Synthesis of Boron Nitride Nanotubes by Self-Propagation High-Temperature Synthesis and Annealing Method

High-quality boron nitride nanotubes were synthesized by annealing porous precursor in flowing NH3 gas at 1150°C. The porous precursor B18Ca2(MgO)9 was produced by self-propagation high-temperature synthesis (SHS) method using Mg, B2O3, and CaB6 as the starting materials, which played an important role in synthesis of BN nanotubes in large quantities. Samples were characterized by SEM, TEM, EDX, HRTEM, X-ray powder diffraction (XRD), Raman, and Fourier transform infrared (FTIR) spectroscopy. The as-synthesized BN nanotubes have an average diameter of about 150 nm with a wall/diameter ratio of 2/3. Mean length of the BN nanotubes was more than 10 μm. The effects of temperature, time, and the possible mechanism of the growth of the BN nanotubes were also discussed

Imprint of the Atlantic Multidecadal Oscillation on Tree-Ring Widths in Northeastern Asia since 1568

We present a new tree-ring reconstruction of the Atlantic Multidecadal Oscillation (AMO) spanning 1568–2007 CE from northeast Asia. Comparison of the instrumental AMO index, an existing tree-ring based AMO reconstruction, and this new record show strongly similar annual to multidecadal patterns of variation over the last 440 years. Warm phases of the AMO are related to increases in growth of Scots pine trees and moisture availability in northeast China and central eastern Siberia. Multi-tape method (MTM) and cross-wavelet analyses indicate that robust multidecadal (∼64–128 years) variability is present throughout the new proxy record. Our results have important implications concerning the influence of North Atlantic sea surface temperatures on East Asian climate, and provide support for the possibility of an AMO signature on global multidecadal climate variability

Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

In recent years, the development of deep learning is noticeably influencing the progress of computational fluid dynamics. Numerous researchers have undertaken flow field predictions on a variety of grids, such as MAC grids, structured grids, unstructured meshes, and pixel-based grids which have been many works focused on. However, predicting unsteady flow fields on unstructured meshes remains challenging. When employing graph neural networks (GNNs) for these predictions, the message-passing mechanism can become inefficient, especially with denser unstructured meshes. Furthermore, unsteady flow field predictions often rely on autoregressive neural networks, which are susceptible to error accumulation during extended predictions. In this study, we integrate the traditional finite volume method to devise a spatial integration strategy that enables the formulation of a physically constrained loss function. This aims to counter the error accumulation that emerged in autoregressive neural networks during long-term predictions. Concurrently, we merge vertex-center and cell-center grids from the finite volume method, introducing a dual message-passing mechanism within a single GNN layer to enhance the message-passing efficiency. We benchmark our approach against MeshGraphnets for unsteady flow field predictions on unstructured meshes. Our findings indicate that the methodologies combined in this study significantly enhance the precision of flow field predictions while substantially minimizing the training time cost. We offer a comparative analysis of flow field predictions, focusing on cylindrical, airfoil, and square column obstacles in two-dimensional incompressible fluid dynamics scenarios. This analysis encompasses lift coefficient, drag coefficient, and pressure coefficient distribution comparison on the boundary layers

The NF-kappa B inhibitor, celastrol, could enhance the anti-cancer effect of gambogic acid on oral squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Gambogic acid (GA) is a major active ingredient of gamboge, a widely used traditional Chinese medicine that has been reported to be a potent cytotoxic agent against some malignant tumors. Many studies have shown that the NF-kappa B signaling pathway plays an important role in anti-apoptosis and the drug resistance of tumor cells during chemotherapy. In this study, the effects and mechanisms of GA and the NF-kappa B inhibitor celastrol on oral cancer cells were investigated.</p> <p>Methods</p> <p>Three human oral squamous cell carcinoma cell lines, Tca8113, TSCC and NT, were treated with GA alone, celastrol alone or GA plus celastrol. Cytotoxicity was assessed by MTT assay. The rate of apoptosis was examined with annexin V/PI staining as well as transmission electronic microscopy in Tca8113 cells. The level of constitutive NF-kappa B activity in oral squamous cell carcinoma cell lines was determined by immunofluorescence assays and nuclear extracts and electrophoretic mobility shift assays (EMSAs) <it>in vitro</it>. To further investigate the role of NF-kappa B activity in GA and celastrol treatment in oral squamous cell carcinoma, we used the dominant negative mutant SR-IκBα to inhibit NF-kappa B activity and to observe its influence on the effect of GA.</p> <p>Results</p> <p>The results showed that GA could inhibit the proliferation and induce the apoptosis of the oral squamous cell carcinoma cell lines and that the NF-kappa B pathway was simultaneously activated by GA treatment. The minimal cytotoxic dose of celastrol was able to effectively suppress the GA-induced NF-kappa B pathway activation. Following the combined treatment with GA and the minimal cytotoxic dose of celastrol or the dominant negative mutant SR-IκBα, proliferation was significantly inhibited, and the apoptotic rate of Tca8113 cells was significantly increased.</p> <p>Conclusion</p> <p>The combination of GA and celastrol has a synergistic antitumor effect. The effect can be primarily attributed to apoptosis induced by a decrease in NF-kappa B pathway activation. The NF-kappa B signaling pathway plays an important role in this process. Therefore, combining GA and celastrol may be a promising modality for treating oral squamous cell carcinoma.</p

AMGNET: multi-scale graph neural networks for flow field prediction

Solving partial differential equations of complex physical systems is a computationally expensive task, especially in Computational Fluid Dynamics(CFD). This drives the application of deep learning methods in solving physical systems. There exist a few deep learning models that are very successful in predicting flow fields of complex physical models, yet most of these still exhibit large errors compared to simulation. Here we introduce AMGNET, a multi-scale graph neural network model based on Encoder-Process-Decoder structure for flow field prediction. Our model employs message passing of graph neural networks at different mesh graph scales. Our method has significantly lower prediction errors than the GCN baseline on several complex fluid prediction tasks, such as airfoil flow and cylinder flow. Our results show that multi-scale representation learning at the graph level is more effective in improving the prediction accuracy of flow field

High-Order Spectral Volume Method for 2D Euler Equations

The Spectral Volume (SV) method is extended to the 2D Euler equations. The focus of this paper is to study the performance of the SV method on multidimensional non-linear systems. Implementation details including total variation diminishing (TVD) and total variation bounded (TVB) limiters are presented. Solutions with both smooth features and discontinuities are utilized to demonstrate the overall capability of the SV method

The PPP Precision Analysis Based on BDS Regional Navigation System

BeiDou navigation satellite system(BDS) has opened service in most of the Asia-Pacific region, it offers the possibility to break the technological monopoly of GPS in the field of high-precision applications, so its performance of precise point positioning (PPP) has been a great concern. Firstly, the constellation of BeiDou regional navigation system and BDS/GPS tracking network is introduced. Secondly, the precise ephemeris and clock offset accuracy of BeiDou satellite based on domestic tracking network is analyzed. Finally, the static and kinematic PPP accuracy is studied, and compared with the GPS. The actual measured numerical example shows that the static and kinematic PPP based on BDS can achieve centimeter-level and decimeter-level respectively, reaching the current level of GPS precise point positioning