NSNO: Neumann Series Neural Operator for Solving Helmholtz Equations in Inhomogeneous Medium

In this paper, we propose Neumann Series Neural Operator (NSNO) to learn the solution operator of Helmholtz equation from inhomogeneity coefficients and source terms to solutions. Helmholtz equation is a crucial partial differential equation (PDE) with applications in various scientific and engineering fields. However, efficient solver of Helmholtz equation is still a big challenge especially in the case of high wavenumber. Recently, deep learning has shown great potential in solving PDEs especially in learning solution operators. Inspired by Neumann series in Helmholtz equation, we design a novel network architecture in which U-Net is embedded inside to capture the multiscale feature. Extensive experiments show that the proposed NSNO significantly outperforms the state-of-the-art FNO with at least 60\% lower relative $L^2$-error, especially in the large wavenumber case, and has 50\% lower computational cost and less data requirement. Moreover, NSNO can be used as the surrogate model in inverse scattering problems. Numerical tests show that NSNO is able to give comparable results with traditional finite difference forward solver while the computational cost is reduced tremendously

Study of the 15N(p,n)15O reaction as a monoenergetic neutron source for the measurement of differential scattering cross sections

The 15N(p,n) reaction is a promising candidate for the production of monoenergetic neutrons with energies of up to 5.7 MeV at the facilities where the T(p,n)3He reaction cannot be used. The characteristic properties of this reaction were studied focusing on its suitability as a source of monoenergetic neutrons for the measurement of differential scattering cross sections in the neutron energy range of 2 MeV to 5 MeV. For this purpose differential and integral cross sections were measured and the choice of optimum target conditions was investigated. The reaction has already been used successfully to measure of elastic and inelastic neutron scattering cross sections for natPb in the energy range from 2 MeV to 4 MeV and for 209Bi and 181Ta at 4 MeV

Trap characterization in composite of solid-liquid using dual-level trap model and TSDC method

Charge trap is considered to be one of the effective characteristic parameters for qualitatively evaluating the aging status of insulating material. In this paper, the trap characteristics in oil-impregnated paper with different aging types (non-treatment, thermal treatment and electrical treatment) are investigated using a dual-level (shallow and deep energy) trap model based on space charge profiles and thermally stimulated depolarization current (TSDC) data. The simulated results based on the model are well consistent with the experimental results. Onthe other hand, the TSDC method can acquire much information related to the shallower traps, and the dual–level trap model can obtain much charge dynamicscharacteristics. It has been observed that thermally aging makes the shallow trap energy become deeper while electrically aging makes it shallower. Moreover, thetrap density in oil-impregnated paper increases after aging regardless of thermal or electrical aging

Revisit the calibration errors on experimental slant total electron content (TEC) determined with GPS

This is a pre-print of an article published in GPS Solutions. The final authenticated version is available online at: https://doi.org/10.1007/s10291-018-0753-7. The study is funded by National Key Research and Development Program of China (2016YFB0501902), National Natural Science Foundation of China (41574025, 41574013, 41731069), Spanish Ministry of Science and Innovation project (CGL2015-66410-P), The Hong Kong RGC Joint Research Scheme (E-PolyU501/16) and State Key Laboratory of Geo-Information Engineering (SKLGIE2015-M-2-2).The calibration errors on experimental slant total electron content (TEC) determined with global positioning system (GPS) observations is revisited. Instead of the analysis of the calibration errors on the carrier phase leveled to code ionospheric observable, we focus on the accuracy analysis of the undifferenced ambiguity-fixed carrier phase ionospheric observable determined from a global distribution of permanent receivers. The results achieved are: (1) using data from an entire month within the last solar cycle maximum, the undifferenced ambiguity-fixed carrier phase ionospheric observable is found to be over one order of magnitude more accurate than the carrier phase leveled to code ionospheric observable and the raw code ionospheric observable. The observation error of the undifferenced ambiguity-fixed carrier phase ionospheric observable ranges from 0.05 to 0.11 total electron content unit (TECU) while that of the carrier phase leveled to code and the raw code ionospheric observable is from 0.65 to 1.65 and 3.14 to 7.48 TECU, respectively. (2) The time-varying receiver differential code bias (DCB), which presents clear day boundary discontinuity and intra-day variability pattern, contributes the most part of the observation error. This contribution is assessed by the short-term stability of the between-receiver DCB, which ranges from 0.06 to 0.17 TECU in a single day. (3) The remaining part of the observation errors presents a sidereal time cycle pattern, indicating the effects of the multipath. Further, the magnitude of the remaining part implies that the code multipath effects are much reduced. (4) The intra-day variation of the between-receiver DCB of the collocated stations suggests that estimating DCBs as a daily constant can have a mis-modeling error of at least several tenths of 1 TECU.Peer ReviewedPostprint (author's final draft

Updated and revised neutron reaction data for 236,238

Nuclear data with high accuracy for minor actinides play an important role in nuclear technology applications, including reactor design and operation, fuel cycle, estimation of the amount of minor actinides in high burn-up reactors and the minor actinides transmutation. Based on the evaluated experimental data, the updated and revised evaluation of a full set of n+237Np nuclear data from 10−5 eV ∼ 20 MeV are carried out and recommended. Mainly revised quantities are neutron multiplicities from fission reaction, inelastic, fission, (n, 2n) and (n, γ) reaction cross sections as well as angular distribution and so on. The promising results are obtained when the renewal evaluated data of 237Np will be used to instead of the evaluated data in CENDL-3.1 database

Theoretical calculations and analysis for n + 6

R-matrix theory is an important methodology for applications on light, medium and heavy mass nuclides nuclear reaction in the resonance energy range. Full R-matrix formalism contains the diagonal elements of the energy levels matrix and it is a rigorous theory. Because of different assumptions and approximations, many kinds of R-matrix derived methods are obtained. The new R-matrix code FDRR is presented and includes 4 kinds of R-matrix applications. It can be used for calculating integral cross sections and angular distributions of 2-bodies reactions. The cross sections and angular distributions of n+ 6Li reaction are calculated and analyzed by FDRR code. The results are in good agreement with experimental data below 20 MeV

The evaluation of experimental data in fast range for n + 56

Iron is one of the five materials selected for evaluation within the pilot international evaluation project CIELO. Analysis of experimental data for n+56Fe reaction is the basis for constraining theoretical calculations and eventual creation of the evaluated file. The detail analysis was performed for inelastic cross sections of neutron induced reactions with 56Fe in the fast range up to 20 MeV where there are significant differences among the main evaluated libraries, mainly caused by the different inelastic scattering cross section measurements. Gamma-ray production cross sections provide a way to gain experimental information about the inelastic cross section. Large discrepancies between experimental data for the 847-keV gamma ray produced in the 56Fe(n,n1'γ) reaction were analyzed. In addition, experimental data for elastic scattering cross section between 9.41∼11 MeV were used to deduce the inelastic cross section from the unitarity constrain

A reference human induced pluripotent stem cell line for large-scale collaborative studies.

Human induced pluripotent stem cell (iPSC) lines are a powerful tool for studying development and disease, but the considerable phenotypic variation between lines makes it challenging to replicate key findings and integrate data across research groups. To address this issue, we sub-cloned candidate human iPSC lines and deeply characterized their genetic properties using whole genome sequencing, their genomic stability upon CRISPR-Cas9-based gene editing, and their phenotypic properties including differentiation to commonly used cell types. These studies identified KOLF2.1J as an all-around well-performing iPSC line. We then shared KOLF2.1J with groups around the world who tested its performance in head-to-head comparisons with their own preferred iPSC lines across a diverse range of differentiation protocols and functional assays. On the strength of these findings, we have made KOLF2.1J and its gene-edited derivative clones readily accessible to promote the standardization required for large-scale collaborative science in the stem cell field

Heterogeneous investment opportunities in multiple-segment firms and the incremental value relevance of segment accounting data

Applying an option-based valuation approach, we develop and test a model that addresses the incremental value relevance of segment data beyond firm-level accounting data. Prior studies (e.g., Zhang 2000; Biddle et al. 2001) show that firm value relates to accounting data (in part) because accounting data are useful for guiding capital investment decisions that underlie value creation. In this study, we establish that the incremental value relevance of segment data relates to heterogeneity of investment decisions across segments, as informed by segmental profitability and investment opportunities. Equity value is predicted to depend not only on aggregate firm-level accounting data but also on the extent to which segments differ in profitability and investment opportunities. Our empirical tests confirm the predicted incremental effect of segment data