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Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"2519",leadTitle:null,fullTitle:"Applications of Calorimetry in a Wide Context - Differential Scanning Calorimetry, Isothermal Titration Calorimetry and Microcalorimetry",title:"Applications of Calorimetry in a Wide Context",subtitle:"Differential Scanning Calorimetry, Isothermal Titration Calorimetry and Microcalorimetry",reviewType:"peer-reviewed",abstract:"Calorimetry, as a technique for thermal analysis, has a wide range of applications which are not only limited to studying the thermal characterisation (e.g. melting temperature, denaturation temperature and enthalpy change) of small and large drug molecules, but are also extended to characterisation of fuel, metals and oils. Differential Scanning Calorimetry is used to study the thermal behaviours of drug molecules and excipients by measuring the differential heat flow needed to maintain the temperature difference between the sample and reference cells equal to zero upon heating at a controlled programmed rate. Microcalorimetry is used to study the thermal transition and folding of biological macromolecules in dilute solutions. Microcalorimetry is applied in formulation and stabilisation of therapeutic proteins. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"54781",title:"Fractal to Non-Fractal Morphological Transitions in Stochastic Growth Processes",doi:"10.5772/67941",slug:"fractal-to-non-fractal-morphological-transitions-in-stochastic-growth-processes",body:'\nIn nature, fractal structures emerge in a wide variety of systexms as a local optimization of diverse growth processes restricted to the entropic and energetic inputs from the environment. Even more, the fractality of these systems determines many of their physical, chemical, and/or biological properties. Thus, to comprehend the mechanisms that originate and control the fractality is highly relevant in many areas of science and technology [1–3].
\nOne of the most successful approaches to this problem employs stochastic growth processes of particle aggregation. In general, aggregation phenomena are out-of-equilibrium processes of fractal pattern formation that are ubiquitous in nature [4]. As such, since the introduction of the diffusion-limited aggregation (DLA) and ballistic aggregation (BA) models, a plethora of studies has been developed trying to understand the ultimate aspects of the aggregation dynamics that give rise to self-similar or fractal clusters, the relationship of this fractality with their physical and chemical properties, and the most effective methods and techniques to control the fractal growth.
\nIn particular, one striking feature of these systems is the morphological transition that they undergo as a result of the interplay of the entropic and energetic aspects of their growth dynamics that ultimately manifest themselves in the geometry of their structure [5]. It is here, where despite of their complexity, great insight can be obtained into the fundamental elements of their dynamics from the powerful concepts of fractal geometry [6, 7].
\nOne example of this is the well-known dielectric breakdown model (DBM) or generalized Laplacian growth model, which has importantly contributed to our understanding of far-from-equilibrium growth phenomena, to such extent that seemingly unrelated patterns found in nature, as river networks or bacterial colonies, are understood in terms of a single framework of complex growth [8, 9]. However, we are still in need for a complete scaling theory of growth for systems far-from-equilibrium, as well as a comprehensive description of the fractality of systems that exhibits fractal to nonfractal morphological transitions [10].
\nIn this chapter, starting from the mean-field result for the fractal dimension of Laplacian growth, we present a theoretical framework for the study of these transitions. Using a statistical approach to fundamental particle-cluster aggregation dynamics, under which it is possible to create four nontrivial fractal to nonfractal transitions that will capture all the main features of fractal growth, we show that, regardless of their space symmetry-breaking mechanism, they are well described by a universal dimensionality function, including the Laplacian one.
\nIn order to show this, we consider the following: first, we introduce a general dimensionality function that is able to describe the measured fractal dimensions and scaling of clusters generated form particle aggregation. Second, we apply this equation to a set of fractal to nonfractal morphological transitions, created by identifying the fundamental dynamics that drive the fractal growth in particle aggregation and by combining three fundamental off-lattice particle-cluster aggregation models, the DLA, BA, and a recently introduced infinite-range mean-field (MF) attractive model [11, 12] under two different schemes. Afterwards, the scaling of the clusters along the transitions is measured for different values of their control parameters using two standard methods: the two-point density correlation function and the radius of gyration. Finally, we show how all measurements for the scaling of the DLA-MF, and BA-MF transitions collapse to a single universal curve valid for any embedding Euclidean space, under the appropriate variable transformations of the general dimensionality function.
\nIn the Laplacian theory of growth, the growth probability at a given point in space, \n
Schematic diagram of the fundamental aggregation models (top row) used in this work, where particles, that are launched one-by-one into the system from \n\n\n\nr\nL\n\n\n\n with uniform probability in position and direction, (a) follow straight-line trajectories before aggregation in BA, (b) perform a random walk in DLA, and (c) get radially attached to the closest particle in the cluster as a result of an infinite-range radial interaction in MF. The morphology of MF emerges solely from its long-range interaction, as opposed to the stochastic BA and DLA. The corresponding characteristic cluster with its fractal dimension \n\n\n\nD\n0\n\n\n\n is shown in the bottom row.
For \n
Here, for \n
However, one of the most challenging aspect of the theory comes when the growth is not purely limited by diffusion, e.g., when it takes place under the presence of long-range attractive interactions, where strong screening and anisotropic effects must be considered [1, 5, 7]. In this case, a clever generalization to the Laplacian growth process was proposed within the context of the DBM, assuming \n
provides a good approximation to the dimensions of this transition but due to its mean-field limitations, it does not have a good correspondence with the numerical results [25, 26]. Nonetheless, as shown here, Eq. (3) is the starting point to clarify this aspect of the theory and, even, to establish a suitable and general framework to analyze more complex morphological transitions in stochastic growth processes.
\nThis is done by considering that the fundamental dynamical elements of aggregation, which drive the fractal growth, are mainly two: a stochastic one, coming from the particles’ trajectories randomness, and an energetic one, coming from attractive interactions. With regard to the latter, there are two physical mechanisms related to these interactions and two models that are able to reproduce their effects. First, the model we will refer to as \n
Schematic diagrams of the energetic aggregation schemes. For the \n\nλ\n\n-model: (a) every particle in the cluster is provided with an effective radius of aggregation \n\nλ\n\n. (b) A particle “collides” with the cluster when its trajectory intersects for the first time the interaction boundary of any aggregated particle. (c) The particle is aggregated to the closest particle along its direction of motion. This is determined by the position of the aggregated particles projected onto the direction of motion of the incoming particle. For the \n\np\n\n-model: (d) a Monte Carlo approach to aggregation is established through the variable \n\n\np\n∈\n[\n0,1\n]\n\n\n, that controls the probability of aggregation under MF dynamics.
In the following and as explained below, all data for \n
In all of the numerical calculations, we choose as a unit of distance, the particles’ diameter here is set to one. For generating aggregates based on BA or MF (Figure 1a and 1c), a standard procedure was used in which particles are launched at random, with equal probability in position and direction of motion, from a circumference of radius \n
Regarding the \n
In all measurements, we performed an ensemble average over \n
where the double bracket indicates an average over all possible origins \n
where \n
In particular, for the \n
(a) Multiscaling aggregates based on DLA, containing \n\n\nN\n=\n150\n×\n\n\n10\n\n3\n\n\n\n particles each, for \n\n\nλ\n=\n1,10,100\n\n\n and 1000 units, visualized at 5, 10, 30 and 100% of their total size. The squares display the multiscaling evolution of the structure. (b) Radius of gyration, \n\n\n\nR\ng\n\n\n\n, and (c) fractal dimension, \n\nD\n\n, versus the number of aggregated particles, \n\nN\n\n, in log-log and lin-log plots, respectively. Notice that, when \n\n\nλ\n→\n∞\n\n\n, the structure of the aggregates tends to MF \n\n\n(\nD\n=\n1\n)\n\n\n. (d) Evolution of the growing front for the first two stages of growth. (e) Typical structure of an MF aggregate.
(a) Multiscaling aggregates based on BA, containing \n\n\nN\n=\n300\n×\n\n\n10\n\n3\n\n\n\n particles each, for \n\n\nλ\n=\n1,10,100\n\n\n and 1000 units, visualized at 5, 10, 30 and 100% of their total size. (b) Radius of gyration, \n\n\n\nR\ng\n\n\n\n, and (c) fractal dimension, \n\nD\n\n, versus the number of aggregated particles, \n\nN\n\n, in log-log and lin-log plots, respectively. (d) Evolution of the growing front for the first two stages of growth. (e) Typical structure of an MF aggregate.
In (a) and (b), aggregates grown with specific values of \n\nε\n\n in the interval \n\n\n[\n0.01,1\n]\n\n\n with the \n\nλ\n\n-model (top row) and log-log plots for \n\n\n\nR\ng\n\n\n\n (middle row) for (a) BA and (b) DLA with \n\n\nN\n\n\n=\n10\n\n5\n\n\n\n particles. One can appreciate the difference in the morphology of these monofractal-aggregates with respect to \n\nε\n\n. Additionally, the specific entropic and energetic contributions to the clusters fractal dimension \n\n\nD\n(\nε\n)\n\n\n are shown in the bottom panes. (c) Clusters based in BA (left) and DLA (right) with the same fractal dimension, from top to bottom \n\n\nD\n=\n1.51\n\n\n and \n\n\n1.31\n\n\n, grown with a very high precision around the desired value.
Clusters of \n\n\n1.5\n×\n\n\n10\n\n5\n\n\n\n particles grown with the indicated values of \n\np\n\n, are shown at different magnifications for the (a) DLA-MF and (b) BA-MF transitions. Particles aggregated under DLA/BA are colored in light-grey while those through MF in black. These transitions exhibit fast morphological transformations as \n\np\n\n increases, from unstable tip-splitting (DLA) or dense branching (BA), through (inhomogeneous) dendritic, to needle-like growth (MF). (c-d) \n\n\nC\n(\nr\n)\n\n\n and \n\n\n\nR\ng\n\n(\nN\n)\n\n\n display deviations from a well-defined linear behavior for different \n\np\n\n, revealing the inhomogeneity or crossover effects in these clusters. Arrows indicate the direction of the transition as function of p:0→1. This is better seen at low scales, where the stochasticity of DLA or BA dominate the local growth, whereas MF tends to dominate the global morphology as \n\n\np\n→\n1\n\n\n. In both cases, the dynamical growth-regime changes at \n\n\np\n≈\n0.1\n\n\n. Labels \n\n\n\nα\nI\n\n\n\n, \n\n\n\nα\n\nI\nI\n\n\n\n\n, \n\n\n\nβ\nI\n\n,\n\n\n and \n\n\n\nβ\n\nI\nI\n\n\n\n\n indicate the scales used for the scaling analysis.
Despite the complexity leading to morphological transitions, simple models can be established to describe their fractality or scaling as a function of the control parameter, in our case, the branching parameter \n
with \n
In this form, by setting \n
where \n
with \n
Furthermore, let us introduce the reduced parameter, \n
where
is an effective parameter associated to a generalized screening/anisotropy-driven force. Its first-order approximation is then,
where the effective parameter is now given as,
With this prescription, the dynamical change in growth regime is now located at \n
In the first approach to morphological transitions, we will consider the case when long-range attractive interactions are introduced in the growth dynamics. In this case, the way to obtain self-similar clusters, that is, clusters with a single fractal dimension, is to maintain a proper balance between the energetic and entropic contributions to the growth process. This can be done by considering an aggregation radius, \n
For example, for \n
It can be appreciated that this growth presents three well-defined stages as illustrated in Figures 3d and 4d. In the first one, the growth is limited by the interactions and is characterized by \n
However, taking into account that the spatial size of the clusters is proportional to the radius of gyration \n
Additionally, this model allows one to estimate \n
In the second approach, a general stochastic aggregation process can be model under a Monte Carlo scheme involving three fundamental and simple off-lattice models of particle-cluster aggregation. On one hand, the well-known BA and DLA models provide disordered/fractal structures through their stochastic (entropic) dynamics (Figure 1a and 1b). On the other, we introduce a mean-field (MF) model of long-range interactive particle-cluster aggregation [11, 12] that provides the most energetic (and noiseless) aggregation dynamics that, simultaneously, acts as the main source of anisotropy. We must remark that this anisotropy is purely generated by the growth dynamics and not from lattice effects [28] (see Figure 1c). Then, the statistical combination of these models results in an off-lattice DLA-MF and BA-MF dynamics, whose morphological transitions can be controlled by the mixing parameter \n
It is necessary to remark that the DLA-MF and BA-MF transitions in the \n
Model | \nTransition | \nMethod | \nScale | \nΛ | \nχ | \nD0 | \npi, qi | \n
---|---|---|---|---|---|---|---|
p | \nDLA-MF | \nC (r) | \n\n\n | \n15.4 | \n2.24 | \n1.67 | \n0.29 | \n
\n | \n\n | \n\n | \n\n\n | \n71.5 | \n1.82 | \n\n | \n0.08 | \n
\n | \n\n | \nRg (N) | \n\n\n | \n33.8 | \n1.41 | \n1.71 | \n0.03 | \n
\n | \n\n | \n\n | \n\n\n | \n101.6 | \n1.32 | \n\n | \n0.01 | \n
\n | \nBA-MF | \nC (r) | \n\n\n | \n11.6 | \n1.61 | \n1.94 | \n0.18 | \n
\n | \n\n | \n\n | \n\n\n | \n45.4 | \n1.38 | \n\n | \n0.04 | \n
\n | \n\n | \nRg (N) | \n\n\n | \n124.8 | \n1.95 | \n1.95 | \n0.06 | \n
\n | \n\n | \n\n | \n\n\n | \n1547.7 | \n2.05 | \n\n | \n0.02 | \n
\n\n | \nDLA-MF | \nRg (N) | \n103 to 105 | \n6.10 | \n1.52 | \n1.70 | \n0.21 | \n
\n | \nBA-MF | \nRg (N) | \n103 to 105 | \n6.35 | \n1.43 | \n1.95 | \n0.19 | \n
p | \nDLA-MF | \nC (r) | \n– | \n– | \n1.69 | \n1.67 | \n1.0 | \n
\n | \n\n | \nRg (N) | \n– | \n– | \n1.34 | \n1.71 | \n1.0 | \n
\n | \nBA-MF | \nC (r) | \n– | \n– | \n1.39 | \n1.94 | \n1.0 | \n
\n | \n\n | \nRg (N) | \n– | \n– | \n1.88 | \n1.95 | \n1.0 | \n
\n\n | \nDLA-MF | \nRg (N) | \n– | \n– | \n1.52 | \n1.70 | \n1.0 | \n
\n | \nBA-MF | \nRg (N) | \n– | \n– | \n1.43 | \n1.95 | \n1.0 | \n
Parameters for the plots of \n
In the first block, we present the parameter values used to describe \n
Scaling analysis for the \n\np\n\n-model: (a) Plots of \n\n\nD\n(\np\n)\n\n\n for the DLA-MF and BA-MF transitions obtained from \n\n\nC\n(\nr\n)\n\n\n (left), at small (\n\n\n\nα\nI\n\n\n\n) and large (\n\n\n\nα\n\nI\nI\n\n\n\n\n) scales, and \n\n\n\nR\ng\n\n(\nN\n)\n\n\n (right), at medium (\n\n\n\nβ\nI\n\n\n\n) and large (\n\n\n\nβ\n\nI\nI\n\n\n\n\n) scales, in correspondence to Figures 6c and 6d, respectively. These results are described by the solid and dotted curves given by equations (8) and (9), respectively, for different values of the parameters \n\nΛ\n\n and \n\nχ\n\n. (b) By plotting \n\nD\n\n as a function of \n\n\nq\n=\np\n/\n\np\ni\n\n\n\n (where \n\n\n\np\ni\n\n\n\n is calculated for each curve), data collapses into single master curves, \n\n\nD\n(\nq\n)\n\n\n, according to Eqs. (10) and (12), respectively. Note the common point of regime change at \n\n\n\nq\ni\n\n=\n1\n\n\n, marked with the vertical dashed lines. The curves for the DBM given by Eqs. (6) and (7), respectively, with \n\n\nΛ\n=\n1\n\n\n and \n\n\nd\n=\n\nd\nw\n\n=\n2\n\n\n, are also included. (c) In the description with the function \n\n\nD\n(\nΦ\n)\n\n\n, all of the morphological transitions approach common transitional points where clusters have fully collapsed to an ordered structure, independently of the stochastic model used. (d) The corresponding scaling analysis is performed for the BA- and DLA-MF transitions obtained by using the \n\nλ\n\n-model. In this case, Eqs. (8), (10) and the exponential form of (14), were used. For further details about the parameter values used, see Table 1.
Model | \nData | \nD0 | \nχ | \n\n\n | \nqt | \n\n\n | \nqt | \nD(q = 1) | \n
---|---|---|---|---|---|---|---|---|
p | \nBA-MF (\n | \n1.94 | \n1.39 | \n2.3 | \n4.5 | \n3.0 | \n5.4 | \n1.72 | \n
\n | \nDLA-MF (\n | \n1.67 | \n1.69 | \n2.3 | \n2.8 | \n3.0 | \n3.2 | \n1.46 | \n
\n | \nBA-MF (\n | \n1.95 | \n1.88 | \n9.0 | \n6.0 | \n19.0 | \n9.0 | \n1.73 | \n
\n | \nDLA-MF (\n | \n1.71 | \n1.34 | \n9.0 | \n21.7 | \n19.0 | \n37.8 | \n1.62 | \n
\n\n | \nBA-MF | \n1.95 | \n1.43 | \n2.3 | \n4.2 | \n3.0 | \n5.0 | \n1.70 | \n
\n | \nDLA-MF | \n1.71 | \n1.52 | \n2.3 | \n3.5 | \n3.0 | \n4.2 | \n1.50 | \n
Transitional points for which the reduced co-dimension \n
The labels \n
By observing the description of the transitions based on the function \n
A final important implication of the previous findings is that the DBM and BA-MF transitions (for both \n
where the effective parameter \n
(a) Snapshots of typical clusters present in fractal to nonfractal morphological transitions obtained from the \n\nλ\n\n-model with the branching parameter \n\nε\n\n as the control parameter, and the \n\np\n\n-model with the mixing parameter \n\np\n\n as the control parameter. (b) By plotting \n\n\n\nD\n*\n\n(\nΦ\n)\n\n\n and \n\n\n\nD\n*\n\n\n\n(\nΦ\n)\n\n\n(\n1\n)\n\n\n\n\n, the data for the morphological transitions DLA-MF, BA-MF, and DBM collapse to universal curves described by Eqs. (14) and (15). Under this prescription, these universal fractal to nonfractal morphological transitions are independent of the initial fractal dimension, \n\n\n\nD\n0\n\n\n\n, the symmetry-breaking process that drives the transition, even crossover effects, and, quite remarkably, the Euclidean dimension, \n\nd\n\n, of its embedding space. All of the numerical data comes from Figure 7.
It has been stated above that the entropic and energetic elements are the two aspects of the complex aggregation dynamics which in nature are strongly correlated. Nonetheless, this reductionist approach that essentially encapsulates the information of all the finer details of the dynamics into an effective interaction (in the \n
Additionally, the descriptive framework for the scaling of fractal to nonfractal morphological transitions in stochastic growth processes, which includes the concept of an effective screening/anisotropy force and reduced codimensionality transformations, has revealed that the DLA-MF, BA-MF, and DBM transitions exhibit a well-defined universal scaling \n
The results and models discussed in this chapter represent an important unifying step toward a complete scaling theory of fractal growth and far-from-equilibrium pattern formation. Additionally, the possibility of applying the dimensionality function to discuss complex structures in other research areas, ranging from biology [4, 1], intelligent materials engineering [31, 32] to medicine [33], seems to be in some cases straightforward.
\nThe authors gratefully acknowledge the computing time granted on the supercomputers MIZTLI (DGTIC-UNAM) and THUBAT-KAAL (CNS-IPICyT) and on XIUHCOATL (CINVESTAV) through M.A. Rodriguez (ININ, Mexico). We acknowledge the partial financial support by CONACyT and VIEP-BUAP through the grants: 257352, DIRV-EXC16-I, and CAEJ-EXC16-G.
\nThe field of surgery has experienced a revolution in the present era with a dramatic shift from the traditional open surgery to minimally invasive surgery (MIS). This has been associated to numerous advantages over open surgery, mainly for patients, such as a reduction of tissue trauma and smaller postoperative scars, which in turn involves shorter hospital stays, reduction of the postoperative pain, and faster recovery.
\nApart from all these meaningful benefits, this evolution in surgery also results in many technical challenges for surgeons. Relative to open surgery, surgeons lose direct vision, and only two-dimensional indirect vision through a display is available. This indirect vision sometimes takes the sense of orientation and depth perception away from surgeons. The precise manipulation of the laparoscopic instrument tip is restricted mainly because these instruments are generally slim and long and with limited dexterity. Most of the instruments are straight and do not have flexible tips. This surgical tools also lead to a reduction of sensory feedback during surgery due to surgeons cannot directly touch the organs in the body. Some of these limitations make the development of common surgical procedures in open surgery not as straightforward and simple through minimally invasive surgery.
\nThis book is just a step forward for the readers to learn further the recent surgical techniques and technologies that have emerged in order to deal with the aforementioned challenges in minimally invasive surgery.
\nLaparoscopic surgeons are required long training time, experience, and practices in order to deal with the technical limitations introduced by laparoscopic surgery and become proficient. Due to the steep learning curve that laparoscopic surgery demands in certain surgical procedures, advanced and structured training programs and methods are constantly being introduced [1, 2]. Recently, there is a paradigm shift from traditional subjective assessment methods of trainees to more objective assessment tools that can accredit surgeons as competent in laparoscopic surgery [3, 4].
\nDespite the many advantages laparoscopic surgery offers to patients, laparoscopy also entails a number of technical limitations for surgeons. The performance of this surgical technique implies important restrictions on freedom of movement, mainly due to the use of rigid and long surgical instruments with poor ergonomic design, the location of the screens, the use of pedals to control the diathermy system, and by the fixed surgical ports for the instruments. These limitations result in an increased incidence of static postures in surgeons and the adoption and maintenance of forced body postures for long periods of time, which potentially affect performance and accuracy during surgery and increase the incidence of physical fatigue and musculoskeletal disorders. In order to address some of these technical limitations, new surgical instruments and devices have been developed aiming to enhance the dexterity, accuracy, and ergonomics of laparoscopic instruments [5]. In addition, new methodological approaches and instrumental techniques for ergonomic analysis have been implemented to improve the working conditions of surgeons, as well as the design of the laparoscopic material [6].
\nSince the introduction of laparoscopic surgery several decades ago, it has been constantly evolving to the emergence of more sophisticated approaches such as the laparoendoscopic single-site surgery (LESS), natural orifice transluminal endoscopic surgery (NOTES), or transanal surgery, which are intended to reduce the patient’s invasiveness and surgical outcomes.
\nLESS surgery could possibly result in even better postoperative outcomes than multi-port laparoscopic surgery, especially concerning cosmetic outcomes and pain [7, 8]. By reducing the number of transcutaneous points of access, the approach offers numerous advantages including, but not limited to, improving postoperative recovery time and pain, enhancing cosmetics, and minimizing port-related complications. Instrument collision, lack of triangulation, and in-line vision are among the main challenges of LESS surgery. Several techniques and advancements have been introduced to overcome constraints associated with this surgical approach such as novel access devices and curved, articulated, or pre-bent instruments [9, 10]. The feasibility of LESS for almost all types of upper gastrointestinal procedures has been proved [11, 12].
\nTo date, several NOTES procedures have been performed using mainly stomach, rectum, and vagina as the portal of entry into the peritoneal cavity. The main benefits of this surgical technique in comparison to conventional laparoscopic surgery include no scars, less external pain, and lower cost. However, there are also some barriers when using this technique, some of them include difficulty in the closure of enterectomy, anastomotic techniques, spatial orientation, long learning curve, lack of triangulation of instruments, control of hemorrhage, and prevention of the transluminal spread of infection [13]. In order to address some of these technical difficulties in NOTES surgery, novel devices and robotic platforms using a flexible endoscope are appearing as a new trend in the field of MIS [14, 15].
\nRectal cancer surgery has undergone a rapid change over the last few decades. We have come a long way from abdominoperineal resection to minimally invasive sphincter-preserving techniques. Minimally invasive surgical techniques have been applied to rectal surgery for several procedures such as transanal polyp excision, local excision of rectal cancer, or transanal total mesorectal excision (taTME), among others [16, 17]. Currently, the two most popular options for local excision are transanal endoscopic microsurgery (TEM) and transanal minimally invasive surgery (TAMIS) [18, 19]. TEM utilizes a rigid platform to access intraluminal lesions in the rectum, maintaining stable the pneumoperitoneum. TAMIS utilizes conventional laparoscopic devices and a single incision port rather than a specialized platform.
\nOne of the technological fields that has most recently affected laparoscopic surgery is robotics. Robotic surgery is a further advancement in the field of laparoscopic surgery, which has gained global acceptance, and a large number of centers are performing robotic surgery as a routine. Laparoscopic robotic surgery has made tremendous progress in a relatively short period of time, resulting in improvements for both the patient and surgeon. Generally speaking, the robot for laparoscopic surgery provides three-dimensional vision, dexterity, and intuitiveness. The majority of robotic surgery applications are in urology, gynecology, and colorectal application, providing comparable clinical results to conventional laparoscopic approaches for the most popular procedures in these fields [20, 21]. The da Vinci surgical system is the most extended robotic platform worldwide for laparoscopic surgery. However, recently many other robotic systems are under development, including additional features such as enhanced portability and force feedback [22, 23].
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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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