Advancing Wound Filling Extraction on 3D Faces:An Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network.Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions.To achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the trainedmodels.The selectedmodel demonstrates exceptional segmentation performance for complex 3D facial wounds.Furthermore,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study.Our method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous method.From this result,we use 3D printing technology to illustrate the shape of the wound filling.The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design.By automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient outcomes.Additionally,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants.Our source code is available at https://github.com/SIMOGroup/WoundFilling3D.

Stabilization for Equal-Order Polygonal Finite Element Method for High Fluid Velocity and Pressure Gradient

This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system.This technique is constructed by a local pressure projection which is extremely simple,yet effective,to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique.In this research,some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method.

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems.The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays.Relied on learning an amount of information from given data,the long short-term memory(LSTM)method and multi-layer neural networks(MNN)method are applied to predict solutions.Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio,single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale.The predicted results by deep learning algorithms are well-agreed with experimental data.

The Interaction between Microcapsules with Different Sizes and Propagating Cracks

The microcapsule-contained self-healing materials are appealing since they can heal the cracks automatically and be effective for a long time.Although many experiments have been carried out,the influence of the size of microcapsules on the self-healing effect is still not well investigated.This study uses the two-dimensional discrete element method(DEM)to investigate the interaction between one microcapsule and one microcrack.The influence of the size of microcapsules is considered.The potential healing time and the influence of the initial damage are studied.The results indicate that the coalescence crack is affected by the size of holes.The elastic modulus,the compressive strength and the coalescence stress decrease with the rising radius of holes.The initial damage in experiments should be greater than 95%of the compressive strength to enhance the self-healing effect.The large microcapsules require slight initial damage.Both a new type of displacement field near the crack and a new category of coalescence crack are observed.The influence of sizes of holes on the cracking behavior of concrete with a circular hole and a pre-existing crack is clarified.

A Correlation Coefficient Approach for Evaluation of Stiffness Degradation of Beams Under Moving Load

This paper presents a new approach using correlation and cross-correlation coefficients to evaluate the stiffness degradation of beams under moving load.The theoretical study of identifying defects by vibration methods showed that the traditional methods derived from the vibration measurement data have not met the needs of the actual issues.We show that the correlation coefficients allow us to evaluate the degree and the effectiveness of the defects on beams.At the same time,the cross-correlation model is the basis for determining the relative position of defects.The results of this study are experimentally conducted to confirm the relationship between the correlation coefficients and the existence of the defects.In particular,the manuscript shows that the sensitivity of the correlation coefficients and cross-correlation is much higher than the parameters such as changes in stiffness(EJ)and natural frequency values(Δf).This study suggests using the above parameters to evaluate the stiffness degradation of beams by vibration measurement data in practice.

Polygonal Finite Element for Two-Dimensional Lid-Driven Cavity Flow

This paper investigates a polygonal finite element(PFE)to solve a two-dimensional(2D)incompressible steady fluid problem in a cavity square.It is a well-known standard benchmark(i.e.,lid-driven cavity flow)-to evaluate the numerical methods in solving fluid problems controlled by the Navier-Stokes(N-S)equation system.The approximation solutions provided in this research are based on our developed equal-order mixed PFE,called Pe1Pe1.It is an exciting development based on constructing the mixed scheme method of two equal-order discretisation spaces for both fluid pressure and velocity fields of flows and our proposed stabilisation technique.In this research,to handle the nonlinear problem of N-S,the Picard iteration scheme is applied.Our proposed method’s performance and convergence are validated by several simulations coded by commercial software,i.e.,MATLAB.For this research,the benchmark is executed with variousReynolds numbers up to the maximum Re=1000.All results then numerously compared to available sources in the literature.

Three-Dimensional Meshfree Analysis of Interlocking Concrete Blocks for Step Seawall Structure

This study adapts the flexible characteristic of meshfree method in analyzing three-dimensional(3D)complex geometry structures,which are the interlocking concrete blocks of step seawall.The elastostatic behavior of the block is analysed by solving the Galerkin weak form formulation over local support domain.The 3D moving least square(MLS)approximation is applied to build the interpolation functions of unknowns.The pre-defined number of nodes in an integration domain ranging from 10 to 60 nodes is also investigated for their effect on the studied results.The accuracy and efficiency of the studied method on 3D elastostatic responses are validated through the comparison with the solutions of standard finite element method(FEM)using linear shape functions on tetrahedral elements and the well-known commercial software,ANSYS.The results show that elastostatic responses of studied concrete block obtained by meshfree method converge faster and are more accurate than those of standard FEM.The studied meshfree method is effective in the analysis of static responses of complex geometry structures.The amount of discretised nodes within the integration domain used in building MLS shape functions should be in the range from 30 to 60 nodes and should not be less than 20 nodes.

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures.Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time.To address this issue,we explore the potential of deep learning(DL)to increase the efficiency of crack detection and forecasting crack growth.However,there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary.In the paper,we present DL models for identifying cracks,especially on concrete surface images,and for predicting crack propagation.Firstly,SegNet and U-Net networks are used to identify concrete cracks.Stochastic gradient descent(SGD)and adaptive moment estimation(Adam)algorithms are applied to minimize loss function during iterations.Secondly,time series algorithms including gated recurrent unit(GRU)and long short-term memory(LSTM)are used to predict crack propagation.The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results.For evaluation of crack propagation,GRU and LSTM are used as DL models and results show good agreement with the experimental data.

Deep learning-based signal processing for evaluating energy dispersal in bridge structures

In this paper,we use deep learning to investigate a loss factor function(LF)for measuring energy dispersal in bridge structures in Ho Chi Minh City,Vietnam.The LF is calculated from the power spectral density(PSD)of random vibration signals to account for the mechanical parameters required for detecting structural changes.

Data-driven approach to solve vertical drain under time-dependent loading

Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(AI)approach has been applied.Thus,in this study,a new hybrid model based on deep neural networks(DNNs),particle swarm optimization(PSO),and genetic algorithms(GAs)is proposed to solve this problem.The DNN can effectively simulate any sophisticated equation,and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model.In the present study,analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under timedependent loading.The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.