Parallel Numerical Simulation of Complex Unsteady Multi-Component Three-Dimensional Flow Field of Nonequilibrium Chemical Reaction

In this paper, the gridless method, which is known for its complete independence of grids, is combined with parallel method to obtain a dynamic parallel multi-component three-dimensional (3D) gridless method to compute the complex unsteady multi-component 3D flow field of nonequilibrium chemical reaction (NCR). Specifically, the flow field was described with a multi-component arbitrary Lagrangian-Eulerian (ALE) control equation, which contains the source term of the chemical reaction. The flow term was decoupled from the chemical reaction term, and the stiff problem of the latter term was solved by time splitting. To control the convective flux in the control equation, the multi-component artificially upstream flux vector splitting (AUFS) scheme was derived for the 3D space. In addition, 3D local point cloud reconstruction was carried out to reconstruct the abnormal point cloud near the large moving boundary in real time. Besides, geometrical zoning was adopted for the parallel part to dynamically balance the computing load across different zones. The message passing interface (MPI) was selected to realize the communication between the zones. After that, the proposed multi-component gridless algorithm was proven accurate through two examples: hydrogen combustion reaction in a vessel, and shock-induced combustion with blunt projectile. Finally, the proposed dynamic parallel multi-component 3D gridless method was applied to compute the 3D muzzle flow field of prefilled serial-connected projectiles. The evolution of the complex flow field was obtained for projectile 2. The parallel efficiency of our method surpassed 79%

Discovery of An Active Intermediate-Mass Black Hole Candidate in the Barred Bulgeless Galaxy NGC 3319

We report the discovery of an active intermediate-mass black hole (IMBH) candidate in the center of nearby barred bulgeless galaxy $\rm NGC~3319$. The point X-ray source revealed by archival Chandra and XMM-Newton observations is spatially coincident with the optical and UV galactic nuclei from Hubble Space Telescope observations. The spectral energy distribution derived from the unresolved X-ray and UV-optical flux is comparable with active galactic nuclei (AGNs) rather than ultra-luminous X-ray sources, although its bolometric luminosity is only $3.6\times10^{40}~\rm erg~s^{-1}$. Assuming an Eddington ratio range between 0.001 and 1, the black hole mass (M_\rm{BH}) will be located at $3\times10^2-3\times10^5~M_{\odot}$, placing it in the so-called IMBH regime and could be the one of the lowest reported so far. Estimates from other approaches (e.g., fundamental plane, X-ray variability) also suggest M_\rm{BH}\lesssim10^5~M_{\odot}.Similar to other BHs in bulgeless galaxies, the discovered IMBH resides in a nuclear star cluster with mass of $\sim6\times10^6~M_{\odot}$. The detection of such a low-mass BH offers us an ideal chance to study the formation and early growth of SMBH seeds, which may result from the bar-driven inflow in late-type galaxies with a prominent bar such as $\rm NGC~3319$.Comment: ApJ accepted, 2 tables, 6 figure

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%

Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater

Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.Comment: 17 pages, 5 figures, 1 table, submitted to Advanced Intelligent Systems for revie