Genuine output and genuine productivity of China\u27s provinces : a multiregional input-output analysis

This paper recalculates value added, capital formation, capital stock, and related multifactor productivity for China\u27s provinces by expanding on the genuine savings method proposed by the World Bank. Specifically, we construct China\u27s time-series multiregional input?output tables to account for the natural resource depletion and environmental damage that affect genuine output when considering inter-provincial trade. The results show that although the loss of natural capital in China\u27s provinces in terms of value added and investment has declined, the impact on productivity during the past decades is still significant and has even increased during the past decades

Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, temperature-dependent nonlinearity and material property uncertainty of heterogeneous materials are considered within the proposed self-optimization wavelet-learning framework. Firstly, meso- and micro-structural modeling of random heterogeneous materials are achieved by the proposed computer representation method, whose simulated hierarchical configurations have relatively high volume ratio of material inclusions. Moreover, temperature-dependent nonlinearity and material property uncertainties of random heterogeneous materials are modeled by a polynomial nonlinear model and Weibull probabilistic model, which can closely resemble actual material properties of heterogeneous materials. Secondly, an innovative stochastic three-scale homogenized method (STSHM) is developed to compute the macroscopic nonlinear thermal conductivity of random heterogeneous materials. Background meshing and filling techniques are devised to extract geometry and material features of random heterogeneous materials for establishing material databases. Thirdly, high-dimensional and highly nonlinear material features of material databases are preprocessed and reduced by wavelet decomposition technique. The neural networks are further employed to excavate the predictive models from dimension-reduced low-dimensional data

PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations

We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the forward, inverse, and mixed problems of stochastic differential equations. In these scenarios, the governing equations are known, but only a limited number of sensor measurements of the system parameters are available. We integrate the governing physical laws into PI-VEGAN with automatic differentiation, while introducing a variational encoder for approximating the latent variables of the actual distribution of the measurements. These latent variables are integrated into the generator to facilitate accurate learning of the characteristics of the stochastic partial equations. Our model consists of three components, namely the encoder, generator, and discriminator, each of which is updated alternatively employing the stochastic gradient descent algorithm. We evaluate the effectiveness of PI-VEGAN in addressing forward, inverse, and mixed problems that require the concurrent calculation of system parameters and solutions. Numerical results demonstrate that the proposed method achieves satisfactory stability and accuracy in comparison with the previous physics-informed generative adversarial network (PI-WGAN).Comment: 23 page

Multi-scale modelling of the human left ventricle

In this paper, a multi-scale computational frame is proposed to simulate dynamics of the human left ventricle. First of all, a modified Level Set method is used to segment the cardiac magnetic resonance imaging and then reconstruct the 3D computational domain of the left ventricle. The Holzapfel-Ogden's nonlinear and anisotropic model is imposed to calculate the passive elastic response. The Fenton-Karma model with stimulus current is optimized to produce the reasonable membrane potential and intracellular calcium concentration. Based on the obtained calcium concentration, the active tension is calculated. Finally, the passive elastic response and the active tension of the left ventricle are coupled with the blood and the obtained fluid structure interaction is solved by the immersed boundary method. Our numerical results at end-diastole and end-systole are generally in good agreement with the clinical measurement and the earlier studies, which verifies the efficiency of the method

Influences of S-Wave Velocity to the Seismic Response of Silt Ground

By using of one-dimension equivalent linearzation seismic response analysis method, the study is performed to the different silt grounds. The influences of s-wave velocity uncertainty to the seismic acceleration peak, duration, response spectrum of silt ground are discussed in the paper. Following conclusions will he expected. (1) The relationship between the difference of seismic peak acceleration and the difference of s-wave velocity is in linear distribution approximately. The seismic peak acceleration is changed with the S-wave velocity. The seismic peak acceleration is much effected by the uncertainty of S-wave velocity. (2) The uncertainty of shear wave velocity has little influence on the seismic duration. (3) The long-period seismic response spectrum is much effected by the decreasing of shear wave velocity. Conversely, the moderate-period and short-period seismic response spectrum is much effected by the increasing of shear wave velocity. With the depth of silt layers extended, the seismic response spectrum is greater influenced by the uncertainty of shear wave velocity

Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture

Data-driven predictive control (DPC) has been studied and used in various scenarios, since it could generate the predicted control sequence only relying on the historical input and output data. Recently, based on cloud computing, data-driven predictive cloud control system (DPCCS) has been proposed with the advantage of sufficient computational resources. However, the existing computation mode of DPCCS is centralized. This computation mode could not utilize fully the computing power of cloud computing, of which the structure is distributed. Thus, the computation delay could not been reduced and still affects the control quality. In this paper, a novel cloud-edge collaborative containerised workflow-based DPC system with disturbance observer (DOB) is proposed, to improve the computation efficiency and guarantee the control accuracy. First, a construction method for the DPC workflow is designed, to match the distributed processing environment of cloud computing. But the non-computation overheads of the workflow tasks are relatively high. Therefore, a cloud-edge collaborative control scheme with DOB is designed. The low-weight data could be truncated to reduce the non-computation overheads. Meanwhile, we design an edge DOB to estimate and compensate the uncertainty in cloud workflow processing, and obtain the composite control variable. The UUB stability of the DOB is also proved. Third, to execute the workflow-based DPC controller and evaluate the proposed cloud-edge collaborative control scheme with DOB in the real cloud environment, we design and implement a practical workflow-based cloud control experimental system based on container technology. Finally, a series of evaluations show that, the computation times are decreased by 45.19% and 74.35% for two real-time control examples, respectively, and by at most 85.10% for a high-dimension control example.Comment: 58 pages and 23 figure

MC-NeRF: Muti-Camera Neural Radiance Fields for Muti-Camera Image Acquisition Systems

Neural Radiance Fields (NeRF) employ multi-view images for 3D scene representation and have shown remarkable performance. As one of the primary sources of multi-view images, multi-camera systems encounter challenges such as varying intrinsic parameters and frequent pose changes. Most previous NeRF-based methods often assume a global unique camera and seldom consider scenarios with multiple cameras. Besides, some pose-robust methods still remain susceptible to suboptimal solutions when poses are poor initialized. In this paper, we propose MC-NeRF, a method can jointly optimize both intrinsic and extrinsic parameters for bundle-adjusting Neural Radiance Fields. Firstly, we conduct a theoretical analysis to tackle the degenerate case and coupling issue that arise from the joint optimization between intrinsic and extrinsic parameters. Secondly, based on the proposed solutions, we introduce an efficient calibration image acquisition scheme for multi-camera systems, including the design of calibration object. Lastly, we present a global end-to-end network with training sequence that enables the regression of intrinsic and extrinsic parameters, along with the rendering network. Moreover, most existing datasets are designed for unique camera, we create a new dataset that includes four different styles of multi-camera acquisition systems, allowing readers to generate custom datasets. Experiments confirm the effectiveness of our method when each image corresponds to different camera parameters. Specifically, we adopt up to 110 images with 110 different intrinsic and extrinsic parameters, to achieve 3D scene representation without providing initial poses. The Code and supplementary materials are available at https://in2-viaun.github.io/MC-NeRF.Comment: This manuscript is currently under revie