Feasibility of Neutron Coincidence Counting for Spent Fuel

High-temperature gas reactors rely on TRIstructural-ISOtropic (TRISO) fuel for enhanced fission product retention. Accurate fuel characterization would improve monitoring of efficient fuel usage and accountability. We developed a new neutron multiplicity counter (NMC) based on boron coated straw (BCS) detectors and used it in coincidence mode for 235U assay in TRISO fuel. In this work, we demonstrate that a high-efficiency version of the NMC encompassing 396 straws is able to estimate the 235U in used TRISO-fueled pebbles or compacts with a relative uncertainty below 2.5% in 100 s. We performed neutronics and fuel depletion calculation of the HTR-10 pebble bed reactor to estimate the neutron and gamma-ray source strengths of used TRISO-fueled pebbles with burnup between 9 and 90 GWd/t. Then, we measured a gamma-ray intrinsic efficiency of 10^-12 at an exposure rate of 340.87 R/h. The low gamma-ray sensitivity and high neutron detection efficiency enable the inspection of used fuel.Comment: 25 pages, 19 figure

Development of gamma-ray and neutron radiation shielding using geopolymer-particulate composites

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Purdue Conference on Active Nonproliferation

One major problem with nuclear security measurements involves source identification inthe presence of low signal-to-background ratio. This scenario iscommon to several applications, ranging from radiation identification atportal monitors to radiation source search with unmanned vehicles. In this context of identification of a large variety of sources, including natural and medical sources, sensitive sources of particular interest, but also potentially new/unknown sources for which no reference measurement is available, statistical methods are particularly appealing for their ability to capture the random nature of the measurements. Among them, Bayesian methods form a generic framework allowing for uncertainty quantification and propagation, which is of prime interest for detection (of known and unknown sources), classification, and quantification of smuggled nuclear and radiological materials. We demonstratethe use of Bayesian models for the identificationof mixed gamma sources, measured with organic scintillatorswithinshort acquisition times. We alsocompare the estimation performance using two different materials: liquid EJ-309 and stilbene crystal

A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators

We propose a hierarchical Bayesian model and state-of-art Monte Carlo sampling method to solve the unfolding problem, i.e., to estimate the spectrum of an unknown neutron source from the data detected by an organic scintillator. Inferring neutron spectra is important for several applications, including nonproliferation and nuclear security, as it allows the discrimination of fission sources in special nuclear material (SNM) from other types of neutron sources based on the differences of the emitted neutron spectra. Organic scintillators interact with neutrons mostly via elastic scattering on hydrogen nuclei and therefore partially retain neutron energy information. Consequently, the neutron spectrum can be derived through deconvolution of the measured light output spectrum and the response functions of the scintillator to monoenergetic neutrons. The proposed approach is compared to three existing methods using simulated data to enable controlled benchmarks. We consider three sets of detector responses. One set corresponds to a 2.5 MeV monoenergetic neutron source and two sets are associated with (energy-wise) continuous neutron sources ($^{252}$Cf and $^{241}$AmBe). Our results show that the proposed method has similar or better unfolding performance compared to other iterative or Tikhonov regularization-based approaches in terms of accuracy and robustness against limited detection events, while requiring less user supervision. The proposed method also provides a posteriori confidence measures, which offers additional information regarding the uncertainty of the measurements and the extracted information.Comment: 10 page

Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography

In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods

The Microbiome: A New Target for Research and Treatment of Schizophrenia and its Resistant Presentations? A Systematic Literature Search and Review

Background: The gastrointestinal system hosts roughly 1,800 distinct phyla and about 40,000 bacterial classes, which are known as microbiota, and which are able to influence the brain. For instance, microbiota can also influence the immune response through the activation of the immune system or through the release of mediators that are able to cross the brain blood barrier or that can interact with other substances that have free access to the brain, such as tryptophan and kynurenic acid, which is a metabolite of tryptophan and which has been involved in the pathogenesis of schizophrenia. Objectives: This paper reviews the possible relationships between microbiome, schizophrenia and treatment resistance. Given the possibility of a role of immune activation and alterations, we also describe the relationship between schizophrenia and immune inflammatory response. Finally, we report on the studies about the use of probiotic and prebiotics in schizophrenia. Methods: Cochrane library and PubMed were searched from the year 2000 to 2018 for publications about microbiome, immune-mediated pathology, schizophrenia and neurodevelopmental disorders. The following search string was used: (microbiome or immune mediated) AND (schizophrenia OR neurodevelopmental disorder). Associated publications were hand-searched from the list of references of the identified papers. A narrative review was also conducted about the use of probiotics and prebiotics in schizophrenia. Results: There exists a close relationship between the central nervous system and the gastrointestinal tract, which makes it likely that there is a relationship between schizophrenia, including its resistant forms, and microbiota. This paper provides a summary of the most important studies that we identified on the topic. Conclusions: Schizophrenia in particular, remain a challenge for researchers and practitioners and the possibility of a role of the microbiome and of immune-mediated pathology should be better explored, not only in animal models but also in clinical trials of agents that are able to alter gut microbiota and possibly influence the mechanisms of gastrointestinal inflammation. Microbiome targeted treatments have not been well-studied yet in patients with mental illness in general, and with schizophrenia in particular. Nonetheless, the field is well worth of being appropriately investigated. Copyright © 2018 Cuomo, Maina, Rosso, Beccarini Crescenzi, Bolognesi, Di Muro, Giordano, Goracci, Neal, Nitti, Pieraccini and Fagiolini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

Characteristics and patterns of care of endometrial cancer before and during COVID-19 pandemic

Objective: Coronavirus disease 2019 (COVID-19) outbreak has correlated with the disruption of screening activities and diagnostic assessments. Endometrial cancer (EC) is one of the most common gynecological malignancies and it is often detected at an early stage, because it frequently produces symptoms. Here, we aim to investigate the impact of COVID-19 outbreak on patterns of presentation and treatment of EC patients. Methods: This is a retrospective study involving 54 centers in Italy. We evaluated patterns of presentation and treatment of EC patients before (period 1: March 1, 2019 to February 29, 2020) and during (period 2: April 1, 2020 to March 31, 2021) the COVID-19 outbreak. Results: Medical records of 5,164 EC patients have been retrieved: 2,718 and 2,446 women treated in period 1 and period 2, respectively. Surgery was the mainstay of treatment in both periods (p=0.356). Nodal assessment was omitted in 689 (27.3%) and 484 (21.2%) patients treated in period 1 and 2, respectively (p<0.001). While, the prevalence of patients undergoing sentinel node mapping (with or without backup lymphadenectomy) has increased during the COVID-19 pandemic (46.7% in period 1 vs. 52.8% in period 2; p<0.001). Overall, 1,280 (50.4%) and 1,021 (44.7%) patients had no adjuvant therapy in period 1 and 2, respectively (p<0.001). Adjuvant therapy use has increased during COVID-19 pandemic (p<0.001). Conclusion: Our data suggest that the COVID-19 pandemic had a significant impact on the characteristics and patterns of care of EC patients. These findings highlight the need to implement healthcare services during the pandemic