The examination of a recently proposed model solution for shear behaviour of infilled natural rock joints based on multi-scale laboratory investigations

In the last decade, numerical models have become an increasingly important tool in biological and medical science. Numerical simulations contribute to a deeper understanding of physiology and are a powerful tool for better diagnostics and treatment. In this paper, a nonlinear multi-scale model framework is developed for blood flow distribution in the full vascular system of an organ. We couple a quasi one-dimensional vascular graph model to represent blood flow in larger vessels and a porous media model to describe flow in smaller vessels and capillary bed. The vascular model is based on Poiseuille’s Law, with pressure correction by elasticity and pressure drop estimation at vessels' junctions. The porous capillary bed is modelled as a two-compartment domain (artery and venous) using Darcy’s Law. The fluid exchange between the artery and venous capillary bed compartments is defined as blood perfusion. The numerical experiments show that the proposed model for blood circulation: (i) is closely dependent on the structure and parameters of both the larger vessels and of the capillary bed, and (ii) provides a realistic blood circulation in the organ. The advantage of the proposed model is that it is complex enough to reliably capture the main underlying physiological function, yet highly flexible as it offers the possibility of incorporating various local effects. Furthermore, the numerical implementation of the model is straightforward and allows for simulations on a regular desktop computer.

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Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model

PeerJ ◽

10.7717/peerj.10285 ◽

2020 ◽

Vol 8 ◽

pp. e10285

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Muhammad Faisal ◽

Alaa Mohamd Shoukry ◽

Mohammed Abdel Wahab Sharkawy ◽

...

Keyword(s):

River Basin ◽

Hybrid Model ◽

Moving Average ◽

Ensemble Empirical Mode Decomposition ◽

Dependence Structure ◽

Absolute Deviation ◽

River Inflow ◽

Multi Scale ◽

Proposed Model ◽

Vine Copula

Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For the application of the proposed two-step architecture, daily river inflow data of the Indus River Basin is used. The proposed two-stage methodology is compared with only the first stage proposed model, Vector Autoregressive and copula-based Autoregressive Integrated Moving Average models. The four evaluation measures, that is, Mean Absolute Relative Error (MARE), Mean Absolute Deviation (MAD), Nash-Sutcliffe Efficiency (NSE) and Mean Square Error (MSE), are used to observe the prediction performance. The results demonstrated that the proposed model outperforms significantly with minimum MARE, MAD, NSE, and MSE for two case studies having significant joint dependance. Therefore, it is concluded that the prediction can be improved by appropriately modeling the dependance structure of the multi-site river inflow data.

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Quantitative evaluation of mechanical properties of the natural rock joints for analyzing behavior of structures in discontinuous rock masses.

Doboku Gakkai Ronbunshu ◽

10.2208/jscej.1999.624_231 ◽

1999 ◽

pp. 231-243 ◽

Cited By ~ 3

Author(s):

Yujing JIANG ◽

Mitsuo NAKAGAWA ◽

Tetsuro ESAKI

Keyword(s):

Mechanical Properties ◽

Quantitative Evaluation ◽

Rock Joints ◽

Rock Masses ◽

Natural Rock ◽

Discontinuous Rock

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Shear Behaviour of Rock Joints

10.1201/9781003211297 ◽

2021 ◽

Author(s):

Buddhima Indraratna ◽

Asadul Haque

Keyword(s):

Rock Joints ◽

Shear Behaviour

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Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3850 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2618-2625

Author(s):

R. T. Subhalakshmi ◽

S. Appavu Alias Balamurugan ◽

S. Sasikala

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Recurrent Neural Network ◽

Automatic Segmentation ◽

Ct Images ◽

Superior Performance ◽

Multi Scale ◽

Proposed Model ◽

Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

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Class Ratio Transform with an Application to Describing the Roughness Anisotropy of Natural Rock Joints

Advances in Civil Engineering ◽

10.1155/2020/5069627 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Rui Yong ◽

Lei Huang ◽

Qinkuan Hou ◽

Shigui Du

Keyword(s):

Roughness Parameter ◽

Rock Joints ◽

Anisotropic Model ◽

Roughness Parameters ◽

Transform Method ◽

Smooth Coefficient ◽

Natural Rock ◽

Smoothing Process ◽

Regular Roughness ◽

Parameter Values

In this study, we explore the potential of class ratio transform with an application to describing the roughness anisotropy of natural rock joints. Roughness smooth coefficient, used for suitably smoothing the roughness parameter values to realize an anisotropic model, is proposed to represent the apparent anisotropy of surface roughness. The geometric irregularities of roughness parameters in polar plots allow transforming to a regular roughness asperity pattern, which can be readily approximated by the ellipse function. The joint roughness coefficients in different orientations of natural rock joints were measured and revealed to be identical after applying the smoothing process using the class ratio transform method. The results show that the roughness smooth coefficient increases with sample size but decreases as azimuthal interval narrows. This method demonstrates the ability in describing the roughness anisotropy and inferring the roughness parameters Z2, Rp, and θmax∗/C+12 D.

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