Evaluation Of Cross-sectional Data Research Articles

Application of Kalman filtering technique for evaluation of neutron cross section data of [formula omitted]Mo (n, 2n) [formula omitted]Mo reaction

In this paper, for the first time, evaluation of neutron cross section data of 100Mo (n, 2n) 99Mo reaction is performed using experimental data available in IAEA-EXFOR database library and nuclear model-based data generated using Talys 1.9 code by applying a novel method of combining Kalman filtering technique with Machine Learning (ML) regression algorithms. The neutron cross section data evaluation has been performed after a detailed study of all the EXFOR papers corresponding to 100Mo (n, 2n) 99Mo reaction and nuclear model based cross section generation by executing T6 random input files using Talys 1.9 code. The evaluated curve generated is then compared with the existing evaluated curve of 100Mo (n, 2n) 99Mo reaction from nuclear data libraries such as ENDF/B-VIII.0, JEFF-3.3, JENDL-4.0, CENDL-3.1 and TENDL 2017 and found to be in good agreement with them. Chi-square and generalized Chi-square tests were employed to assess the proposed evaluation techniques and found them to be good in estimating evaluated mean values and evaluated uncertainties of cross section.

Symptoms in first-degree relatives of patients with rheumatoid arthritis: evaluation of cross-sectional data from the symptoms in persons at risk of rheumatoid arthritis (SPARRA) questionnaire in the PRe-clinical EValuation of Novel Targets in RA (PREVeNT-RA) Cohort.

BackgroundFirst-degree relatives (FDRs) of people with rheumatoid arthritis (RA) have a fourfold increased risk of developing RA. The Symptoms in Persons At Risk of Rheumatoid Arthritis (SPARRA) questionnaire was developed to document symptoms in persons at risk of RA. The aims of this study were (1) to describe symptoms in a cohort of FDRs of patients with RA overall and stratified by seropositivity and elevated CRP and (2) to determine if patient characteristics were associated with symptoms suggestive of RA.MethodsA cross-sectional study of FDRs of patients with RA, in the PREVeNT-RA study, who completed a study questionnaire, provided a blood sample measured for rheumatoid factor, anti-CCP and CRP and completed the SPARRA questionnaire. Moderate/severe symptoms and symmetrical, small and large joint pain were identified and described. Symptoms associated with both seropositivity and elevated CRP were considered suggestive of RA. Logistic regression was used to determine if symptoms suggestive of RA were associated with patient characteristics.ResultsEight hundred seventy participants provided all data, 43 (5%) were seropositive and 122 (14%) had elevated CRP. The most frequently reported symptoms were sleep disturbances (20.3%) and joint pain (17.9%). Symmetrical and small joint pain were 11.3% and 12.8% higher, respectively, in those who were seropositive and 11.5% and 10.7% higher in those with elevated CRP. In the logistic regression model, seropositivity, older age and feeling depressed were associated with increased odds of small and symmetrical joint pain.ConclusionsThis is the first time the SPARRA questionnaire has been applied in FDRs of patients with RA and has demonstrated that the presence of symmetrical and small joint pain in this group may be useful in identifying people at higher risk of developing RA.

Monte Carlo simulation of γ and fission transfer-induced probabilities using extended 𝓡-matrix theory: Application to the 237U∗ system

This paper deals with simultaneous neutron-induced average partial cross sections and surrogate-like probability simulations over several excitation and de-excitation channels of the compound nucleus. Present calculations, based on one-dimensional fission barrier extended 𝓡-matrix theory using Monte Carlo samplings of both first and second well resonance parameters, avoid the surrogate-reaction method historically taken for surrogate data analyses that proved to be very poor in terms of extrapolated neutron-induced capture cross sections. Present theoretical approach is portrayed and subsequent results can be compared for the first time with experimental γ-decay probabilities; thanks to brand new simultaneous 238 U(3 He,4 Heγ) and 238 U(3 He,4 He f) surrogate measurements. Future integration of our strategy in standard neutron cross section data evaluation remains tied to the developments made in terms of direct reaction population probability calculations.

A New Formulation of the Unified Monte Carlo Approach (UMC-B) and Cross-Section Evaluation for the Dosimetry Reaction 55Mn(n,γ)56Mn

Abstract Two relatively new approaches to neutron cross section data evaluation are described. They are known collectively as Unified Monte Carlo (versions UMC-G and UMC-B). Comparisons are made between these two methods, as well as with the well-known generalized least-squares (GLSQ) technique, through the use of simple, hypothetical (toy) examples. These new Monte Carlo methods are based on stochastic sampling of probability functions that are constructed with the use of theoretical and experimental data by applying the principle of maximum entropy. No further assumptions are involved in either UMC-G or UMC-B. However, the GLSQ procedure requires the linearization of non-linear terms, such as those that occur when cross section ratio data are included in an evaluation. It is shown that these two stochastic techniques yield results that agree well with each other, and with the GLSQ method, when linear data are involved, or when the perturbations due to data discrepancies and nonlinearity effects are small. Otherwise, there can be noticeable differences. The present investigation also demonstrates, as observed in earlier work, that the least-squares approach breaks down when these conditions are not satisfied. This paper also presents an actual evaluation of the 55Mn(n,γ)56Mn neutron dosimetry reaction cross section in the energy range from 100 keV to 20 MeV, which was performed using both GLSQ and UMC-G approaches.

An Investigation of the Performance of the Unified Monte Carlo Method of Neutron Cross Section Data Evaluation

The Unified Monte Carlo method (UMC) has been suggested to avoid certain limitations and approximations inherent to the well-known Generalized Least Squares (GLS) method of nuclear data evaluation. This contribution reports on an investigation of the performance of the UMC method in comparison with the GLS method. This is accomplished by applying both methods to simple examples with few input values that were selected to explore various features of the evaluation process that impact upon the quality of an evaluation. Among the issues explored are: i) convergence of UMC results with the number of Monte Carlo histories and the ranges of sampled values; ii) a comparison of Monte Carlo sampling using the Metropolis scheme and a brute force approach; iii) the effects of large data discrepancies; iv) the effects of large data uncertainties; v) the effects of strong or weak model or experimental data correlations; and vi) the impact of ratio data and integral data. Comparisons are also made of the evaluated results for these examples when the input values are first transformed to comparable logarithmic values prior to performing the evaluation. Some general conclusions that are applicable to more realistic evaluation exercises are offered.

Serum carotenoids and cerebral white matter lesions: the Rotterdam scan study.

To study the relation between serum levels of carotenoids and white matter lesions (WMLs) on magnetic resonance imaging (MRI). Evaluation of cross-sectional data from a cohort study. The Rotterdam Scan Study. Two hundred and three nondemented older persons, age 60 to 90, from the Rotterdam Scan Study. Serum levels of carotenoids were determined. WMLs on MRIs were rated separately into periventricular and subcortical WMLs. Odds ratios (ORs) for the presence of severe WMLs (upper decile) were calculated per standard deviation (SD) increase in serum carotenoid level and per SD increase in overall carotenoid serum level. Effect modification by smoking status was studied through stratified analyses. Increasing levels of all the separate carotenoids were associated with less severe periventricular WMLs, which reached statistical significance for the overall carotenoid serum level (OR 0.4 per SD; 95% confidence interval (CI) = 0.2-0.9). We found no association between carotenoid levels and the presence of severe subcortical WMLs (OR 1.2 per SD; 95% CI = 0.7-2.0). The association of carotenoid levels with severe periventricular WMLs was more marked in those who ever smoked (OR 0.1 per SD; 95% CI = 0.0-0.9) than in those who had never smoked (OR 0.9 per SD; 95% CI = 0.4-2.1). These findings are compatible with the view that high levels of carotenoids may protect against WMLs in the periventricular region, in particular in smokers. Longitudinal studies with repeated measurements of both carotenoids and WMLs are necessary to explore this hypothesis further.