Novel ensemble algorithms for random two-domain parabolic problems

In this paper, three efficient ensemble algorithms are proposed for fast-solving the random fluid-fluid interaction model. Such a model can be simplified as coupling two heat equations with random diffusion coefficients and a friction parameter due to its complexity and uncertainty. We utilize the Monte Carlo method for the coupled model with random inputs to derive some deterministic fluid-fluid numerical models and use the ensemble idea to realize the fast computation of multiple problems. Our remarkable feature of these algorithms is employing the same coefficient matrix for multiple linear systems, significantly reducing the computational cost. By data-passing partitioned techniques, we can decouple the numerical models into two smaller sub-domain problems and achieve parallel computation. Theoretically, we derive that both algorithms are unconditionally stable and convergent. Finally, numerical experiments are conducted not only to support the theoretical results but also to validate the exclusive feature of the proposed algorithms

Expression levels of apoptotic factors in a rat model of corticosteroid-induced femoral head necrosis

Purpose: To study the expression levels of apoptotic factors in corticosteroid-mediated femoral head necrosis (FHN) in rats. Methods: Sprague-Dawley (SD) rats (n = 60) bred adaptively for one week were randomly assigned to control and model groups (30 rats/group). A rat model of corticosteroid-induced femoral head necrosis was established. Then, 3 mL of blood drawn from the inferior vena cava of each rat was used for the assay of the expression levels of osteoprotegerin (OPG) and osteoclast differentiation factor (RANKL) in each group using enzyme-linked immunosorbent assay (ELISA). The caspase-3- and Bcl-2-+ve cells in each group were determined with immunohistochemical method. Results: Relative to control, serum OPG level of model group was significantly decreased, while the RANKL level was markedly raised (p < 0.05). The degree of empty lacunae in the model rats was markedly increased, relative to control. Caspase-3-+ve cells were more numerous in the model group than in control, while Bcl-2-positive cells were markedly decreased compared to control (p < 0.05). Conclusion: Apoptosis occurs in the rat model of femoral head necrosis. Glucocorticoids may regulate the apoptotic process by  upregulating caspase-3 and inhibiting Bcl-2. This provides a novel lead for FHN therapy. Keywords: Femoral head necrosis, Corticosteroid, Glucocorticoid, Apoptosi

Learning to infer inner-body under clothing from monocular video

Accurately estimating the human inner-body under clothing is very important for body measurement, virtual try-on and VR/AR applications. In this paper, we propose the first method to allow everyone to easily reconstruct their own 3D inner-body under daily clothing from a self-captured video with the mean reconstruction error of 0.73 cm within 15 s. This avoids privacy concerns arising from nudity or minimal clothing. Specifically, we propose a novel two-stage framework with a Semantic-guided Undressing Network (SUNet) and an Intra-Inter Transformer Network (IITNet). SUNet learns semantically related body features to alleviate the complexity and uncertainty of directly estimating 3D inner-bodies under clothing. IITNet reconstructs the 3D inner-body model by making full use of intra-frame and inter-frame information, which addresses the misalignment of inconsistent poses in different frames. Experimental results on both public datasets and our collected dataset demonstrate the effectiveness of the proposed method. The code and dataset is available for research purposes at http://cic.tju.edu.cn/faculty/likun/projects/Inner-Body

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

Ensemble Domain Decomposition Algorithm for the Fully-mixed Random Stokes-Darcy Model with the Beavers-Joseph Interface Conditions

In this paper, an efficient ensemble domain decomposition algorithm is proposed for fast solving the fully-mixed random Stokes-Darcy model with the physically realistic Beavers-Joseph (BJ) interface conditions. We utilize the Monte Carlo method for the coupled model with random inputs to derive some deterministic Stokes-Darcy numerical models and use the idea of the ensemble to realize the fast computation of multiple problems. One remarkable feature of the algorithm is that multiple linear systems share a common coefficient matrix in each deterministic numerical model, which significantly reduces the computational cost and achieves comparable accuracy with the traditional methods. Moreover, by domain decomposition, we can decouple the Stokes-Darcy system into two smaller sub-physics problems naturally. Both mesh-dependent and mesh-independent convergence rates of the algorithm are rigorously derived by choosing suitable Robin parameters. Especially, for small hydraulic conductivity in practice, the almost optimal geometric convergence can be obtained by finite element discretization. Finally, two groups of numerical experiments are conducted to validate and illustrate the exclusive features of the proposed algorithm

Sb@Ni6 superstructure units stabilize Li-rich layered cathode in the wide voltage window

In the practical operations of Li-ion batteries, inevitable deep charge/discharge happens locally due to the intrinsic (de)lithiation inhomogeneity at the electrode and particle level, which would damage the health of batteries and even cause the safety concern. It is essential to develop the stable cathodes operating in a wide voltage window to ensure the health and safety of Li-ion batteries. Herein, we comprehensively investigate the charge/discharge behaviors of a representative Li-rich cathode Li1.2Mn0.54Ni0.13Co0.13O2 in a wide voltage window of 1.0–4.8 V, and reveal that, deep-lithiation would drive violent TM migration and severe Li/TM mixing, thereby leading to the irreversible structural transformation from layered to spinel then to rock salt, eventually causing the fast decay in electrochemical performance. Based on these understandings, a novel Li-rich cathode Li[Li1/4Mn1/2Ni1/6Sb1/12]O2 is successfully synthesized through introducing aromatic Sb@Ni6 superstructure units in the TM layers. The introduced Sb@Ni6 superstructure units can effectively tune the local oxygen environment, suppress TM migration, and stabilize the layered framework under deep lithiation. Finally, a stable charge/discharge is achieved in 1.0–4.8 V. This work deepens the understanding into the structural stability of Li-rich cathodes in a wide voltage window, and benefits the development of high-energy-density and safe cathodes

Visualization of Electronic Multiple Ordering and Its Dynamics in High Magnetic Field: Evidence of Electronic Multiple Ordering Crystals

Constituent atoms and electrons determine matter properties together, and they can form long-range ordering respectively. Distinguishing and isolating the electronic ordering out from the lattice crystal is a crucial issue in contemporary materials science. However, the intrinsic structure of a long-range electronic ordering is difficult to observe because it can be easily affected by many external factors. Here, we present the observation of electronic multiple ordering (EMO) and its dynamics at the micrometer scale in a manganite thin film. The strong internal couplings among multiple electronic degrees of freedom in the EMO make its morphology robust against external factors and visible via well-defined boundaries along specific axes and cleavage planes, which behave like a multiple-ordered electronic crystal. A strong magnetic field up to 17.6 T is needed to completely melt such EMO at 7 K, and the corresponding formation, motion, and annihilation dynamics are imaged utilizing a home-built high-field magnetic force microscope. The EMO is parasitic within the lattice crystal house, but its dynamics follows its own rules of electronic correlation, therefore becoming distinguishable and isolatable as the electronic ordering. Our work provides a microscopic foundation for the understanding and control of the electronic ordering and the designs of the corresponding devices