\r\n\tThe book also covers the more specialized areas of energy consumption, riding comfort, noise and vibration.
\r\n\tEscalators and passengers conveyors should also be addressed, as these devices complement elevator system in moving passenger around the building.
\r\n\tModern developments are hope to be covered within the relevant chapters, some of which are listed as follows: Modern electrical safety systems,Modern shaft and motor feedback devices, Modern electrical drive system, Two elevator cars in the same shaft, Multiple elevator car systems in the same shaft, Evacuation systems using elevators, Modern calculation and simulation tools and software packages, Ropeless elevator systems.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"8d5766ef86475867198610aeb050233c",bookSignature:"Dr. Lutfi Al-Sharif",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10040.jpg",keywords:"Elevator Traffic Engineering, Simulation, Elevator Mechanical Engineering, Safety Gear System, Drive Systems, Control Systems, Energy Consumption, Power, Riding Comfort, Noise and Vibration, Escalators, Passenger Conveyors",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:1,numberOfTotalCitations:2,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 14th 2019",dateEndSecondStepPublish:"February 28th 2020",dateEndThirdStepPublish:"April 28th 2020",dateEndFourthStepPublish:"July 17th 2020",dateEndFifthStepPublish:"September 15th 2020",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"314726",title:"Dr.",name:"Lutfi",middleName:null,surname:"Al-Sharif",slug:"lutfi-al-sharif",fullName:"Lutfi Al-Sharif",profilePictureURL:"https://mts.intechopen.com/storage/users/314726/images/system/314726.png",biography:"Lutfi Al-Sharif is currently the dean of Engineering Technology and professor of Electrical Engineering at Al-Hussein Technical University in Amman/Jordan, and jointly professor of Building Transportation Systems at the Department of Mechatronics Engineering, the University of Jordan. He received his Ph.D. in elevator traffic analysis in 1992 from the University of Manchester, U.K. He worked for 10 years for the London Underground, United Kingdom, focusing on elevators and escalators. He has over 50 papers published in peer-reviewed journals and conferences in vertical transportation systems and is the co-inventor of four patents, coauthor of the 2nd edition of the Elevator Traffic Handbook, and author of the “Indoor Transportation” chapter in the Elsevier Encyclopaedia of Transportation.",institutionString:"Al-Hussein Technical University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Jordan",institutionURL:null,country:{name:"Jordan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"194667",firstName:"Marijana",lastName:"Francetic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/194667/images/4752_n.jpg",email:"marijana@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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PCs are blood extracts, applied to surgical sites, injuries, or wounds, a safe and effective way to promote soft tissue healing and bone growth. The most frequently used substrate, platelet-rich fibrin (PRF) is a member of platelet concentrates developed by Choukroun and colleagues [1]. PRF is an autologous matrix, in which cytokines and cells are released after a short period [2].
\nThe aim of this chapter is to describe the potential functions of PRF for bone grafting in oral and maxillofacial surgery.
\nThere are different types of PRF in local use.
\nPure platelet-rich fibrin (P-PRF) in other terms leukocyte-poor platelet-rich fibrin is a preparation without leukocytes having a high-density fibrin network. This platelet concentrate exists in a strongly activated gel form. It cannot be injected and used like traditional fibrin glues. On the other hand, with its strong fibrin matrix facility, P-PRF can be handled like a real solid material for several applications [3].
\nDuring preparation, only very low amounts of leucocytes are collected because of the specific separator gel, which is used to obtain P-PRF. However, the platelet collection level is high, and the preservation of the platelets during the procedure seems to be acceptable [4]. The main disadvantage of this platelet concentrate protocol is its cost and complex procedure if it is compared to the other forms of PRF, which are available nowadays [3].
\nThe second platelet concentrate type is leukocyte and platelet-rich fibrin (L-PRF), which is known as leukocyte and platelet-rich fibrin concentrate, called Choukroun’s PRF. This product was invented by Choukroun in 2000 [1, 2]. The main concept of this technique differs from the other protocols. Patient’s blood is taken and immediately softly centrifuged. This provides the formation of a fibrin clot in the middle of the tube, between the red blood cell base at the bottom and the acellular plasma at the top. This clot includes nearly all the platelets and more than 50% of the leukocytes from the initial blood harvest [5, 6]. The high quantity of leukocytes provides immune and antibacterial properties, wound healing and growth factor regulation. But, it depends on which leukocytes, in which quantity and in which state the centrifugation process can softly activate the white blood cells [7]. This product therefore only exists in an activated form and cannot be injected like a suspension. Therefore, L-PRF is a practical solid material with strong fibrin scaffold and used in oral and maxillofacial surgery, periodontology, implant dentistry and ear nose throat surgery [6].
\nThe amount of WBCs effect vascularization and bone remodeling. As a result, researchers made new modifications in the centrifugation speeds and times to prevent cell loss within the PRF matrix [8]. These recent modifications of the PRF protocol have led to the improvement of advanced platelet-rich fibrin (A-PRF), which uses lower G-forces to gain higher growth factors compared to PRF [9]. It maintains higher amount of WBCs in the fibrin matrix and has special glass tubes, which are designed to make clotting faster. After the centrifugation, the tubes are removed and placed in their holders and left for 5 min to start clot formation. The fibrin clot which is rich with WBCs provides higher growth factors and recent research emphasizes that A-PRF enhances collagen matrix synthesis and supplement of progenitor cells [8].
\nOne of the recent developments in the PRF technology is injectable PRF (i-PRF). Standard PRF is prepared as a gel which is inconvenient to inject [10]. i-PRF protocol necessitates short centrifugation period in order to produce a liquid platelet concentrate, which includes primarily liquid fibrinogen and thrombin prior to fibrin formation [11]. The plastic tubes with hydrophobic surface are used, and therefore coagulation process does not effectively start. Hence, all the blood components reach the top of the tube under the centrifugation force with short centrifugation time (i.e., 2–4 min). The light yellow-colored layer, which is the combination of plasma and platelets, is aspirated from the top of the tube to obtain i-PRF. Nowadays, it is used with bone grafts to keep graft particles tightly encapsulated in the fibrin matrix. With the coagulation process, i-PRF forms a gel consistency holding bone graft together. Also, the release of growth factor is beneficial for the graft. This has the potential to convert any osteoconductive graft to osteopromotive, which would provide faster and better bone formation. Another type of graft prepared with i-PRF is the PRF block. For its preparation, i-PRF is mixed with a combination of bone graft and shredded PRF clot [10].
\nSome physicians worry about damage for the patient with glass-evacuated blood collection tubes with silica activators. O’Connell emphasized that silica contact cannot be avoided with glass tubes. These silica particles are dense enough to settle in the sediment and might reach the patient when the product is used during procedures [12]. Recently, Tunali et al. used medical grade titanium tubes to produce titanium prepared platelet-rich fibrin (T-PRF) [10]. This biocompatible material was tried to eliminate the potential negative effects of silica from dry glass or glass-coated plastic tubes [12]. The research showed that T-PRF supply is a more organized network than L-PRF. Furthermore, its fibrin network covers larger area and has thicker fibrin clot. In a human study, wound healing in the palatal mucosa is found better with the T-PRF application [10].
\nA newer application of PRF is the prepared platelet-rich lysate (PRF-L). In this technique, after PRF preparation, it is incubated at 37°C in a humidified atmosphere of 5% CO2 and 95% air. The exudate, which is collected, is referred to as PRF lysate. It is said to be a good source of several growth factors. In a study, it is found that PRF-L can significantly improve the proliferation index, collagen deposition and migration rates in chronically UVA-irradiated human dermal fibroblasts [13]. This is a new technique that requires further studies.
\nVenous blood of patient is drawn into 10 mL, anticoagulant-free tubes and then immediately spun in a tabletop centrifuge at 3000 rpm for 10 min. Due to the lack of anticoagulant, platelet activation occurs once the blood contacts the glass tube walls and subsequently initiates the coagulation cascade. By centrifugation, fibrinogen is concentrated in the top and middle of the tube and then circulating thrombin transforms it into fibrin. This action leads to the development of a fibrin clot in the middle of the tube, between the RBCs at the bottom and serum at the top. The upper straw-colored layer is removed from the centrifuge tube and blotted to remove serum and RBC to result in a dense matrix. Timing is also critical to the success of the PRF technique. PRF must be prepared immediately before application to the surgical site (Figure 1) [1].
\nPreparation of PRF membrane. A: Collection of venous blood into the tube; B: centrifuge device; C: platelet-rich fibrin clots; D: PRF clots were pressed to obtain more dense membranes; E: PRF membranes.
In the past, PRF could be useful for both hemostasis and wound healing. Nowadays, sinus lift technique is considered to be one of the most common and valid procedures for the use of PRF in oral surgery. PRF is also used for augmenting the alveolar ridge, improve soft tissue healing and periodontal regeneration and preserving the alveolar ridge height after extraction. It has been studied that PRF improves wound healing in multiple extractions by reducing healing time [14].
\nThe use of platelet concentrates may reduce and control the risks associated with delayed bone and soft tissue healing. The success for the healing of a bone graft is certainly related with intact soft tissue coverage.
\nPatients using antiresorptive therapies exhibit a reduced healing potential in bone and soft tissue that can lead to medication related osteonecrosis. In order to stimulate healing and obtain soft tissue coverage, PRF can be placed to the surgical or exposed area without tension. Even if there is a need for researches in this field, PRF presented valuable solutions till today [15].
\nThe other indication of PRF usage is the method of socket preservation. When implant restoration is considered after extraction of a tooth, this must be kept in mind that the resorption process starts immediately. Thus, the volume of the socket, soft tissue closure and thickness of gingiva must be protected as possible as early, for a complete and quick bone regeneration. If the wound closure and remodeling are achieved properly, implant can be placed in the desired position [15].
\nSeveral oral and maxillofacial surgical procedures have used membranes for guided bone regeneration (GBR). The potential applications of PRF in this field have been shown to be effective in different clinical situations. However, this method may not be superior as a barrier membrane for regeneration when compared to conventional membranes, and therefore appropriate cases should be selected in order to benefit from the properties of PRF in GBR (Figure 2) [16, 17].
\nEnucleation of radicular cyst resulted from left central incisor tooth and apical resection were performed at the same time. A: After eliminating the whole lesions, PRF placed into the intrabony defect to accelerate healing and prevent soft tissue collapse. B and C: PRF membrane was used to cover the buccal wall of the cavity as a barrier.
Sinus lift is one of the most frequent grafting procedures in implantology and has proven to be a successful and predictable technique for providing bone height for implant placement. In the literature, no application using PRF was more studied than the sinus lift procedure. This model generating to obtain bone height, provides a closed, protected and stable environment for healing. Thus, bone healing in sinus cavity can be evaluated more accurate than the other procedures that are in contact with the oral environment [18].
\nSubsinus cavity floored by the Schneiderian membrane following sinus lift has a capacity of bone regeneration due to the high osteogenic properties, though several bone materials can yield good results in terms of bone healing [19].
\nMost researches have focused on bone biomaterials to improve bone quality and to accelerate healing for early operations. The main mechanism of subsinus cavity healing is to be filled with blood clot that is promoting neoangiogenesis and proliferation. Similarly, the aim of using PRF into the cavity is to provide space and optimize healing as a biologic agent [20]. The first study reported on PRF by Choukroun et al. represented that the use of PRF in addition to the bone grafts accelerated healing period for sinus floor augmentation [21]. It is important to reduce healing time before implantation.
\nThe incidence of perforation of the membrane that is a common intraoperative finding was reported ranging from 10 to 60% [22]. The rupture of the membrane can lead to postoperative complications, so has to be repaired before grafting. Otherwise, a biomaterial can migrate into the sinus cavity leading to sinus infection and impairment of healing. Some biomaterials such as collagen membrane and platelet concentrates have been commonly used to prevent these complications. In addition to the use of PRF for grafting, it is also used as an adhesion material and frequently preferred for repair of possible perforations of the schneiderian membrane during sinus lift.
\nThe acceleration of healing in bone defect is of particular interest when dental implants would be inserted in this area. Due to reduction of time, early functional loading and better primary stability of the implant will be obtained after the regeneration [19]. Thus, some biologic agents may be needed to shorten this period. For this purpose, PRF-like substances can be used. The potential of these substances as a biologic agent in oral and maxillofacial surgery relies on the growth factors stored within platelet alpha granules containing platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), insulin-like growth factor (IGF), platelet-derived angiogenic factor and transforming growth factor-beta (TGF-β). Moreover, PRF acts as a biological connector between different graft elements and serves as a matrix that eases forming new bone tissue into the defect. In fact, due to the lack of standardization and absence of characterization of the studied PRF materials, it is difficult to explain the exact function of platelet concentrates on bone regeneration [23].
\nPeri-implant bone defects are encountered as a result of peri-implantitis and secondly inadequate bone volume for implantation and thirdly immediate implantation. Although the mechanisms of peri-implantitis are considered to be bacterial accumulation or prosthetic overload of the implant, it is believed that the main mechanism of peri-implantitis is related to bone/implant interface breakdown [24].
\nIn case of small amount of bone, it is inevitable to make lateral perforation while placing implant or bone resorption around particularly the collar of the implant. In order to reinforce the bone amount around the implant to prevent resorption and gain more esthetic appearance, autogenous and/or synthetic materials can be used to cover the open surface of these implants.
\nPeri-implant bone defects can be developed after immediate implantation (Figure 3). After the insertion of the implant in a fresh avulsion socket, a large peri-implant defect must be filled with a material to prevent soft tissue invagination [25].
\nPeri-implant bone defect after immediate implant placement. A: Buccal and palatinal bone defects around the implant; B, C: A mix of PRF and xenogeneic bone graft constituted a sticky mass; D, E: PRF with bone graft placed into the cavity; F: PRF membranes were used to cover the whole defect.
For those three clinical situations, bone regeneration between the cavity/defect and implant surface can be achieved by using PRF in addition to bone grafts. With its strong fibrin architecture and specific 3D cell distribution, especially L-PRF may offer positive results when used in combination with bone biomaterials for the filling of peri-implant bone defects. On the whole, there are still only few studies published in the literature about the use of L-PRF in peri-implant bone defects [26].
\n\n
Eliminating transmission of diseases (the patient’s own blood)
Promoting soft tissue healing
Protection extraction socket against resorption and infection
Minimal invasive and inexpensive method
Role as a carrier matrix for bone grafts
Supporting neoangiogenesis
Minimal immunological reaction
As a sticky material, holding different substances together
Growth factors
PRF seems to be an accepted minimally invasive approach with good clinical results. With the ease of preparation, low cost and biologic properties, PRF could be considered as a beneficial treatment option. On the other hand, due to the lack of standardization and long-term follow up clinical studies, the major effect of PRF on bone grafting has not been explained yet.
\nA new intellectual movement in the field of cognitive science1 has been developed, above all, in the last two decades of the current century, starting from debates that took place, mainly, in the philosophy of science at the end of the twentieth century. This movement has been described more broadly by many authors as a “new mechanistic philosophy” [4, 5, 6, 7]. Strongly influenced by recent advances in computer science, neuroscience, and artificial intelligence, the theoretical framework developed by some of the movement’s most prominent authors offers a new physicalist (or materialist) and mechanistic view of human cognition2 [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21].
The theory formulated from the application of the neo-mechanistic philosophy to cognitive science and, specifically, to human cognition, can be called the
One of the most central elements present in the framework of MTHC is a “model of human cognitive computation” [9, 10, 11, 13, 15], which is also part of the attempt made by several influential authors to provide some type of unification or integration for the field of cognitive science [9, 10, 23, 24, 25]. However, some complex cognitive capacities and some particular aspects of human cognition still present a challenge for explanations constructed by using this theoretical structure [22].
My central goal in this chapter, therefore, is to present an argument to show that human cognition cannot yet be completely understood and explained in terms of mechanistic computation and that this view indeed presents many substantial limitations.
To develop my argument, I present, firstly, some of the central elements of this neo-mechanistic framework and its application to cognitive science. Secondly, I present the mechanistic model of human cognitive computation, as it is currently framed, and, based on the specialized literature, I show in what dimensions it helps our understanding of some aspects of human cognitive capacities, such as visual perception and memory consolidation. Thirdly, I show that to understand and explain some human cognitive capacities, such as self-consciousness and conscious informal reasoning and decision making, the neo-mechanistic framework shows substantial limitations. I conclude the chapter by suggesting that the notion of human artificial cognitive computation can be useful for several projects, but to fully understand natural human cognition we will most certainly have to consider theories that go beyond the current neo-mechanistic model of human cognitive computation.
The contemporary movement of neo-mechanistic philosophy has been historically associated with ideas already present in the period of Ancient Philosophy. Philosophers, such as Democritus, Leucippus, Aristotle, Epicurus, and Lucretius [9, 14, 26], for example, have been mentioned in the specialized literature as precursors. Although there is no unity of thought regarding this philosophical tradition, these thinkers would arguably have launched, in Western philosophical thought, the first notions linked to mechanistic reflections. In other words, these philosophers would have proposed the general idea that many phenomena in nature must be explained through their basic components, their forms of movement, their properties, and their interactions since these phenomena are also composed of these basic elements.
In Modern Philosophy, the history of what might be called “mechanistic philosophy” is quite complex, given the many debates over definitions of the term and the variety of positions that can be considered within a more general view of what the term means in this period. In any case, many authors consider that the movement of mechanistic philosophy in the seventeenth century is a reaction to Aristotelian natural philosophy and various natural philosophies of the Renaissance period [27]. The French philosopher René Descartes (1596–1650), for example, is considered one of the main figures who laid the foundations of modern mechanistic philosophy, especially with regard to explanations of biological natural phenomena [9, 27, 28, 29, 30, 31]. Des Chene [30] argues that Descartes united a mechanistic ontology, on the one hand, with a method of mechanistic explanation, on the other, applying these ideas to numerous biological phenomena, including the behavior of non-human animals and the human body.
Shortly thereafter, this reasoning would also be applied quite influentially to human beings and their mental capacities. One of the most prominent advocates of this view was the French philosopher and physician Julien Offray de La Mettrie (1709–1751), who published
At the beginning of the twentieth century, the debate about the best explanation for the complex phenomenon of “life” was still quite strong [32]. The controversy was over whether or not this phenomenon could be explained in mechanistic terms. In this context, a very influential work was that of the German-born American physiologist and biologist Jacques Loeb (1859–1924), published in 1912,
In the second half of the twentieth century, philosophers of science sought to analyze, in a more precise way, this mechanistic explanatory strategy. One of the most influential analyzes is present in the work of the American philosopher Ernest Nagel (1901–1985),
It was also during this period that some philosophers of science working in the field of biology began the task of elaborating, in an even more robust and systematic way, notions related to mechanistic explanations in science – mainly in biology. Along these lines, some pioneering works were the following: Herbert Simon,
Within this line of philosophical thinking, the work of William Bechtel and Robert Richardson,
It is also important to point out that in the development of the neo-mechanist movement, at the end of the twentieth century, we can distinguish, more generally, two main trends [5]. One of them focuses more on metaphysical and ontological directions. Authors who work in this line seek, above all, to answer what mechanisms are as real things in the world. The other strand followed in the direction of a greater elaboration of the philosophy of science with epistemological and methodological discussions about scientific explanations, mainly in the area of biology. They seek to explain how something works and not make claims about the ultimate reality of things. These two strands of the new mechanism have been elaborated in an enormous specialized literature that covers several scientific and philosophical areas, dominating a great part of the central debates. Despite being two dimensions that can be separated in the debate, ontological and epistemological discussions are deeply related in many works, both directly and indirectly.
The neo-mechanistic philosophy began to be applied with greater emphasis to cognitive science since the decade of 1990 – with this application becoming stronger in the first decade of the twenty-first century – and it has been better elaborated since then until the present days in central works of very influential authors [9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 35, 36, 37, 38, 39, 40, 41, 42, 43]. According to this view, human cognition, specifically, as well as biological cognition, in general, can be understood and explained through complex models of multilevel neurocognitive mechanisms. At these levels, there are causal processes related to cognitive information processing, cognitive representation, cognitive computing, as well as processes related to chemical and physical reactions that can be used to explain a given cognitive phenomenon. These are, in fact, autonomous processes of causation, which take place at all these different levels and are relevant to the explanation of the phenomenon of interest [44]. According to this theory of human cognition, namely, MTHC, all these causal levels and processes, although autonomous, can be related in a pluralistic mechanistic explanation, where the relevant scientific theories are integrated. As a result, MTHC includes not only a theory of human cognition but also a theory of the human neurocognitive relationship; that is, the theoretical framework suggests a possible solution to the problem of how we are to understand and explain the connection between human neural and cognitive phenomena, thus attempting to relate neuroscience and cognitive science.
The main objective of a mechanistic scientific explanation in scientific areas, such as biology, cognitive neuroscience, and cognitive science, is to identify the parts of a mechanism, its operations, its organization, and thus show how these elements constitute the system’s relationship with the phenomenon that must be explained [9, 10, 45]. Particularly, in cognitive science, the central idea present in the theory is that human neurocognitive processes are a type of information processing performed by neural systems (mechanisms). These processes and the components that carry them out can be decomposed into subparts, and these subparts are decomposed again, as far as necessary for the understanding of the investigated phenomenon. After that, these components and activities have to be located in the brain as spatiotemporal parts of a complex multilevel neurobiological mechanism. As a result, there may be multiple levels of mechanistic composition in a human neurocognitive mechanism.
Another important feature of MTHC is that it was developed within a broad physicalist context that is present in a vast amount of work in contemporary cognitive science, philosophy of cognitive science, and philosophy of mind. In this physicalist context, the theory tries to combine central ideas present in traditional cognitive science with the main ideas present in certain fields of neuroscience that investigate human cognition. In this sense, some authors argue that this mechanistic physicalist framework can provide a consistent way to build a unified science of cognition and integrate cognitive science and neuroscience [23, 24, 25, 40].
Indeed, integrating and unifying, from a physicalist background, traditional cognitive science and traditional neuroscience to understand and investigate human cognition is an old dream held by many authors. Patricia Churchland, in 1986, calls for the unification of cognitive research and neural research in her book
It is possible to argue that MTHC was articulated with the objective of providing this integration and unification in a more precise theoretical way and within a clear physicalist background. The influential version of MTHC by William Bechtel is a clear example. He considers the human phenomenon “mind-brain” as “a set of mechanisms for controlling behavior” [9], and he explains that cognitive phenomena (e.g., perception, attention, memory, problem solving, and language) can be characterized as “information-processing mechanisms” [9]. Bechtel [9] states that scientific disciplines that aim to explain cognitive activities recognize that “in some way, these activities depend upon our brain.” Or, to put it in another way: “Psychological phenomena are realized in brains comprised of neurons” [45]. This means that cognitive phenomena are physical and need to be explained in some physical (neural) way.
Craver and Tabery [47] describe the physicalist commitment quite clearly—“many mechanists opt for some form of explanatory anti-reductionism, emphasizing the importance of multilevel and upward-looking explanations, without rejecting the central ideas that motivate a broad physicalist world-picture.” Therefore, in this approach, there is no space for any form of dualism, pluralism, or non-physicalism of any kind in relation to the ontology of human cognition. There is, indeed, a clear commitment to a form of ontological monism, namely, physicalism, that underlies the neo-mechanistic theory of human cognition.
Neo-mechanistic ideas about human cognitive phenomena are becoming increasingly dominant in fields related to theoretical cognitive science and cognitive neuroscience [48]. Consequently, the neo-mechanistic framework is often presented as one of the main theories, or the main theory, to explain human cognition in the twenty-first century.
Formulations of the idea that human cognition can be considered in computational terms can already arguably be found in the works of Thomas Hobbes (1588–1679) and Gottfried Leibniz (1646–1716). However, it is in the first half of the twentieth century that new developments in this tradition made the thesis gain great strength [49]. Alan Turing (1912–1954), with his work on computation, made a solid mathematical contribution to advances in the attempt to build machines capable of thinking like humans. And with the development of the computer and the emergence of studies in computer science and artificial intelligence, there was an even greater push for the acceptance of these ideas in the period. Indeed, these were crucial factors in the development of cognitive psychology in the 1950s and cognitive science (in the specific sense) in the 1970s. In discussing the foundations of cognitive science, Gardner [3] states that “there is the faith that central to any understanding of the human mind is the electronic computer.” Furthermore, according to him: “Involvement with computers, and belief in their relevance as a model of human thought, is pervasive in cognitive science” [3].
The first formulations of the philosophical foundations and the most central bases of the “computational theory of cognition” were presented, above all, in central works by Hilary Putnam (1926–2016) and Jerry Fodor (1935–2017). It is mainly based on works like these that the “classical model of cognitive computation” was formulated [49]. According to this proposal, the human mind is a computational system similar in important respects to a “Turing machine,” which works through “Turing-style computations.” In this view, cognitive processes, such as problem solving, decision making, and formal reasoning, are performed through computations similar to those of a Turing machine.
Another line of work, however, developed an alternative notion of cognitive computation. Inspired by research in the field of neurophysiology, some authors in the 1980s proposed that cognitive computation was something very different from Turing-style computation [50]. The correct format of cognitive computation for them was that of neural networks, in which, very briefly, data nodes are connected in a particular way so that when the network is activated through an input, it can provide an output. This framework became known as connectionism, and it has been developed in numerous works since then. Many cognitive models of different phenomena were built based on this view, such as object recognition, speech perception, and sentence comprehension.
The notion of “cognitive mechanistic computation” is part of this tradition, and it is especially related to the model of neural networks. Craver [10], for example, writes about the “computational properties of brain regions” and “computational properties of neural systems,” without giving much detail about what exactly this means. In any case, it is clear that the supposed computation is much more related to concrete properties of neural systems than to abstract functional properties of psychological capacities considered in terms of Turing computation or something similar. Milkowski [11], in turn, presents a proposal that holds that neurocognitive processing occurs over states that contain information, but he does not elaborate much on the content and the semantic dimension of cognitive information or of putative cognitive computations.
Bechtel [9, 19] considers mental mechanisms as information-processing mechanisms that operate through neural representations and neural computations about vehicles and content. In his view, the “control theory of dynamical systems” shows how content is placed in this context. And Thagard [14, 15] thinks that mental mechanisms operate through computations that take place on representations at the cognitive level and computations that take place at the neural and molecular levels. In Thagard’s work, there is also recourse to the “theory of dynamical systems” (as in Bechtel’s); however, just in his version of the mechanistic theory, there is a definite number of mechanistic levels and extensive discussion about the “semantic pointers theory” of Chris Eliasmith.
Finally, there is the work of Piccinini [12, 13, 51], which is one of the most theoretically sophisticated and detailed among neo-mechanists regarding such issues. The author defends a mechanistic neurocomputational theory of human cognition. In his view, the human nervous system is a functional mechanism that produces computations through the activation of neurons, while the processing occurs in vehicles according to rules. Cognitive capacities are explained then by multilevel neurocognitive mechanisms that perform neural computations over neural representations. Besides, he thinks that neural computation (i.e., computations defined on the functionally relevant elements of neural activity) is not purely digital, as classically understood, nor purely analog, as alternatively understood; in his view, neural computation is
One does not need to enter so deep into these individual theories to see that they differ significantly. Craver mentions computations but does not offer an elaborated account. Thagard is the only one mentioning semantic pointers as central to the account. Milkowski and Piccinini attempt to avoid the problems with content, by means of focusing on formal properties. And Bechtel uses control theory to deal with the issue of content. As a result, it is not possible to derive from those accounts a single theory, as each author develops his/her own point of view with its significant particularities. There is, therefore, no theoretical substantial unity among these proponents.
However, one can try to find common aspects to evaluate at least the most basic and important tenets. To do that, an analysis of two cases where this mechanistic view on human cognitive computation can be applied will be helpful.
One of the best examples found in the specialized literature of a concrete application of this view to particular cognitive phenomena is related to memory, which, indeed, has been traditionally an object of study in the field of psychology [9, 10]. Functional analyses of the human memory capacity reveal the existence of many sub-capacities, such as short-term memory, long-term memory, phonological memory, visuospatial memory, semantic memory, episodic memory, and memory consolidation. In mechanistic terms, one of the best-understood phenomena in this memory system is memory consolidation. Roughly put, this is the phenomenon of transforming short-term memories (which are liable and easy to disrupt) into long-term memories, which are robust and enduring, when consolidation takes place and permits the organism to remember important events for a longer period of time and modify its behavior accordingly [52]. To explain this phenomenon, all the relevant regions in the brain responsible for the functions that compose the neuro-cognitive mechanism of memory consolidation, including all relevant mechanistic levels of decomposition, must be identified, that is, all the particular component parts and component operations of the whole mechanism must be determined, as shown on Figure 1. Finally, the causal processes and causal interactions within the mechanism functions need also to be understood, that is, the general organization of the mechanism.
An example of a simple model of a neuro-cognitive biological mechanism (M1). In this model, M1 is composed, at the level L1, by its component parts C1, C2, and C3, which perform the functions (or activities) f1, f2, and f3. The component parts can be decomposed into smaller components, as it happens with C3, which is composed, at level L2, of the sub-components SC1, SC2, SC3, and SC4. The component SC3 can be further decomposed, at level L3, into its subcomponents ssc1, ssc2, and ssc3.
The explanation starts at the highest level of the whole mechanism. At this level, it is necessary to correctly identify all the large neural network that is responsible for memory consolidation. Secondly, it must be established whether this large neural system is indeed all that is relevant for the explanation of the phenomenon. The mechanistic explanation at this level also needs to clarify how the neural network process information about new memory episodes through
Once this has been clarified, the explanation turns to the second level of description in which the large neural system is decomposed into particular sub-neural systems localized in more specific regions. Here the goal is to understand the information processing and computational operations (e.g., spiking patterns in populations of neurons) of these smaller neural networks and how they contribute to the performance of the whole mechanism composed of such neural nets.
Moreover, a further stage of decomposition must be reached that concerns the processes underlying memory at an intercellular level. The explanation at this particular level aims at describing the components of a particular neural network and at understanding how a small number of neurons operate (e.g., how they depolarize and fire in the process of propagation of action potentials, or how they are responsible for synaptic processes, neurotransmitters being released, and so on). Here it is possible to measure spiking rates of neurons, or spiking frequency and record neural activity in general.
Finally, the explanation can go even to another lower mechanistic level—the intracellular and molecular level. At this level, the description is in terms of the activity of relevant proteins, molecules, and ions. As one can see, this kind of explanation “exhibits a progression from the behavioral-level characterization of memory consolidation to the identification of important components in the process at progressively lower levels.” [52]. All levels are equally important to achieve the complete multilevel mechanistic explanation of the particular phenomenon in the end.
Another example is related to human visual perception [9, 13, 40], which is roughly understood as the capacity to acquire and process visual information from objects and events in the environment. In the biological mechanism related to human visual perception, the occipital lobe is central, since many studies on humans show deficits in visual processing due to damage in the occipital lobe. The mechanism also includes a projection of the optic tract going from the eye, passing by the lateral geniculate nucleus (LGN), which is an area of the thalamus, and achieving the occipital lobe. Besides, it includes the eyes, optic nerves, and other brain areas responsible for visual perception. All these areas can be decomposed in working components and their operations, and each decomposition is considered to be a lower level in the entire constitution of the mechanism. The occipital lobe, for instance, can be itself decomposed in areas responsible for particular visual functions, such as the striate cortex, also known as Brodmann area 17, or V1 (primary visual cortex, or visual area 1).
The same procedure can be done for all the other areas in the brain that are also part of the mechanism responsible for visual perception; for instance, V2, V3, V4, and V5/MT. It is necessary to identify also the cells (including visual receptor cells in the retina of the eye, such as cones and rods), networks of cells, or larger neural systems in these areas that are responsible for
As one can observe by looking at these two examples, the notion of “computation” in the mechanistic framework stands for some causal interactions within the nervous system and this is how different brain regions “compute” different information. Each brain region “stands for” some kind of particular information—related to perception, sensation, memory, language, reasoning, emotion, etc. The substantial problems with such an account of human cognition will be analyzed in what follows.
A great deal of criticism has arisen in the specialized literature concerning the notion of human cognitive computation. It is nearly impossible to review all of the works, but I will make some considerations of some of the most influential critics.
Fodor [53, 54, 55], for instance, claims that many mental representations (e.g., beliefs) and mental processes (e.g., abductive reasoning) are sensitive to global properties (i.e., properties that beliefs, for instance, have so that they are determined by a set of other beliefs which they are members of). For example, a belief about a tennis racket being broken may complicate the plan of playing tennis on the weekend, but not the plan of playing soccer. This means that a mental representation, such as an intention to play tennis, will depend on the context at the moment—whether there is a racket available for the game or not. Fodor argues, though, that classical symbolic computing models are only sensitive to local properties, and neural network models cannot handle this feature of human cognition.
Dreyfus [56], in turn, claims that much human knowledge cannot be captured by symbolic manipulation and formal rules, since this knowledge is constructed through direct contact and practicing in the world. Nagel [57] brings attention to the problem of phenomenal consciousness—roughly, the issue of what it feels like to experience something subjectively. Following this line of thinking, we can also say that a computer cannot know (if it can know anything) what it feels like to taste the flavor of chocolate. It has no idea of what it is like to eat chocolate, something that is quite basic for any child that does it. More than that, computers do not feel pain or pleasure, which is quite basic for human beings. Furthermore, Searle [58] brings attention to the difficulties related to intentionality, understanding, and meaning, with his famous “Chinese room argument.” And, additionally, Putnam [59] develops the idea that mental states cannot be identified with computational states, consequently arguing vigorously against computational reductionism3.
The case of Bruner’s critics is also very interesting. One of the names most frequently mentioned in influential works of historical reconstruction of the events and studies that contributed to the beginning and development of the cognitive movement in psychology is the American psychologist Jerome Bruner (1915–2016) [1, 2, 3, 60, 61]. He is recognized for having founded, together with George Miller (1920–2012), the Center for Cognitive Studies at Harvard University, in 1960. In addition, Bruner published, together with colleagues, in 1956,
One of the most interesting points in Bruner’s work, however, is his strong criticism of the very cognitive movement he helped to develop. He has presented this criticism in key works, such as
In
However, in Bruner’s view, this is not the way forward. In
One of the major problems pointed out by Bruner in the computationalist approach is that the production of meaning is often extremely complex, sensitive to the context, and involves the difficulty of clear and precise understanding [64]. This is not the same as establishing computational procedures for the processing of input and output information to the system, whether this is computational processing in digital format or the form of neural networks. For Bruner, meaning making is not merely information processing; it is something more profound and more complex. Culture, in his view, has a fundamental role in human life and it is only through it and in it that certain processes and mental structures are formed and used.
The human being, in Bruner’s view, was able to develop a way of life in which reality is represented by a symbolism shared by members of a cultural community, and human life is organized and built from this symbolism that is conserved, elaborated, and transmitted through successive generations [64]. Although meaning is in the mind and is produced by it, it also has its origins in culture and has its importance within the culture in which it was generated. And for the production of meanings, the human mind creates and makes use of symbolic cultural systems. Thus, in this view, thinking and learning are always situated in a cultural context [64]. Computer systems, however, are not capable of producing meanings. They only deal with a certain set of formalized and operationalized meanings, but they do not make interpretations of human and cultural phenomena.
Furthermore, there is no very clear reason to suppose that processes and relationships between all mental phenomena are literally computational in nature, nor that all mental representations have this same character. The application of the concept of computation to these phenomena investigated in the tradition of psychological research is based only on a working hypothesis present in a certain particular theoretical system. Nevertheless, there is as yet no concrete proof that all human cognition works according to a type of computational processing x, y, or z. In fact, finding out what kind of computational processing is related to the human mind has become an extremely debated issue internally by adherents of any computational model of human cognition [49]. It is no accident that comprehensive theoretical systems were developed precisely with the intention of questioning the computational model of cognition.
Now, to illustrate more concretely some of the difficulties mentioned with the notion of human cognitive computation, let us consider some cases involving conscious complex informal reasoning and conscious complex decision making where explanations for human behavior might be required [22].
Consider, firstly, a case where a person is dissatisfied with her marriage and is thinking about getting a divorce. To make such a decision, she has been consciously reflecting for months on the current state of the marriage, her beliefs about the relationship, her emotions about her partner, her desires and expectations in life, the beliefs of her family and closest friends about the issue and what are the reasons to take action in this regard. After thinking carefully for a very long time, being aware that she really does not feel comfortable and happy at all, she decides to go for a divorce.
Consider also a second example. A person needs to decide which candidate she will vote for as president of her country. To make this decision, she needs to use her conscious informal reasoning ability. Thus, she reflects on the arguments put forward by politicians running for the election, the arguments put forward by commentators, scientists, and political analysts, as well as journalists writing on the subject, and the arguments of friends and family she finds relevant and credible. After three months of thinking, she has not decided yet but is rather still in doubt concerning her vote in the major candidates A and B. When someone asks her which candidate she is going to vote, she says: “I still don’t know.” Then, some surprising news arises in a serious newspaper with charges of corruption against candidate A, and she is a frequent reader of this newspaper, so she becomes immediately aware of this. Upon reflection on the matter and related issues, she takes the new information seriously and she finally decides that voting for candidate B is the best option. The major reason is that there is no charge whatsoever of corruption against him. When she is asked now which candidate she is going to vote for, she answers immediately: “candidate B.” After she made up her mind, she finally goes to the appropriate place on the proper day and time to cast her vote.
A third example is the case of a college student who suffers from difficulties related to his excessive anxiety. Through a general psychological assessment, it can be seen that the factors related to student anxiety are financial difficulties, difficulties in family life where physical and psychological violence occurs, difficulties in finding leisure time to relax and have fun (since they need to work and study at the same time) and difficulties with excessive concerns about the uncertain future, as he believes that it will not be easy to find a job when he graduates. All of these factors seem to contribute to generate in the student’s mind distorted and dysfunctional negative thoughts about himself and his life, and it seems very plausible that these distorted thoughts are strongly associated with his excessive anxiety. This interpretation is, indeed, supported by numerous works in the specialized literature in clinical psychology. Thus, we observe that the most relevant causal factors to explain this psychological phenomenon are not merely computational, but psychological, social, and environmental.
Psychological scientific explanations, in these cases, need considerations that go beyond the investigation of computations being performed in nervous systems or even in any abstract functional system. What explains the psychological phenomenon of belief formation and decision making in the first example and the excessive anxiety in the third example is the meaning formation and interaction of beliefs, desires, and intentions to act (according to logical rules, practical rules, and interpretation of reality), which are strongly affected by emotions, physical environment, and social factors.
In the second example, evidently, an informative explanation would have to mention an important causal factor—the event of the corruption charges against candidate A, appearing in a serious newspaper. Moreover, the explanation would have to mention that the person becomes aware of this event, accepts it as reliable, accepts the charges as true and accurate, and now this content is present in one or some of her beliefs. In possession of this content, she can rationally justify herself when engaging in discussions about the topic with family, friends, and other people, providing reasons for her related beliefs and her related behaviors. Thus, the influence of the event on her is external and affects the internal logic and content of her systems of beliefs, emotions, desires, and intentions. This explanation involves then particular properties of human cognitive systems, present for instance in belief and intention systems. These properties are clearly different than those involved in merely describing supposed automatic computational activities in her neural networks or describing what is happening in terms of physical and chemical neural processes. The explanation for this phenomenon of belief formation, therefore, would also have to account for how this new information could change a particular belief given her system of beliefs about the topic.
In the examples above, there are cognitive processes that often necessitate consciousness and complex informal reasoning about belief systems that are often linked to particular perceptions, sensations, emotions, desires, intentions, attitudes, as well as related to each other and the external environment. Some of these beliefs have great value, such as some moral beliefs, which makes this whole dynamic even more complex. In these cases, blind computation might even occur at some level, but what is most relevant are environmental, social, cultural, historical, and psychological factors (such as beliefs, emotions, desires, and intentions) that acquire meaning in a given cognitive system.
The relevant explanation of the actions in such cases is made through considerations—(1) about the creation and alteration of the content of perceptions, beliefs, sensations, emotions, maxims, wills, desires, intentions, etc.; (2) about their internal relationships; and (3) about their external relationships with the physical, social, historical, and cultural context. Rigorous empirical scientific research can aid in discovering strong and systematic (stable) regularities in human behavior explained in such terms without the need for the notion of computation. Statistical tools and analysis, through the mathematical application, can bring greater objectivity, avoiding both an extremely subjective and confusing vocabulary, as well as unproductive speculation and mere common sense.
Moreover, self-consciousness here is crucial, since we humans have the ability to
Therefore, human beings have the ability to form original belief systems and relate them according to logical and interpretative rules, building arguments to support their point of view, which often influences their behavior. Human beings are also able to think about different types of relevant information for months or years to make an important and complex decision. To make a difficult decision, a human being can take into account information related to plans for the very distant future, in which many scenarios are considered. A human may wonder what happened in the very distant past, or what might have happened, even if he or she knew what really happened. And complex informal reasoning and complex decision making are things that humans do naturally and often in their daily lives.
Thus, in cognitive science, it is necessary to deal with extremely complex phenomena, given that human beings show great differences when compared to other animals in nature. Human beings have a cumulative, complex, dynamic, and elaborate culture that is passed on through generations. Humans are also involved in understanding and writing their own history. They have natural languages with enormous, complex, and refined expressive power and sophisticated grammar. Human beings practice and appreciate art, such as literature, painting, cinema, and music. They engage in purely formal or very abstract thoughts when they do mathematics, logic, and engage in certain religious thoughts. They create legal laws for their societies and think about morality, building moral systems. They build artificial intelligence machines that are able to learn with a certain level of autonomy and are able to explore other planets. Furthermore, humans are involved in politics, science, and philosophy.
Computers, by contrast, so far, do not form beliefs on their own, they do not have the capacity to evaluate and improve them by themselves, and they do not interact in the social environment neither using natural language with a huge degree of sophistication as humans do nor engaging in social and cultural practices. If we look at the problem from a very concrete and objective point of view, we observe that even the most advanced computer systems, the most advanced robots, and the most advanced artificial neural and cognitive architectures today are still very far from behaving like human beings in relation to language and actions that involve consciousness and informal rationality. Humans are capable of playing chess, cooking pizza, making coffee, having a conversation about politics, creating a new song on a guitar, and playing tennis on the same day. No computational artificial system is currently capable of this generality in cognition. So, as a matter of current fact, computational artificial cognition cannot be used to fully explain the major capacities of human cognition and intelligence.
It is no surprise, then, that mechanistic accounts of psychological capacities usually suggest only
Difficulties with the notion of cognitive computation are recognized by influential neo-mechanists themselves. Milkowski [21], for instance, concludes his work by admitting that we “still don’t know how to model consciousness mechanistically.” Additionally, there are several alternative models of cognitive computation in cognitive science nowadays—syntactic computation; algorithmic computation; causal computation; and semantic computation [65]. None of the models has gained significant prominence over the others concerning the understanding and explanation of human cognition. Finally, there is strong criticism even of the attempt by neo-mechanists to propose that good computational explanations in cognitive science must be also mechanistic explanations [66, 67].
Therefore, if we think about the issue from the point of view of current facts, we need to recognize that the neo-mechanistic proposal for human cognition is still far from being able to be considered the best or most plausible understanding and explanation of human cognition. It is just one view among many.
The mechanistic framework has been offering significant contributions to the field of cognitive science, on the one hand. One of its best contributions is the promotion of debates on the issue of human cognitive computation. In this sense, there is a search for a better understanding of what this notion actually means. All this effort is very worthwhile and welcome. More generally, the theoretical debate about fundamental questions in cognitive science promoted by new mechanists is also very important, as well as their effort to clarify what a “biological mechanism” and a “cognitive mechanism” are and what a “mechanistic explanation” in cognitive science is. Furthermore, another contribution of the new mechanistic philosophy is to encourage historical research and current debate, in cognitive science and beyond, about the relationship among “mechanism,” “materialism,” “reductionism” and “computationalism”, so that these concepts are not confused and that the positions adopted by the authors, as well as the different dimensions of the debate, are appreciated in a fair and correct way. Finally, the new mechanistic philosophy applied to cognitive science is also contributing to the important debate concerning the unity, integration, and plurality in the field.
On the other hand, however, many of the current promises of the new mechanism for cognitive science are quite difficult to fulfill. Firstly, neo-mechanistic philosophy is a philosophy of science built primarily from examples from the biological sciences and neuroscience that is serving as the basis for building a philosophy of the science of mind. We live in a period in which neuroscience and artificial intelligence research have gained great prestige and recognition. A great deal of economic investment has been made in these areas and this is very attractive. In part, this also influences “the new wave of mechanism,” and the necessity of some authors to expand the framework. However, numerous particularities related to psychology and human cognition are being neglected in this theoretical structure, as I tried to show.
Secondly, there is considerable disagreement among leading neo-mechanists over the most plausible formulation of MTHC regarding fundamental issues, such as the idea of human cognitive computation. Thus, there is a considerable difficulty related to the internal articulation and unification of the theory. Furthermore, many alternative major theories, and the research programs based on them, strongly threaten the neo-mechanistic framework in current cognitive science, since they are also seeking predominance in the field, or just for having more space and recognition.
Given this, we can conclude that the mechanistic model of human cognitive computation cannot provide substantial theoretical or explanatory unification or integration to the field of cognitive science today, since there is no unification between the proponents themselves. Moreover, their different proposals are often unclear on many important aspects concerning traditional problems of intentionality, consciousness, and self-consciousness. The accounts are sometimes internally not well-articulated; and, externally, there is serious criticism of them, with countless debates and controversies on several fundamental questions. In addition, there are several alternative models competing for predominance on this particular issue. And it is yet by no means clear whether the explanatory power of any of them is greater than the explanatory power of the others.
This analysis shows, therefore, that the neo-mechanistic proposal concerning human cognitive computation has serious weaknesses. But the problem is not to use the idea of cognitive computing to advance models of biological and artificial cognitive architectures, since many human cognitive abilities can already be simulated. Indeed, it is very interesting to see that our science has advanced to the point where a computer can win against the best chess and go game players in the world. In fact, advancements within computational artificial systems and robotics could well be applied to improve our educational and health systems. For example, inspired by scientific developments in the field of cognitive science, artificial cognitive systems could possibly be developed to help children with the learning process of mathematics, natural language, or history at schools, or even at the university level. Artificial systems could possibly be developed to help people with excessive anxiety symptoms, as well. This could be extremely worthwhile. Moreover, better and more advanced artificial cognitive systems and robotic systems can contribute to improving theories of human cognition, as much as better and more correct theories of human cognition can help in faster advancements of cognitive artificial systems and robotic systems. But there is good reason to keep these efforts separated and to consider human cognition as a very complex and particular phenomenon in nature.
The problem arises only with the untenable suggestion that we already have, or that we are very close to getting, the complete and definitive understanding and explanation of all the major capacities of human cognition in computational terms. This, yes, is a mistake.
The author declares no conflict of interest.
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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:null},{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:"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:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{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:null},{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. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"92",type:"subseries",title:"Health and Wellbeing",keywords:"Ecology, Ecological, Nature, Health, Wellbeing, Health production",scope:"\r\n\tSustainable approaches to health and wellbeing in our COVID 19 recovery needs to focus on ecological approaches that prioritize our relationships with each other, and include engagement with nature, the arts and our heritage. This will ensure that we discover ways to live in our world that allows us and other beings to flourish. We can no longer rely on medicalized approaches to health that wait for people to become ill before attempting to treat them. We need to live in harmony with nature and rediscover the beauty and balance in our everyday lives and surroundings, which contribute to our well-being and that of all other creatures on the planet. This topic will provide insights and knowledge into how to achieve this change in health care that is based on ecologically sustainable practices.
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