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The immiscibility of hydrogen-helium mixture under the temperature and pressure conditions of planetary interiors is crucial for understanding the structures of gas giant planets (e.g., Jupiter and Saturn). While the experimental probe at such extreme conditions is challenging, theoretical simulation is heavily relied in an effort to unravel the mixing behavior of hydrogen and helium. Here we develop a method via a machine learning accelerated molecular dynamics simulation to quantify the physical separation of hydrogen and helium under the conditions of planetary interiors. The immiscibility line achieved with the developed method yields substantially higher demixing temperatures at pressure above 1.5 Mbar than earlier theoretical data, but matches better to the experimental estimate. Our results suggest a possibility that H-He demixing takes place in a large fraction of the interior radii of Jupiter and Saturn, i.e., 27.5% in Jupiter and 48.3% in Saturn. This indication of an H-He immiscible layer hints at the formation of helium rain and offers a potential explanation for the decrease of helium in the atmospheres of Jupiter and Saturn. The immiscibility of H-He mixtures in gas giants remains poorly understood. Here the authors use large scale machine learning accelerated molecular dynamics simulations to suggest that H-He demixing may occur within large interior radii of Jupiter and Saturn. [ABSTRACT FROM AUTHOR]

With the push towards sustainable alloy production, using recycled material in casting Al alloys has become essential. However, high recycle content (HRC) aluminum alloys typically have a high iron content, leading to the formation of Fe-bearing intermetallic particles (Fe-IMCs) that affect the mechanical performance and formability of the alloy. Historically, 2D microscopy-based characterization techniques have been used to assess the size and morphology of these Fe-IMCs. While widely used, these 2D techniques are often incapable of capturing the complex 3D interconnected morphologies of the Fe-IMCs. In this work, we present a methodology for the high-throughput compositional and 3D morphological characterization of Fe-IMCs in a primary (AA 5182) and a high recycle content (HRC alloy) in the as-cast and homogenized states, using a combination of 3D X-ray Computed Tomography (XCT) and energy-dispersive X-ray spectroscopy (EDS). To capture the differences in morphology of the Fe-IMCs in the commercial and HRC alloys, we introduce a new 3D morphological descriptor—the particle-to-convex hull volume ratio (p/h). Finally, the effect of homogenization on the Fe-IMCs morphology was tracked using p/h, and a comprehensive analysis of the Fe-IMCs' compositional and morphological evolution was presented. [ABSTRACT FROM AUTHOR]

The coupling of excited states and ionic dynamics is the basic and challenging point for the materials response at extreme conditions. In the laboratory, the intense laser produces transient nature and complexity with highly nonequilibrium states, making it extremely difficult and interesting for both experimental measurements and theoretical methods. With the inclusion of laser-excited states, we extend an ab initio method into the direct simulations of whole laser-driven microscopic dynamics from solid to liquid. We construct the framework of combining the electron-temperature-dependent deep neural-network potential energy surface with a hybrid atomistic-continuum approach, controlling non-adiabatic energy exchange and atomistic dynamics, which enables consistent interpretation of experimental data. By large-scale ab initio simulations, we demonstrate that the nonthermal effects introduced by hot electrons play a dominant role in modulating the lattice dynamics, thermodynamic pathway, and structural transformation. We highlight that the present work provides a path to realistic computational studies of laser-driven processes, thus bridging the gap between experiments and simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a user scheduling algorithm for massive multiple-input multiple-output (MIMO) systems over more general correlated Rician fading channels. To achieve low latency and high throughput, a new user scheduling algorithm based on deep learning (DL) is proposed, which exploits only statistical channel state information. The proposed scheduling network is trained to grasp the mapping from the statistical signal and interference pattern to the user scheduling decision through supervised learning. It can predict the optimal scheduling scheme from statistical CSI without iterative calculation after offline training. Simulation results demonstrate the superior performance of the proposed algorithm in terms of calculation time, and it achieves almost the same throughput as the optimal scheduling algorithm which is obtained through exhaustive search. Furthermore, with the normalization of the input data, the proposed scheduling network is robust to the change of the channel environment and the number of transmit antennas. [ABSTRACT FROM AUTHOR]

In this study, we aimed to investigate the feasibility of using Materialise's interactive medical image control system (MIMICS) to measure urinary calculi volume. We used a cylinder measuring to measure the same polymer clay volume in different groups. Polymer clay was made into an oval shape, an antler type, and a multiple irregular shapes by hand. They are divided into three groups, that is, A, B, and C, each of which has seven polymer clays. The computer tomography (CT) 3D images of each sample were obtained by 256iCT scanning. The CT 3D image was imported into MIMICS to measure the theoretical volume and average CT value of polymer clay. The differences between the volume and CT values measured by MIMICS and 256iCT were evaluated. The volume of each polymer clay that was measured by a measuring cylinder was 34.7 ml. The average CT values of groups A, B, and C measured by 256iCT were 1121.3 ± 35.8, 1071.3 ± 22.2, and 1083.9 ± 6.3 Hu, respectively. The theoretical volume and CT values of the ceramics measured by MIMICS were as follows: the averaged volume of group A was 35.1 ± 0.4 ml, and the average CT value was 1065.7 ± 5.3 Hu. The average volume of group B was 34.5 ± 0.2 ml, and the average CT value was 1008.9 ± 7.7 Hu. The average volume of group C was 34.4 ± 0.5 ml, and the average CT value was 980.9 ± 6.1 Hu. MIMICS was reliable in measuring urinary stone volume. The difference between the CT values measured by MIMICS and 256iCT was statistically significant. MIMICS had a slightly lower CT value than that of 256iCT. However, from the data point of view, the difference between the two methods was small. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is a worldwide disease that affects a large number of males. Although prostate-specific antigen (PSA) screening is used, the specificity is limited. This study analyzes the sensitivity and specificity of adenomatous polyposis coli (APC) methylation for PCa detection in body fluids and tissues. Combining search results from PubMed and Embase, 19 studies were included, 5 involving body fluids and 14 involving prostate tissue, with 2344 subjects. In body fluid subgroups, the pooled sensitivity and specificity was 0.53 (95% confidence interval (CI): 0.28-0.78) and 0.92 (95% CI: 0.86-0.95), respectively. From tissue studies, the results presented as 0.84 (95% CI: 0.70-0.92) and 0.91 (95% CI: 0.77-0.97). To confirm the results, we conducted a further analysis by removing studies which introduced high heterogeneity due to the type of cases and controls. The same degree of sensitivity and specificity was presented in two subgroups (urine: sensitivity 0.46, 95% CI: 0.39-0.53; specificity 0.87, 95% CI: 0.64-0.96; tissue: sensitivity 0.87, 95% CI: 0.72-0.94; specificity 0.89, 95% CI: 0.68-0.97). In addition, analysis of the interaction between APC methylation and PCa showed strong association in the whole data set (odds ratio (OR)=24.91, 95% CI: 12.86-48.24, I2=72.5%). Pooling the same two main subgroups (tissue/fluid) gave a pooled OR of 33.54 (95% CI: 14.88-75.59; I2=70.7%) and 8.20 (95% CI: 2.84-23.74, I2=64.2%), respectively. From this study, the results suggest that APC promoter methylation may be the potential testing for PCa diagnosis and provide a new viewpoint in the treatment of PCa. [ABSTRACT FROM AUTHOR]

An invertible linear map φ on a Lie algebra L is called a triple automorphism of it if φ([ x, [ y, z]]) = [ φ( x), [ φ( y), φ( z)]] for ∀ x, y, z ∈ L. Let g be a finite-dimensional simple Lie algebra of rank l defined over an algebraically closed field F of characteristic zero, p an arbitrary parabolic subalgebra of g. It is shown in this paper that an invertible linear map φ on p is a triple automorphism if and only if either φ itself is an automorphism of p or it is the composition of an automorphism of p and an extremal map of order 2. [ABSTRACT FROM AUTHOR]

A new scaffold material composed of extracellular matrix (ECM) and thermal sensitive hydrogel (HG), and evaluated its biocompatibility were investigated. We cultured bladder smooth muscle cells with this compound material, and then observed with phase contrast microscopy and scanning electron microscope (SEM) to assess the cell growth and morphology. The cell adhesion and proliferation were detected with MTT assay and cell count. Results show the ECM/HG compounds appeared as a net-like and red-stained construction with enough meshes and without any cellular fragments. 6 h after implantation, cells were observed adhere on the compounds and extend spurious along the fibers 12 h later. Under SEM even some ECM was observed to be secreted. MTT assay shows there was obvious statistic difference among 3 groups ( P<0.05). ECM/HG compound materials show a good biocompatibility, which confirms that it would be an ideal tissue engineering scaffolds. [ABSTRACT FROM AUTHOR]