Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"21",leadTitle:null,fullTitle:"Ferroelectrics",title:"Ferroelectrics",subtitle:null,reviewType:"peer-reviewed",abstract:"Ferroelectric materials exhibit a wide spectrum of functional properties, including switchable polarization, piezoelectricity, high non-linear optical activity, pyroelectricity, and non-linear dielectric behaviour. These properties are crucial for application in electronic devices such as sensors, microactuators, infrared detectors, microwave phase filters and, non-volatile memories. This unique combination of properties of ferroelectric materials has attracted researchers and engineers for a long time. This book reviews a wide range of diverse topics related to the phenomenon of ferroelectricity (in the bulk as well as thin film form) and provides a forum for scientists, engineers, and students working in this field. The present book containing 24 chapters is a result of contributions of experts from international scientific community working in different aspects of ferroelectricity related to experimental and theoretical work aimed at the understanding of ferroelectricity and their utilization in devices. It provides an up-to-date insightful coverage to the recent advances in the synthesis, characterization, functional properties and potential device applications in specialized areas.",isbn:null,printIsbn:"978-953-307-439-9",pdfIsbn:"978-953-51-4917-0",doi:"10.5772/554",price:139,priceEur:155,priceUsd:179,slug:"ferroelectrics",numberOfPages:466,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:null,bookSignature:"Indrani Coondoo",publishedDate:"December 14th 2010",coverURL:"https://cdn.intechopen.com/books/images_new/21.jpg",numberOfDownloads:73495,numberOfWosCitations:120,numberOfCrossrefCitations:25,numberOfCrossrefCitationsByBook:4,numberOfDimensionsCitations:71,numberOfDimensionsCitationsByBook:5,hasAltmetrics:0,numberOfTotalCitations:216,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 13th 2010",dateEndSecondStepPublish:"May 11th 2010",dateEndThirdStepPublish:"September 15th 2010",dateEndFourthStepPublish:"October 15th 2010",dateEndFifthStepPublish:"December 14th 2010",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"289832",title:"Dr.",name:"Indrani",middleName:null,surname:"Coondoo",slug:"indrani-coondoo",fullName:"Indrani Coondoo",profilePictureURL:"https://mts.intechopen.com/storage/users/289832/images/system/289832.jpeg",biography:"Dr. Indrani Coondoo has been working as a research scientist at\nthe University of Aveiro, Portugal since 2019. She has worked\nas an FCT postdoctoral researcher at the same university (2010-\n2017). She was a visiting researcher at the University of Puerto\nRico, U.S.A. (2010). She obtained her Doctorate degree from\nthe Faculty of Applied Sciences, University of Delhi (2008). She\nserved as a CSIR Research Associate and Project Fellow at the\nNational Physical Laboratory, India (2008-2009). Her research interest lies in the\nstudy of lead-free piezoelectrics, layered ferroelectrics, and multiferroics. She has\npublished more than fifty research papers in journals of international repute. She\nhas one U.S.A. patent to her credit. She has edited a book entitled “Ferroelectrics”\nand written many book chapters, as well as review articles.",institutionString:"University of Aveiro",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"505",title:"Electrochemistry",slug:"chemistry-physical-chemistry-electrochemistry"}],chapters:[{id:"12488",title:"Principle Operation of 3-D Memory Device based on Piezoacousto Properties of Ferroelectric Films",doi:"10.5772/13238",slug:"principle-operation-and-3-d-architectures-of-universal-memory-device-based-on-piezoacousto-propertie",totalDownloads:2917,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"Ju. H. 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1. Introduction
The mathematical model of moving fluid includes a set of equations, which are usually written as transport equations of main physical parameters—density, velocity, energy, etc. These equations are conservation laws in fluid flows. Traditionally the component form of the equations is usually used, but at the same time, the componentless form (Gibbs approach) could be applied to obtain and transform these equations. In this chapter several main conservation laws are discussed and represented in tensor form, which has many advantages against usually used component form, like simplicity and compactness, independence on reference frames, less errors in transformations, etc. Below we obtain and analyze continuity and momentum equations and vorticity and energy transport equations, and we discuss also about the divergent form of transport Eqs.
2. Continuity equation
Continuity equation is the mass conservation law for a fluid flow and is presented as a scalar equation, which connects density ρ and velocity of fluid particles V→, and for any liquid it could be written as
∂ρ∂t+V→⋅∇→ρ+ρ∇→⋅V→=0,E1
where ∇→=e→i∂∂xi=e→1∂∂x1+e→2∂∂x2+e→2∂∂x3 is the Hamilton operator and the dot is a symbol of a scalar product.
Hereinafter, the Einstein summation convention is used by default.
It also can be written in two other equivalent forms [1, 2]:
dρdt+ρ∇→⋅V→=0and∂ρ∂t+∇→⋅ρV→=0.E2
In the case of incompressible fluid, we could obtain its simplified form:
∇→⋅V→=0.E3
Let us apply gradient operator to continuity equation (Eq. (1)):
∇→∂ρ∂t+∇→V→⋅∇→ρ+∇→ρ∇→⋅V→=0.
As a result, we obtain vector equation:
∂∂t∇→ρ+∇→ρ⋅∇→V→T+V→⋅∇→∇→ρ+∇→⋅V→∇→ρ+ρ∇→∇→⋅V→=0,E4
which could be written in a more compact form:
ddt∇→ρ+∇→V→⋅∇→ρ+∇→⋅V→∇→ρ+ρ∇→∇→⋅V→=0E5
or by a little bit different way:
ddt∇→ρ+∇→V→⋅∇→ρ+∇→ρ∇→⋅V→=0.E6
These equations contain gradient of vector V→ divergence, which [3] equal to
∇→∇→⋅V→=ΔV→+∇→×∇→×V→.
For incompressible fluid the left part of this relation is equal to zero; therefore for rotation of velocity vector, we can write:
∇→×∇→×V→=−ΔV→.E7
In the case of compressible fluid in accordance with Eq. (6), we have additional terms in the right part of the equation:
∇→×∇→×V→=−ΔV→+1ρddt∇→ρ+1ρ∇→V→⋅∇→ρ+∇→⋅V→ρ∇→ρ.E8
Continuity equation can be also written in tensor form:
Finally, continuity equation can be written in form
∂ρ∂t+tr∇→ρ⊗V→+ρ∇→V→=0.E11
Convective derivatives of density and pressure (and any another scalar quantitatives) also can be written in tensor form:
V→⋅∇→ρ=trV→⊗∇→ρ;V→⋅∇→p=trV→⊗∇→p,
i.e., convective derivative is equal to trace of corresponding tensor.
In addition, for the divergence of the product of scalar and vector functions, we can obtain the following relation:
∇→⋅ρV→=∇→ρ⊗V→+ρ∇→V→:E¯.
3. Equations of motion of fluid with constant and variable properties
The equation of a motion in terms of stress [4, 5] is
ρdV→dt=∇→⋅σ¯+ρf→,E12
where f→ is the body force per unit mass and ∇→⋅σ¯ is the divergence of stress tensor σ¯. In accordance with Newton’s law, tensor σ¯ for an incompressible fluid is
σ¯=−pE¯+2μS¯,E13
where p is the pressure; μ is the fluid shear (dynamic) viscosity; and S¯=12∇→V→+∇→V→T is the rate of strain tensor. Due to relation ∇→⋅V→=0, when μ≠const divergence of stress tensor σ¯ is written as
∇→⋅σ¯=−∇→p+∇→μ⋅2S¯+2μ∇→⋅S¯=−∇→p+∇→μ⋅2S¯+μΔV→.E14
Then equation of motion of incompressible fluid (Navier–Stokes equation) at μ≠const is
ρdV→dt=−∇→p+μΔV→+∇→μ⋅2S¯+ρf→.E15
Additional term ∇→μ⋅2S¯ relates to changing of shear viscosity. In Cartesian coordinates Eq. (15) has the form:
ρdVidt=−∂p∂xi+μΔVi+∂μ∂xj∂Vj∂xi+∂Vi∂xj+ρfi.
In case of compressible fluid with variable viscosity, the equation will contain a term with divergence ∇→⋅V→, which is not equal to zero now. Rheological relation is this case has the form [2]:
considering viscosity variability is especially important for turbulent flow modeling using the Boussinesq hypothesis with turbulent viscosity μt.
Let us apply divergence operation to the Navier–Stokes equation for compressible fluid with variable viscosity. With this purpose we shall apply operation ∇→⋅ to each vector term of Eq. (20):
If the fluid motion occurs in gravity force field, then there is potential U=gz, where z is the vertical coordinate and the body force per unit mass is f→=−∇→U. In this case ∇→⋅ρf→=∇→ρ⋅f→+ρ∇→⋅f→=−∇→ρ⋅∇→U−ρΔU.
Function U is linear; therefore ΔU=0 and
∇→⋅ρf→=−∇→ρ⋅∇→U.
As a result of applied divergence operation to Navier–Stokes equation at μ≠const, we obtain scalar equation:
Now we consider the general case of fluid motion, taking into account its compressibility.
The set of equations of motion of an incompressible fluid contains two—Navier–Stokes and continuity (one vector equation and one scalar equation) [2, 3]:
ρdV→dt=−∇→p+μΔV→+ρf→∇→⋅V→=0.E24
This set of two equations is closed: it contains two unknown quantities—velocity vector V→ and pressure per two equations. The set describes laminar flows; in turbulent flows it becomes unclosed because Reynolds stress tensor appeared.
In case of compressible flows at ρ≠const, divergence of velocity is ∇→⋅V→≠0, and the Navier–Stokes equation (Eq. (19)) of a fluid motion at μ=const has the form:
ρdV→dt=−∇→p′+μΔV→+μ∇→∇→⋅V→+ρf→,E25
where p′ is defined by Eq. (17). Continuity equation is written in the form of Eq. (1). If ρ≠const, the set of equations (Eq. (25) and Eq. (1)) becomes unclosed, because density will also be unknown. To close the set of equation, energy equation is used, which contains one more unknown scalar quantity—temperature T. To determine temperature T, state equation is used; usually in fluid dynamics, it is the Mendeleev-Clapeyron equation. Energy equation could be written as the equation of specific internal energy transport:
ρdudt=−∇→⋅q→+σ¯:∇→V→+ρqs,E26
where u is the specific internal energy (for ideal gas it could be expressed with the help of the isochore heat capacity, du=cvdT); q→ is the heat flux vector (in laminar flow by Fourier’s law, q→=−λ∇→T); λ is the thermal conductivity of the material; and qs is the heat flux from internal or external sources.
Mendeleev-Clapeyron equation has the form
p=ρRT,E27
where R is the universal gas constant. In case of real gas or fluid, the state equation becomes more complicated.
For liquids it usually supposes ρ = const. This condition is applicable for gas motions and also in case the velocities of gas particles are less than 1/3 of sound velocity.
Eqs. (1), (25), (26), and (27) are valid for laminar regime of motion. In case of turbulent regime in these equations, correlations will appear, caused by velocity, density, and temperature pulsations. For closure of the set of equations of turbulent motion, additional relations are required.
4. Vorticity vector and its associated tensor
Vorticity ω→ is a vector quantity, which characterizes velocity field:
ω→=12rotV→=12∇→×V→,E28
in component form
ω→=12∂Vj∂xiεkije→k,
where εijk is the Levi-Civita tensor in component form. Components of ω→ vector are
Vorticity vector ω→ and spin tensor Ω¯ are connected with each other through Levi-Civita tensor 3ε¯=εijke→ie→je→k. These quantities are mutually associated. They say that tensor Ω¯=12∇→V→−∇→V→T is associated to vector ω→=12∇→×V→, because the following relations are satisfied:
Ω¯:3ε¯=3ε¯:Ω¯=−2ω→=−∇→×V→.E30
And vice versa, vector ω→ is associated with tensor Ω¯ since the following expression is valid:
3ε¯⋅ω→=ω→⋅3ε¯=Ω¯.E31
In Eq. (30) spin tensor Ω¯ is translated to the vector ω→; in Eq. (31) vector ω→ is associated with spin tensor.
Let us assume that the fluid is incompressible, μ = const, and its motion occurs in the field of potential mass forces. In this case with the help of Eq. (34), we can obtain the Navier–Stokes equation in the form:
∂V→∂t+∇→V22+P+U+∇→×V→×V→=νΔV→;
now we apply curl operation (rot=∇→×) to the left and right parts of this equation:
∂∂t∇→×V→+∇→×∇→×V→×V→=νΔ∇→×V→.
We could rewrite the second term of the left part in this form:
V→⋅∇→∇→×V→−∇→×V→⋅∇→V→+∇→×V→∇→⋅V→−V→∇→⋅2ω→,
but ∇→⋅ω→=0, and fluid is incompressible (∇→⋅V→=0); therefore, this term finally can be written as
2V→⋅∇→ω→−2ω→⋅∇→V→.
As a result, we can obtain transport equation of vortices in an incompressible viscous fluid, which is named as the generalized Helmholtz equation:
∂ω→∂t+V→⋅∇→ω→−ω→⋅∇→V→=νΔω→,E36
or in more compact form:
dω→dt−ω→⋅∇→V→=νΔω→.E37
It is necessary to note that in the case of compressible fluid and at μ≠const, this equation becomes much more complicated.
This transport equation can be written in another form, considering the equality V→⋅∇→ω→−ω→⋅∇→V→=∇→×ω→×V→. Then
∂ω→∂t+∇→×ω→×V→=νΔω→.E38
If we apply divergence operation to Eq. (36), then for incompressible fluid we obtain
It is easy to see that in sum the left part is the material derivative of kinetic energy of a fluid particle—quantity ddtV22. Then Eq. (46) takes the form:
ρddtV22=V→⋅∇→⋅σ¯+V→⋅ρf→.E47
Usually stress tensor is defined as the sum σ¯=−pE¯+τ¯, where τ¯ is the shear stress tensor. Then the first term of the right part of Eq. (46) takes the form:
V→⋅∇→⋅σ¯=−V→⋅∇→⋅pE¯+V→⋅∇→⋅τ¯.
Now we can represent equation of mechanical energy balance (Eq. (46)) considering ∇→⋅pE¯=∇→p as follows:
ρddtV22=−V→⋅∇→p+V→⋅∇→⋅τ¯+ρV→⋅f→.E48
The first member of the right part of Eq. (46) is power of stresses V→⋅∇→⋅σ¯, which can be written in the form:
V→⋅∇→⋅σ¯=∇→⋅V→⋅σ¯−σ¯:∇→V→.E49
It is easy to be proven if we rewrite this expression in component form in Cartesian coordinates. In this case the left part of Eq. (49) is
Due to the symmetry of the stress tensor σ¯=σ¯T, this expression is an identity (i.e., the left side is equal to the right one). Indeed, the second term of the right-hand side of this relation, after re-designating the index i by k and vice versa, takes the form Vk∂σki∂xi, but σki=σik; therefore both parts of the expression are equal to each other. Thus, equality (49) is valid.
We could simplify the last term of the right part of Eq. (46) if we introduce potential U of mass forces in field of gravity (z axis is positive upwards) as earlier f→=−∇→U, U=gz=r→⋅g→, where g→ is the gravity acceleration vector in the field of gravity. Then
After substituting the above expressions into Eq. (46), we obtain the equation for mechanical energy of a fluid flow:
ρddtV22+U=∇→⋅V→⋅σ¯−σ¯:∇→V→.E51
In the left part of this equation, we observe the total mechanical energy of a fluid flow as the sum of kinetic and potential energy of the flow [6]. Often the right part of Eq. (51) is written in another form, where stress tensor is written as the sum σ=−pE¯+τ¯. Then the right part of Eq. (51) will have the form:
When we substitute these terms in Eq. (52) and then in Eq. (51), the equation for mechanical energy of a fluid flow will take the form:
ρddtV22+U=−V→⋅∇→p+∇→V→⋅τ¯−τ:∇→V→.E53
As a result, we could conclude that the rate of change of total mechanical energy of a flow is equal to the sum of the powers of the pressure forces and viscous friction.
Navier–Stokes equation for a steady flow of viscous incompressible fluid is
ρV→⋅∇→V→=−∇→p+μΔV→+ρf→.E54
The Laplacian of velocity in the right part can be written in the form:
ΔV→=∇→∇→⋅V→−∇→×∇→×V→.E55
It can be obtained by consideration of operation ∇→×∇→×V→:
Using Eqs. (34) and (55) and considering the mass force in field of gravity with the help of potential U, U=−gz (z axis is positive upwards), we obtain instead Eq. (54):
∇→V22−V→×∇→×V→=−1ρ∇→p+ν∇→∇→⋅V→−ν∇→×∇→×V→+∇→U,
where ν=μρ is the kinematic (momentum) viscosity of fluid.
In incompressible fluid ρ=const, ∇→⋅V→=0 and then we have
∇→V22+pρ+gz=V→×∇→×V→−ν∇→×∇→×V→⏟V→−ν∇→×∇→×V→.E56
The gradient of total mechanical energy of a fluid particle E=V22+pρ+gz depends on vortex structure of the flow. When ∇×V→=0 the right part of Eq. (56) is zero, and then E=const in the whole area of the flow.
It is possible to obtain the divergence form of Eq. (54) for incompressible fluid considering ∇→⋅V→=0 and using Eq. (55) and the relation:
Using concepts of identity tensor I¯ and Levi-Civita tensor 3ε¯=εijke→ie→je→k, we can write members of the right part in the divergence form, and as a result, the whole Navier–Stokes equation for steady flow of incompressible fluid can be written as
∇→⋅ρV→⊗V→+pI¯−ρUI¯+ρν3ε⋅∇→×V→=0.E59
The last term of Eq. (59) can be considered in a more simple form due to relation for incompressible fluid ΔV→=∇→⋅∇→V→=∇→⋅S¯. Finally, the divergence form of the Navier–Stokes equation for steady flow of an incompressible fluid is
∇→⋅ρV→⊗V→+p+ρgzI¯−μS¯=0.E60
7. Energy equation for moving fluid
The first law of thermodynamics connects internal energy, heat, and work. In the case of moving fluid, it can be written as follows:
ρdudt=−∇→⋅q→+σ¯:∇→V→+qv,E61
where t is the time; u is the specific internal energy; q→ is the heat flux density vector due to thermal conductivity; σ¯ is the stress tensor; and qv is the value of heat entering into the particle volume from action of external or internal sources per unit time. In this expression, the colon denotes double scalar product of tensors; in this case these are the stress tensor and velocity gradient tensor.
The physical meaning of this equation is that the rate of change of internal energy per unit volume is equal to rate of energy supply due to heat conduction, due to dissipation of mechanical energy of the flow, and due to heat from external or internal sources. Since stress tensor σ¯ can be written as σ¯=−pE¯+τ¯, where τ¯ is the shear stress tensor, taking into account material derivative definition, we can rewrite Eq. (61) in this form:
ρ∂u∂t+V→⋅∇→u=−∇→⋅q→−p∇→⋅V→+τ¯:∇→V→+qv.E62
This is the energy equation in terms of transfer of specific internal energy u.
Vector q→ in the energy equation is determined by Fourier’s law:
q→=−λ∇→T,E63
where T is the temperature and λ is the coefficient of thermal conductivity.
Fourier’s law of thermal conductivity can also be written in terms of enthalpy, which for an ideal gas is related to temperature by the formula h=cpT, where ср is the isobar heat capacity. Then considering λ=ρcpa, where a is the thermal diffusivity, heat flux density vector can be written in the form:
q→=λ∇→T=ρcpa∇→T=ρνaν∇→cpT=ρνPr∇→h=μPr∇→h,
where Pr=a/ν is the Prandtl number.
In Cartesian coordinates Eq. (62) can be written as follows:
ρ∂u∂t+Vj∂u∂xj=∂∂xjλ∂T∂xj−p∂Vj∂xj+τij∂Vi∂xj+qv.E64
The terms p∇→⋅V→ and τ¯:∇→V→ show us in a moving fluid heating or cooling can occur. The term p∇→⋅V→ may cause significant change of temperature, when gas expands (compresses) rapidly. The term τ¯:∇→V→ is always positive; it characterizes dissipation of mechanical energy and its transformation to heat energy. This scalar quantity is usually named as Rayleigh dissipation function [6] and denoted as τ¯:∇→V→=Ф. Let us write this function in Cartesian coordinates for Newtonian viscous fluid, when rheological relation has the form:
τ¯=−23μ∇→⋅V→E¯+2μS¯,E65
where μ is the fluid shear viscosity and S¯ is the strain rate tensor.
Now we could write the dissipative term τ¯:∇→V→ in Eq. (62) by simple transformations:
This function can also be written in the componentless form:
Ф=τ¯:∇→V→=−23μ∇→⋅V→2+μ∇→V→:∇→V→+∇→V→:∇→V→T.E68
For perfect gases [6, 7] internal energy is connected with temperature by the relation du=cvdT, where cv is the isochore thermal capacity. Then, instead of Eq. (62) with the help of expression for vector q→, we can write equation for temperature transport in the form:
ρcv∂T∂t+V→⋅∇→T=∇→⋅λ∇→T−pV→⋅∇→+τ¯:∇→V→+qv.E69
The energy equation (Eq. (62)) can be also written in terms of enthalpy h=u+pρ. With this purpose we need to add the term ρddtpρ to the left and right parts of the equation. Then we obtain in the left part ρdhdt, but in the right part, we shall get this term in transformed form shown as follows:
ρddtpρ=dpdt−pρdρdt=dpdt+ρ∇→⋅V→.E70
Here we also used continuity equation (Eq. (2)). Finally, we can obtain energy equation in the form of enthalpy transport as
ρdhdt=−∇→⋅q→+dpdt+τ¯:∇→V→+qv.E71
This is the second form of energy equation for the perfect gas in which dh=cpdT, where cp is the isobar heat capacity, which leads to the following transport equation for temperature:
ρcpdTdt=∇→⋅λ∇→T+dpdt+τ¯:∇→V→+qv.E72
One more form of the energy equation can be written if we introduce stagnation enthalpy h+V22. To do it we need to add equations for mechanical energy (Eq. (48) to Eq. (71)); as a result we obtain the equation:
ρddth+V22=−∇→⋅q→+∂p∂t+∇→⋅τ¯⋅V→+ρV→⋅f→+qv.E73
Here we used the relation:
∇→⋅τ¯⋅V→=V→⋅∇→⋅τ¯+τ¯:∇→V→,E74
which is easy to be proven if we write it down in the component form considering symmetry of stress tensor τ.
As a result, the dissipative term in Eq. (73) can be written as follows:
∇→⋅τ¯⋅V→=Ψ+Ф,E75
where Ф is the Rayleigh dissipation function and Ψ=V→⋅∇→⋅τ¯− is the scalar quantity, which can be named as additional dissipation function. This additional dissipation function in Cartesian coordinates for Newtonian Stokes liquid can be written as
The fourth form of the energy equation can be written in terms of entropy s transport. According to the fundamental thermodynamic relation,
Tds=dh−1ρdp.E77
Hence we have
Tdsdt=dhdt−1ρdpdt,
and then if we substitute quantity dhdt from Eq. (77) to Eq. (71), we obtain
ρTdsdt=−∇→⋅q→+τ¯:∇→V→+qv.E78
All forms of the equation energy (in terms of internal energy, enthalpy, stagnation enthalpy, and entropy) are equivalent.
Equation for temperature field of an arbitrary gas in the form of equation of transport of temperature T can be obtained from Eq. (62) or Eq. (71). In these cases quantities dudt and dhdt for arbitrary gases and liquids must be specified using known formulas, which follow from Maxwell’s relations:
du=cvdT−1ρ2T∂p∂Tρ−pdρ,E79
dh=cpdT+1ρ2ρ+T∂ρ∂Tpdp.E80
The subscripts in derivatives here fix the parameters, with the constancy of which the derivatives are calculated. From these formulas the expressions for derivatives can be obtained:
dudt=cvdTdt−1ρ2T∂p∂Tρ−pdρdt,E81
dhdt=cpdTdt+1ρ2ρ+T∂ρ∂Tpdpdt.E82
If we substitute them into Eq. (62) and Eq. (71), we obtain two forms of the equation energy in terms of temperature transport.
In heat transfer problems, boundary conditions are specified in three different kinds—the first, second, and third kind:
The boundary conditions of the first kind consist in setting the temperature on the surface of the body.
The boundary conditions of the second kind are setting of the distribution of the heat flux density q on the surface of the body.
The boundary conditions of the third kind consist in setting the temperature of the flow over the surface of the body and the heat transfer conditions on its surface.
8. Divergence form of transport equations
Material derivative of any physical quantity Θ multiplied by density ρ always can be written in the “divergent” form as
ρdΘdt=∂∂tρΘ+∇→⋅ρV→Θ.E83
This directly follows from the continuity equation [Eq. (2)].
Let us consider in detail the following cases for three ranks of a certain physical quantity Θ.
8.1 Quantity Θ is a scalar
Let us assume that quantity Θ is temperature T:
ρdTdt=∂∂tρT+∇→⋅ρV→T.E84
We could prove this equality if we write the left and right parts in component form. For the left part, we have
Therefore, the expression (Eq. (83)) is valid in case Θ is a vector.
8.3 Quantity Θ is a tensor
Let us assume that Θ is a tensor, for instance, stress tensor σ¯. Stress tensor is a second-rank symmetric tensor, which in Cartesian coordinates can be written in the form σ¯=σije→ie→j. Let us prove the equality:
ρdσ¯dt=∂∂tρσ¯+∇→⋅ρV→σ¯.E86
The left part of this equation in the component form can be written as follows:
Therefore, the expression (Eq. (83)) is also valid in case Θ is a tensor.
It is necessary to note the derivative ddtσ¯, which contains local and convective parts ddt=∂∂t+V→⋅∇→, can, at first glance, be used in fluid models when writing the defining equation in the form of a differential transport Equation [2, 5]. However, careful analysis shows that the material derivative for a second-rank tensor is not an invariant quantity [5, 8, 9]. By this reason, instead of derivative ddt, derivative DDt is usually used as the material derivative for a second-rank tensor, which contains also rotational part (deviatoric stress rate), which provides symmetry relative to rotations. The rotational part cannot be written in divergent form.
There are different forms of deviatoric stress rate for an arbitrary second-rank tensor A, for instance:
At present, the question of which derivative is more appropriate to use when constructing rheological equations is unclear. The most common is the rotational derivative by Gustav Jaumann. The corresponding material derivative in the form by Jaumann, for an arbitrary tensor of the second rank, has the form
DA¯Dt=∂A¯∂t+V→⋅∇→A¯+A¯⋅Ω¯+A¯⋅Ω¯T.E87
Material derivative in the form by Rivlin is written as follows:
DA¯Dt=∂A¯∂t+V→⋅∇→A¯+A¯⋅∇→V→+A¯⋅∇→V→T.E88
It is easy to see that Rivlin’s derivative differs from Jaumann’s one by the additional term A¯⋅S¯+A¯⋅S¯T, which is neutral by itself.
Rotational derivative of a symmetric tensor is also a symmetric tensor. As an example, let us consider the rotational derivative of strain-rate tensor and spin tensor:
As a result, we have obtained the symmetrical second-rank tensor.
9. Conclusions
In this chapter, some applications of tensor calculus in fluid dynamics and heat transfer are presented. Typical transformations of equations and governing relations are discussed. Main conservation equations are given and analyzed. The governing equations of fluid motion and energy were obtained.
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State Marine Technical University, St. Petersburg, Russia
State Marine Technical University, St. Petersburg, Russia
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1. Introduction
Explainable artificial intelligence (xAI) is one of the research topics that has been intriguing in recent years. Today, even if we are at the beginning of understanding this type of models, the studies that show interesting results about this issue are getting more and more intensive. In the near future, it is predicted that there will be years when the interpretability of artificial intelligence and deep meta-learning models is frequently explored [1]. It is thought to be a solution to overcome constraints in classical deep learning methods.
In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. Currently, in classical deep learning methods, input data and target (class) information can be trained with high performance and tested with new data input [2]. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms [3]. Many layers in a deep network allow it to recognize things at different levels of abstraction. For example, in a structure designed to recognize dogs, the lower layers recognize simple things such as outlines or color; the upper layers recognize more complex things like fur or eyes, and the upper layers define them all as a dog. Presumably speaking, the same approach can be applied to other inputs that lead a machine to teach itself. For example, it can be easily applied to the sounds that make up the words in the speech, the letters and words that form the sentences in the text, or the steering movements required to drive.
However, there are important shortcomings that current deep learning models are currently inadequate [4]. For deep learning, huge data sets are needed to train on, and these data sets must be inclusive/unbiased, and of good quality [5]. In addition, traditional deep learning requires a lot of time to train models for satisfying their purpose with an admissible amount of accuracy and relevancy [6]. Although deep learning is autonomous, it is highly susceptible to errors. Assume that an algorithm is trained with data sets small enough to not be inclusive [4]. The models trained by this way cause to irrelevant responses (biased predictions coming from a biased training set) being displayed to users [7]. One of the most important problems in artificial learning models is transparency and interpretability [8]. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable [9]. In other words, when you receive an output data against the input data, the deep learning model cannot provide the information for which reason the output is generated. The user cannot fully grasp the internal functions of these models and cannot find answers to question why and how the answers the models produce [10]. This situation creates difficulties in the application areas of these models in many aspects. For example, you stopped a taxi and got on it. The driver is such a driver that when he takes you to your destination, he turns right, turns left, and tries to get you on a strange route than you expect, but when you ask why he did so, he cannot give you a satisfactory answer. Would you be nervous? If there is no problem for you, you can ride an autonomous vehicle without a driver. As another example, when you go to the doctor, the doctor you send your complaint asks for tests and when you have those tests and send it to the doctor, the doctor tells you what your illness is. Even though he says his treatment, he does not give explanatory information about the cause of your illness. In this case, questions remain about what caused the disease and you would not be satisfied with the doctor. This is an important open point in artificial neural networks and deep learning models.
The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. One of these areas is the end user explanation section that includes social sciences. This area provides artificial intelligence to gain cognitive abilities. Another area is the human machine interface, where it can demonstrate the ability to explain; because explainable artificial intelligence needs a very high-level interaction with the user. And finally, deep learning models are an important part of an explicable artificial intelligence approach (Figure 1).
Figure 1.
Explainable artificial intelligence (xAI) [8].
In this new approach, it is aimed to provide the user with the ability to explain the output data produced as well as being trained at high performance with the input data and target (class) information and tested with the new data input as in the classical machine learning models. This will create a new generation artificial intelligence approach that can establish a cause and effect relationship between input and output. It will also be the mechanism of monitoring the reliability of artificial intelligence from the user point of view. While a classic deep learning model can answer “what” or “who” questions, learning models in explainable artificial intelligence approaches can also answer “why,” “how,” “where,” and “when” questions [10] (Figure 2).
Figure 2.
How can explainable artificial intelligence (xAI) be reliable [11]?
Explainability and accuracy are two separate domains. In general, models that are advantageous in terms of accuracy and performance are not very successful in terms of explainability. Likewise, methods with high explainability are also disadvantageous in terms of accuracy. When methods such as classical deep learning models, artificial neural networks support vector machines are utilized, they do not give reasons why, and how their outputs created in terms of explainability. On the other hand, they are very successful in accuracy and performance. Rule-based structures, decision trees, regression algorithms, and graphical methods are good explainability but not advantageous in terms of performance and accuracy. At this point, explanatory artificial intelligence (xAI), which is targeted to be at the highest level of both explainability and accuracy and performance, reveals its importance at this point (Figure 3).
Figure 3.
Machine learning models with respect to accuracy-explainability domain [12].
2. Related works
There is a transformation of machine learning that has been going on since the 1950s, sometimes faster and sometimes slower. The most studied and remarkable area in the recent past is artificial learning, which aims to model the live decision system, behavior, and responses. Successful results in the field of artificial learning led to the rapid increase of AI applications. Further studies promise to be autonomous systems capable of self-perception, learning, decision-making, and action [13].
Especially after the 1990s, although deep learning concept and foundations go back to the past, the accompanying recurrent neural networks, convolutional neural networks, deep reinforcement learning, and adversarial generative networks have achieved remarkable successes. Although successful results are obtained, these systems are insufficient in terms of explaining the decisions and actions to human users and there are limits.
The U.S. Department of Defense (DoD) explains that it is facing the challenges posed by autonomous and symbiotic systems, which are becoming smarter with each passing day. Explaining artificial intelligence or especially explanatory machine learning is important in terms of being a preview that users will encounter machines with human-like artificial intelligence in the future [14, 15]. Explained artificial intelligence is one of the Defense Advanced Research Projects Agency (DARPA) programs aimed at the development of a new generation of artificial intelligence systems, where they understand the context and environment in which machines operate and build descriptive models that enable them to characterize the real world phenomenon over time. For this purpose, DARPA recently issued a call letter for the Explainable Artificial Intelligence (XAI)—Explanatory Artificial Intelligence project [15]. Within the scope of the project, it is aimed to develop a system of machine learning techniques that focus on machine learning and human-machine interaction, and produce explanatory models that will enable end users to understand, trust, and manage emerging artificial intelligence systems. According to the researchers from DARPA, the striking successes in machine learning have led to a huge explosion in new AI capabilities that enable the production of autonomous systems that perceive, learn, decide, and act on their own. Although these systems provide tremendous benefits, their effectiveness is limited due to the inability to explain machine decisions and actions to human users.
The Explanatory Artificial Intelligence project aims to develop the machine learning and computer-human interaction tools to ensure that the end user, who depends on decisions, recommendations, or actions produced by the artificial intelligence system, understands the reason behind the system’s decisions [1]. For example, an intelligence analyst who gets recommendations from big data analytics algorithms may need to understand why the algorithm advises to examine a particular activity further. Similarly, the operator, who tests a newly developed autonomous system, has to understand how he makes his own decisions to determine how the system will use it in future tasks.
The xAI tools will provide end users with explanations of individual decisions, which will enable them to understand the strengths and weaknesses of the system in general, give an idea of how the system will behave in the future, and perhaps teach how to correct the system\'s mistakes. The XAI project addresses three research and development challenges: how to build more models, how to design an explanation interface, and how to understand psychological requirements for effective explanations [2].
For the first problem, the xAI project aims to develop machine learning techniques to be able to manufacture explanatory models. To solve the second challenge, the program envisions integrating state-of-the-art human-machine interaction techniques with new principles, strategies, and techniques to produce effective explanations. To solve the third problem, the xAI project plans to summarize, disseminate, and apply existing psychological theory explanations. There are two technical areas in the program: the first is to develop an explanatory learning system with an explanatory model and an explanation interface; and the second technical area covers psychological theories of explanation [8].
In 2016, a self-driving car was launched on quiet roads in Monmouth County, New Jersey. This experimental tool developed by researchers at chip maker Nvidia did not look different from other autonomous cars; however, Google was different from what Tesla or General Motors introduced and showed the rising power of artificial intelligence. The car had not even followed a single instruction provided by an engineer or a programmer. Instead, it relied entirely on an algorithm that allowed him to learn to drive by watching a person driving [3]. It was an impressive success to have a car self-driving in this way. But it was also somewhat upsetting as it was not entirely clear how the car made its own decisions. The information from the vehicle’s sensors went directly to a huge artificial neural network that processes the data and then delivers the commands needed to operate the steering wheel, brakes, and other structures. The results seem to match the reactions you can expect from a human driver. But what if one day something unexpected happens; hits a tree or stops at the green light? According to the current situation, it may be difficult to find the cause. The system is so complex that even the engineers who designed it can find it difficult to pinpoint the cause of any action. Moreover, you cannot ask this; there is no obvious way to design such a system that can always explain why it does what it does. The mysterious mind of this vehicle points to a vague-looking issue of artificial intelligence. Artificial intelligence technology, which is located at the base of the car and known as deep learning, has proven to be very strong in problem-solving in recent years, and this technology has been widely applied in works such as image content estimation, voice recognition, and language translation. Now the same methods can be used to diagnose lethal diseases, make million-dollar business decisions, etc. to change all industries.
Currently, the mathematical models are used to help determine who will be on parole, who will be approved to borrow money, and who will be hired. If you can access these mathematical models, it is possible to understand their reasoning. But banks, the military, employers, and others are now turning their attention to more complex machine learning approaches. These approaches can make automated decision-making completely incomprehensible. The most common of these approaches represents deep learning, a fundamentally different way of programming computers. Whether it is an investment decision or a medical decision, or a military decision, you do not want to rely solely on a “black box” method [1]. There is already a debate that it is a fundamental legal right to question a system of artificial intelligence about how it arrived at its conclusions. Starting in the summer of 2018, the European Union may require companies to provide users with an explanation of the decisions made by automated systems. This may be impossible even for systems that look comparatively simple on the surface, such as applications and Websites that use deep learning to offer advertising or song suggestions. Computers performing these services have programmed themselves and have done so in ways we cannot understand. Even the engineers who build these applications cannot fully explain their behavior.
As technology advances, we can go beyond some thresholds where using artificial intelligence in recent times requires a leap of faith. The mankind, of course, are not always able to fully explain our thought processes; but we find a variety of methods to intuitively trust people and measure them. Will this be possible for machines that think and make decisions differently than a person does? We have never built machines that operate in ways that their manufacturers do not understand. How long can we hope to communicate and deal with intelligent machines that can be unpredictable or incomprehensible? These questions take a journey toward new technology research on artificial intelligence algorithms, from Google to Apple and many other places between them, including a conversation with one of the greatest thinkers of our time.
3. Explainable artificial intelligence (xAI)
You cannot see how the deep neural network works just by looking inside. The reasoning of a network is embedded in the behavior of thousands of nerves, which are stacked and tied to tens or even hundreds of layers, mixed together. Each of the nerves in the first layer receives an input, such as the voltage of a pixel in an image, and then performs a calculation before sending a new signal as an output. This output is sent to the next layer in a complex network, and this process continues until a general output is produced. There is also a process known as back propagation that modifies the calculations of individual nerves so that a network learns to produce a desired output. Because deep learning is inherently a dark black box by nature, artificial learning models designed with millions of artificial nerve cells with hundreds of layers like traditional deep learning models are not infallible [1]. Their reliability is questioned when simple pixel changes can be seriously misleaded by causing significant deviations in the weight values in all layers of the neural network, especially in an example such as a one-pixel attack [16]. So, it becomes inevitable to ask the question of how it can succeed or fail. With the success of this type of advanced applications, its complexity also increases and its understanding/clarity becomes difficult.
It is aimed to have the ability to explain the reasons of new artificial learning systems, identify their strengths and weaknesses, and understand how they will behave in the future. For an ideal artificial intelligence system, the best accuracy and best performance, as well as the best explainability and the best interpretability are required within the cause-effect relationship. The strategy developed to achieve this goal is to develop new or modified artificial learning techniques that will produce more explicable models. These models are aimed to be combined with state-of-the-art human-computer interactive interface techniques that can be translated into understandable and useful explanation dialogs for the end user (Figure 4).
Figure 4.
Explainable artificial intelligence (xAI) project proposed by DARPA [14, 15].
In this structure, unlike the classical deep learning approaches, two different elements draw attention as well as a new machine learning process. One of these is the explanatory model and the other is the explanation interface. The process of deep neural network-based machine learning is explained at the core of the artificial intelligence approach. Among the known deep learning models, autoencoder, convolutional, recurrent (LSTM), deep belief network, or deep reinforcement learning can be preferred. However, it is also possible to use a hybrid structure where several deep learning approaches are used together. Autoencoder-type model of deep neural networks are multilayered perceptron structure. In convolution neural network-type models, layers consist of convolutional layer, ReLU activation function, and max pool layer. A conventional component of the LSTM is composed of a memory cell including input, output, and forget gates. For training, the backpropagation through time algorithm can be preferred. Although the most common form of deep reinforcement learning models is deep Q network (DQN), many different variations of this model can be addressed. Many different algorithms are used as optimization algorithm. Gradient-based algorithms are the most common form of these algorithms (Figure 5).
Figure 5.
Deep learning models: (a) autoencoder [17], (b) convolutional neural network [18], and (c) recurrent (LSTM) neural network [19].
Explainable model is an adaptive rule-based reasoning system. It is a structure that reveals the cause-effect relations between input data and the results obtained from the machine learning process. This causal structure learns the rules with its own internal deep learning method. In this way, the explanatory artificial intelligence model allows it to explore the causes and develop new strategies against different situations [20].
The explanation interface is a part of the user interaction. It is similar to the question-answer interface in voice digital assistants. This interface consists of a decoder that evaluates the demands of the user and an encoder unit that enables the responses from the explanatory model, which constitutes the causal mechanism of the explainable artificial intelligence, to the user (Figure 6).
Figure 6.
Semantic knowledge matching for explainable artificial intelligence model [21].
In fact, the large networks of semantic technologies (entities) and relationships associated with Knowledge Graphs (KGs) provide a useful solution for the issue of understandability, several reasoning mechanisms, ranging from consistency checking to causal inference [21]. The ontologies realizing these reasoning procedures provide a formal representation of semantic entities and relationships relevant to a particular sphere of knowledge [21]. The input data, hidden layers, encoded features, and predicted output of deep learning models are passed into knowledge graphs (KGs) or concepts and relationships of ontologies (knowledge matching) [21]. Generally, the internal functioning of algorithms to be more transparent and comprehensible can be realized by knowledge matching of deep learning components, including input features, hidden unit and layers, and output predictions with KGs and ontology components [21]. Besides that, the conditions for advanced explanations, cross-disciplinary and interactive explanations are enabled by query and reasoning mechanisms of KGs and ontologies [21].
Although explanatory artificial intelligence forms are of very different structures, all modules such as this explanation interface, explanatory model, and deep learning work in coordination with each other. For example, while a deep learning process estimates classes, such as the explanatory artificial intelligence model (xAI tool) developed by IBM, the concept features data obtained from this process, and another deep learning process using the same input data set produces an explanatory output for the predicted class label output [22] (Figure 7).
Figure 7.
Explainable artificial intelligence (xAI) tool developed by IBM [22].
At this point, the explainable artificial intelligence (xAI) tool developed by IBM is referred as a self-explaining neural network (SENN) which can be trained end-to-end with back-propagation in case of that g depends on its arguments in a continuous way [18]. The input is transformed into a small set of interpretable basis features by a concept encoder [22]. The relevance scores are produced by an input-dependent parametrizer. A prediction to be generated is merged by an aggregation function. The full model to behave locally as a linear function on h(x) with parameters θ(x), producing interpretation of both concepts and relevances, is induced by the robustness loss on the parametrizer [22]. θ(x) modeling capacity is important so that the model richness realizing higher-capacity architectures is sustained although the concepts are chosen to be raw inputs (i.e., h is the identity).
4. Meta-learning
As research and technology on machine learning progresses, artificial intelligence agents consistently display impressive learning performances that meet and exceed the cognitive skills of people in different fields. However, most AI programs are based on computing technology and even reinforcement learning (RL) models that try to regularly improve their knowledge to match human performance. By contrast, people can quickly learn new skills of new skills, simply by having a new skill [23]. The learning of the human brain so efficiently has surprised neuroscientists for years.
In traditional deep learning approaches, the system develops a data-specific model that is transmitted to it by learning from the data. The learning system will perform a certain task only for a certain environment. In the case of another environment, when a very different data is transmitted to it, this deep learning model will be insufficient to perform the task [24]. This issue reveals hard constraints in utilizing machine learning or data mining methods, since the relationship between the learning problem and the effectiveness of different learning algorithms is not yet understood. Under ideal conditions, a system should be designed in which the quality of the data given to the system differs and it can easily adapt to changes in different environments [25]. The deep learning methods used in the current situation are not successful in these situations. At this point, meta-learning, which learns to learn, is an integrated and hierarchical learning model over several different environmental models [26, 27]. As a subfield of machine learning, meta-learning learning algorithms are applied on metadata about machine learning experiments. Instead of classical machine learning approaches that only learn a specific task with single massive dataset, meta-learning is a high-level machine learning approach that learns other tasks together. Therefore, this approach requires a hierarchical structure that learns to learn a new task with distributed hierarchically structured metadata. It is generally applied for hyper parameter adjustment; recent applications have started to focus on a small number of learning. For example, if the system has already learned a few different models or tasks, meta-learning can generalize them and learn how to learn more efficiently. In this way, it can learn new tasks efficiently and create a structure that can easily adapt to changes in multiple tasks in different environments.
People are good at figuring out the meaning of a word after seeing it used only in a few sentences. Similarly, we want our ML algorithms to be generalized to new tasks, without the need for a large data set each time, and to change behavior after a few samples. In typical learning (on a single dataset), each sample targets pair functions as a training point. However, in a small number of learning situations, each “new” sample area is actually another task in itself. In other words, understanding the way that you use unique words in a particular social environment becomes a new task for your language-understanding model, and when you enter a different social environment, it means that the system can adapt to a different language-understanding model than before since it requires to dominate the words that are specific to that social environment. To make sure an ML framework can behave similarly, we have to train it on multiple tasks on its own, so we make each data set a new example of training [28] (Figure 8).
Figure 8.
Meta-learning approach [29].
An alternative is to handle the task consecutively as a sequential input array and create a repetitive model that can create a representation of this array for a new task. Typically, in this case, we have a single training process with a memory or attention repetitive network [30]. This approach also gives good results, especially when the installations are properly designed for the task. The calculation performed by the optimizer during the meta-forward transition is very similar to the calculation of a repetitive network [31]. It repeatedly applies the same parameters over a series of inputs (consecutive weights and gradients of the model during learning). In practice, this means that we meet a common problem with repetitive networks. Since the models are not trained to get rid of training errors, they have trouble returning to a safe path when they make mistakes, and the models have difficulty generalizing longer sequences than those used in the order in which they were used. In order to overcome these problems, if the model learns an action policy related to the current educational situation, reinforcement learning approaches can be preferred [32] (Figure 9).
Figure 9.
(a) Meta-reinforcement learning (stack of sub-policies representation) [33] and (b) meta-reinforcement learning (inner-outer loop representation) [34].
Formal reinforcement learning algorithm learns a policy for only single task.
θ∗=argmaxθEπθτRτE1
In meta-reinforcement learning, there are two distinct processes. One of them is adaptation (inner-loop) behaving ordinary RL policy learning to produce sub-policy where ϕi=fθMi for each environment (task) Mi.
θ∗=argmaxθ∑i=1nEπϕiτRτE2
Another process is meta-training (outer-loop), which is described as meta-policy learning from all sub-policies in the adaptation process (inner-loop).
One of the main differentiers between the human brain and artificial intelligence structures such as deep neural networks, is the brain that utilizes different chemicals known as neurotransmitters to perform different cognitive functions. A new study by DeepMind believes that one of these neurotransmitters plays an important role in the brain\'s ability to quickly learn new topics. Dopamine acts as a reward system that strengthens connections between neurons in the brain.
The DeepMind team has used different meta-reinforcement learning techniques that simulate the role of dopamine in the learning process. Meta-learning trained a repetitive neural network (representing the prefrontal cortex) using standard deep reinforcement learning techniques (representing the role of dopamine) and then compared the activity dynamics of the repetitive network with actual data from previous findings in neuroscience experiments [27]. Recurrent networks are a good example of meta-learning because they can internalize past actions and observations and then use these experiences while training on various tasks.
The meta-learning model recreated the Harlow experiment by saying a virtual computer screen and randomly selected images, and the experiment showed that the “meta-RL agent” was learned in a similar way to the animals found in the Harlow Experiment, even when presented with the Harlow Experiment. All new images were never seen before. The meta-learning agent quickly adapted to different tasks with different rules and structures.
5. Explainable meta-reinforcement learning (xMRL)
In this section, we will discuss the development of deep reinforcement learning models with an explicable approach to artificial intelligence. Deep reinforcement learning models are machine learning models that learn what action to take according to status and reward information by maximizing reward [27]. Generally, it is widely preferred in robotic, autonomous driverless vehicles, unmanned aerial vehicles, and games. Explanatory artificial intelligence, on the other hand, provides the knowledge of why action should be taken against the situation and reward for deep reinforcement learning models. In this way, it will be possible to gain the causal decision-making ability of the model by revealing the relational links between the input and output of the developed agent (Figure 10).
Figure 10.
(a) Reinforcement learning and (b) inverse reinforcement learning [35].
In addition, it is possible to learn the reward derivation mechanism by using the inverse reinforcement learning model [36, 37]. In this case, unlike the previous approach, a meta-cognitive artificial intelligence model that can adapt to other environments instead of just one environment is developed [38, 39]. Taken together with the explainable artificial intelligence approach, it will be possible for the developed agent to develop his own strategy by establishing a cause-effect relationship. For example, the explainable meta-reinforcement learning agent to be developed means that in terms of meta-learning, it can learn to play Go, chess, checkers, and even learn and adapt when it is encountering a new game, and in terms of explainable artificial intelligence, it means that being aware of why it is doing any specific action against a move made by the opponent, it can explain this.
6. Discussion and concluding remarks
Next generation artificial intelligence structures are expected to have a hierarchical meta-learning ability that can adapt to many different environments, besides being a causal and explanatory power by establishing a cause-effect relationship. For this, serious effort is still needed to create flexible and interpretable models that can hold opinions from many different disciplines together and work in harmony.
We cannot ignore the advantages this will give us. For example, if we start with a medical application, after the patient data is examined, both the physician must understand and explain to the patient why he/she suggested that the explanatory decision support system suggested to the related patient that there was a “risk of heart attack.” At the same time, as a meta-learning agent of this system, it has the same ability against all other diseases and it will be possible to develop appropriate treatment strategies.
While coming to this stage, what data is evaluated first is another important criterion. It is also necessary to explain what data is needed and why, and what is needed for proper evaluation. In the future, next generation deep learning and artificial intelligence forms are expected to reach the level of intelligence (singularity), which has higher performance and ability than human level. Artificial intelligence and deep learning structures mentioned in this section are thought to shed light on reaching these levels. In particular, it can be said that meta-learning approaches are capable of supporting the formation of structures that learn and adapt to multiple tasks and are also called general artificial intelligence (AGI). In the same way, it can be stated that artificial intelligence structures will help the formation of self-awareness and artificial consciousness structures based on content and causality.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"explainable artificial intelligence (xAI), meta-learning, deep learning",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72398.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72398.xml",downloadPdfUrl:"/chapter/pdf-download/72398",previewPdfUrl:"/chapter/pdf-preview/72398",totalDownloads:1159,totalViews:0,totalCrossrefCites:10,dateSubmitted:"November 14th 2019",dateReviewed:"March 18th 2020",datePrePublished:"June 25th 2020",datePublished:"December 9th 2020",dateFinished:"June 4th 2020",readingETA:"0",abstract:"The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72398",risUrl:"/chapter/ris/72398",signatures:"Evren Dağlarli",book:{id:"9963",type:"book",title:"Advances and Applications in Deep Learning",subtitle:null,fullTitle:"Advances and Applications in Deep Learning",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",bookSignature:"Marco Antonio Aceves-Fernandez",coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83962-879-5",printIsbn:"978-1-83962-878-8",pdfIsbn:"978-1-83962-880-1",isAvailableForWebshopOrdering:!0,editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"168350",title:"Dr.",name:"Evren",middleName:null,surname:"Daglarli",fullName:"Evren Daglarli",slug:"evren-daglarli",email:"evren.daglarli@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Istanbul Technical University",institutionURL:null,country:{name:"Turkey"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related works",level:"1"},{id:"sec_3",title:"3. Explainable artificial intelligence (xAI)",level:"1"},{id:"sec_4",title:"4. Meta-learning",level:"1"},{id:"sec_5",title:"5. Explainable meta-reinforcement learning (xMRL)",level:"1"},{id:"sec_6",title:"6. Discussion and concluding remarks",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Adadi A, Berrada M. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138-52160'},{id:"B2",body:'Došilović FK, Brčić M, Hlupić N. Explainable artificial intelligence: A survey. In: 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE; 2018. pp. 0210-0215'},{id:"B3",body:'Core MG, Lane HC, Van Lent M, Gomboc D, Solomon S, Rosenberg M. Building explainable artificial intelligence systems. 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Reinforcement Meta-Learning Optimizes Visuomotor Learning. 2020. bioRxiv'},{id:"B38",body:'Parisotto E, Ghosh S, Yalamanchi SB, Chinnaobireddy V, Wu Y, Salakhutdinov R. Concurrent Meta Reinforcement Learning. 2019. arXiv preprint arXiv:1903.02710'},{id:"B39",body:'Jabri A, Hsu K, Gupta A, Eysenbach B, Levine S, Finn C. Unsupervised curricula for visual meta-reinforcement Learning. In: Advances in Neural Information Processing Systems; 2019. pp. 10519-10530'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Evren Dağlarli",address:"evren.daglarli@itu.edu.tr",affiliation:'
Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, Turkey
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All IntechOpen published chapters and articles are available OPEN ACCESS and can be read without the requirement for registration of any kind, immediately upon publication, without any barrier.
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The HTML version, as well as the PDF version of publications dated before 2012 that are accessible through a reader, are available to readers with no restriction.
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The full content of chapters and articles can be read, copied and printed from the link location of the chapter/article and these actions are not limited or restricted in any way.
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Registration is requested only to download the PDF of the chapter/article. There are no subscription fees and there is no charge to user groups.
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IntechOpen chapters and articles are distributed under CC BY 3.0 licences allowing users to “copy, use, distribute, transmit and display the work publicly and to make and distribute derivative works, in any digital medium for any responsible purpose, subject to proper attribution of authorship...” and there is no non-commercial restriction.
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Authors may post published works to any repository or website with no delay, and Authors and Editors of IntechOpen books have direct access to the PDF of the full book.
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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:1,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. 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His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. 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He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. 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He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. 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He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"4",type:"subseries",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. 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It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. 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