1 Introduction
Meshfree Methods (MMs) [1, 2] have been proposed as an alternative to the widely used Finite Element Method (FEM) for applications in solid mechanics, due to their attractive property to alleviate the mesh requirement for the approximation of an unknown field function. In meshbased techniques, such as FEM, the approximation accuracy is deteriorated in problems that involve large deformations due to the mesh distortion. MMs are more suited for simulations involving large deformations. In MMs, the spatial domain is discretized by a set of nodes arbitrarily distributed without any interconnectivity. Since the introduction of Smoothed Particle Hydrodynamics (SPH) [3, 4], MMs proliferated with developments such as the Meshless Collocation Methods (MCMs) [5, 6, 7], the Meshless Local PetrovGalerkin (MLPG) [8] and the ElementFree Galerkin (EFG) [9] methods. A detailed overview of meshfree methods and their advantages can be found in [1, 10].
In MCMs, the strong form solution of a PDE system is approximated using nodal collocation on the field nodes. Such methods demonstrate high efficiency, easy implementation, and direct Essential Boundary Conditions (EBC) imposition. However, MCMs often suffer from low accuracy when natural boundary conditions are involved due to the lower precision in the approximation of highorder derivatives [11]. In weak form MMs, such as the MLPG and EFG methods, the order of derivation is reduced. Natural boundary conditions are satisfied in a straightforward manner. Weak form MMs have demonstrated high numerical stability and accuracy in large strain problems [12, 13].
The Meshless Total Lagrangian Explicit Dynamics (MTLED) [14, 15, 16] is an efficient variant of the EFG method that was introduced to deal with large deformations. In MTLED, all calculations refer to the initial configuration of the continuum (totalLagrangian formulation). MTLED implements a central difference scheme for explicit time integration, where basis functions and spatial derivatives are computed once, during the preprocessing stage. In addition, the explicit time integration demonstrates advantages over implicit integration schemes, such as (i) straightforward consideration of geometrical and material nonlinearity; (ii) easy implementation; (iii) suitability for massive parallelism [14]. Originally, basis functions approximation in MTLED was performed using the Moving Least Squares (MLS) scheme [14]. An improved version of the MLS with advanced approximation capability named as the Modified MLS (MMLS) has been introduced in [17]. The drawback of the approximation schemes of the MLS group is that the approximation of the field function does not possess the Kroneckerdelta property and the imposition of Essential Boundary Conditions (EBC) requires special treatment.
Methods that modify the weak form to impose EBC (e.g., Lagrange multipliers, penalty methods) are not suitable for explicit time integration. Joldes et al. [18] have proposed the EBC imposition in explicit meshless (EBCIEM) method for EBC imposition in MTLED. In EBCIEM, a displacement correction applies by distributing force on EBC nodes through Finite Element shape functions. EBCIEM applies exact imposition of EBC up to machine precision. However, the need for a Finite Element layer on the EBC boundary imposes restrictions regarding the applicability of the method to arbitrary geometries. A simplified version (SEBCIEM) has been also proposed [18], where the distributed forces are lumped on the EBC nodes.
The EBC correction requirement can be alleviated if basis functions that possess the Kroneckerdelta property are used. Approximation schemes of the Maximum Entropy (MaxEnt) group possess a weak Kroneckerdelta property, which allows imposing EBC directly as in FEM. Different MaxEnt variations have been proposed up to date. Sukumar in [19] used the maximization of information entropy to formulate meshfree interpolants on polygonal elements. Aroyo and Ortiz in [20]
used a Pareto compromise between locality of approximation and maximization of information entropy to create the Local Maximum Entropy (LME) approximants. LME approximants use generalized barycentric coordinates based on Jayne’s principle of maximum entropy
[21] and provide a seamless transition between nonlocal approximation and simplicial interpolation on a Delaunay triangulation (linear interpolants in the context of FEM).LME approximants have already found a large number of applications [22, 23, 24, 25, 26]. LME approximants are smooth and nonnegative approximants with local support that possess the weak Kroneckerdelta property in the sense that basis functions on the boundary are not influenced by internal nodes. The LME support domain "locality" is controlled by a nondimensional parameter . A variational approach to adapt in the context of LME has been proposed in [27]. Cellbased Maximum Entropy (CME) approximants have been proposed as an alternative to LME in the Galerkin framework [28]. CME capitalizes on the background mesh connectivity only to define compact support for basis functions on rings () of connected cells while the basis functions values are computed without using the connectivity information. Up to date, CME approximants have been implemented to approximate field functions in .
The purpose of this work is to extend the CME approximants in and use them in the context of threedimensional (3D) largestrain elastodynamic problems. Due to the previously described advantages of the explicit time integration over the implicit time integration for the solution of largestrain problems, we introduce the CME approximants in the MTLED method. While MTLED is an efficient method to address large deformation problems, the limitation of the special EBC treatment in MMLS can be restrictive in some scenarios. Our objective is to simplify the MTLED method and enable direct EBC imposition by replacing the MMLS with the CME. The rest of this paper is structured as follows. In Section 2, we provide a compact and cogent formulation of the Cellbased Maximum Entropy approximation in , based on the minimum relative entropy framework. In Section 3, we extend the Cellbased Maximum Entropy approximation in . In Section 4, we present benchmark examples, illustrating the accuracy and robustness of the proposed scheme in simple and complex geometries. Finally, in Section 5, we discuss our conclusions.
2 Cellbased Maximum Entropy Approximants Formulation In E^{2}
CME approximants belong to the class of convex approximation schemes. They have compact support which leads to linear algebraic systems with small bandwidth and hence reduced memory requirements. CME are more time efficient compared to approximants with densenodal supports. Minimal supports are constructed for triangulated domains in by replacing Gaussian prior weights functions (commonly used in LME) with smooth distance approximation functions using the Rfunctions technology [29]. The smooth distance approximation of a point to the boundary of the support ring, , is computed to derive the prior weight functions (Figure 1). Following, we give a brief overview of the minimum relative entropy framework (Subsection 2.1) along with the nodal prior weight construction using Rfunctions (Subsection 2.2). For a detailed description of the CME formulation we refer the reader to Millán et al. [28].
2.1 Minimum Relative Entropy Framework
For a scalarvalued function and a set of unstructured points , the MaxEnt approximation is given by
(1) 
where are nodal coefficients, and are nonnegative basis functions that fulfill the zeroth and first order reproducing conditions:
(2) 
The basis functions are defined from an optimization problem that is established at each evaluation point for the linear constraints given by Equation (2
). Due to the nonnegative and partition of unity (zeroth order reproducing condition) properties, the basis functions can be interpreted as a discrete probability distribution. The informational entropy provides a canonical measure to the uncertainty associated with a discrete probability distribution. The leastbiased approximation scheme that is consistent with the linear constraints is provided by the principle of maximum entropy. The formulation of MaxEnt approximants is derived by maximizing the ShannonJaynes entropy measure
[21] when nodal prior weights are used:(3) 
where is the relative entropy measure and the corresponding variational principle is given by the principle of minimum relative (cross) entropy [30]. The MaxEnt basis functions optimization problem can be stated in the variational formulation:
(4)  
subject to  
Duality methods can be employed to solve the convex optimization problem in Equation (4) efficiently and robustly [30, 31]. The basis functions are then:
(5) 
where
(6) 
is the partition function and
is the Lagrange multiplier vector. The Lagrange multiplier vector is obtained solving the unconstrained convex optimization problem:
(7) 
where is the converged solution of at . The resulting basis functions from the solution of are noninterpolating, except on the boundary of the nodal set’s convex hull where the weak Kroneckerdelta property holds (Figure 2). The weak Kroneckerdelta property provides a strong advantage over other approximant types, such as the MLS, where special treatments are required for the imposition of EBC [18]. Moreover, the basis functions inherit the nodal prior weight functions smoothness [32, 31]. Various nodal prior weight functions, such as Gaussian weight function [20, 22, 33], quartic polynomial weight function[34, 35, 36], level set based nodal weight function [37, 38], exponential nodal weight function [39, 40], and approximate distance function to planar curves [28] have been used to construct maximum entropy approximants with specific desired properties. The expression of the gradient for the MaxEnt basis functions for a node evaluated on a point is given by:
(8) 
where the superscript on any function indicates evaluation at , is the prior weight function for node , , and is given by:
(9) 
where
is the identity matrix,
, and . A detailed description of the derivation of is given in Refs. [28, 34].2.2 Nodal Prior Weights Construction For CME In E^{2}
To satisfy the required properties of CME (i.e., smoothness, compact support, unimodality), smooth approximations of the distance function to planar curves [41] are considered in the construction of prior weight functions. The theory of Rfunctions [29] is used to approximate the distance function of each node in the nodal support of a basis function to the boundary polygon of the ring support domain (Figure 1). Any realvalued function , where , is considered a Rfunction if its sign is determined only by the sign of its arguments and not their magnitude. Similarly to logical functions, Rfunctions can be written as a composition of elementary Rfunctions using operations such as negation, disjunction, conjunction, and equivalence [29, 41].
Using the composition property of Rfunctions, the approximate distance function of any given node from the boundary of its polygonal support domain is expressed as the composition of elementary Rfunctions corresponding to the piecewise linear segments of the polygonal curve . For each line segment belonging to with endpoints and , the signed distance function from a point to the line passing from and is defined by:
(10) 
where is the length of the line segment. In addition, the line segment defines a disk of radius . This disk can be expressed by the trimming function:
(11) 
where is normalized to first order and defines the disk with center . From Equations (10), (11) the signed distance function from a point to the line segment with endpoints and is given by the first order normalized function :
(12) 
where is differentiable for any point that does not lay on the line segment. For line segments s.t. the normalized approximation, up to order , of the distance function for the node is given by the Requivalence formula [41]:
(13) 
The Requivalence formula has the desirable properties of preserving the normalization up to the order and being associative, while the Rconjunction is normalized up to the order and it is not associative. Finally, the prior weight function for node is obtained by:
(14) 
where are the indices of the ring nodal neighbors of the point , is a smoothness modulation factor and is normalized s.t. . It should be noted that the Requivalence formula in Equation (13) does not preserve the normalization of the distance function on the joining vertices of the piecewise linear segments and hence introduces undesired bulging effect at these points. Increasing the smoothness modulation factor , the bulging effect is reduced.
3 Extension Of Cellbased Maximum Entropy Approximants In E^{3}
MaxEnt approximants (Equation (5)) are naturally generalized to , with . However, CME approximants have been limited to due to the construction of nodal prior weight functions as smooth approximations to the distance function of 2D polygonal curves. In this section we extend the CME approximants to by constructing nodal prior weight functions as smooth approximations to the distance functions of polyhedral surfaces following the development of Biswas and Shapiro [41].
3.1 Nodal Prior Weights Construction For CME In E^{3}
We consider the discretization of a finite domain in tetrahedra . The ring support domain for any vertex of is defined by the union of the piecewise triangular faces belonging to the ring of attached tetrahedra to the vertex (Figure 1). Rfunctions are employed to construct the approximation distance field to the boundary of the ring support domain by joining the fields of the individual boundary triangular faces.
The distance field approximation to any triangle is constructed considering the intersection of the triangle’s carrier plane and a trim volume defined by the planes , that pass through the edges of the triangle and are orthogonal to the carrier plane (Figure 3). The trim volume can be constructed joining the planes according to the conjunction formula:
(15) 
where , are constants controlling the shape of the Rfunction’s zero set. For points in that do not lay on the carrier plane the Equation (15) is , while for points on the carrier plane () it reduces to the standard conjunction formula. Therefore, is given by:
(16) 
where . The function defines a smooth trimming volume tangent to the three edges of the triangle. The coefficient controls the smoothness of the trimming volume, where for large the trimming volume flattens out and for it becomes identical to the unbounded prism constructed by the combination of the planes (Figure 3). While the unbounded prism trimming volume () does not maintain differentiability on the triangle edges, the smooth volume () is differentiable at any point except the triangle vertices where the bulging effect arises. Substituting and in Equation (12), the normalized distance function from any point to the triangle is approximated and the nodal prior weights are obtained by Equations (13), (14), similarly to the case.
3.2 Derivation Of The CME Approximants Gradient In E^{3}
To compute the gradient of the CME approximants (Equation (8)), we should first compute the gradient of the prior function . Following the abbreviation in [28], to derive , for a node at a point , we first derive the gradient of the normalized approximation to the distance function:
(17) 
where is the distance field function of the triangular patch of the support domain’s boundary for node . The gradient of the distance field function is given by:
(18) 
where is omitted for notation simplicity, is the gradient of the triangle’s carrier plane , which is identical to the plane’s normal vector, and is the gradient of the trim volume function:
(19)  
where , is the gradient of the perpendicular plane to the edge of the triangle given by its normal vector and is given by:
(20)  
4 Evaluation Of The CME Approximants In The MTLED Method
We construct the matrix of the basis function derivatives using the CME approximation, instead of the MMLS, to derive the CMEMTLED method. We perform a series of numerical examples for threedimensional large strain hyperelasticity at steady state to assess the efficiency of the CME approximants in the MTLED method. In all the examples, we use the hyperelastic neoHookean material which is described by the hyperelastic strain energy density function [42]:
(21) 
where and are the Lamé parameters, is the first strain invariant and is the determinant of the deformation gradient. The evaluation of spatial integrals is applied on quadrature points, generated on a background unstructured tetrahedral mesh using a 4point quadrature rule [43]. We use dynamic relaxation, described in detail in [44, 45], to ensure fast convergence for the explicit time integration scheme to the steady state solution. All the simulations are performed using a multithreaded implementation of the MTLED algorithm on an Intel Core i78700K CPU using 12 threads and 16GB DDR4 RAM.
4.1 Cube Under Unconstrained Compression
We model the compression of an unconstrained cube with softtissuelike constitutive properties (hyperelastic neoHookean material). The cube has side length , Young modulus , Poisson ratio , and density . It is subjected to unconstrained compression (Figure 4) by displacing the top surface by ( of the initial height). In this simple case, the vertical component of displacement () is given by the analytical solution , ( refers to the reference configuration).
Simulations are performed for two successively fined nodal distributions, consisting of and nodes, respectively. Computations are conducted using the CMEMTLED method. We evaluate the accuracy of the proposed scheme using the Normalized Root Mean Square Error ():
(22) 
where is the number of nodes, is the axis displacement component of the numerical solution, and the axis displacement component of the analytical solution. Table 1 shows the computed for the two considered nodal distributions. Good agreement with the analytical solution is achieved even for the coarse grid ( nodes, quadrature points). Additionally, due to the weak Kroneckerdelta property of the CME approximants, the essential boundary conditions defined on faces , , and are imposed exactly (absolute error is zero up to machine precision).
Nodes  Quadrature points  

152  1896  1.75E3 
4594  92876  3.85E4 
4.2 Cube Under Constrained Deformation
We further demonstrate the accuracy of the proposed scheme by considering the extension and compression of a cube with side length . One face () of the cube is rigidly constrained, while the opposite face () is displaced (Figure 5). A maximum displacement loading of ( of the initial height) is smoothly applied. A hyperelastic neoHookean material model with , and is chosen to capture the material response of soft tissue.
The cube is discretized using six successively denser nodal distributions (: , : ). The CME approximants are used to approximate the unknown field functions. Simulations are performed to test the convergence of the proposed scheme. The simulation characteristics such as the critical explicit integration time step (), the number of execution steps (), and the total execution time () are evaluated for CME with and parameters ; ; ; . The simulations are also performed using the MMLS approximants with the SEBCIEM correction [18]. The dilatation coefficient (parameter controlling support size) is used in MMLS. The simulation characteristics are given in Table 2.
The for CME is found higher for all the parameter sets compared to MMLSSEBCIEM. The gain is reduced for increasing and ; with increasing
the gradient becomes steeper, leading to larger eigenvalues in the spatial derivatives matrix (
). The CME performs better, in terms of computational efficiency, since it can reach steady state with less time steps than the MMLSSEBCIEM.Approx.  ^{†}  ^{‡}  (ms)  (s)  

Extension  
CME  2  0.2  2.173  0.260  2963  10455  1.4  3173.5 
1.6  1.972  0.243  2814  12707  1.6 – 3906.9  
2.0  1.918  0.238  3341 13122  1.4  3911.0  
4  0.2  1.527  0.205  3396  16582  1.9  4652.4  
1.6  1.230  0.176  3529  20900  1.8  5711.7  
2.0  1.216  0.174  3509  21167  1.9  6070.6  
MMLSSEBCIEM  1.6  0.2  0.902  0.192  3208  16827  1.4  4603.5 
Compression  
CME  2  0.2  2.173  0.260  3172  10442  1.7  3184.9 
1.6  1.972  0.243  3093  17288  1.5  4630.3  
2.0  1.918  0.238  3117  16501  1.5  4414.7  
4  0.2  1.527  0.205  3279  31402  1.7  7231.9  
1.6  1.230  0.176  3664  24885  2.0  6297.4  
2.0  1.216  0.174  3735  18563  1.7  5557.1  
MMLSSEBCIEM  1.6  0.2  0.902  0.192  3975  18315  1.8  5710.6 

CME smoothness modulation factor

CME Rfunction zero set shape factor or MMLS dilatation coefficient
An analytical solution is not available for the given problem. Therefore, to evaluate the convergence and accuracy of the method we use the Signed Relative Error in strain energy density () as a convergence measure similar to [20]. We compute the for to discretization with respect to the (finest) discretization (Figure 6). The is obtained by:
(23) 
where is the mean value of the strain energy density for the discretization, . The for CME is comparable with the for MMLSSEBCIEM. While the MMLSSEBCIEM lead to lower for higher nodal density (), the CME results in lower for lower nodal density ( and ).
For additional comparison, the for to discretization for a Finite Element Method (FEM) simulation is given. FEM simulations are performed with the FEBio v software [46] using isoparametric tetrahedral elements and the NewtonRaphson implicit time integration method. Linear tetrahedral elements are known for being prone to volumetric “locking”. For this reason, FEBio implements a nodally integrated tetrahedron with enhanced performance for finite deformation and nearincompressibility compared to the standard constant strain tetrahedron [47]. The for FEM simulations is found an order of magnitude higher from CME in extension.
Similar results are acquired for compression. The use of CME in MTLED leads to higher and lower compared to the MMLSSEBCIEM (Table 2). The in compression is found lower for CME compared to MMLSSEBCIEM and FEM for all the discretization levels (Figure 6). The setup of and ; ; ; for CME is found to be a good tradeoff between accuracy and efficiency in MTLED for both cases of extension and compression (Table 2). The use of CME instead of the MMLSSEBCIEM in MTLED eliminates the necessity for EBC correction and the execution time is reduced. The execution time reduction is more evident for dense nodal discretization (up to  ). Moreover, the evaluation of the demonstrates the improved accuracy of the MTLED method over the FEM for large strain problems for both extension and compression conditions. Especially for severe distortion, such as in the compression case, the FEM simulation leads to poor results for coarse nodal discretization compared to MTLED using either CME or MMLSSEBCIEM approximants (Figure 7).
4.3 Cylinder Under Locally Applied Indentation
We highlight the applicability of the proposed CMEMTLED method against extreme indentation and demonstrate its applicability beyond what is possible with FEM. In detail we consider indentation of a cylindrical domain with height and diameter , modelled by the hyperelastic neoHookean constitutive law as in [16]. The subregion of the cylinder’s top surface (illustrated in Figure 8) undergoes a vertical displacement of ( of the initial height) while the nodes belonging to the bottom surface are fixed to zero displacements at all directions. The assigned material properties are similar to the test cases in Subsections 4.1 and 4.2 (; ; ). The cylindrical domain consists of nodes and quadrature points (4point quadrature rule). The CME approximants are constructed using the setup of and ; ; ; . The deformation of the indented cylinder at steady state is given in Figure 8. The described displacements at the bottom and top surfaces are exactly satisfied.
The effect of the quadrature on the accuracy of the proposed method is evaluated. The mean strain energy density is measured for several simulations using 1, 4, 8, 16, and 32 quadrature points per integration cell. Quadrature with 8, 16, and 32 quadrature points is performed by using the 4point quadrature rule after subdividing the cells of the original background mesh using 2, 4, and 8 subdivisions respectively. It is shown that for increasing the number of the quadrature points, the strain energy density is reduced monotonically (Figure 9). The difference of the mean strain energy density for quadrature using 4 and 32 points per integration cell is found . Therefore, performing integration with the 4point quadrature rule appears to be a good compromise between accuracy and efficiency. Additional improvement of the accuracy with minimum computational overhead could be achieved by using the adaptive integration method described in [48].
4.4 Cardiac Model Geometry Under Locally Applied Indentation
Changes in myocardium stiffness are related to different cardiac conditions or diseases (e.g., diastolic dysfunction [49], tachycardiainduced cardiomyopathy [50]
). Evaluation of myocardial material properties under such circumstances is relevant for disease assessment and surgical planning. Material properties evaluation is based on the estimation of the force/displacement relation for a predefined displacement
[51, 52]. In this context, we simulate the indentation of a 3D cardiac biventricular geometry. Our objective is to demonstrate the applicability of the CMEMTLED method on domains with arbitrary shape and its ability to satisfy the prescribed displacement conditions on such domains. We use a mesh composed of nodes and tetrahedral cells which was generated from MRI cardiac segmentation images using TetGen [53]. The segmentation images were made available from [54]. Tissue indentation in the axis direction is simulated imposing a displacement on a patch region at the right cardiac ventricle while the bottom and top faces of the ventricles are constrained in all directions (Figure 10). We use the neoHookean constitutive model with parameters ; ; to describe the mechanical response of the cardiac tissue. We assume that the cardiac biventricular geometry is not beating. It should be noted that the selected essential boundary conditions and constitutive model are not based on available experimental data and do not represent neither invivo boundary conditions nor the realistic response of the heart. They are rather chosen aiming to demonstrate the capabilities of the CMEMTLED method. A realistic biomechanical simulation is outofscope of the current study.The indentation simulation is performed using both the CME (, ) and the MMLSSEBCIEM. The characteristics , , and the EBC values at steady state for the two approximation schemes are given in Table 3. It is shown that using CME, the EBC can be imposed exactly without the need of any special treatment such as SEBCIEM or EBCIEM. In addition, solution stability can be achieved using longer explicit integration time steps leading to a reduction in the computational time.
Approx.  (ms)  (h)  Displaced EBC^{†} (m)  Fixed EBC^{†} (m)  

CME  0.636  36073  1.06  0.01 1.0E17  0.00 0.00 
MMLSSEBCIEM  0.455  49692  1.53  0.01 3.3E15  0.00 1.7E15 

Mean EBC values at steady state for fixed and displaced nodes with mean variation
5 Concluding remarks
In the present study, we presented the extension of Cellbased Maximum Entropy (CME) approximants in the threedimensional Euclidean space () using the Rfunctions technology based on the work of Millán et al [28], and Biswas and Shapiro [41]. The CME approximants were used specifically in the MTLED method, however they could be easily introduced in other solvers (explicit, implicit, semiimplicit). We evaluated the accuracy and efficiency of the CME in the Meshless Total Lagrangian Explicit Dynamics (MTLED) method through comparison with Modified Moving Least Squares (MMLS) and Finite Element Method (FEM). We presented two numerical examples to verify the capability of CME MTLED to generate accurate solutions to nonlinear equations of solid mechanics governing the behavior of soft, deformable tissues. We also verified the accuracy of the proposed scheme against extreme indentation, with the indentation depth reaching of the initial height of the sample. The evaluation was performed by analyzing the signed relative error in strain energy density for several combinations of parameters used in CME. Finally, we verified the accuracy of essential boundary conditions (EBC) imposition on domains of arbitrary shape in an indentation simulation of a cardiac biventricular model derived from cardiac MRI segmentation images.
In all the numerical examples, the use of the CME approximants allowed longer explicit time integration step without compromising the numerical stability. A gain in computational time up to was achieved for CME, while demonstrating similar convergence to MMLS. The weak Kroneckerdelta property of CME allowed to directly impose EBC, avoiding special EBC imposition treatments, which have been previously proposed to deal with the lack of the Kronecker delta property in MMLS. The smooth derivatives of CME led to derivatives matrix with smaller eigenvalues compared to MMLS and hence longer explicit time integration steps. CME was proven a valuable alternative to the group of MLS approximants in the context of MTLED and other similar EFG frameworks.
Acknowledgments
This work was supported by the European Research Council through grant ERC2014StG 638284, by MINECO (Spain) through project DPI201675458R and by European Social Fund (EU) and Aragón Government through BSICoS group (T39_17R). Computations were performed by the ICTS NANBIOSIS (HPC Unit at University of Zaragoza). The authors wish to thank CONICET and grant PICTO20160054 from UNCuyoANPCyT for funding the third author and the Department of Health, Western Australia, for its financial support to the fourth author through a Merit Award. The fifth and last authors acknowledge the funding from the Australian Government through the Australian Research Council ARC (Discovery Project Grants DP160100714, DP1092893, and DP120100402).
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