Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

Comparing the excessive daytime sleepiness of obese and non-obese patients

Background: Obesity, particularly morbid obesity, has various physical and mental complications. Excessive daytime somnolence (EDS) is a sleep disorder that reduces individuals� performance capability and the accuracy of their short-term memory and causes learning problems. This retrospective study aimed to document the presence of EDS in a sample of obese patients in comparison to patients with a normal weight. Objectives: This article compares the excessive daytime sleepiness of obese and non-obese patients in the minimally invasive surgery research center in Tehran, Iran. Patients and Methods: In this case-control study, we compared excessive daytime sleepiness in 55 obese patients who were candidates for laparoscopic surgery, with a body mass index (BMI) of equal to or greater than 30 kg/m2, with 55 controls with a normal BMI (19.5-24.9 kg/m2). The process of selecting the control group in our case-control study is matching in group levels, so that the controls are similar to the case group with regard to certain key characteristics, such as age, sex, and race. The sleep assessment was based on the Epworth sleepiness scale (ESS) questionnaire. Analysis of variance (ANOVA) was used to compare the means of quantitative data, such as the ESS score of groups. Results: Sleepiness was not affected by gender in cases or controls. The sleepiness prevalence was 29 (52.7) in the cases group and 17 (30.9) in the control group (OR = 2.493 (95 CI 1.144-5.435)). The mean ESS scores in cases and controls were 7.82 ± 3.86 and 10.54±6.15, respectively (P = 0.007). Moreover, the prevalence of sleepiness and the mean ESS scores in class III of obesity differed significantly from the controls (16 (57.1) vs. 17 (30.9)) (OR = 2.980 (95 CI 1.162-7.645)) and (11.04±5.93 vs. 7.82±3.86) (P = 0.013), respectively. Conclusions: Our findings suggest a strong relationship between EDS and obesity, particularly morbid obesity. Therefore, physicians must be familiar with EDS as a mixed clinical entity indicating careful assessment and specific treatment planning. © 2016, Iranian Red Crescent Medical Journal

Radial evolution of the April 2020 stealth coronal mass ejection between 0.8 and 1 AU - Comparison of Forbush decreases at Solar Orbiter and near the Earth

Aims. We present observations of the first coronal mass ejection (CME) observed at the Solar Orbiter spacecraft on April 19, 2020, and the associated Forbush decrease (FD) measured by its High Energy Telescope (HET). This CME is a multispacecraft event also seen near Earth the next day. Methods. We highlight the capabilities of HET for observing small short-term variations of the galactic cosmic ray count rate using its single detector counters. The analytical ForbMod model is applied to the FD measurements to reproduce the Forbush decrease at both locations. Input parameters for the model are derived from both in situ and remote-sensing observations of the CME. Results. The very slow (~350 km/s) stealth CME caused a FD with an amplitude of 3 % in the low-energy cosmic ray measurements at HET and 2 % in a comparable channel of the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) on the Lunar Reconnaissance Orbiter, as well as a 1 % decrease in neutron monitor measurements. Significant differences are observed in the expansion behavior of the CME at different locations, which may be related to influence of the following high speed solar wind stream. Under certain assumptions, ForbMod is able to reproduce the observed FDs in low-energy cosmic ray measurements from HET as well as CRaTER, but with the same input parameters, the results do not agree with the FD amplitudes at higher energies measured by neutron monitors on Earth. We study these discrepancies and provide possible explanations. Conclusions. This study highlights that the novel measurements of the Solar Orbiter can be coordinated with other spacecraft to improve our understanding of space weather in the inner heliosphere. Multi-spacecraft observations combined with data-based modeling are also essential to understand the propagation and evolution of CMEs as well as their space weather impacts

First year of energetic particle measurements in the inner heliosphere with Solar Orbiter's Energetic Particle Detector

Context. Solar Orbiter strives to unveil how the Sun controls and shapes the heliosphere and fills it with energetic particle radiation. To this end, its Energetic Particle Detector (EPD) has now been in operation, providing excellent data, for just over a year. Aims. EPD measures suprathermal and energetic particles in the energy range from a few keV up to (near-) relativistic energies (few MeV for electrons and about 500 MeV nuc−1 for ions). We present an overview of the initial results from the first year of operations and we provide a first assessment of issues and limitations. In addition, we present areas where EPD excels and provides opportunities for significant scientific progress in understanding how our Sun shapes the heliosphere. Methods. We used the solar particle events observed by Solar Orbiter on 21 July and between 10 and 11 December 2020 to discuss the capabilities, along with updates and open issues related to EPD on Solar Orbiter. We also give some words of caution and caveats related to the use of EPD-derived data. Results. During this first year of operations of the Solar Orbiter mission, EPD has recorded several particle events at distances between 0.5 and 1 au from the Sun. We present dynamic and time-averaged energy spectra for ions that were measured with a combination of all four EPD sensors, namely: the SupraThermal Electron and Proton sensor (STEP), the Electron Proton Telescope (EPT), the Suprathermal Ion Spectrograph (SIS), and the High-Energy Telescope (HET) as well as the associated energy spectra for electrons measured with STEP and EPT. We illustrate the capabilities of the EPD suite using the 10 and 11 December 2020 solar particle event. This event showed an enrichment of heavy ions as well as 3He, for which we also present dynamic spectra measured with SIS. The high anisotropy of electrons at the onset of the event and its temporal evolution is also shown using data from these sensors. We discuss the ongoing in-flight calibration and a few open instrumental issues using data from the 21 July and the 10 and 11 December 2020 events and give guidelines and examples for the usage of the EPD data. We explain how spacecraft operations may affect EPD data and we present a list of such time periods in the appendix. A list of the most significant particle enhancements as observed by EPT during this first year is also provided.Ministerio de Economía y CompetitividadAgencia Estatal de Investigació

The quijote simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 2563, 5123, or 10243 particles in a box of 1 h -1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions

Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization

Radio Frequency Interference (RFI) is one of the systematic challenges preventing 21cm interferometric instruments from detecting the Epoch of Reionization. To mitigate the effects of RFI on data analysis pipelines, numerous inpaint techniques have been developed to restore RFI corrupted data. We examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that capable of inpainting RFI corrupted data in interferometric instruments. We train our network on simulated data and show that our network is capable at inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modeling are best suited for inpainting over narrowband RFI. We also show that with our fiducial parameters Discrete Prolate Spheroidal Sequences (DPSS) and CLEAN provide the best performance for intermittent ``narrowband'' RFI while Gaussian Progress Regression (GPR) and Least Squares Spectral Analysis (LSSA) provide the best performance for larger RFI gaps. However we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We find these results to be consistent in both simulated and real visibilities. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that in the future, as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities of HERA data.Comment: 26 pages, 18 figure