Application of a Spectral Method to Simulate Quasi-Three-Dimensional Underwater Acoustic Fields

The solution and synthesis of quasi-three-dimensional sound fields have always been core issues in computational ocean acoustics. Traditionally, finite difference algorithms have been employed to solve these problems. In this paper, a novel numerical algorithm based on the spectral method is devised. The quasi-three-dimensional problem is transformed into a problem resembling a two-dimensional line source using an integral transformation strategy. Then, a stair-step approximation is adopted to address the range dependence of the two-dimensional problem; because this approximation is essentially a discretization, the range-dependent two-dimensional problem is further simplified into a one-dimensional problem. Finally, we apply the Chebyshev--Tau spectral method to accurately solve the one-dimensional problem. We present the corresponding numerical program for the proposed algorithm and describe some representative numerical examples. The simulation results ultimately verify the reliability and capability of the proposed algorithm.Comment: 43 pages, 20 figures. arXiv admin note: text overlap with arXiv:2112.1360

Two Chebyshev Spectral Methods for Solving Normal Modes in Atmospheric Acoustics

The normal mode model is important in computational atmospheric acoustics. It is often used to compute the atmospheric acoustic field under a time-independent single-frequency sound source. Its solution consists of a set of discrete modes radiating into the upper atmosphere, usually related to the continuous spectrum. In this article, we present two spectral methods, the Chebyshev-Tau and Chebyshev-Collocation methods, to solve for the atmospheric acoustic normal modes, and corresponding programs are developed. The two spectral methods successfully transform the problem of searching for the modal wavenumbers in the complex plane into a simple dense matrix eigenvalue problem by projecting the governing equation onto a set of orthogonal bases, which can be easily solved through linear algebra methods. After the eigenvalues and eigenvectors are obtained, the horizontal wavenumbers and their corresponding modes can be obtained with simple processing. Numerical experiments were examined for both downwind and upwind conditions to verify the effectiveness of the methods. The running time data indicated that both spectral methods proposed in this article are faster than the Legendre-Galerkin spectral method proposed previously

Study on experiments and numerical simulation of coal combustion characteristics under different thermal environments

In order to study heat release rate and flue gas generation during residual coal in goaf combustion, a cone calorimeter was used to research on combustion characteristics of bituminous coal under different heat radiation flux conditions. A novel coal fire FOAM solver for coal combustion is developed, numerically simulated by varying the gas atmosphere, and compared with the above experimental results. The results showed that with the increase in heat radiation flux, the shorter the time to reach the peak heat release rate, the greater the maximum heat release rate. When the coal does not enter the ignition stage, the CO production rate is higher than that after ignition, and the CO2 production rate is lower than that after ignition. Compared with the experimental results of the cone calorimeter, the difference between the two results is very small, which verified the feasibility and accuracy of the coal combustion model. After the injection of the composite inert gas, the heat release rate and mass loss rate of coal combustion have varying degrees of lag. The numerical simulation results can provide a theoretical basis for injecting inert compounds into the goaves of coal mines to prevent the spontaneous combustion of coal

Discovery of Pan-Cancer Related Genes Via Integrative Network Analysis

Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method’s potential for application in identifying driver gene candidates for further biological experimental verification

Janus particle-engineered structural lipiodol droplets for arterial embolization

Abstract Embolization (utilizing embolic materials to block blood vessels) has been considered one of the most promising strategies for clinical disease treatments. However, the existing embolic materials have poor embolization effectiveness, posing a great challenge to highly efficient embolization. In this study, we construct Janus particle-engineered structural lipiodol droplets by programming the self-assembly of Janus particles at the lipiodol-water interface. As a result, we achieve highly efficient renal embolization in rabbits. The obtained structural lipiodol droplets exhibit excellent mechanical stability and viscoelasticity, enabling them to closely pack together to efficiently embolize the feeding artery. They also feature good viscoelastic deformation capacities and can travel distally to embolize finer vasculatures down to 40 μm. After 14 days post-embolization, the Janus particle-engineered structural lipiodol droplets achieve efficient embolization without evidence of recanalization or non-target embolization, exhibiting embolization effectiveness superior to the clinical lipiodol-based emulsion. Our strategy provides an alternative approach to large-scale fabricate embolic materials for highly efficient embolization and exhibits good potential for clinical applications