The semi-analytical method for damping of tubular transition layer damping structure

To solve the limited vibration consumption of the traditional tubular damping structure (TTDS), the tubular transition layer damping structure (TTLDS) is proposed; Based on viscoelastic materials and theories of thin cylindrical shells, the governing equation, the first order matrix differential equation describing vibration of TTLDS under harmonic excitation, is derived by considering the interaction between all layers and the dissipation caused by the shear deformation for transition layer and damping layer. By using the extended homogeneous capacity precision integration method to solve the control equation, a semi-analytical method for studying the vibration and damping characteristics of TTLDS is given. By way of comparison, the correctness of the method provided in paper is verified. At last, the influence of thickness, material and location of transition layer on damping effect is analyzed. The results show that the change for the thickness or material of the transition layer can make the structural damping effect change greatly, while the change for location of the transition layer plays only a few roles on the structural damping effect

Tumor-associated macrophages regulate gastric cancer cell invasion and metastasis through TGF beta 2/NF-kappa B/Kindlin-2 axis

Objective: Recent studies have shown that tumor-associated macrophages (TAMs) play an important role in cancer invasion and metastasis. Our previous studies have reported that TAMs promote the invasion and metastasis of gastric cancer (GC) cells through the Kindlin-2 pathway. However, the mechanism needs to be clarified. Methods: THP-1 monocytes were induced by PMA/interleukin (IL)-4/IL-13 to establish an efficient TAM model in vitro and M2 macrophages were isolated via flow cytometry. A dual luciferase reporter system and chromatin immunoprecipitation (ChIP) assay were used to investigate the mechanism of transforming growth factor beta 2 (TGF beta 2) regulating Kindlin-2 expression. Immunohistochemistry was used to study the relationships among TAM infiltration in human GC tissues, Kindlin-2 protein expression, clinicopathological parameters and prognosis in human GC tissues. A nude mouse oncogenesis model was used to verify the invasion and metastasis mechanisms in vivo. Results: We found that Kindlin-2 expression was upregulated at both mRNA and protein levels in GC cells cocultured with TAMs, associated with higher invasion rate. Kindlin-2 knockdown reduced the invasion rate of GC cells under coculture condition. TGF beta 2 secreted by TAMs regulated the expression of Kindlin-2 through the transcription factor NF-kappa B. TAMs thus participated in the progression of GC through the TGF beta 2/NF-kappa B/Kindlin-2 axis. Kindlin-2 expression and TAM infiltration were significantly positively correlated with TNM stage, and patients with high Kindlin-2 expression had significantly poorer overall survival than patients with low Kindlin-2 expression. Furthermore, Kindlin-2 promoted the invasion of GC cells in vivo. Conclusions: This study elucidates the mechanism of TAMs participating in GC cell invasion and metastasis through the TGF beta 2/NF-kappa B/Kindlin-2 axis, providing a possibility for new treatment options and approaches.Peer reviewe

Full-length single-cell RNA-seq applied to a viral human cancer:applications to HPV expression and splicing analysis in HeLa S3 cells

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied HeLa is a well characterized HPV+ cervical cancer cell line Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins Conclusion: Our results reveal the heterogeneity of a virus-infected cell line It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers

A Study on the Thermal Effect of the Current-Carrying Capacity of Embedded Underground Cable

The current paper aims to study embedded underground cable and the effect of temperature that surrounds it. Determining the carrying capacity of the cable is important to predict the temperature changesin the embedded pipe. Simulating the temperature field and the laying environment according to the IEC standard enables the calculation of the carrying capacity of the buried region. According to the theoryof heat transfer, the embedded pipe tube model temperature field should be coupled with a numerical model. The domain and boundary conditions of the temperature field should also be determined using the 8.7/15kV YJV 400 cable. In conducting numerical calculation and analysis using the temperature field model, the two-dimensional temperature distribution of the emission control area should be determined. The experimental results show that the simulation isconsistent with the IEC standard. Furthermore, in identifying the cable ampacity, the different seasons and different cable rows should be taken into account using the finite element method. Finally, theappropriate choice of root and circuit numbers of the cable will improve the cable’s the carrying capacity

Feature-Enhanced Deep Learning Network for Digital Elevation Model Super-Resolution

The high-resolution digital elevation model (HR DEM) plays an important role in hydrological analysis, cartographic generalization, and national security. As the main high-precision DEM data supplementary method, DEM super-resolution (DEM SR) based on deep learning has been widely studied. However, its accuracy has fallen into a bottleneck at present, which is more prominent in complex regions. The reason for this issue is that the existing methods are difficult to capture enough local features from the low-resolution input data, and a part of the global information (contour information of long-distance features, such as rivers and ridges) will also be lost in the network transmission process. To resolve this issue, a novel feature-enhanced deep learning network (FEN) is designed in this article. The proposed FEN includes a global feature SR (GFSR) module and a local feature SR (LFSR) module. The former provides global information by using an interpolation method (Kriging), including geographical laws (spatial autocorrelation). The latter fully captures the features in the input data by integrating powerful feature extraction modules and then provides sufficient local features for DEM SR tasks. Thus, DEM SR tasks for complex regions can be realized by integrating the results of GFSR and LFSR modules. Extensive experiments show that FEN achieves state-of-the-art performance in DEM SR tasks facing complex regions. Specifically, compared with the existing DEM SR method (TfaSR, SRResNet, Bicubic, SRCNN, and Kriging), the result by FEN is closer to HR DEM and can retain more local DEM features. Meanwhile, the FEN is more than 20% ahead of other DEM SR methods based on deep learning in elevation accuracy

A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution

High-resolution DEMs can provide accurate geographic information and can be widely used in hydrological analysis, path planning, and urban design. As the main complementary means of producing high-resolution DEMs, the DEM super-resolution (SR) method based on deep learning has reached a bottleneck. The reason for this phenomenon is that the DEM super-resolution method based on deep learning lacks a part of the global information it requires. Specifically, the multilevel aggregation process of deep learning has difficulty sufficiently capturing the low-level features with dependencies, which leads to a lack of global relationships with high-level information. To address this problem, we propose a global-information-constrained deep learning network for DEM SR (GISR). Specifically, our proposed GISR method consists of a global information supplement module and a local feature generation module. The former uses the Kriging method to supplement global information, considering the spatial autocorrelation rule. The latter includes a residual module and the PixelShuffle module, which is used to restore the detailed features of the terrain. Compared with the bicubic, Kriging, SRCNN, SRResNet, and TfaSR methods, the experimental results of our method show a better ability to retain terrain features, and the generation effect is more consistent with the ground truth DEM. Meanwhile, compared with the deep learning method, the RMSE of our results is improved by 20.5% to 68.8%

Grid-Connected Adaptive Analysis and Control of the DC Wind Turbine Under the Grid-Side Transient Faults

The grid-side transient fault ride-through problem seriously affects the stable operation of the grid-connected wind turbine. At present, the grid-connected adaptability analysis of DC wind turbine (DCWT) under the grid-side transient faults is still a blank. In order to study the grid-connected adaptability of the DC wind turbine under the grid-side transient faults, based on the grid-connected system of a DC wind turbine, this paper analyzed the operation characteristics of the DC wind turbine during the period of the grid-side transient faults, and proposed a grid-side transient fault ride-through control strategy. During the period of the grid-side transient faults, additional energy absorbing circuit was attached to the DC wind turbine to absorb the surplus power and stabilize the low voltage side voltage and the dynamic reactive power compensation scheme was proposed in the grid-side converter to provide maximum reactive power support for the fault grid. The simulation model of the grid-connected system based on the DC wind turbine was built for PSCAD/EMTDC. Through the simulation of the model under the fault conditions of grid-voltage drop, rise and continuous change, it was verified that the proposed control strategy effectively improved the grid-connected adaptability of the DC wind turbine under the grid-side transient faults and ensured the safe and stable operation of the DC wind turbine

Some basic theorems and formulas for building fractal nonlinear wave models

As a tool to deal with the phenomenon in layered or porous media, many theoretical problems need to be improved urgently. In this paper, the definitions of two kinds of fractal derivatives based on scale transformation are developed from the perspective of mathematics, and the four operation rules, chain rules, Rolle theorem, mean value theorem, Taylor formula, variable upper limit integral derivation rule, definite integral basic principle, partial integral formula and double partial integral formula of the fractal version are given. Better still, we derive the fractal Euler-Lagrangian equation by using the variational method, and demonstrate the steps and effectiveness of deriving the fractal nonlinear wave equation based on the fractal Euler-Lagrangian equation through the example of the functionally graded beam

STUDY ON ANFIS CONTROL STRATEGY OF ENGINEERING VEHICLE SEAT SUSPENSION (MT)

Taking the seat vibration system of a certain type of engineering vehicle as an object, using the characteristics of adaptive network-based fuzzy inference system（ANFIS） self-learning and fuzzy reasoning, a semiactive control method of seat suspension vibration based on ANFIS was proposed, which solved the problem of excessive seat vibration in the working process of engineering vehicles. Firstly, the vibration process of the seat suspension of engineering vehicles was analyzed, and the dynamic model of the seat suspension was established. Then, the fuzzy neural control system of seat suspension was designed by adding feed-forward and feedback links. Finally, the control effects of different vibration excitation frequencies, different controller control objects and the input of measured seat signals were observed respectively. The results show that the fuzzy neural control system reduces the vibration energy and frequency of the seat, and the root mean square value of the seat vibration displacement is 52% to 66% of the passive suspension seat