainty of fitting a HMM to multivariate time-series data. We explain the usefulness of uncertainty quantification and visualization with evaluating the performance of clustering models, as well as how information exploitation of time-series datasets can be enhanced. We implement our methods to cluster patterns of scanpaths from raw eye tracking data.
A new evaluation of the ENDL cross section set for Neptunium (Z=93) is developed using the TALYS statistical model cross section code. The primary goal of this effort is to produce an evaluation that attempts to match as closely as possible fission cross sections developed through surrogate reaction techniques on actinide targets 237 Np and 239 Np.Additional experimental data on the long-lived 237 Np target including (n,g), (n,2n), (n,total), and (n,elastic) isalso considered. This evaluation effort and theprocessing needed to render its results into data libraries is a necessary step in making the efforts of nuclear experimentalists useful to the broad community of researchers engaged in simulations of nuclear fusion for basic and applied science. Anotheraspect, verification and validation against various AGEX experiments, is also presented. The end-product is an updated library that includes the latest measurements of key fission cross sections for comparison against those measured via traditional techniques.All the steps in the evaluation, processing, validation and verification, and library release are described in the following sections. For completeness, the appendices contain all the parameters used in the TALYS evaluation for the 237 Np and 239 Np cross sections.
Project 1: Our previous work demonstrated that the CES biomarker can be an approximate measure for the levels of chromosomal instability (CIN) in cancer, and that the CES (for Centromere and kinetochore gene Expression Score) biomarker effectively predicts which cancer patients benefit from adjuvant chemotherapies in lung and other cancer types. Such a utility can potentially reduce overtreatment that is common in lung cancer treatment. We determined performance of the multiplex Quantigene RNA assay platform for measuring mRNA levels of CES genes using clinical tumor samples. These experiments determined sensitivity, specificity and detection limit of 12 CES genes and 5 control genes using clinical tumor samples with paired normal tissue samples. We determined how sample concentration and storage conditions affect measurement sensitivity and specificity, and whether sample storage over time affects result reproducibility. Importantly, we confirmed that early stage lung cancer CES values are significantly higher than those of paired normal tissues in all cases. This result greatly extended our bioinformatic data mining by confirming that we can measure CES values directly from patient tumor tissue, and that tumors have higher CES values than normal tissues. Unfortunately, our CRADA partner in China did not finish measuring CES expression levels for a larger group of ~100 tumor samples, due to the long period time required for human subject review in China. This delay prevented us from generating enough additional data to generate a significant statistical model. We finished analyzing all the data acquired during the CRADA, and are trying to work collaboratively with the Shanghai University Lung Cancer Hospital to complete the analysis of more tumor samples. Although no publications have resulted from this research, the findings are crucial for ongoing efforts to obtain funding to develop the technology to address problems in deciding on appropriate treatment regimens for individual patients, in clinical settings. Project 2: The overarching milestones of CRADA Project 2 were to investigate the role of FAM83 family members in cancer progression. Two publications resulted from this work which are highlighted below. The first major goal of the CRADA was to broadly assess the role of FAM83 family oncogenes in human cancer using a multi-omic bioinformatic approach (Snijders et al 2017 Mol. Oncol 11). The second major goal was to specifically assess the role of FAM83D in a cancer progression and metastasis (Yin et al 2019 Cell Oncol; in revision). Role of FAM83 family oncogenes in human cancers Recent studies from our laboratory as well as other groups have demonstrated that FAM83 family members have oncogenic properties and have significantly elevated expression levels in multiple human tumor types, including breast cancers (BC). We performed an integrative multi-omics bio-informatic analysis of the role of FAM83 family oncogenes in human cancers. Our findings were published in the journal Molecular Oncology (Snijders et al; Mol Oncol. 2017 Feb;11(2):167-179). Highlights from this manuscript are: • Transcriptional levels of FAM83 family members are frequently and dramatically elevated across tumor types in human • DNA copy number increase appears to be a potential mechanism for increased expression of FAM83 genes • FAM83 family gene signature is associated with poor survival across human tumor types • FAM83 gene alterations are significantly correlated with TP53, PIK3CA and Ecadherin mutations. In conclusion, this analysis of FAM83 family members opened up a new horizon for further understanding the significance of FAM83 genes in cancer and clinical applications in diagnosis, prognosis and therapy. Role of FAM83D in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy. Lung cancer is one of the most common malignant tumors in the world and non-small cell lung cancer (NSCLC) accounts for 80~85% of all diagnosed lung cancer cases, one of the deadliest cancers worldwide. FAM83D has been proposed as an oncoprotein in several types of human cancer, yet its role and molecular mechanism in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy are poorly understood. We evaluated the expression of FAM83D in NSCLC cells and cancerous tissues and its prognostic significance, then investigated the effect of FAM83D on tumorigenic phenotypes such as cell proliferation, EMT and invasive capability, and finally assessed impact of FAM83D on NSCLC chemo-drug resistance. The results of this study are currently in revision at the journal Cellular Oncology. Highlights from this manuscript are: • FAM83D expression is elevated in NSCLC cells and tissues and associated with poor prognosis • FAM83D promotes NSCLC cell proliferation • FAM83D induces EMT in NSCLC cells • FAM83D promotes NSCLC cell migration and invasion • FAM83D promotes EMT and invasive capability of NSCLC cells by regulating AKT/mTOR pathway • FAM83D silencing increases cisplatin sensitivity of NSCLC cells In summary, this second study demonstrated for the first time that FAM83D promoted EMT and metastasis of NSCLC cells through AKT/mTOR signal pathway as well as increased the sensitivity of NSCLC to cisplatin. This study may provide potential targets for advanced NSCLC treatment and personalized therapy.
This research project is part of the U.S. Nuclear Regulatory Commission’s (NRC’s) Probabilistic Flood Hazard Assessment (PFHA) research plan in support of developing a risk-informed licensing framework for flood hazards and design standards at proposed new facilities and significance determination tools for evaluating potential deficiencies related to flood protection at operating facilities. The PFHA plan aims to build upon recent advances in deterministic, probabilistic, and statistical modeling of extreme precipitation events to develop regulatory tools and guidance for NRC staff with regard to PFHA for nuclear facilities. The tools and guidance developed under the PFHA plan will support and enhance the NRC’s capacity to perform thorough and efficient reviews of license applications and license amendment requests. They will also support risk-informed significance determination of inspection findings, unusual events, and other oversight activities. For Year 1, specifically, Pacific Northwest National Laboratory (PNNL) staff: Prepared an annual report that summarizes recent scientific findings, reports on activities of federal agencies with direct responsibility for climate change science, and provide a qualitative assessment of these findings relevant to NRC concerns on a regional level (i.e., increasing air and water temperatures, decreasing water availability, increasing frequency and intensity of storms and flooding, and sea level rise). Participated in annual webinars with NRC staff at NRC Headquarters in Rockville, Maryland. Presented updates summarizing recent scientific findings at the NRC PFHA Workshop Headquarters in Rockville, Maryland. PNNL staff have identified neither technical nor programmatic issues in proceeding with Year 2 activities. PNNL staff updated this report in March and April 2019.
A new evaluation of the ENDL cross section set for Thorium (Z=90) is developed using the TALYS statistical model cross section code. The primary goal of this effort is to produce an evaluation that attempts to match as closely as possible fission cross sections developed through surrogate reaction techniques on actinide targets 230 Th and 231 Th. This evaluation effort and the processing needed to render its results into data libraries is a necessary step in making the efforts of nuclear experimentalists useful to the broad community of researchers engaged in simulations of nuclear fusion for basic and applied science. Another aspect, verification and validation against various AGEX experiments, is also presented. The end-product is an updated library that includes the latest measurements of fission cross sections for comparison against those measured via traditional techniques. All the steps in the evaluation, processing, validation and verification, and library release are described in the following sections. For completeness, the appendix contains all the parameters used in the TALYS cross section evaluation for neutrons incident on 230 Th and 231 Th.
We consider the problem of designing a sensor network to most efficiently locate the source of a seismic event. The left panel of Figure 1 shows 982 possible sensor locations in a region of interest. The objective is to infer the unknown location of an earthquake, indicated by the gold dot for illustration purposes. Assume that three sensors have already been placed for data collection. Travel times of the P-wave from the source to the three sensors are observed and used to infer the unknown source location via a Bayesian statistical model described subsequently. The right panel of Figure 1 shows samples from the resulting posterior distribution for source location along with a posterior estimate of this location.
Plasma physics is an exciting field of study with a wide variety of nonlinear processes that come into play. Examples of such processes include the interaction of small-scale turbulence with large-scale plasma structures and the nonlinear saturation of plasma instabilities, for example those of magneto-hydrodynamical nature. During this Truman LDRD project, I studied a collection of nonlinear problems that are of interest to the field of plasma physics. This LDRD report summarizes four main research accomplishments. First, a new statistical model for describing inhomogeneous drift-wave turbulence inter- acting with zonal flows was developed. This new model includes the effects of nonlinear wave-wave collisions, which are expected to change the spectrum of the underlying DW turbulence and therefore the generation of zonal flows. Second, a new mathematical formalism was proposed to systematically apply the non- linear WKB approximation to general field theories, including those often used in fluid dynamics. This formalism represents an interesting tool for studying physical systems that show an explicit scale separation. Third, a weakly nonlinear model was developed to describe the magneto-Rayleigh-Taylor instability. This instability is of paramount importance to understand as it can reduce the performance of magnetic-inertial-fusion (MIF) platforms. The developed models captures the effects of harmonic generation and saturation of the linear growth of the instability. Finally, a framework was proposed for scaling magneto-inertial fusion (MIF) targets to larger pulsed-power drivers. From this framework, a set of scaling rules were derived that conserve the physical regimes of MIF systems when scaling up in peak current. By doing so, deleterious nonlinear processes that affect MIF performance may be kept at bay.
Focal Area(s): This proposal aims to develop and evaluate statistical models and machine learning algorithms for detecting and tracking features in spatiotemporal remotely sensed data with uncertainty quantification. We focus a particular application on the detection of sea ice leads and ridges in the Arctic and use these key sea ice features for model calibration and to gain insight into the physics of sea ice thermodynamics and deformation.