Advances in Embryo Transfer",title:"胚胎移植新进展",subtitle:"Advances in Embryo Transfer",reviewType:"peer-reviewed",abstract:"本书阐述了生殖医学相关的技术知识,以21世纪最新进展和发展趋势为重点,注重创新性、实用性。 其内容从最佳的卵巢刺激方案、授精技术新进展,到胚胎移植操作技巧、胚胎冷冻保存以及子宫内膜容受性的最新研究成果等都做了详尽的描 述。本书旨在帮助更多从事辅助生殖技术的人员了解本领域最新进展,更新此领域中科学研究和临床诊治观念,以提高诊疗水平达到最佳活产 率。
Embryo transfer has become one of the prominent high businesses worldwide. This book updates and reviews some new developed theories and technologies in the human embryo transfer and mainly focus on discussing some encountered problems during embryo transfer, which gives some examples how to improve pregnancy rate by innovated techniques so that readers, especially embryologists and physicians for human IVF programs, may acquire some new and usable information as well as some key practice techniques. Major contents include the optimal stimulation scheme for ovaries, advance in insemination technology, improved embryo transfer technology and endometrial receptivity and embryo implantation mechanism. Thus, this book will greatly add new information for readers to improve human embryo transfer pregnancy rate.
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Human body is a complex thermal system, Arsene d’Arsonval and Claude Bernard have shown that the temperature difference between arterial blood and venous blood is due to oxygenation of blood [1]. A large number of research papers in bio-heat transfer over the past few decades have focused on an understanding of the impact of blood flow on the temperature distribution within living tissues. Pennes [2] was the first attempt to explain the temperature distribution in human tissue with blood flow effect. The improvement of numerical models for determination of temperature distribution in living tissues has been a topic of interest for numerous researchers. Askarizadeh and Ahmadikia [3] introduced analytical solutions for the transient Fourier and non-Fourier bio-heat transfer equations. Li et al. [4] studied the bio-thermomechanical interactions within the human skin in the context of generalized thermoelasticity.
Analytical solutions for the current problem [5, 6] are very difficult to obtain, so numerical methods have become the main way for solving these problems [7, 8, 9, 10]. The boundary element method (BEM) [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] is one of the numerical methods used to solve the current general problem [22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. Generally, Laplace-domain fundamental solutions are easier to obtain than time-domain fundamental solutions for engineering and scientific problems [32, 33]. consequently, the BEM is very helpful for problems that did not have time-domain fundamental solutions, because it requires the Laplace-domain fundamental solutions of the problem’s governing equations. So, BEM expands the range of engineering problems that can be solved with the classical time-domain BEM.
The main aim of this chapter is to propose a new boundary element fractional model for describing the bio-thermomechanical properties of anisotropic soft tissues. The dual reciprocity boundary element method has been used to solve the TWMBT for obtaining the temperature distribution, and then the BEM has been used to obtain the displacement and stress at each time step. The linear systems from BEM were solved by the TFQMR solver with the ILUT preconditioner which reduces the number of iterations and the total CPU time.
A brief summary of the chapter is as follows: Section 1 introduces the background and provides the readers with the necessary information to books and articles for a better understanding of bio-thermomechanical problems in anisotropic soft tissues Section 2 describes the BEM modeling of the bio-thermomechanical interactions and introduces the partial differential equations that govern its related problems. Section 3 outlines the dual reciprocity boundary element method (DRBEM) for temperature field. Section 4 discusses the convolution quadrature boundary element method (CQBEM) for poro-elastic field. Section 5 presents the new numerical results that describe the bio-thermomechanical problems in anisotropic soft tissues.
Consider an anisotropic soft tissue in the Cartesian coordinate system
Geometry of the current problem.
The governing equations which model the fractional bio-thermomechanical problems in anisotropic soft tissues can be written as follows [34, 35].
where the fluid was modeled by the following Darcy’s law [36].
The fractional order equation which describes the TWMBT can be expressed as [37].
where
According to Bonnet [39], our problem can be expressed as a matrix system as [40].
where
Through this chapter, we supposed that
According to finite difference scheme of Caputo [22] and using the fundamental solution of difference equation resulting from fractional bio-heat Eq. (11) [41], we can write the following dual reciprocity boundary integral equation
in which
where
where
The discretization process for Eq. (12) leads to
After interpolation and integration processes over boundary elements, Eq. (15) can be expressed as
The matrix form of Eq. (16) can be written using (14) as
which also can be written
where
The boundary and initial conditions
The time discretization has been performed as follows
Substituting from Eqs. (22)–(25) into (20), we obtain
Thus, with the temperature
The representation formula of (8) that describes the unknown field
where
For anisotropic case, the Laplace domain fundamental solution
where the solid displacement fundamental solution
with
which can be expressed as [36].
The fundamental solution of solid displacement
in which
The remaining parts of
On the basis of limiting process
According to limiting process
where
and
By using (39)-(42), we can write
By applying the inverse Laplace transform, we obtain
where
According to [40], the fundamental solution is
On the basis of Stokes theorem, we obtain
which can be expressed as
On the basis of [40], we get
in which
By applying (48) to a formula
Making use of (34) and (45), we can express
On the basis of [40], we obtain
which may be expressed using (34) as
By applying (29) (53), we obtain
Based on [42], we have
In the in right-side of (55), we can write second term as follows
in which
According to [40], we can write
By augmenting
According to [41], we get
where
On the basis of Lubich [44, 45], the integration weights
Polar coordinate transformation
Substitution of Eq. (63) into Eq. (61), we get
with
According to the procedure [43], the convolution operator (44) can be expressed as
which may be written as
Let the boundary
Now, we assume that we have
where
where
Thus, from (67), we can write the following
In the current study, a Krylov subspace iterative method is used for solving the resulting linear systems. In order to reduce the number of iterations, a dual threshold incomplete LU factorization technique (ILUT) which is one of the well-known preconditioning techniques is implemented as a robust preconditioner for TFQMR (Transpose-free quasi minimal residual) [46] to accelerate the convergence of the solver TFQMR.
To illustrate the numerical calculations computed by the proposed technique, the physical parameters for transversely isotropic soft tissue are given as follows [47]:
The elasticity tensor
in which
where
and therefore
where
Since for strongly anisotropic soft tissue both bulk moduli are positive, we used the following physical parameters for anisotropic soft tissue [48].
and therefore
and other constants considered in the calculations are as follows.
The domain boundary of the current problem has been discretized into 21 boundary elements and 42 internal points as depicted in Figure 2. The computation was done using Matlab R2018a on a MacBook Pro with 2.9GHz quad-core Intel Core i7 processor and 16GB RAM.
Boundary element model of the current problem.
Figure 3 shows the variation of the temperature
Variation of the temperature
Figure 4 illustrates the variation of the displacement
Variation of the displacement
Figure 5 shows the variation of the displacement
Variation of the displacement
Figure 6 shows the variation of the fluid pressure
Variation of the fluid pressure
Figure 7 shows the variation of the bio-thermal stress
Variation of the bio-thermal stress
Since there are no findings available for the problem under consideration. Therefore, some literatures may be regarded as special cases from our general problem. In the special case under consideration, the results of the bio-thermal stress caused by coupling between the temperature and displacement fields are plotted in Figure 8 to illustrate the variation of the bio-thermal stress
Variation of the bio-thermal stress
A novel boundary element model based on the TWMBT and Biot’s theory was established for describing the bio-thermomechanical interactions in anisotropic soft tissues.
The bio-heat transfer equation has been solved using the dual reciprocity boundary element method (DRBEM) to obtain the temperature distribution.
The mechanical equation has been solved using the convolution quadrature boundary element method (CQBEM) to obtain the displacement and fluid pressure for different temperature distributions at each time step.
Due to the advantages of DRBEM and CQBEM such as dealing with more complex shapes of soft tissues and not needing the discretization of the internal domain, also, they have low RAM and CPU usage. Therefore, they are a versatile and powerful methods for modeling of fractional bio-thermomechanical problems in anisotropic soft tissues.
The linear systems resulting from BEM have been solved by TFQMR solver with the ILUT preconditioner which reduces the number of iterations and the total CPU time.
Numerical findings are presented graphically to show the effect of fractional order parameter on the problem variables temperature, displacements and fluid pressure.
Numerical findings confirm the validity, efficiency and accuracy of the proposed BEM technique.
The proposed technique can be applied to a wide variety of fractional bio-thermomechanical problems in anisotropic soft tissues.
For open boundary problems of soft tissues, such as the considered problem, the BEM users need only to deal with real geometry boundaries. But for these problems, FDM and FEM use artificial boundaries, which are far away from the real soft tissues. Also, these artificial boundaries are also becoming a big challenge for FDM users and FEM users. So, BEM becomes the best method for the considered problem.
The presence of fractional order parameter in the current study plays a significant role in all the physical quantities during modeling and simulation in medicine and healthcare.
From the research that has been performed, it is possible to conclude that the proposed BEM is an easier, effective, predictable, and stable technique in the treatment of the bio-thermomechanical soft tissue models.
It can be concluded from this chapter that Biot’s equations for the dynamic response of poroelastic media can be combined with the bio-heat transfer models to describe the fractional bio-thermomechanical interactions of anisotropic soft tissues.
Current numerical results for our complex and general problem may provide interesting information for researchers and scientists in bioengineering, heat transfer, mechanics, neurophysiology, biology and clinicians.
A=ϕ1+Q/R | Biot’s coefficient |
Bx˜e | linear elastostatics operator |
Γ | considered boundary |
ΓD | Dirichlet boundary |
ΓN | Neumann boundary |
C | specific heat of soft tissue |
C | shape factor |
Cb | specific heat of the blood |
Cpjkl | specific heat of the blood |
F | bulk body forces |
ĝD | Dirichlet datum |
ĝN | Neumann datum |
K | dynamic permeability |
K | thermal conductivity of soft tissue |
m | iterative parameter |
p | pore pressure |
P0τ | heating power |
Q,R | solid–fluid coupling parameters |
Qmet | metabolic heat source |
Qext | external heat source |
S0 | scattering coefficient |
T | soft tissue temperature |
T⌣b | arterial blood temperature |
Txe | traction derivative |
u | solid displacement |
uf | fluid displacement |
V=Vf+Vs | bulk volume |
Vf | fluid volume |
Vs | solid volume |
Wb | blood perfusion rate |
B | stress-temperature coefficients |
∈ | linear strain tensor |
ζ | fluid volume variation |
ρ=ρs1−ϕ+ϕρf | bulk density |
ρs=Cϕρf | mass density of soft tissue |
ρf | blood density |
σ | total stress tensor |
τ | time |
τq | phase lag for heat flux |
τT | phase lag for temperature gradient |
φiαk | continuous polynomial shape functions |
ϕ=VfV | porosity |
ψjβk | discontinuous polynomial shape functions |
Ω | considered region |
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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