Reprocessing input data validation

The Isotope Correlation Technique (ICT), in conjunction with the gravimetric (Pu/U ratio) method for mass determination, provides an independent verification of the input accountancy at the dissolver or accountancy stage of the reprocessing plant. The Isotope Correlation Technique has been applied to many classes of domestic and international reactor systems (light-water, heavy-water, graphite, and liquid-metal) operating in a variety of modes (power, research, production, and breeder), and for a variety of reprocessing fuel cycle management strategies. Analysis of reprocessing operations data based on isotopic correlations derived for assemblies in a PWR environment and fuel management scheme, yielded differences between the measurement-derived and ICT-derived plutonium mass determinations of ({minus}0.02 {plus minus} 0.23)% for the measured U-235 and (+0.50 {plus minus} 0.31)% for the measured Pu-239, for a core campaign. The ICT analyses has been implemented for the plutonium isotopics in a depleted uranium assembly in a heavy-water, enriched uranium system and for the uranium isotopes in the fuel assemblies in light-water, highly-enriched systems. 7 refs., 5 figs., 4 tabs

Workshop summary:Kaons@CERN 2023

Kaon physics is at a turning point – while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop, held in September 2023 at CERN

Workshop summary -- Kaons@CERN 2023

Kaon physics is at a turning point -- while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop.Comment: 54 pages, Summary of Kaons@CERN 23 workshop, references and clarifications adde

Auditory event-related potentials

Auditory event related potentials are electric potentials (AERP, AEP) and magnetic fields (AEF) generated by the synchronous activity of large neural populations in the brain, which are time-locked to some actual or expected sound event

Model-Based Verification: Guidelines for Generating Expected Properties

This report presents a basic set of guidelines to facilitate the generation of expected properties in the context of Model-Based Verification. Expected properties are natural language statements that express characteristics of the behavior of a system-characteristics that are consistent with user expectations. Through model checking, expected properties of a system, formally expressed as claims, are analyzed against the model. This analysis can detect inconsistencies between models of the system and their expected properties and identify potential system defects

Model-Based Verification: Analysis Guidelines

This technical note provides guidance for the analysis activity that occurs during the interpretation of results produced by model-checking tools. In the model-checking analysis activity, the main question is, "Does the system behave correctly?" To answer this question, a model and a set of expected properties are used as input to a model checker. The expected output is a confirmation or refutation of the specified expected properties. In most cases, if the model checker does not confirm the property, it provides a counterexample. Counterexamples are executions of the model showing the sequence of steps that refutes the expected property. Sometimes the state space to be explored in order to find this counterexample is so large that it cannot be completely covered. This is the state explosion problem. Models must be tuned to reduce the state space; this is a manual and intuitive task. Interpreting the model checker's output can also be difficult. The significance of the output must be assessed; its interpretation may suggest an error in the claims or the model, or a defect in the actual system. This document presents the problems related to interpreting results. It provides strategies to overcome state explosion, analyze results, and provide feedback to the system designers and developers