A Performance Indicator for Reduction in Vulnerability Through Stabilization of Plutonium

The US Department of Energy (DOE) is currently storing several metric tons of plutonium in various forms in a variety of facilities throughout the DOE complex. Since the cessation of weapons production in 1990, many of these facilities with plutonium in storage have not operated. Since the shutdown was regarded as temporary, little attempt was made at that time to empty the process lines of plutonium, or to place the plutonium in containers or packages that would provide safe storage for extended periods of time. As a result, the packages and containers providing interim storage are vulnerable to failure through leakage, rupture and other modes, and pose potential hazards to facility workers, the public and the environment. Here, an approach to measuring and tracking the reduction in vulnerabilities resulting from stabilizing and repackaging plutonium is developed and presented. The approach utilizes results obtained by the DOE Working Group on the vulnerabilities associated with plutonium storage

NRC support for the Kalinin (VVER) probabilistic risk assessment

The US Nuclear Regulatory Commission (NRC) and the Federal Nuclear and Radiation Safety Authority of the Russian Federation have been working together since 1994 to carry out a probabilistic risk assessment (PRA) of a VVER-1000 in the Russian Federation. This was a recognition by both parties that this technology has had a profound effect on the discipline of nuclear reactor safety in the West and that the technology should be transferred to others so that it can be applied to Soviet-designed plants. The NRC provided funds from the Agency for International Development and technical support primarily through Brookhaven National Laboratory and its subcontractors. The latter support was carried out through workshops, by documenting the methodology to be used in a set of guides, and through periodic review of the technical activity. The result of this effort to date includes a set of procedure guides, a draft final report on the Level 1 PRA for internal events (excluding internal fires and floods), and progress reports on the fire, flood, and seismic analysis. It is the authors belief that the type of assistance provided by the NRC has been instrumental in assuring a quality product and transferring important technology for use by regulators and operators of Soviet-designed reactors. After a thorough review, the report will be finalized, lessons learned will be applied in the regulatory and operational regimes in the Russian Federation, and consideration will be given to supporting a containment analysis in order to complete a simplified Level 2 PRA

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Quantifying Absolute Neutralization Titers against SARS-CoV-2 by a Standardized Virus Neutralization Assay Allows for CrossCohort Comparisons of COVID-19 Sera

The global coronavirus disease 2019 (COVID-19) pandemic has mobilized efforts to develop vaccines and antibody-based therapeutics, including convalescent-phase plasma therapy, that inhibit viral entry by inducing or transferring neutralizing antibodies (nAbs) against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein (CoV2-S). However, rigorous efficacy testing requires extensive screening with live virus under onerous biosafety level 3 (BSL3) conditions, which limits high-throughput screening of patient and vaccine sera. Myriad BSL2-compatible surrogate virus neutralization assays (VNAs) have been developed to overcome this barrier. Yet, there is marked variability between VNAs and how their results are presented, making intergroup comparisons difficult. To address these limitations, we developed a standardized VNA using CoV2-S pseudotyped particles (CoV2pp) based on vesicular stomatitis virus bearing the Renilla luciferase gene in place of its G glyco-protein (VSVDG); this assay can be robustly produced at scale and generate accurate neutralizing titers within 18 h postinfection. Our standardized CoV2pp VNA showed a strong positive correlation with CoV2-S enzyme-linked immunosorbent assay (ELISA) results and live-virus neutralizations in confirmed convalescent-patient sera. Three independent groups subsequently validated our standardized CoV2pp VNA (n . 120). Our data (i) show that absolute 50% inhibitory concentration (absIC50), absIC80, and absIC90 values can be legitimately compared across diverse cohorts, (ii) highlight the substantial but consistent variability in neutralization potency across these cohorts, and (iii) support the use of the absIC80 as a more meaningful metric for assessing the neutralization potency of a vaccine or convalescent-phase sera. Lastly, we used our CoV2pp in a screen to identify ultrapermissive 293T clones that stably express ACE2 or ACE2 plus TMPRSS2. When these are used in combination with our CoV2pp, we can produce CoV2pp sufficient for 150,000 standardized VNAs/week. IMPORTANCE Vaccines and antibody-based therapeutics like convalescent-phase plasma therapy are premised upon inducing or transferring neutralizing antibodies that inhibit SARS-CoV-2 entry into cells. Virus neutralization assays (VNAs) for measuring neutralizing antibody titers (NATs) are an essential part of determining vaccine or therapeutic efficacy. However, such efficacy testing is limited by the inherent dangers of working with the live virus, which requires specialized high-level biocontainment facilities. We there-fore developed a standardized replication-defective pseudotyped particle system that mimics the entry of live SARS-CoV-2. This tool allows for the safe and efficient measurement of NATs, determination of other forms of entry inhibition, and thorough investigation of virus entry mechanisms. Four independent labs across the globe validated our standardized VNA using diverse cohorts. We argue that a standardized and scalable assay is necessary for meaningful comparisons of the myriad of vaccines and antibody-based therapeutics becoming available. Our data provide generalizable metrics for assessing their efficacy.Fil: Oguntuyo, Kasopefoluwa. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Stevens, Christian S.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Hung, Chuan Tien. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Ikegame, Satoshi. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Acklin, Joshua A.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Kowdle, Shreyas S.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Carmichael, Jillian C.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Chiu, Hsin Ping. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Azarm, Kristopher D.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Haas, Griffin D.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Amanat, Fatima. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Klingler, Jéromine. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Baine, Ian. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Arinsburg, Suzanne. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Bandres, Juan C.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Siddiquey, Mohammed N. A.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Schilke, Robert M.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Woolard, Matthew D.. State University of Louisiana; Estados UnidosFil: Zhang, Hongbo. State University of Louisiana; Estados UnidosFil: Duty, Andrew J.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Kraus, Thomas A.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Moran, Thomas M.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Tortorella, Domenico. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Lim, Jean K.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Gamarnik, Andrea Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Hioe, Catarina E.. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Zolla Pazner, Susan. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Ivanov, Stanimir S.. State University of Louisiana; Estados UnidosFil: Kamil, Jeremy. State University of Louisiana; Estados UnidosFil: Krammer, Florian. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Lee, Benhur. Icahn School of Medicine at Mount Sinai; Estados UnidosFil: Ojeda, Diego Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: González López Ledesma, María Mora. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Costa Navarro, Guadalupe Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Pallarés, H. M.. No especifíca;Fil: Sanchez, Lautaro Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Perez, P.. No especifíca;Fil: Ostrowsk, M.. No especifíca;Fil: Villordo, S. M.. No especifíca;Fil: Alvarez, D. E.. No especifíca;Fil: Caramelo, J. J.. No especifíca;Fil: Carradori, J.. No especifíca;Fil: Yanovsky, M. J.. No especifíca

Methods for Evaluation and Comparison of Display Indicators

This paper summarizes a procedure and associated methodologies for evaluating the operational characteristics of display indicators. This research was conducted as a part of the Risk-Based performance Indicator (RBPI) project under the auspices of the US Nuclear Regulatory Commission (NRC) for the Office of Research (Res.). The recommended procedure and associated methodologies have been utilized to evaluate the capabilities of four candidate system unavailability indicators presently under study by NRC/Res

Seismic Qualification of Equipment by Means of Probabilistic Risk Assessment. [PWR]

Upon the sponsorship of the Equipment Qualification Branch (EQB) of NRC, Brookhaven National Laboratory (BNL) has utilized a risk-based approach for identifying, in a generic fashion, seismically risk-sensitive equipment. It is anticipated that the conclusions drawn therefrom and the methodology employed will, in part, reconcile some of the concerns dealing with the seismic qualification of equipment in operating plants. The approach taken augments an existing sensitivity analysis, based upon the WASH-1400 Reactor Safety Study (RSS), by accounting for seismicity and component fragility with the Kennedy model and by essentially including the requisite seismic data presented in the Zion Probabilistic Safety Study (ZPSS). Parametrically adjusting the seismic-related variables and ascertaining their effects on overall plant risk, core-melt probability, accident sequence probability, etc., allows one to identify those seismically risk-sensitive systems and equipment. This paper describes the approach taken and highlights the results obtained thus far for a hypothetical pressurized water reactor (PWR)

<span style="font-size:10.0pt;font-family: "Times New Roman";mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Mangal;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language: HI" lang="EN-US">Effect of crucible shape on the solution hydrodynamic in the growth of KTiOPO₄single crystals by top-seeded solution growth method: A numerical analysis</span>

675-680Three-dimensional solution flow and temperature field simulations were performed to model the growth of KTiOPO4 single crystals by TSSG method. Steady flow and temperature field for four crucible shapes were computed using finite volume method. Our results show the effect of crucible shape on axial flow which has direct effect on homogeneity of solution, morphology of crystal and mass transport during the growth of crystals from solution. In order to consider the simulation results, KTiOPO4 single crystals were grown by TSSG method in crucibles with different shapes. </span

Probabilistic Evaluation of Fire Protection Features Found in Nuclear Power Plants

This paper describes a method which can be used to evaluate, on a relative basis, the NRC Fire Protection (FP) guidelines as found in Section 9.5.1 (Fire Protection) of the Standard Review Plan (SRP). The approach, a hybrid of existing physical models for fire propagation determinations and probabilistic models for fire-mitigation system reliability, can potentially be used as an adjunct to the present fire safety review process