Number of iterations for the GMRES method without and with the two preconditioners
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis 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.
\n\nWe 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.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\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:"1350",leadTitle:null,fullTitle:"Neuroimaging for Clinicians - Combining Research and Practice",title:"Neuroimaging for Clinicians",subtitle:"Combining Research and Practice",reviewType:"peer-reviewed",abstract:"Neuroimaging for clinicians sourced 19 chapters from some of the world's top brain-imaging researchers and clinicians to provide a timely review of the state of the art in neuroimaging, covering radiology, neurology, psychiatry, psychology, and geriatrics. Contributors from China, Brazil, France, Germany, Italy, Japan, Macedonia, Poland, Spain, South Africa, and the United States of America have collaborated enthusiastically and efficiently to create this reader-friendly but comprehensive work covering the diagnosis, pathophysiology, and effective treatment of several common health conditions, with many explanatory figures, tables and boxes to enhance legibility and make the book clinically useful. Countless hours have gone into writing these chapters, and our profound appreciation is in order for their consistent advice on the use of neuroimaging in diagnostic work-ups for conditions such as acute stroke, cell biology, ciliopathies, cognitive integration, dementia and other amnestic disorders, Post-Traumatic Stress Disorder, and many more",isbn:null,printIsbn:"978-953-307-450-4",pdfIsbn:"978-953-51-6600-9",doi:"10.5772/1816",price:139,priceEur:155,priceUsd:179,slug:"neuroimaging-for-clinicians-combining-research-and-practice",numberOfPages:438,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"2a13c34467733730cec3ba883cd001a3",bookSignature:"Julio F. P. Peres",publishedDate:"December 9th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/1350.jpg",numberOfDownloads:59091,numberOfWosCitations:24,numberOfCrossrefCitations:10,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:34,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:68,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 22nd 2010",dateEndSecondStepPublish:"December 20th 2010",dateEndThirdStepPublish:"April 26th 2011",dateEndFourthStepPublish:"May 26th 2011",dateEndFifthStepPublish:"July 25th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"62734",title:"Dr.",name:"Julio F. P.",middleName:null,surname:"Peres",slug:"julio-f.-p.-peres",fullName:"Julio F. P. 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In addition to his research at the Institute of Psychiatry at Universidade de São Paulo, he is the professor in charge of Clinical Psychotraumatology training for doctors and psychologists at Hospital Pérola Byington. 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It provides valuable information about biosynthesized active compounds that can be used for the further development of products in various industries. Chapters cover such topics as biosynthesis, characterization, separation, and purification, and applications of bioactive molecules. It describes and discusses bioresources of animal, vegetal, and microbial origin as potential sources of flavonoids, polysaccharides, sterols, polyphenols, amino acids, and others. This book provides insight into future developments in the field and, as such, is an essential resource for academicians, industrial researchers, and practitioners in biomolecules with biological activity.\nKey features:
Creation and development of modern technologies is based on fundamental research. Development of new materials is based on the knowledge of fundamentals of the processes that determine the formation of a material, as well as the correlation of structure and physical properties. Progress in the creation of new materials with certain properties (mechanical, electrical, magnetic) depends on the level of understanding of the processes that underlie the formation of a particular structure. The discovery and development of the materials with new physical properties, in turn, lead to the creation of new instruments and devices. Metallic glass and nanocrystalline metallic materials formed from metallic glasses, of course, apply to such materials.
\nAlthough many years passed after production of the first metallic glass, interest in them is only growing. In 1960, a group of researchers led by Professor Duwez first obtained metal alloy in a strange non‐crystalline state [1]. X‐ray diffraction patterns had no lines corresponding to whatever crystalline phases, and there was only a broad halo. This work [1] is considered to be the first publication, which refers to amorphous metal alloys or metallic glasses. However, a year earlier Miroshnichenko and Sally [2] in Dnepropetrovsk (USSR) have already demonstrated the ability to produce metal alloys in a non‐crystalline state. Obviously, due to not very popular scientific “Factory laboratory” journal, this work went unnoticed. Whatever it was, work [1] is considered to be the first publication devoted to the study of the amorphous phase in metallic systems (as opposed to, e.g., already investigated oxide systems). After the first publication, an avalanche of the works went; an amorphous phase could be obtained in a growing number of systems, and within few years the number of amorphous alloys was already counted in the hundreds. Interest in the metallic glass was due to their unusual structure, quite untypical of alloys, and a whole set of outstanding physical and chemical properties. Amorphous alloys may be high‐strength, magnetically hard and soft magnetic, corrosion‐resistant, and others. Thus, microhardness HV of metallic glasses based on transition metals (Fe, Co, Ni) may exceed 1000, tensile strength may be greater than 4.0 GN/m2. These values exceed the maximum values of strength and hardness of usual metals and alloys used in the industry. For example, the strength of the wire from iron‐based metallic glasses is higher than the strength of piano wire [3]. Iron‐based alloys have very good magnetic properties of low coercivity (0.5–1 A/m) and high saturation magnetization exceeding 1.4 T. Even higher hysteresis properties were obtained for Co70Fe5Si15B10 [4] and Co‐Fe‐P‐B [5] alloys that have almost zero magnetostriction. In general, the main characteristics of the soft magnetic amorphous alloys based on iron, cobalt, and nickel are the high values of residual induction and low magnetic reversal losses; high values of magnetic permeability (high iron content) or close to zero values of magnetostriction (high content of cobalt). Magnetic properties can also be increased with an addition of the alloying elements, the values of the magnetic permeability can be as high as 120,000 [6]. With a slight change in the composition, the properties of metallic glasses can vary quite significantly.
\nMost of the physical properties of solids are structurally sensitive. This dependence is typical for metallic glasses. For example, the soft magnetic properties of amorphous alloys can be improved by relaxation annealing, annealing in a magnetic field; mechanical properties naturally depend on the presence of residual stresses, corrosion properties depend on the chemical composition and state of the surface layer. Partially crystallized metallic glasses (peculiar composite consisting of amorphous and crystalline phases) have a number of very good properties that differ from the properties of both amorphous and crystalline materials. Importantly, the amorphous state is unstable; when heating or aging amorphous phase may decompose with a natural degradation of properties. Therefore, from the standpoint of basic science, and from the perspective of the industrial use of new materials, it is extremely important to study both the actual structure of the amorphous phase in metal alloys and its stability, the transition to the partially crystalline or fully crystalline state, as well as the correlation of structure and properties of the material.
\nThe purpose of this review is to analyze the current state of research of structure evolution in amorphous and nanocrystalline metallic alloys. Particular attention is given to pre‐crystallization processes and specific features of heterogeneous amorphous phase formation. The decomposition of homogeneous amorphous phase and the formation of regions with different chemical compositions and/or different short‐range order are considered for different types of metallic glasses. Formation of a nanocrystalline structure from homogeneous and heterogeneous amorphous phase is studied. Structure‐property correlations are described.
\nThe most common method of producing metallic glass or amorphous alloy is a melt quenching onto a moving substrate. When the melt is cooled, the cooling rate is about 106–109 K/s (depending on the method of quenching). With such a large cooling rate at room temperature, the structure of liquid is frozen and the sample is non‐crystalline.
\nNot all alloys can be obtained in an amorphous state. The most commonly considered three main criteria that determine diffusionless solidification [7]: thermodynamic criterion, morphological criterion and heat criterion. Usually kinetic and structural criteria are also discussed [8–10]. Some alloys may be prepared as amorphous will have less cooling rate [11–24].
\nThe most common method for studying the structure of amorphous alloys is a method of large‐angle X‐ray scattering or as it is often simply called X‐ray method [25, 26]. For detailed information about the structure of the amorphous phase, it is necessary to build the partial radial distribution functions [27–31]. At present, methods of construction of total and partial radial distribution functions are not used very often, although it should be noted the study of Mattern et al. [32] or the study cited in [33]. In analyzing the structure of metallic glasses, as a rule, with the help of Ehrenfest equation, the radius of the first coordination sphere as well as its changes at all types of influences are estimated; and distortion of diffuse maxima (the appearance of an additional shoulder, splitting of the peak, and others.) is determined. In combination with other methods of research, such an approach appears to be more productive.
\nAnother commonly used method for analyzing the structure of the amorphous phase is the method of small‐angle X‐ray (SAXS) and neutron scattering [34]. The most important feature of this method is the possibility of studying heterogeneities in the structure of disordered systems as are metallic glass. In an absolutely homogeneous medium, there is no small‐angle scattering, and the scattering pattern varies considerably with the appearance of any heterogeneities of the electron density in the structure.
\nIn addition to the X‐ray methods for studying the structure, the most important method is the transmission electron microscopy (TEM). Without dwelling on the specifics of the method, it is important to note that the analysis of the structure of amorphous phase is based on both electron diffraction and analysis of bright‐field and dark‐field TEM images. The high‐resolution electron microscopy and micro‐ or nanobeam diffraction methods are used to study the structural features of the early stages of crystallization of the amorphous phase, and the structure of the nanocrystals. The last method allows determining atomic arrangement in amorphous structure, strain in crystalline materials with a high spatial resolution, etc. The development of the nanobeam electron diffraction with a coherent electron beam lesser than 1 nm in diameter has made it possible to obtain two‐dimensional diffraction patterns from a nanoscale region to detect local atomic structure. Combination of the nanobeam diffraction experimental method with ab initio molecular dynamics simulation allows obtaining and developing new knowledge about amorphous structure [35, 36]. The newly developed Cs‐corrected TEM technique offers a great advantage to probe the local atomic structure of disordered metallic glasses since it allows achieving a coherent electron beam as small as ∼3 Å in diameter, which cannot be obtained before by conventional TEM [37].
\nIn studying the structure of the amorphous phase, indirect methods may be used such as the measurement of certain properties, whose changes reflect changes in the structure. For example, structure change can be fixed by measuring the temperature dependence of the magnetic properties. Thus, during heating of the ferromagnetic Fe27Ni63P14B6 amorphous alloy, there was an increase in the Curie temperature [38]. Since the Curie temperature is determined by the nearest environment of atoms, its change, of course, indicates a change in the structure of amorphous phase. In [39], it was shown that after the heat treatment, the amorphous Fe‐P‐B alloy is not uniform, and it is characterized by two different Curie temperatures. Numerous studies of amorphous alloys by Mössbauer spectroscopy showed that depending on the conditions of heat treatment, the parameters of the Mössbauer spectra may vary considerably [40]. Application of NMR spectroscopy revealed that the temperature change can lead to changing type of short‐range order in the amorphous phase [41].
\nA number of researchers have tried to describe the structure of the amorphous phase in metallic glasses by different models. The first model was the model of a chaotic close packing of hard spheres [42], which allowed successfully describing the distribution function of the atoms in the amorphous structure. Model chaotic close packing of hard spheres was initially designed to one‐component systems, and later was adapted to the binary system [43]. Later this model has been improved by the introduction of interatomic potentials (soft‐sphere model), which allowed to build atomic pair distribution function [44] with the more realistic values of the position and intensity of the main part and the shoulder of the second maximum of the distribution function. The next step in the modeling of the structure of metallic glasses was attempts to build a structure made up not of individual atoms and of coordination polyhedra [45, 46]. Another group of models of the metallic glass structure is based on the idea that elements of the crystal structure retained in liquid and amorphous alloys [47]. The review [33] lists all the main issues to date models of the structure of amorphous alloys: model of chaotic packing of hard spheres; model of polyhedra packing; the stereochemical model, model of efficient packing of quasi‐equivalence clusters [47–51] and the model of middle‐range order or fractal packaging [52].
\nAny approach to the description of the amorphous structure suggests that it is a homogeneous isotropic structure. In fact, it was turned out that the structure of the amorphous phase in alloys cannot always be uniform and isotropic. Figure 1 shows a typical X‐ray scattering curve of amorphous alloy. The figure shows intensive first diffuse peak, determining the shortest distance between atoms and the subsequent weak halo. A feature of the amorphous phase in the metal‐metalloid alloys is significantly weaker scattering from metalloid atoms compared to metallic atoms (e.g., an amorphous phase in alloys of Fe‐B); therefore, the scattering is determined primarily by the metal atom. Other situation occurs in the case when the amorphous phase contains two or more metals with comparable scattering amplitude (e.g., Fe‐Zr). In such systems, the appearance of inhomogeneity areas is much more pronounced, since the formation of regions with different chemical compositions (e.g., enriched in iron or zirconium) [53] leads to the appearance of at least two types of the shortest distances between atoms, which naturally affect the X‐ray diffraction pattern. Such changes have been found in a number of systems [54–60].
\nX‐ray diffraction pattern of amorphous alloy.
It should be noted that X‐ray diffraction pattern of the sample shown in Figure 1 does not indicate that the amorphous phase is uniform. The appearance of a shoulder or a second peak occurs when the radii of the coordination spheres in different regions of the sample are different. If the areas, even with different chemical compositions, have the same radius of the first coordination sphere, no features on the X‐ray diffraction patterns will be seen.
\nHeterogeneities in the structure of the amorphous phase were observed not only in as‐prepared state. In some cases, the appearance of heterogeneities in the structure was found after various external influences: heat, irradiation, and others [61–65]. A lot of information about heterogeneities in the amorphous phase was obtained using the method of small‐angle X‐ray scattering [66–70]. It is interesting to note that the occurrence of heterogeneities may be related to the composition of the amorphous phase [56]. The impact of external influences on the structure of the amorphous phase will be discussed in more detail in Section 6.
\nNucleation and growth of the crystals are suppressed at rapid cooling of the melt, and below the melting point metastable supercooled liquid is formed. With the temperature decrease the viscosity increases and the diffusion slows down, that is why at room temperature the solidified structure has the same atomic arrangement as it is in the melt prior to cooling. This non‐equilibrium state is usually called the glass and the temperature at which solidification occurs—a glass transition temperature
The main features of the deformation of metallic glasses are a great amount of elastic deformation (up to 4% in the bulk amorphous alloys), high strength, and fracture toughness [7]. Elementary carriers of deformation in metallic glasses are groups of atoms [82]. In contrast to crystalline materials in which the acts of deformation can be estimated from the behavior of dislocations by transmission electron microscopy, it is virtually impossible to observe such a process in metallic glasses. It is usually assumed that the reason for plastic flow and fracture is the formation of shear bands that develop over time. Deformation of metallic glasses proceeds differently in the two different modes of loading and temperature. At high stresses and relatively low temperatures, deformation occurs heterogeneously and it is much localized. As a result, narrow shear bands formed, the orientation of the shear bands is close to the orientation of the maximum shear stress [83, 84]. The time of formation of shear band is estimated as
It is important to note one more feature of the deformation of metallic glasses. When stress is applied initially, in accordance with Hooke’s law, elastic deformation occurs in which the elongation linearly depends on the stress. At higher loads, this dependence deviates from the linear law. If so‐called mechanical hysteresis loop is observed after the load removing, and the sample does not return to its original shape, one talks about the inelastic deformation of the material. The energy corresponding to the area of the loop, connected with the displacement of atoms in a stable position. The quantity of such displacement in the amorphous alloys is generally about an order of magnitude greater than in crystalline alloys. It is believed that the inelasticity of amorphous alloys is related to the free volume in their structure: if the free space is small, the inelastic deformation is also small. A further plastic deformation of amorphous alloys occurs by the formation and propagation of shear bands. It has been found that the velocity of propagation of shear bands does not depend on the strain rate in the range 2 x 10-4–10-2 s-1 [84]. For such different metallic glasses as ductile Pd40Ni40P20 alloy (value of the plastic strain of about 3%) and fragile Mg58Cu31Y6Nd5 (value of plastic deformation of 0%), the average velocity of propagation of shear bands varies slightly and is 317 and 366 μm/s, respectively [85].
\nAnother feature of plastic deformation of amorphous alloys is the increase in the concentration of free volume in the shear bands, that is, increase in the average distance between the atoms. In general, the features of the shear band structure and, in particular, the reasons for the accelerated mass transfer in these areas are being actively discussed recently. A number of studies assumed that the severe plastic deformation leads to temperature increase up to the melting of the material in shear bands [86–87]. According to another view, shear bands are regions with a disordered amorphous structure of reduced density, so that the diffusion mass transfer in these areas can be facilitated. Probably, mass transfer processes depend both on the temperature rise and on the less dense structure, but these issues are still subject to a more detailed study.
\nThe mechanical properties of amorphous alloys have aroused great interest from the moment when the metallic glasses were first obtained as a ribbon, and it was possible to carry out systematic research. One of the most complete early reviews devoted to the study of the mechanical properties was K. Pampillo’s review [83]. Metallic glasses are high‐strength materials, some of them can be cut, rolled, and even stamp, and it makes metallic glass attractive for technical applications [88–91]. The properties of metallic glasses are highly dependent on the prehistory of the samples and their chemical composition. Since metallic glasses contain fluctuations, atomic structure may vary slightly from place to place. Structural heterogeneities inevitably lead to irregularities in mechanical properties. Areas with less local viscosity are less stable and can be advantageous in some places of localization of inelastic deformation. These areas are places that facilitated the formation of shear bands [92–94]. Johnson and Samwer [95] derived a universal dependence of the yield strength on the temperature (
When heated, amorphous alloys crystallize, but the crystallization is often preceded by separation of the amorphous phase into regions of different chemical compositions and different short‐range order, that is, the formation of two or more amorphous phases. These phases do not separate with sharp interface, and the transformation may exhibit spinodal decomposition characteristics [54, 61]. Changes in the structure of the amorphous phase during annealing were studied for the alloys of various compositions. Figure 2 presents the X‐ray diffraction pattern of Al87Ni8La5 sample annealed at 150°C [98]. After annealing, the samples were amorphous. The structure of the amorphous phase was found to change during annealing, which is revealed in the change of the diffuse maximum in the X‐ray diffraction patterns. The first diffuse maximum in the X‐ray diffraction pattern of the as‐prepared amorphous ribbon was symmetrical, and after annealing it shows a shoulder on the large‐angle side, the degree of the maximum distortion increasing with annealing time. Figure 2 presents the X‐ray diffraction pattern of the sample annealed for 25 h (first diffuse peak region). It is seen that subsequent to heat treatment the diffuse maximum is a superposition of two maxima. The positions of the scattering maxima are known to determine the radius of the first coordination sphere R1,
\nwhere (
Similar results were obtained in studies of the effect of prolonged low‐temperature annealing on the structure of amorphous Fe‐Zr alloy [53]. The annealing of Fe90Zr10 sample results in a shoulder on the smaller angle side of the first maximum in the X‐ray diffraction pattern: the isothermal annealing leads to amorphous phase decomposition and formation of at least two new amorphous phases with different component concentrations. Such a structural change was also mentioned by the authors of [97] in the studies of metallic Fe‐Zr glass by the methods of anomalous X‐ray scattering, nuclear gamma resonance spectroscopy, and magnetic property measurement.
\nX‐ray diffraction pattern of as‐prepared amorphous Al87Ni8La5 alloy (a) and sample annealed at 1500°C for 25 h (b); 1 denotes the experimental spectrum, 2 the summary curve, 3 and 4 lines the diffusion halos from the first and second amorphous phases.
Bright field image (a) and electron diffraction pattern (b) of the sample annealed at 100°C for 6850 h.
Figure 3a presents the alloy structure after annealing at 100°C for 6850 h, and Figure 3b presents the corresponding electron diffraction pattern. After this annealing, the sample contains 10–30 nm size crystals. The nanocrystals have non‐sharp boundaries in the transmission electron microscopy images. The structure is distinguished by diffuse grain boundaries in the microstructure image and persisting blurred rings in the microdiffraction pattern. Such diffusivity cannot be explained by the small grain size since the diffraction patterns of usual nanostructures with crystal sizes of 5–10 nm exhibit much more pronounced ring reflections [99]. With increasing annealing time, the structure becomes more distinct and the boundaries between nanocrystals become sharper, and rings in the electron diffraction patterns begin to split. Figure 4 shows an electron diffraction pattern exhibiting splitting of ring reflection, its more pronounced areas indicated by arrows. It is important to note that these changes in structure occur without appreciable change in scale—the size of nanocrystals is not appreciably changed. The electron diffraction and X‐ray diffraction patterns enabled to establish that two solid solutions of Zr in Fe form in the sample, their lattices having close but different parameters and, naturally, different component ratios. The above characteristic features of transformation indicate a progressive and continuous transition from single‐phase to two‐phase amorphous structure, then to crystalline structure with diffuse grain boundaries, and, finally, to fully crystalline structure with a constant size the regions of composition changes, point to the spinodal nature of transformation.
\nElectron diffraction pattern of the sample annealed at 100°C for 7000 h.
Evolution of the amorphous phase depends on temperature. As it was mentioned above, heating and annealing of the metallic glasses can be carried out in two different temperature ranges: above and below the glass transition temperature. Above the glass transition temperature, the amorphous phase is in a supercooled liquid state, below glass transition temperature it is in fact the amorphous state. It is known that when passing through the glass transition temperature
TEM image of the structure of as‐prepared amorphous Ni70Mo10P20 alloy.
TEM image of the structure of Ni70Mo10P20 alloy annealed above
X‐ray diffraction patterns of as‐prepared and annealed above
High‐resolution electron microscopy image of alloy Ni70Mo10P20 crystallized above
HREM image of Ni nanocrystals formed above the glass transition temperature.
HREM image of Ni(Mo) nanocrystals formed above the glass transition temperature.
Thus, structure formed above and below the glass transition temperature varies markedly. It should be noted that the scale of the chemical composition change may be different: it is about (15–20 nanometers) in (Fe‐Zr system) [102] or more than 50 nm (Ni‐Mo‐B system) [99]. This difference is connected with opportunity of implementing a diffusion mass transport in different temperature conditions. As noted above, the results, showing the development of heterogeneities in an amorphous matrix, were obtained using small‐angle X‐ray scattering method as well [63, 66, 99, 102–104]. Changes in the structure of the amorphous phase do not necessarily have the character of a separation at heat treatment. The type of short‐range order can vary with the temperature [41, 105]. Concentration redistribution of the components in the amorphous phase can occur not only before the onset of crystallization. In some cases, the amorphous alloy separation may accompany the first stage of crystallization [99, 106].
\nThe structure of amorphous phase can vary considerably, not only by heating but also by deformation. The question of a possible change in the structure under the influence of the deformation came into sight from the very beginning of amorphous alloy research. As was mentioned problems of homogeneous deformation and inhomogeneous flow, the fracture processes, their dependence on the temperature and composition was discussed in [83, 107]. In the last few years, new works were published that confirm the change in the structure of the amorphous phase at different types of influences which do not lead to crystallization [108]. A group of studies on the structure evolution carried out directly in the process of deformation of Zr‐based amorphous alloys aroused great interest. The samples were deformed by tension, and synchrotron source was used for structure investigation in‐situ. It allowed detecting the structure occurring at the elastic deformation [109–111]. It has been found that in the absence of plastic deformation, the tensile results in a change of the distance between atoms in an amorphous structure, and these changes depend on the orientation of the applied stress. It was shown that the initial symmetric with respect to zero diffuse peak position becomes asymmetric in the process of deformation. The results indicate that the first coordination sphere, characterizing the arrangement of atoms in the amorphous structure, transformed into an ellipsoid in the deformation process. In the study of elastic deformation of Zr62Al8Ni13Cu17 and La62Al14(Cu5/6Ag1/6)14Ce5Ni5 metallic glasses the deformation was shown [108] to be indeed an anisotropic. The Zr‐based amorphous alloys are brittle, and they practically do not undergo plastic deformation, so only the region of elastic deformation was considered in the above‐mentioned paper. At the same time, it would be interesting to determine whether the observed changes in the structure can be stored when the load is removed and whether they exist at plastic deformation. Such studies have been carried out for the amorphous Pd40Ni40P20 alloy [112]. The deformation by multiple rolling was found to cause formation of anisotropic amorphous structure. The distance between atoms along the rolling direction was found to increase at the deformation, whereas it does not change in the perpendicular direction. Changes in the structure during deformation have been observed using a small‐angle X‐ray scattering method [113, 114].
\nIn addition to the occurrence of the anisotropy in the structure, plastic deformation may lead to separation of the amorphous phase. Figure 11 shows the X‐ray diffraction patterns of amorphous Al88Ni2Y10 and Al88Ni10Y2 alloys after deformation by multiple rolling [115]. It is seen that the first diffuse maximum for Al88Ni10Y2 alloy is asymmetric and, as in the case of heat treatment, it is a superposition of at least two maxima. Note that changing the structure under the influence of plastic deformation depends on the alloy composition. It is evident that the first diffuse maximum from the Al88Ni10Y2 sample (curve 1 in the X‐ray diffraction pattern) is asymmetric and, obviously, it is a superposition of at least two maxima. Two amorphous halos indicate the appearance of regions, which differ in composition. So, rolling of the Al88Ni10Y2 alloy leads to separation of the amorphous phase into regions with different chemical compositions. Deformation of Al88Ni2Y10 alloy (curve 2, Figure 11) leads to formation of a small amount of nanocrystals.
\nX‐ray diffraction patterns of deformed Al88Ni10Y2 (1) and Al88Ni2Y10 (2) alloys.
The physical properties of amorphous alloys are very sensitive to structure evolution; they depend on external influences. Amorphous phase is not thermally stable, so the subsequent annealing, not leading to crystallization, causes the structure change:
\nSince amorphous state is metastable, the amorphous alloys transform into a more stable crystalline state at heating. In most cases, the crystallization of the amorphous phase occurs by the mechanism of nucleation and growth. The most detailed crystallization reactions were studied by U. Koester in Bochum University [120]. The transition of the amorphous phase into crystalline phases depending upon the alloy composition can occur by one of the following reactions.\n
primary (or preferential) crystallization;
eutectic crystallization;
polymorphic crystallization.
When the primary crystallization occurs, concentration gradient arises in front of the growing crystal. The growth rate decreases with time because the reaction involves the atoms diffusing on the long distances. It has been found that the spherical radius
where
The polymorphic and eutectic crystallization mechanisms are studied in [122, 123].
\nThe crystal nucleation can be homogeneous and heterogeneous, in the last case the formation of crystals will be facilitated on the surfaces or so‐called “frozen” crystallization centers [124, 125]. The ratio of nucleation and growth rates is a very important factor in the formation of structure. At low nucleation rate and high growth rate, small number of crystals forms and they can grow to considerable size. In the case of high nucleation rate and a small growth rate, very large number of crystals forms in the amorphous matrix.
\nA diagram of phase transformation: I ‐ α‐Fe + Fe2B, II ‐ α‐Fe + Fe3B(P42/n), III ‐ α‐Fe + Fe3B(P42/n) + Fe3B(I‾4), IV ‐ α‐Fe + Fe3B(I‾4), V – A + α‐Fe + Fe3B(I‾4), VI – A + α‐Fe, IHK ‐ α‐Fe + Fe3B(P42/n) + Fe3B(P), GFI ‐ α‐Fe + Fe3B(P) + Fe3B(Pnma).
In addition to the above reaction of nucleation and growth crystallization, the mechanism of spinodal decomposition may occur at the metallic glass crystallization [126]. The possibility of such a separation in metallic glasses was discussed in a number of papers [127, 128–130]. The crystal structure formed during the crystallization of the amorphous phase can vary significantly in phase composition, morphology, crystal size, mutual arrangement of structural components [131]. In the last decades, much attention has been paid to nanocrystalline structure. The nanocrystalline structure in most cases is formed by the primary crystallization reaction. The Fe‐Cu‐Nb‐Si‐B alloy, called Finemet, was the first alloy obtained in the nanocrystalline structure [132].
\nAn important feature of the crystallization of metallic glasses is the fact that in most cases, crystallization begins with the formation of metastable phases. As an example, crystallization of most extensively studied Fe‐B metal glasses may be considered [133–135]. In accordance with the phase diagram of the system [106], the crystallization of compositions close to the eutectic must occur by α‐Fe and tetragonal Fe2B boride formation. However, the crystallization leads to the formation of α‐Fe is formed of tetragonal Fe3B boride. This boride may form different structures (crystalline lattice of different space group), which undergoes several changes before equilibrium phase turns into Fe2B at heating or annealing. Specific features of metastable Fe3B boride were studied in a lot of work [93, 134, 136, 137]. Figure 12 illustrates a diagram of Fe‐B metallic glass transformation; the regions of existence of different borides are shown.
\nAnother example is the crystallization of the amorphous Ni34Zr66 alloy [138]. This alloy belongs to a small group of metallic glasses, which, in principle, can crystallize by polymorphic crystallization mechanism with formation of equilibrium NiZr2 crystalline phase (tetragonal lattice of I4/mcm space group). However, NiZr2 phase was not observed at the crystallization; a metastable phase with an orthorhombic lattice was formed. In contrast to the equilibrium phase, the structure of the metastable phase has a lower symmetry.
\nThe formation of metastable phases at the crystallization of the amorphous phase is often observed for multi‐component systems. Crystallization of multi‐component Zr‐based alloys and changes in the structure and properties at treatment were examined for the alloys of different composition [139–145]. The crystalline structure forming at amorphous phase decomposition is strongly dependent on the chemical composition. For example, the amorphous Zr65Cu17.5Ni10Al7.5 alloy crystallizes with simultaneous formation of three phases: NiZr2‐type phase with cubic lattice, NiZr2 tetragonal phase and a metastable hexagonal phase, the crystals of all phases are randomly distributed in the amorphous matrix. When reducing the zirconium concentration, the phase composition varies. At the first stage of crystallization of the amorphous Zr50Ti16Cu15Ni19 alloy, three phases are formed, two of them are crystalline and the third phase is quasicrystalline. Quasicrystalline phase was observed in a number of crystallized metallic glasses of different composition [145–149].
\nPart of X‐ray diffraction pattern of Al85Ni10Ce5 after the first stage of crystallization.
One more example: decomposition of the amorphous phase in the Al85Ni10Ce5 metallic glass [150, 151]. The alloy crystallization begins with the formation of metastable phase. Figure 13 shows the X‐ray diffraction pattern of the alloy after the first crystallization stage. After finishing the first stage, the sample is predominantly crystalline, but it retains a certain amount of amorphous phase. All the observed diffraction lines correspond to one crystalline phase with a body‐centered cubic lattice. Figure 13 shows only a part of the investigated spectrum; the sum of the squares of the indices ∑ =
Changes in the structure during the phase transformation from an amorphous phase to the crystalline equilibrium phase are accompanied by a change in the physical properties [118, 152]. Structure formed at the crystallization of metallic glasses varies also during deformation or pressure increase [153–155], icosahedral quasicrystals have been found at the crystallization of a number of metallic glasses [155–158].
\nUnder certain conditions, crystallization leads to the formation of a nanocrystalline structure. Nanocrystalline materials may be single‐phase and multiphase polycrystalline samples with a grain size of up to 100 nm (at least in one direction). Owing to the extremely small size of the grain, the structure of nanocrystalline materials is characterized by a large volume fraction of grain boundaries and interfaces, which can largely determine a variety of physical and chemical properties of the material. Indeed, it has been found that many properties of nanocrystalline material are fundamentally different from those of conventional polycrystalline and amorphous alloys [159, 160]. For instance, nanocrystalline materials may have high strength and hardness, good ductility and toughness, reduced elastic modules, higher diffusion rates, large heat capacity and thermal expansion coefficient, and higher magnetic properties as compared to conventional materials [161–164]. The nanocrystalline materials not only provide an excellent opportunity to study the nature of structure, properties, and structure/properties correlation of solids down to the nanometer range, but they are also very attractive in terms of possible industrial use of new materials [165]. Changes in the structure and properties of amorphous and amorphous-nanocrystalline materials have been studied in a number of papers [166–169]. Small grain size makes a great development and length of the grain boundaries. The grain volume fraction of the grain boundaries in the total amount of the nanocrystalline particles in the material can be estimated in spherical shape approximation as in [170]:\n
where δ is the average thickness of the interface and
The crystal distribution by size after annealing at 473 K for 5 (a) and 15 (b) min: experimental (columns) and calculated (line) data.
Crystallization of amorphous alloys usually takes place under conditions of existence of the large driving forces and low diffusion mobility. These conditions are favorable rather for nucleation and not for crystal growth. Formation and specific features of nanocrystalline structure were studied for different materials [171–175]. Specific nanocrystalline/amorphous composite structure has excellent magnetic or mechanical properties in a number of alloys [176–178].
\nTo establish the mechanism of nanocrystallization, one can use a method of analysis of crystal size distributions. For example, controlled crystallization of the amorphous Al86Ni11Yb3 alloy [179] was found to lead to the formation of nanocrystalline structure with nanocrystal size of 5–12 nm. The nanocrystals are formed according to the mechanism of the non‐stationary heterogeneous nucleation and from frozen‐in crystal nuclei. For the conclusion of the nucleation mechanism, the distributions of nanocrystals by size were plotted for different durations of isothermal annealing; the distributions were analyzed. The size distributions obtained upon treatment for 5 and 15 min are depicted in Figure 14a and b, respectively. It is apparent that the heterogeneous nucleation with a latent period proceeds in our case. This is confirmed by the following facts. (1) Small‐sized crystals are absent in the distribution obtained upon treatment for 15 min. (2) A sharply descending portion in the small‐size range is observed in the distribution at a treatment time of 5 min, which is impossible in the case of homogeneous nucleation. (3) The fraction of large‐sized particles (the right‐hand branch of the distribution) decreases gradually, which is characteristic of the non‐stationary rate of nanocrystal nucleation (with the latent period). Therefore, at the very early stage of metallic glass annealing, there exists a certain time interval during which the stationary size distribution of subcritical nuclei (corresponding to the classical theory) is reached.
\nThe dependence of the average crystal size in the Al86Ni11Yb31 alloy on the annealing time time.
Computer simulations were carried out for heterogeneous nucleation and diffusion controlled growth. The calculated crystal distributions by size for annealed Al86Ni11Yb3 alloy (473 K for 5 and 15 min) are shown by lines in Figure 14; the experimental size distributions are represented by columns. Good agreement with the experimental data was obtained for the following crystallization parameters:
Since metallic glasses are often used as the starting material for producing nanostructures, and problem of deformation of the composite amorphous‐nanocrystalline material is very important. Naturally, an important question is what component (nanocrystal or amorphous phase) determines the mechanical properties of the material and, in particular, if the nanocrystals can be deformed. Questions of the deformation of nanostructures are discussed in detail in [170]. Usually single unloaded volume unit contains a small amount of induced defects or does not contain them at all. The smaller the size of the nano volume, the smaller the number of induced defects it contains. A critical particle size with a free surface dc was assumed to can be estimated if believe that below the probability of the existence of the defect sharply decreases [171, 172]. It was suggested that dc is about 10–100 nm. Such estimates have been made for free nanoparticles. It was assumed that I also believe that the mechanisms of plastic deformation and fracture of nanocrystalline materials are determined by the size effect.
\nIn this way, the solution to this problem largely rests on the question whether the nanocrystals contain dislocations, which are carriers of plastic deformation. In the early stages of research, it has been suggested that there is a critical size below which all the crystals are perfect; and the crystals contain stacking faults, twins, etc. when exceeding this size. In fact, at the initial moment when the crystals nucleate and grow in an isotropic amorphous phase, they really are perfect. When the crystal growing, when the volume per one atom in the crystal is differ then the volume per atom in the amorphous phase, there are tension stresses around the crystals. The value of these stresses increases as the crystals grow and conditions for the formation of linear defects arise. Is there, indeed, a certain critical size of crystal separating perfect and imperfect crystals? The answer is quite important in particular for determining the strength and ductility of the material. For example, it was supposed in [170] that such a critical crystallite size determines the boundaries of the regions where mechanisms of dislocation strengthening and plasticity cease to operate.
\nSpecific features of the nanocrystalline structure were studied in detail in [174–178] for Al‐ and Ni‐based alloys. The results showed that the Al nanocrystals and Ni nanocrystals formed in eutectic Ni‐Mo‐B systems were defect‐free. These nanocrystals have equiaxial shape, small size, and they do not contain linear defects. The nanocrystals of Ni (Mo) solid solution in all studied alloys were defect‐free at the beginning of crystallization, while the size of the nanocrystals is not more than about 5 nm. However, in the fully formed nanostructure the average size is much larger (20–50 nm), and the nanocrystals contain a significant number of defects. In analyzing the reasons of defect formation in the nanocrystals, two factors: the size factor and the energy of stacking fault formation, were considered. This size factor is associated with the inability to generate dislocations in nanocrystals smaller than 100 Burgers vector (operation conditions of dislocation sources, such as the Frank‐Read [184]). It is also obvious that the probability of appearance of defects (e.g., stacking faults) depends on the energy of their formation, and of course, this factor must be taken into account when comparing the degree of perfection of different nanocrystals. In terms of the magnitude of the Burgers vector b, the average size of the nanocrystals is:
\nNi in the alloy with 20 at.% B 8–20
Al ∼40–45
(the Burgers vector is
As was mentioned, when analyzing the degree of nanocrystals perfection in the Al‐ and Ni‐based systems, the influence of the energy of stacking fault formation was considered [174]. The value of the energy of stacking fault formation is 135 erg/cm2 for Al and 240 erg/cm2 for Ni. However, it is known that the energy of stacking fault formation decreases with increasing electron concentration when doping [185]. For example, stacking fault energy in Cu‐Ag alloys decreases ∼10 times with increasing electron concentration to 20% [186, 187]. Unlike aluminum nanocrystals that are one‐component, nickel nanocrystals contain about 17 at.% Mo, which leads to an increase in the electron concentration to about 20%. It is therefore natural to expect a significant reduction (several times) the stacking fault energy. This reduction of the energy of stacking fault formation can explain more perfect structure of Al nanocrystals as compared to Ni(Mo) nanocrystals.
\nHowever, what is a predominant reason of defect formation in the nanocrystals (the size factor or the energy of formation of stacking faults) is not clear.
\nWhat can cause the formation of defects in the nanocrystals? As shown above, a significant number of defects were observed in nanocrystals of Ni (Mo) solid solution. Since the nucleation and nanocrystal growth occur by the primary mechanism of crystallization, it is natural to expect that the concentration of molybdenum in nickel lattice varies from the center to the periphery of the nanocrystal. It is known that [187, 188] peculiar ordered structure can be formed in the crystals of two‐component systems. It can be assumed that the formation of twins in the nanocrystals of Ni(Mo) solid solution can occur in this way, that is, the formation be facilitated due to the uneven distribution of molybdenum atoms in solid solution. Naturally, formation of stacking faults by this mechanism is not possible in the case of single‐component nanocrystals.
\nIt should be noted that the shape of Ni(Mo) nanocrystals with size of 30–50 nm differs from the equilibrium shape, and their growth leads to accumulation of the stresses associated with a crystallographic anisotropy and a difference in the density of the amorphous and crystalline phases. When increasing the size of the nanocrystals, the concentration of molybdenum in Ni (Mo) solid solution reduces, which, as previously noted, leads to increasing energy of stacking fault formation and, hence, decreasing the probability of twin formation by fluctuations of composition. On the other hand, molybdenum concentration in solid solution decreases during the growth of the nanocrystals; and its concentration increases at the reaction front (in the amorphous matrix in front of the crystal growing boundary). Details of the experimental observation of the distribution in front of the growing particle are given in [189].
\nIn the processes of the primary crystallization the amorphous phase of Ni‐based alloys is enriching in molybdenum. Naturally, the Mo concentration is increased near the growing nanocrystal and this increased concentration diminishes with the distance from the nanocrystal. Heterogeneities in the composition of the surrounded regions may be important for the origin of the twins or stacking faults near the nanocrystal boundary and its intergrowth into the nanocrystal. It should also be noted that by increasing the Mo concentration in the nickel lattice, stacking fault energy decreases, that is, formation of defects is facilitated. If these reasons, indeed, define the appearance of defects in the nanocrystals, you should expect the formation of twins and stacking faults in the near‐boundary areas. The studies have shown that nanocrystals with defects in the near‐boundary areas, in fact, present in the nanocrystalline structure. Figure 16 shows a nanocrystal Ni(Mo) solid solution with defect‐free central part and twins and stacking faults in the near‐boundary regions.
High‐resolution electron microscopy image of the Ni(Mo) nanocrystal in the amorphous matrix.
Obviously, chemical heterogeneity is not only reason enough for the defects formation; small nanocrystals (up to ∼ 5 nm) are always defect‐free. Formation of the crystals in the amorphous matrix occurs in the field of stresses caused by the difference in specific volume per atom in the amorphous phase and in crystalline structure. Naturally, these stresses increase with the crystal sizes increasing. At the initial stages of crystal growth in isotropic amorphous phase these stresses are too small to induce the formation of defects in the nanocrystals. In the process of growth the nanocrystals reach the size when there is a growing stresses may cause the formation of defects.
\nIt should be noted that the formation of microtwins and stacking faults cannot be determined by only size factor. One way of determining the degree of structure perfection of the nanocrystals formed in the amorphous phase was based on the following analysis of the diffraction line broadening:
\nbroadening of the diffraction line caused by the small grain size is proportional to 1/cos θ or sec θ (θ is the angle of the reflection);
broadening of the diffraction line caused by randomly distributed dislocation is proportional to tg θ. If the nanocrystals do not contain defects, the half‐widths of the diffraction reflections depend linearly on the secant of the diffraction angle. Figure 17 shows such dependencies for Al‐based alloy (Al82Ni11Ce3Si4) and Ni‐based alloy ((Ni70Mo30)90B10). These alloys contain the nanocrystals with practically the same size: 12 nm nanocrystals in Al82Ni11Ce3Si4 and 13 nm in (Ni70Mo30)90B10. Therefore, curves 1 (for Al82Ni11Ce3Si4 alloy) and 2 (for (Ni70Mo30)90B10 alloy) correspond to two nanocrystalline alloys containing nanocrystals with approximately the same size (∼13 nm). (It should be noted that the lattice parameter of the aluminum and nickel varies markedly, which determines the different quantities of points in the curves.) It is evident that a linear relationship is indeed characteristic of nanocrystals in an aluminum alloy, while linear dependence was not observed for nickel‐based alloy. This means that the nanocrystals of the same size may be defect‐free or contain linear defects in the different systems.
The angular dependence b of the diffraction line half‐width (1 – Al82Ni11Ce3Si4, 2 – (Ni70Mo30)90B10, 3 – Al88Ni10Y2).
However, it is more correct to compare the nanocrystals with the same size, expressed in the Burgers vector units (i.e., with the same number of interatomic distances); these values are 42
In connection with the above, one point should be noted. As already mentioned, some researchers have suggested that there is a certain critical size of the nanocrystal, below which it is defect‐free. For each such material critical size of the nanovolume can be estimated when a probability of the existence of induced defect sharply decreases [170]. Since the yield strength and elastic modulus for nickel are much larger (about three times) than that for aluminum, one would expect that the critical size of dislocation‐free Al nanocrystals will be lower than that for N nanocrystals, but the experimental results do not match. The results show that the size of the nanocrystal, whereby there occur defects, actually, is different for different systems. It depends both on the chemical composition and concentration of the components and on the conditions of nanocrystal formation; one would expect that the formation of the nanocrystal, for example, in thin sections the size of defect‐free crystal should be reduced significantly because the proximity surface will contribute to the resulting stress.
\nObviously, to estimate perfection of the nanocrystalline structure, one should consider both the size of the crystals and the possible chemical heterogeneity, facilitating the formation of defects. The second factor is rather significant for nanocrystals comprising two or more components, the first one is important for any type of nanocrystals.
\nAs noted above, the changes occurring in the amorphous phase structure before the onset of crystallization may significantly affect the morphology and parameters of the crystalline structure forming on subsequent heating [116, 190, 191]. The amorphous phase is the parent phase for nanocrystals, which are formed by thermal and/or deformation effects. By varying the type and effect parameters, one can create amorphous‐nanocrystalline materials with different size and volume fraction of nanocrystals. Nanocrystal formation usually occurs by the primary crystallization mechanism. This reaction leads to change of the amorphous phase composition and to enrich the amorphous phase in refractory components and metalloids. This process can result in an increased stability of the amorphous phase. The crystallization process thus can stop, and the resulting amorphous‐nanocrystalline composites have high thermal stability. Heterogeneities in the amorphous phase may lead to the formation of different crystal phases. The noted structural changes do not exhaust all the possibilities for transformation of the system toward the equilibrium state. This wide variety of structures defines a set of good physical and chemical properties. For example, [116] annealing of amorphous Fe‐B alloy at temperatures below the crystallization temperature was found to alter microhardness and stability of the amorphous phase and ten‐fold increase of the crystal size after subsequent crystallization. It has been found that heat treatment at different temperature ranges leads not only to a change of the phase composition but also to the formation of the structure with substantially smaller crystals upon subsequent heating. Formation of smaller crystals was also observed is the case of crystallization of heterogeneous amorphous structure. This last point is particularly important because it indicates the possibility of structure design, based on the decomposition and separation inside amorphous phase. The separation of amorphous phase makes it possible to produce amorphous samples with crystalline surfaces (Figure 18) and crystalline samples with amorphous surface (Figure 19) in Fe‐B‐P alloys [190].
\nMicrostructure of partially crystallized in‐situ Fe83B10P7 alloy.
Microstructure of annealed and partially crystallized in‐situ Fe83B10P7 alloy.
As most properties are structure‐sensitive, the knowledge of the ways for structure design leads to the possibility of forming materials with the desired properties. Creating new materials with good properties, as already mentioned, is one of the major challenges of modern science.
\nCoupled PDE models have wide applications in the real world. For example, in fluid dynamics, there are two-phase flow models, fluid structure interaction models, heat transfer models in fluids etc. In this work, we focus on two typical models: coupled models for describing fluid flow interacting with porous media flow, and fluid structure interaction (FSI) models. The first type of models have been validated by experiments [1] and then justified by using homogenization theory [2, 3]. Applications include the environmental engineering problem of groundwater contamination through rivers and the geoscience problem of surface flows filtrating in vuggy porous media. The second type of models come from many practical applications. For example, blood flow interacting with vessel wall, compressible fluids interacting with aircraft wings, as well as slamming and whipping response of ship structure to water flow. These models are typical multidomain coupled PDE models with multiphysics. Due to the heterogenenities in subdomain models, it is very difficult to find a unified approach to solve the different subdomain models simultaneously. Moreover, some coupled models have nonlinearity in either subdomain problems or interface coupling terms. To deal with the difficulties caused by the coupling of different submodels and the nonlinearity [4, 5, 6, 7, 8], we discuss some decoupling techniques [4, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], which have shown to be very effective and efficient. Among them, we will emphasize the work proposed by our group members and highlight the novelty and importance of these algorithms.
The rest of the paper is organized as follows. In Section 2, we introduce the coupled fluid flow/porous media interacting models. Some decoupling techniques, specifically, decoupled preconditioners, decoupled two-level and multi-level methods, and partitioned time schemes will be presented and analyzed. In Section 3, we present fluid structure interaction models. Partitioned decoupling algorithms include the Robin-Neumann scheme [14, 15], the
For the linear cases of the coupled models for fluid flow interacting with porous media flow, we refer to the Stokes/Darcy model studied in [7, 11, 18, 20, 24, 25, 26, 27, 28, 29, 30]. For the nonlinear case, we refer to the coupled nonlinear Navier–Stokes/Darcy model [4, 5, 6].
Let
A global domain
For incompressible Newtonian fluid flow, Navier–Stokes equations of the stress-divergence form are usually used [31, 32].
where
is the stress tensor with
In strong form,
because the fluid flow is assumed to be divergence free and
Among various porous media flow models, Darcy’s law is the most favored. The governing variable in
Here,
We assume that
Moreover, the divergence of the seepage velocity equals to the source term. This leads to the following steady state equation:
In the time-dependent case, the governing equations in
where
No matter time-dependent or steady state, the key part of the coupled model is a set of interface conditions, which describe the interaction mechanism of the two different types of flows. The following interface conditions have been extensively used and studied in the literature [1, 2, 3, 7, 27, 28]:
Here,
The proper functional spaces for
Moreover, we denote
where
with
For the wellposedness of the coupled Navier–Stokes/Darcy model, we refer to the recent results in [5, 6, 34]. It is shown in [34] that if the viscosity is sufficiently large and the normal velocity across the interface is sufficiently small, then the problem (14) is wellposed. We follow the assumptions in [34]. In addition to these assumptions on model parameters and variables, we shall frequently use these properties:
We partition
We assume that the solution of (13) is smooth enough and the finite element spaces have the following typical approximation properties: for all
To satisfy the discrete inf-sup condition and the approximation properties (17)–(18), if
As an illustration of decoupled preconditioning techniques, we will consider the linear Stokes/Darcy model, whose weak form is (19) while the nonlinear term is dropped. We note that the discrete model in the operator form is
Here
By discarding the coupling interface terms and plugging in
By keeping one of
Let us denote
We also propose another preconditioner of the block triangular type:
by retaining the divergence operator. This preconditioner is still decoupled as the computation can be carried out in a block forward substitution manner. As an illustration, we illustrate the importance of using preconditioners in Table 1.
268 | 186 | 41 | 20 | |
948 | 432 | 45 | 22 | |
3556 | 48 | 22 | ||
13,764 | 46 | 22 | ||
54,148 | 46 | 22 |
Number of iterations for the GMRES method without and with the two preconditioners
We use
For the mixed Stokes/Darcy model, Mu and Xu in [11] propose a two-grid method in which the coarse grid solution is used to supplement the boundary conditions at the interface for both of the two subproblems. The
Solve the linear part of problem
Solve a modified fine grid problem: find
The main theoretical results for the two grid algorithm are as follows.
Based on this algorithm, some other improvements have been made. For example, in [18, 19, 20], by sequentially solving the Stokes submodel and the Darcy submodel, the authors can make
The extension to a multilevel decoupled algorithm can be found in [26]. The
Solve the linear part of problem
Set
find
end.
In our multilevel algorithm, we refine the grid step by step, the coupled problem is only solved on the coarsest mesh, and linear decoupled subproblems are solved in parallel on successively refined meshes. We see that the algorithm is very effective and efficient. Moreover, the theory of the two grid algorithm guarantees that the approximation properties are good. As an illustration of the effectiveness of the multilevel algorithm [41], we present numerical results in Table 2.
Errors between the solutions of multilevel algorithm and the exact solutions (
In the following, we use steady state NS/Darcy model to illustrate how to apply two-level and multilevel methods to decouple the coupled nonlinear PDE models. The algorithm combines the two-level algorithms and the Newton-type linearization [4, 36, 42]. Our
Solve the coupled problem (19) on a coarse grid triangulation with the meshsize
On a fine grid triangulation with the meshsize
On the same fine grid triangulation, solve two subproblems by using the newly obtained solution.
We remark here that the problem (36) and the problem (34) differ only in the right hand side. Similarly, the problem (35) and the problem (33) have the same stiffness matrix. In sum, the advantages of our work exist in that the scaling between the two meshsizes is better, the algorithm is decoupled and linear on the fine grid level and the two submodels in the last two steps share the same stiffness matrices.
For the coupled problem (19), by using the properties (16)–(18), the error estimates in the energy norm can be derived by using a fixed-point framework [4, 31]. Moreover, the Aubin-Nitsche duality argument can result in the
Moreover, we have the following
The energy norm estimate (37) is the Lemma 2 in [4]. The
The following lemma concludes the error estimate of
From Lemma 1, we note that the optimal finite element solution error in the energy norm is of order
Finally, we would like to make some comments on the mixed Stokes/Darcy model. We note that by dropping those trilinear terms (32), (34) and (36), our two-level algorithm can be naturally applied to the coupled Stokes/Darcy model. We note that the above algorithms can be naturally extended to multi-level algorithms by recursively calling the above two-level algorithms [17, 43]. The extension and the corresponding analysis can be found in [26, 43].
The fully evolutionary Stokes/Darcy equations will be used as the model problem in this subsection to illustrate the partitioned time schemes. We neglect the fluid density and the porosity effects in this subsection. We review some decoupled methods that converge within a reasonable amount of time, and are stable when the physical parameters are small. More precisely, partitioned time methods can efficiently solve the surface subproblem and the subsurface subproblem separately.
For the estimate of the stability, we assume that the solution of the Stokes/Darcy problem is long-time regular [44]:
The simplest time scheme for the evolutionary coupled Stokes/Darcy model is the
However, this scheme is fully coupled and each time step one has to solve a coupled system including both (45) and (46), although, on the other hand, this scheme enjoys the desirable strong stability and convergence properties. In [12], Mu and Zhu propose the following backward Euler forward Euler scheme and combine it with the two-level spatial discretization. We neglect the two-level spatial discretization in this presentation. Here, the Forward Euler means it discretizes the coupling term explicitly.
The analysis of this can be found in [12, 45]. In particular, the longtime stability (cf. (51)) of BEFE method was proved in [45] in the sense that no form of Gronwall’s inequality was used.
Then, BELF algorithm achieves the optimal convergence rate uniformly in time. The solution of the BEFE method satisfies the uniform in time error estimates:
If
If
The advantage of this scheme is that it is parallel. As revealed in the time step restriction (50), the disadvantage of this method is that it may become highly unstable when the parameters
The Crank–Nicolson-Leap-Frog possesses strong stability and convergence properties [47, 48]. Most importantly, the time-step condition for the scheme does not depend on
For the numerical experiments of these partitioned time schemes, we refer the readers to [12].
FSI models include a fluid model whose general form is (1), a structure model, plus certain interface conditions that describe the interaction mechanism (see Figure 2 for an illustration of FSI models in the reference configuration and the deformed configuration). To differentiate the notations in different subdomains, we will use a subscript “
An illustration of fluid structure interaction: the reference configuration (left) and the deformed configuration (right).
In general, the structure model reads as:
Here,
The fluid motion and the structure motion are coupled through certain interface conditions that describe the compatibility of the kinematics and transactions at the fluid–structure interface. For applications with non-slip interface conditions, both velocity and normal stress are continuous across the interface
Here,
There have been many advanced numerical methods for various FSI models. Our focus here is a linear fluid model coupled with a thin wall structure. The reason we choose this model is that this kind of models, if calculated using the standard (Dirichlet-Neumann) explicit decoupling schemes will lead to the so-called added mass effect [50]. Moreover, the algorithms dealing with the added-mass difficulties are the most exciting development in this direction. Therefore, we consider dropping the nonlinear term in (1), and consider the following 1D structure model:
Here,
First of all, we comment here that the decoupled preconditioning techniques can also be naturally applied to FSI models. In the preconditioning step, one can apply either the Multigrid approach [51] or domain decomposition methods [52].
In this presentation, we focus on the two most recent approaches for the linear Stokes model coupled with thin wall structure. The first approach is called partitioned Robin-Neumann scheme, in which the fluid subproblem is imposed with Robin boundary condition at the interface while the structure subproblem is imposed with Neumann boundary conditioner at the interface [14, 15]. The second approach is called kinetically coupled
Partitioned Robin-Neumann scheme:
Given the initial solution
For
End
Here,
Different from the partitioned Robin-Neumann scheme. The kinematically coupled
Given the initial solution
For
End.
Due to different time scales in many FSI problems, it is natural and essential to develop multirate time-stepping schemes [22, 23] that mimic the physical phenomena. For illustration, we will examine the application of the multirate technique to the
In order to apply multirate time-stepping scheme to FSI problems, a question is in which region the larger time step size should be used. Numerical experiments suggest that the version with a larger time step size in the fluid solver (cf. Figure 3) results in a better accuracy. The corresponding method is described in the following. We comment here that the multirate
An illustration of a multirate time stepping technique.
Multirate time-stepping
Given the initial solution
For
End.
In this subsection, we present numerical experiments to demonstrate the convergence and stability performance of the multirate
Geometrical configuration.
The physical parameters are set as follows:
is prescribed on the fluid inlet boundary for
The first experiment is set up to compare the Robin-Neumann scheme with the
Comparisons of the numerical results obtained by the coupled implicit scheme, the RN scheme, and the
We then conduct numerical experiments to investigate the effects of the time-step ratio
Comparison of the
To examine how the stability and approximation are affected when the time step in the fluid region is too large, we fix the time step
Numerical displacements under the settings:
In Figure 8, we present the numerical results of the fluid pressure distribution at
Fluid pressure distribution at
In order to examine the order of convergence, we start with
We compare the numerical solutions of the multirate
Relative errors of primary variables with the spacing
Finally, in order to demonstrate the advantage of the multirate
Implicit scheme | Multirate | Multirate | |
---|---|---|---|
14.90 | 4.02 | 0.74 | |
48.64 | 16.00 | 2.82 | |
179.83 | 66.67 | 11.6 | |
797.76 | 297.96 | 49.23 | |
3165.26 | 1270.30 | 206.32 |
CPU times (in seconds) for the coupled implicit scheme and the multirate
In numerical methods for coupled multidomain PDE models, there are usually two types of methods: the monolithic methods and the partitioned (or decoupled) methods. The monolithic methods usually require a code developed for the coupled problem being solved globally. In contrast, the partitioned approach preserves software modularity because one can use existing subdomain solvers. The advantages of using monolithic methods exist in that they provide better approximation accuracy and usually have better stability than the decoupled methods. The drawback is that the computation costs based on the monolithic approaches are usually high. In comparison, the partitioned approaches allow reusing existing software which is an attractive advantage. However, the accuracy and stability of the partitioned method need to be taken into consideration. Nevertheless, from our research works, we also note that one can apply decoupling techniques in monolithic methods, for example, decoupled preconditioners and schemes which are parallel in time. On the other hand, in partitioned algorithms, one can also try to apply coupling numerical techniques such as extrapolations using previous time-step solutions, interpolation using the coarse-grid solution, or extrapolations between subdomain solutions. In this work, we review the decoupling algorithms for the coupled models of fluid flow interacting with porous media flow and FSI models. We show how to decouple the coupled PDE models using preconditioning, two-level and multi-level algorithms, and partitioned time schemes. We attach importance to the decoupling numerical techniques while also emphasizing how to preserve the coupled multidomain physics features. This review provides a general framework for designing decoupled algorithms for coupled PDE models and exhibits the philosophy of the interplays between PDE models and the corresponding numerical methods.
Mingchao Cai’s work was supported in part by the NSF Awards (170023, 1831950), NIH-BUILD grant through UL1GM118973, NIH-RCMI grant through U54MD013376, and XSEDE HPC resource TG-MCH200022. Mo Mu’s work was supported in part by the HongKong RGC CERG HKUST16301218.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. 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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Several potential applications of this fiber are also mentioned, such as the use of this fiber to fabricate rope, place mats, paper cardboard, string thread, tea bags, high-quality textile materials, absorbent, polymer/fiber composites, etc.",book:{id:"7544",slug:"banana-nutrition-function-and-processing-kinetics",title:"Banana Nutrition",fullTitle:"Banana Nutrition - Function and Processing Kinetics"},signatures:"Asmanto Subagyo and Achmad Chafidz",authors:[{id:"257742",title:"M.Sc.",name:"Achmad",middleName:null,surname:"Chafidz",slug:"achmad-chafidz",fullName:"Achmad Chafidz"},{id:"268400",title:"Mr.",name:"Asmanto",middleName:null,surname:"Subagyo",slug:"asmanto-subagyo",fullName:"Asmanto Subagyo"}]},{id:"40180",title:"Plant Tissue Culture: Current Status and Opportunities",slug:"plant-tissue-culture-current-status-and-opportunities",totalDownloads:66541,totalCrossrefCites:45,totalDimensionsCites:95,abstract:null,book:{id:"3568",slug:"recent-advances-in-plant-in-vitro-culture",title:"Recent Advances in Plant in vitro Culture",fullTitle:"Recent Advances in Plant in vitro Culture"},signatures:"Altaf Hussain, Iqbal Ahmed Qarshi, Hummera Nazir and Ikram Ullah",authors:[{id:"147617",title:"Dr.",name:"Altaf",middleName:null,surname:"Hussain",slug:"altaf-hussain",fullName:"Altaf Hussain"}]},{id:"66996",title:"Ethiopian Common Medicinal Plants: Their Parts and Uses in Traditional Medicine - Ecology and Quality Control",slug:"ethiopian-common-medicinal-plants-their-parts-and-uses-in-traditional-medicine-ecology-and-quality-c",totalDownloads:4157,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"The main purpose of this review is to document medicinal plants used for traditional treatments with their parts, use, ecology, and quality control. Accordingly, 80 medicinal plant species were reviewed; leaves and roots are the main parts of the plants used for preparation of traditional medicines. The local practitioners provided various traditional medications to their patients’ diseases such as stomachaches, asthma, dysentery, malaria, evil eyes, cancer, skin diseases, and headaches. The uses of medicinal plants for human and animal treatments are practiced from time immemorial. Stream/riverbanks, cultivated lands, disturbed sites, bushlands, forested areas and their margins, woodlands, grasslands, and home gardens are major habitats of medicinal plants. Generally, medicinal plants used for traditional medicine play a significant role in the healthcare of the majority of the people in Ethiopia. The major threats to medicinal plants are habitat destruction, urbanization, agricultural expansion, investment, road construction, and deforestation. Because of these, medicinal plants are being declined and lost with their habitats. Community- and research-based conservation mechanisms could be an appropriate approach for mitigating the problems pertinent to the loss of medicinal plants and their habitats and for documenting medicinal plants. Chromatography; electrophoretic, macroscopic, and microscopic techniques; and pharmaceutical practice are mainly used for quality control of herbal medicines.",book:{id:"8502",slug:"plant-science-structure-anatomy-and-physiology-in-plants-cultured-in-vivo-and-in-vitro",title:"Plant Science",fullTitle:"Plant Science - Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro"},signatures:"Admasu Moges and Yohannes Moges",authors:[{id:"249746",title:"Ph.D.",name:"Admasu",middleName:null,surname:"Moges",slug:"admasu-moges",fullName:"Admasu Moges"},{id:"297761",title:"MSc.",name:"Yohannes",middleName:null,surname:"Moges",slug:"yohannes-moges",fullName:"Yohannes Moges"}]},{id:"70658",title:"Factors Affecting Yield of Crops",slug:"factors-affecting-yield-of-crops",totalDownloads:4126,totalCrossrefCites:30,totalDimensionsCites:44,abstract:"A good understanding of dynamics involved in food production is critical for the improvement of food security. It has been demonstrated that an increase in crop yields significantly reduces poverty. Yield, the mass of harvest crop product in a specific area, is influenced by several factors. These factors are grouped in three basic categories known as technological (agricultural practices, managerial decision, etc.), biological (diseases, insects, pests, weeds) and environmental (climatic condition, soil fertility, topography, water quality, etc.). These factors account for yield differences from one region to another worldwide. The current chapter will discuss each of these three basic factors as well as providing some recommendations for overcoming them. In addition, it will provide the importance of climate-smart agriculture in the increase of crop yields while facilitating the achievement of crop production in safe environment. This goes in line with the second goal of 2030 Agenda for Sustainable Development of United Nations in transforming our world formulated as end hunger, achieve food security, improve nutrition and promote sustainable agriculture.",book:{id:"8153",slug:"agronomy-climate-change-food-security",title:"Agronomy",fullTitle:"Agronomy - Climate Change & Food Security"},signatures:"Tandzi Ngoune Liliane and Mutengwa Shelton Charles",authors:[{id:"313819",title:"Dr.",name:"Liliane",middleName:null,surname:"Tandzi",slug:"liliane-tandzi",fullName:"Liliane Tandzi"},{id:"314316",title:"Prof.",name:"Charles Shelton",middleName:null,surname:"Mutengwa",slug:"charles-shelton-mutengwa",fullName:"Charles Shelton Mutengwa"}]},{id:"59402",title:"Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB",slug:"robotic-harvesting-of-fruiting-vegetables-a-simulation-approach-in-v-rep-ros-and-matlab",totalDownloads:2807,totalCrossrefCites:8,totalDimensionsCites:9,abstract:"In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.",book:{id:"6265",slug:"automation-in-agriculture-securing-food-supplies-for-future-generations",title:"Automation in Agriculture",fullTitle:"Automation in Agriculture - Securing Food Supplies for Future Generations"},signatures:"Redmond R. Shamshiri, Ibrahim A. Hameed, Manoj Karkee and\nCornelia Weltzien",authors:[{id:"182449",title:"Prof.",name:"Ibrahim",middleName:"A.",surname:"Hameed",slug:"ibrahim-hameed",fullName:"Ibrahim Hameed"},{id:"203413",title:"Dr.",name:"Redmond R.",middleName:null,surname:"Shamshiri",slug:"redmond-r.-shamshiri",fullName:"Redmond R. Shamshiri"},{id:"241193",title:"Dr.",name:"Manoj",middleName:null,surname:"Karkee",slug:"manoj-karkee",fullName:"Manoj Karkee"},{id:"241194",title:"Dr.",name:"Cornelia",middleName:null,surname:"Weltzien",slug:"cornelia-weltzien",fullName:"Cornelia Weltzien"}]}],onlineFirstChaptersFilter:{topicId:"5",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82476",title:"Joint Action of Herbicides on Weeds and Their Risk Assessment on Earthworm (Eisenia fetida L.)",slug:"joint-action-of-herbicides-on-weeds-and-their-risk-assessment-on-earthworm-eisenia-fetida-l",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105462",abstract:"Frequent and intensive use of similar modes of action herbicides increases selection pressure resulting in nature adapt and a number of herbicide-resistant weeds. The most effective methods to prevent and delay herbicide-resistant weeds are herbicide tank mixture and adjuvant mixed herbicides. This chapter intends to explain the advantages of herbicide tank mixture and adjuvant mixed herbicides. In addition, the models of estimated herbicide mixture interaction response have been explained. Although herbicide mixtures have benefits, they may present risks leading to soil pollution and affecting soil fauna such as earthworms. Therefore, we discussed the negative effect of mixture herbicides on Eisenia fetida. On the other hand, various models to calculate mixture herbicide toxicity on earthworms will be present in this chapter.",book:{id:"11610",title:"New Insights in Herbicide Science",coverURL:"https://cdn.intechopen.com/books/images_new/11610.jpg"},signatures:"Mohammad Taghi Alebrahim, Elham Samadi Kalkhoran and Te-Ming Paul Tseng"},{id:"82937",title:"Abiotic Stress in Plants",slug:"abiotic-stress-in-plants-1",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105944",abstract:"Stress in plants refers to external conditions, which drastically affect the growth, development, or productivity of plants. Stress triggers a wide range of plant responses, such as altered gene expression, cellular metabolism, changes in growth rates, and crop yields. Some abiotic stresses, such as low or high temperature, deficient water, and ultraviolet radiation, make plant growth and development unfavorable, leading to a fall in crop yield worldwide. The following writeup incorporated the abiotic stress factors related to the growth and development of plants, such as temperature, drought, heat, cold, and many more. Abiotic stress factors are the nonliving factors influencing the metabolism, growth, and development of the plant tissues at that particular time when such abiotic stress affects them. As a result of such abiotic stresses, the plants have generated many stress tolerance factors. Various stress-responsive genes are thus being formulated in response to the abiotic stresses, so the plants can survive even in such extreme conditions as well. Henceforth, it can be concluded that the abiotic stress factors imposed on the plants adversely impact their growth and developmental procedures, and at the same time, they also produce some stress tolerance factors to minimize the damage.",book:{id:"11330",title:"Plant Response Mechanisms to Abiotic Stresses",coverURL:"https://cdn.intechopen.com/books/images_new/11330.jpg"},signatures:"Shubham Dey and Ayan Raichaudhuri"},{id:"82943",title:"Laboratory Diagnosis of Candidiasis",slug:"laboratory-diagnosis-of-candidiasis",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.106359",abstract:"The burden of Candidiasis continues to increase and so does the Candida species. Although Candida species are closely similar phenotypically, they differ from each other in terms of epidemiology, genetic characteristics, antifungal susceptibility and virulence profile. Therefore, reliable and accurate laboratory methods for identification of Candida species can determine the Candidiasis burden and enable the administration of the most appropriate antifungal drug therapy to reduce fungal mortality rates. Conventional and biochemical methods are often used in identification of Candida species. However, these techniques are specific and sensitive enough in detecting the non albicans candida (NAC) species. Molecular techniques have improved the laboratory diagnosis and management of Candidiasis due to improved sensitivity and specificity threshold. This chapter provides an overview of different laboratory methods for diagnosis of Candidiasis.",book:{id:"11608",title:"Candida and Candidiasis",coverURL:"https://cdn.intechopen.com/books/images_new/11608.jpg"},signatures:"Benson Musinguzi, Obondo J. Sande, Gerald Mboowa, Andrew Baguma, Herbert Itabangi and Beatrice Achan"},{id:"82732",title:"Role of Microorganisms in Alleviating the Abiotic Stress Conditions Affecting Plant Growth",slug:"role-of-microorganisms-in-alleviating-the-abiotic-stress-conditions-affecting-plant-growth",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105943",abstract:"Agriculture is one of the main sectors that participate in building up world economy, and offers the main source of food, income, and employment to their rural populations. Despite the necessity of doubling agricultural production, quantitatively and qualitatively, to cope with the worsening increase in the global population and to meet the increasing humanitarian needs, the agricultural sector faces many abiotic stress conditions. Additionally, the great climate changes lead to an increase in the negative impact of these stressors. There are many conventional and nonconventional ways that could directly or indirectly mitigate the adverse effects of these stressors, each of them has its advantages and disadvantages. The biological tool is one of the promising methods; it depends on the effective use of beneficial microorganisms to alleviate stress conditions that affect plant growth, development, and therefore productivity. This method is economically inexpensive and eco-friendly toward the environment. Beneficial soil microorganisms such as PGPRs and AMF colonize the root zone of many plant species and help to enhance plant growth and development. Thus, this chapter is aiming to highlight the role of microorganisms in alleviating the abiotic stress conditions affecting in plant growth.",book:{id:"11330",title:"Plant Response Mechanisms to Abiotic Stresses",coverURL:"https://cdn.intechopen.com/books/images_new/11330.jpg"},signatures:"Talaat El Sebai and Maha Abdallah"},{id:"81786",title:"Mycotoxins … Silent Death",slug:"mycotoxins-silent-death",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.104382",abstract:"There are many types of fungi that produce secondary metabolites called mycotoxins. These compounds are very dangerous to humans and animals, as exposure to them causes acute or chronic toxicity. Temperature, humidity and pH are important environmental factors in the production of mycotoxins. There are about 500 types of mycotoxins that are found in many agricultural products such as peanut, cereals, wines, fruit juice, dried fruits, feed, and other foodstuffs. Among the most important genera of fungi that produce mycotoxins are Aspergillus, Penicillium, Altenaria, Fusarium, and others. Some of them infect plants in the field and produce mycotoxin, while others infect agricultural crops, foodstuffs, and feed in the store and produce mycotoxin during storage conditions. Mycotoxins are divided into various groups according to the degree of their impact and danger, into highly toxic, low toxic, carcinogenic, and mutagenic. This is depends on the chemical composition of the different types of mycotoxins, which are an open hydrocarbon chain with low molecular weights ranging between 100 and 697 Da. The biological effects of mycotoxins include damage to living tissues, suppression of immunity, and neurological disorders. Aflatoxins are one of the most dangerous mycotoxins as they are the main cause of hepatocellular carcinoma and the fifth most common carcinogen in the world.",book:{id:"11023",title:"Mycotoxins and Food Safety - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11023.jpg"},signatures:"Azhar A. Alhaddad"},{id:"82849",title:"Influence of Abiotic Stresses on Seed Production and Quality",slug:"influence-of-abiotic-stresses-on-seed-production-and-quality",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.106045",abstract:"Climate change is exerting detrimental impacts on agriculture through various biotic and abiotic stresses. Abiotic stresses such as drought, flood, temperature extremes, salinity, chemicals, heavy metals, nutrient scarcity/toxicity, wind and light in combination more adversely affect the seed production and quality by hampering plant’s morphological, physiological, cellular, biochemical and molecular activities than alone, resulting in poor production of high-quality seeds. Deterioration of yield and quality arises also under abiotic stresses. Under abiotic stresses, plant activates its own defensive mechanisms by escaping, avoiding and tolerating stresses. Some of the plant’s defensive mechanisms include plant’s morphological, cellular, physiological, biochemical and molecular changes to adapt the stresses, synthesis of compounds such as ABA, proline, polyamines increasing the activities of ROS quenchers, expression of stress-resisting genes and activation of enzymes. Further, exogenous application of phytohormones, stress-alleviating compounds, modification of agronomic management, modern breeding strategies such as development of resistant varieties can also help to cope up with stresses and produce quality seeds. Financial and policy support of government or NGOs regarding development of infrastructure, research technologies and thereby, multi-locational trials as well as technology transfusion through extension activities are needed to curtail down the devastating impact of abiotic stresses on quality seed production.",book:{id:"11322",title:"Seed Biology Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11322.jpg"},signatures:"Rupa Das and Saikat Biswas"}],onlineFirstChaptersTotal:320},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:18,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"7",title:"Bioinformatics and Medical Informatics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",isOpenForSubmission:!0,editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",slug:"slawomir-wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",biography:"Professor Sławomir Wilczyński, Head of the Chair of Department of Basic Biomedical Sciences, Faculty of Pharmaceutical Sciences, Medical University of Silesia in Katowice, Poland. His research interests are focused on modern imaging methods used in medicine and pharmacy, including in particular hyperspectral imaging, dynamic thermovision analysis, high-resolution ultrasound, as well as other techniques such as EPR, NMR and hemispheric directional reflectance. Author of over 100 scientific works, patents and industrial designs. Expert of the Polish National Center for Research and Development, Member of the Investment Committee in the Bridge Alfa NCBiR program, expert of the Polish Ministry of Funds and Regional Policy, Polish Medical Research Agency. Editor-in-chief of the journal in the field of aesthetic medicine and dermatology - Aesthetica.",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},{id:"8",title:"Bioinspired Technology and Biomechanics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",isOpenForSubmission:!0,editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",slug:"adriano-andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",biography:"Dr. Adriano de Oliveira Andrade graduated in Electrical Engineering at the Federal University of Goiás (Brazil) in 1997. He received his MSc and PhD in Biomedical Engineering respectively from the Federal University of Uberlândia (UFU, Brazil) in 2000 and from the University of Reading (UK) in 2005. He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. 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Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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