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Mitochondrial Remodeling in Endothelial Cells under Cyclic Stretch is Independent of Drp1 Activation
Full text linkMitochondria in endothelial cells remodel morphologically when supraphysiological cyclic stretch is exerted on the cells. During remodeling, mitochondria become shorter, but how they do so remains elusive. Drp1 is a regulator of mitochondrial morphologies. It shortens mitochondria by shifting the balance from mitochondrial fusion to fission. In this study, we hypothesized that Drp1 activation is involved in mitochondrial remodeling under supraphysiological cyclic stretch. To verify the involvement of Drp1, its activation was first quantified with Western blotting, but Drp1 was not significantly activated in endothelial cells under supraphysiological cyclic stretch. Next, Drp1 activation was inhibited with Mdivi-1, but this did not inhibit mitochondrial remodeling. Intracellular Ca2+ increase activates Drp1 through calcineurin. First, we inhibited the intracellular Ca2+ increase with Gd3+ and thapsigargin, but this did not inhibit mitochondrial remodeling. Next, we inhibited calcineurin with cyclosporin A, but this also did not inhibit mitochondrial remodeling. These results indicate that mitochondrial remodeling under supraphysiological cyclic stretch is independent of Drp1 activation. In endothelial cells under supraphysiological cyclic stretch, reactive oxygen species (ROS) are generated. Mitochondrial morphologies are remodeled by ROS generation. When ROS was eliminated with N-acetyl-L-cysteine, mitochondrial remodeling was inhibited. Furthermore, when the polymerization of the actin cytoskeleton was inhibited with cytochalasin D, mitochondrial remodeling was also inhibited. These results suggest that ROS and actin cytoskeleton are rather involved in mitochondrial remodeling. In conclusion, the present results suggest that mitochondrial remodeling in endothelial cells under supraphysiological cyclic stretch is induced by ROS in association with actin cytoskeleton rather than through Drp1 activation
Influence of Water Stability on Bond Performance Between Magnesium Phosphate Cement Mortar and Steel Fibre
Full text linkThe fibre pullout test was conducted to investigate the influence of the water stability on the bond behaviour between the Magnesium phosphate cement (MPC) matrix and the steel fibre. The composition of the MPC-matrix and the immersion age of the specimens are experimentally investigated. The average bond strength and the pullout energy are investigated by analysing the experimental results. In addition, the microscopic characteristics of the interface transition zone are investigated using scanning electron microscopy (SEM). The experimental results showed that the bond performance between the MPC-matrix and the steel fibre decreased significantly with the increase of the duration of immersion in water. The average bond strength between the steel fibre and the MPC-matrix reduced by more than 50% when the specimens were immersed in the water for 28 days. The effect of the water on the interface between the steel fibre and the MPC-matrix was found to be more significant compared to the composition of the MPC-matrix. In addition, the MgO-KH2PO4 mole ratio of the MPC significantly influenced the water stability of the interface zone between the steel fibre and MPC-matrix
Low-Dose CT Image Denoising Based on Improved WGAN-gp
Full text linkIn order to improve the quality of low-dose computational tomography (CT) images, the paper proposes an improved image denoising approach based on WGAN-gp with Wasserstein distance. For improving the training and the convergence efficiency, the given method introduces the gradient penalty term to WGAN network. The novel perceptual loss is introduced to make the texture information of the low-dose images sensitive to the diagnostician eye. The experimental results show that compared with the state-of-art methods, the time complexity is reduced, and the visual quality of low-dose CT images is significantly improved
A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System
No full textInternet of Things (IoT) is an emerging paradigm involving intelligent sensor networks that incorporates embedded technology for collecting data, communicating with external environments. Recently, cloud computing together with fog computing has become an important research area of the Internet of Things because of big data processing capabilities. It is a promising technology that utilizes cloud or fog computing / architecture to improve sensor computing, storage, and communication capabilities. However, recently it has been shown that this computing/architecture may be vulnerable to various attacks because of the openness nature of the wireless network. Therefore, it becomes more and more important to ensure the security and privacy in these scenes. Encryption security and information hiding technology can provide authentication, confidentiality, integrity, anti-eavesdropping, availability and so on for these computing models or architectures. The purpose of this review is to look for original articles with novel ideas and solutions to address encryption security and information hiding technologies in cloud or fog-based Internet of Things systems. We hope this review will provide an opportunity for scientists, researchers and industry engineers to study original manuscripts and know developments in all aspects of security, privacy, trust, and covert communication issues in cloud or fog computing/architecture Internet of Things systems
Remaining Useful Life Prediction of Rolling Bearings Based on Recurrent Neural Network
No full textIn order to acquire the degradation state of rolling bearings and achieve predictive maintenance, this paper proposed a novel Remaining Useful Life (RUL) prediction of rolling bearings based on Long Short Term Memory (LSTM) neural net-work. The method is divided into two parts: feature extraction and RUL prediction. Firstly, a large number of features are extracted from the original vibration signal. After correlation analysis, the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model. In the part of RUL prediction, LSTM that making full use of the network’s memory in time is used to improve the accuracy of RUL prediction. The proposed method is validated by life cycle experimental data of bearings, and the RUL prediction results of LSTM model are compared with Support Vector Regression (SVR) and Light Gradient Boosting Machine (LightGBM) models respectively. The results show that the proposed method is more suitable for RUL prediction of rolling bearings
A Simple FEM for Solving Two-Dimensional Diffusion Equation with Nonlinear Interface Jump Conditions
No full textIn this paper, we propose a numerical method for solving parabolic interface problems with nonhomogeneous flux jump condition and nonlinear jump condition. The main idea is to use traditional finite element method on semi-Cartesian mesh coupled with Newton’s method to handle nonlinearity. It is easy to implement even though variable coefficients are used in the jump condition instead of constant in previous work for elliptic interface problem. Numerical experiments show that our method is about second order accurate in the L∞ norm
An Immersed Method Based on Cut-Cells for the Simulation of 2D Incompressible Fluid Flows Past Solid Structures
No full textWe present a cut-cell method for the simulation of 2D incompressible flows past obstacles. It consists in using the MAC scheme on cartesian grids and imposing Dirchlet boundary conditions for the velocity field on the boundary of solid structures following the Shortley-Weller formulation. In order to ensure local conservation properties, viscous and convecting terms are discretized in a finite volume way. The scheme is second order implicit in time for the linear part, the linear systems are solved by the use of the capacitance matrix method for non-moving obstacles. Numerical results of flows around an impulsively started circular cylinder are presented which confirm the efficiency of the method, for Reynolds numbers 1000 and 3000. An example of flows around a moving rigid body at Reynolds number 800 is also shown, a solver using the PETSc-Library has been prefered in this context to solve the linear systems
Frequency Domain Filtering SAR Interferometric Phase Noise Using the Amended Matrix Pencil Model
No full textInterferometric phase filtering is one of the key steps in interferometric synthetic aperture radar (InSAR/SAR). However, the ideal filtering results are difficult to obtain due to dense fringe and low coherence regions. Moreover, the InSAR/SAR data range is relatively large, so the efficiency of interferential phase filtering is one of the major problems. In this letter, we proposed an interferometric phase filtering method based on an amended matrix pencil and linear window mean filter. The combination of the matrix pencil and the linear mean filter are introduced to the interferometric phase filtering for the first time. First, the interferometric signal is analyzed, and the interferometric phase filtering is transformed into a local frequency estimation problem. Then, the local frequency is estimated using an amended matrix pencil at a window. The local frequency can represent terrain changes, thus suggesting that the frequency can be accurately estimated even in dense fringe regions. Finally, the local frequency is filtered by using a linear window mean filter, and the filtered phase is recovered. The proposed method is calculated by some matrices. Therefore, the computational complexity is reduced, and the efficiency of the interferometric phase filtering is improved. Experiments are conducted with simulated and real InSAR data. The proposed method exhibits a better filtering effect and an ideal efficiency as compared with the traditional filtering method
A Data-Intensive FLAC<sup>3D</sup> Computation Model: Application of Geospatial Big Data to Predict Mining Induced Subsidence
No full textAlthough big data are widely used in various fields, its application is still rare in the study of mining subsidence prediction (MSP) caused by underground mining. Traditional research in MSP has the problem of oversimplifying geological mining conditions, ignoring the fluctuation of rock layers with space. In the context of geospatial big data, a data-intensive FLAC3D (Fast Lagrangian Analysis of a Continua in 3 Dimensions) model is proposed in this paper based on borehole logs. In the modeling process, we developed a method to handle geospatial big data and were able to make full use of borehole logs. The effectiveness of the proposed method was verified by comparing the results of the traditional method, proposed method, and field observation. The findings show that the proposed method has obvious advantages over the traditional prediction results. The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7% and the standard deviation of the prediction results (which was 70 points) decreased by 39.4%, on average. The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence prediction
3D Bounding Box Proposal for on-Street Parking Space Status Sensing in Real World Conditions
No full textVision-based technologies have been extensively applied for on-street parking space sensing, aiming at providing timely and accurate information for drivers and improving daily travel convenience. However, it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera. This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle. A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion. It predicts 3D box candidates on multi-scale feature maps with five different 3D anchors, which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set. Subsequently, vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces, where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle’s box and parking space edge. In space status inference, to further eliminate mutual vehicle interference, three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit. Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference