Detection of a methanol megamaser in a major-merger galaxy

We have detected emission from both the 4_{-1}-3_{0} E (36.2~GHz) class I and 7_{-2}-8_{-1} E (37.7~GHz) class II methanol transitions towards the centre of the closest ultra-luminous infrared galaxy Arp 220. The emission in both the methanol transitions show narrow spectral features and have luminosities approximately 8 orders of magnitude stronger than that observed from typical class I methanol masers observed in Galactic star formation regions. The emission is also orders of magnitude stronger than the expected intensity of thermal emission from these transitions and based on these findings we suggest that the emission from the two transitions are masers. These observations provides the first detection of a methanol megamaser in the 36.2 and 37.7 GHz transitions and represents only the second detection of a methanol megamaser, following the recent report of an 84 GHz methanol megamaser in NGC1068. We find the methanol megamasers are significantly offset from the nuclear region and arise towards regions where there is Ha emission, suggesting that it is associated with starburst activity. The high degree of correlation between the spatial distribution of the 36.2 GHz methanol and X-ray plume emission suggests that the production of strong extragalactic class I methanol masers is related to galactic outflow driven shocks and perhaps cosmic rays. In contrast to OH and H2O megamasers which originate close to the nucleus, methanol megamasers provide a new probe of feedback (e.g. outflows) processes on larger-scales and of star formation beyond the circumnuclear starburst regions of active galaxies.Comment: Accepted for publication in ApJ

The operation modal analysis of the structure crack fault diagnosis based on pseudo-successive data

In order to monitor the crack propagation of the structure in the working state for a long time, an operation modal analysis method based on pseudo-successive data is proposed. The vibration response signals of the cantilever beam under white noise excitation are collected and the modal parameters are extracted by the time-frequency operation modal analysis method based on the complex Morlet wavelet. In comparison with the experimental modal analysis results of hammering method, it is revealed that the error of the time-frequency operation modal analysis method is less than 10 %. By setting cracks of different lengths on the cantilever beam, the vibration response signals are extracted, and the modal parameters are extracted by the operation modal analysis method separately. By comparing those modal parameters above, it is found that the natural frequencies of the second, the fourth and the sixth orders decrease with the increase of the crack depth, and the changes of natural frequencies show the monotonicity. So, it can be used as an index for quantitative identification of crack damage. The pseudo continuous data monitoring signals of crack propagation can be constructed by means of “first discrete, then continuous”. The modal parameters changes of the whole crack propagation can be observed in one time plane by means of the operation modal analysis method. Therefore, the effective monitoring and diagnosis of the structure can be completed in case of excessive data of long-time vibration monitoring signals

Synthesis, characterization, and mercury adsorption properties of hybrid mesoporous aluminosilicate sieve prepared with fly ash

AbstractA novel hybrid mesoporous aluminosilicate sieve (HMAS) was prepared with fly ash and impregnated with zeolite A precursors. This improved the mercury adsorption of HMAS compared to original MCM-41. The HMAS was characterized by X-ray diffraction (XRD), nitrogen adsorption–desorption, Fourier transform infrared (FTIR) analysis, transmission electron microscopy (TEM) images and 29Si and 27Al magic angle spinning nuclear magnetic resonance (MAS NMR) spectra. These showed that the HMAS structure was still retained after impregnated with zeolite A. But the surface area and pore diameter of HMAS decreased due to pore blockage. Adsorption of mercury from aqueous solution was studied on untreated MCM-41and HMAS. The mercury adsorption rate of HMAS was higher than that of origin MCM-41. The adsorption of mercury was investigated on HMAS regarding the pH of mercury solution, initial mercury concentration, and the reaction temperature. The experimental data fit well to Langmuir and Freundlich isotherm models. The Dublin–Radushkevich isotherm and the characterization show that the mercury adsorption on HMAS involved the ion-exchange mechanisms. In addition, the thermodynamic parameters suggest that the adsorption process was endothermic in nature. The adsorption of mercury on HMAS followed the first order kinetics

Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions

Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is physics-informed neural networks (PINN), which integrate physical principles into neural networks. This transforms the process of solving PDEs into optimization problems for neural networks. To address a collection of advection-diffusion equations (ADE) in a range of difficult circumstances, this paper proposes a novel network structure. This architecture integrates the solver, a multi-scale deep neural networks (MscaleDNN) utilized in the PINN method, with a hard constraint technique known as HCPINN. This method introduces a revised formulation of the desired solution for ADE by utilizing a loss function that incorporates the residuals of the governing equation and penalizes any deviations from the specified boundary and initial constraints. By surpassing the boundary constraints automatically, this method improves the accuracy and efficiency of the PINN technique. To address the “spectral bias” phenomenon in neural networks, a subnetwork structure of MscaleDNN and a Fourier-induced activation function are incorporated into the HCPINN, resulting in a hybrid approach called SFHCPINN. The effectiveness of SFHCPINN is demonstrated through various numerical experiments involving ADE in different dimensions. The numerical results indicate that SFHCPINN outperforms both standard PINN and its subnetwork version with Fourier feature embedding. It achieves remarkable accuracy and efficiency while effectively handling complex boundary conditions and high-frequency scenarios in ADE

Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks

Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe into solving a class of elliptic partial differential equations (PDEs) with multiple scales by utilizing Fourier-based mixed physics informed neural networks (dubbed FMPINN), its solver is configured as a multi-scale deep neural network. In contrast to the classical PINN method, a dual (flux) variable about the rough coefficient of PDEs is introduced to avoid the ill-condition of neural tangent kernel matrix caused by the oscillating coefficient of multi-scale PDEs. Therefore, apart from the physical conservation laws, the discrepancy between the auxiliary variables and the gradients of multi-scale coefficients is incorporated into the cost function, obtaining a satisfactory solution of PDEs by minimizing the defined loss through some optimization methods. Additionally, a trigonometric activation function is introduced for FMPINN, which is suited for representing the derivatives of complex target functions. Handling the input data by Fourier feature mapping will effectively improve the capacity of deep neural networks to solve high-frequency problems. Finally, to validate the efficiency and robustness of the proposed FMPINN algorithm, we present several numerical examples of multi-scale problems in various dimensional Euclidean spaces. These examples cover low-frequency and high-frequency oscillation cases, demonstrating the effectiveness of our approach. All code and data accompanying this manuscript will be publicly available at https://github.com/Blue-Giant/FMPINN

Increased expression of collagens, transforming growth factor-β1, and -β3 in gluteal muscle contracture

<p>Abstract</p> <p>Backgroud</p> <p>Gluteal muscle contracture (GMC) is a multi-factor human chronic fibrotic disease of the gluteal muscle. Fibrotic tissue is characterized by excessive accumulation of collagen in the muscle's extracellular matrix. Transforming growth factor (TGF)-β1 and -β2 are thought to play an important role in fibrogenesis, while TGF-β3 is believed to have an anti-fibrotic function. We hypothesize that the expression of collagen and TGF-βs would be up-regulated in GMC patients.</p> <p>Methods</p> <p>The expression of collagen type I, type III and TGF-βs were studied in 23 fibrotic samples and 23 normal/control samples in GMC patients using immunohistochemistry, reverse transcription and polymerase chain reaction (RT-PCR) and western bolt analysis.</p> <p>Results</p> <p>Compared to the unaffected adjacent muscle, increased expression of TGF-β1 and -β3 was associated with deposition of collagen type I and type III in the fibrotic muscle of the GMC patients at the mRNA level. Strong up-regulation of these proteins in fibrotic muscle was confirmed by immunohistochemical staining and western blot analysis. TGF-β2 was not up-regulated in relation to GMC.</p> <p>Conclusion</p> <p>This study confirmed our hypothesis that collagen types I, III, TGF-β1 and TGF-β3 were up-regulated in biopsy specimens obtained from patients with GMC. Complex interaction of TGF-β1 with profibrotic function and TGF-β3 with antifibrotic function may increase synthesis of collagens and thereby significantly contribute to the process of gluteal muscle scarring in patients with GMC.</p