\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"5153",leadTitle:null,fullTitle:"Diamond and Carbon Composites and Nanocomposites",title:"Diamond and Carbon Composites and Nanocomposites",subtitle:null,reviewType:"peer-reviewed",abstract:"During the past few years, scientists have achieved significant successes in nanoscience and technology. Nanotechnology is a branch of science that deals with fine structures and materials with very small dimensions - less than 100 nm. The composite science and technology have also benefits from nanotechnology. This book collects new developments about diamond and carbon composites and nanocomposites and their use in manufacturing technology.",isbn:"978-953-51-2454-2",printIsbn:"978-953-51-2453-5",pdfIsbn:"978-953-51-6657-3",doi:"10.5772/61410",price:119,priceEur:129,priceUsd:155,slug:"diamond-and-carbon-composites-and-nanocomposites",numberOfPages:182,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"c358194cd9d33671b03808b346f354dc",bookSignature:"Mahmood Aliofkhazraei",publishedDate:"June 29th 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5153.jpg",numberOfDownloads:13289,numberOfWosCitations:36,numberOfCrossrefCitations:12,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:37,numberOfDimensionsCitationsByBook:4,hasAltmetrics:0,numberOfTotalCitations:85,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 24th 2015",dateEndSecondStepPublish:"October 25th 2015",dateEndThirdStepPublish:"January 11th 2016",dateEndFourthStepPublish:"February 10th 2016",dateEndFifthStepPublish:"May 26th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"155413",title:"Dr.",name:"Mahmood",middleName:null,surname:"Aliofkhazraei",slug:"mahmood-aliofkhazraei",fullName:"Mahmood Aliofkhazraei",profilePictureURL:"https://mts.intechopen.com/storage/users/155413/images/3939_n.jpg",biography:"Dr. Mahmood Aliofkhazraei works in the corrosion and surface engineering group at the Tarbiat Modares University. He is the head of Aliofkhazraei research group (www.aliofkhazraei.com). Dr. Aliofkhazraei has received several honors, including the Khwarizmi award and the best young nanotechnologist award of Iran. He is a member of the National Association of Surface Sciences, Iranian Corrosion Association, and National Elite Foundation of Iran. His research focuses on materials science, nanotechnology, and its use in surface and corrosion science.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"13",institution:{name:"Tarbiat Modares University",institutionURL:null,country:{name:"Iran"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"926",title:"Condensed Matter Physics",slug:"materials-science-composite-materials-condensed-matter-physics"}],chapters:[{id:"50602",title:"Hardness of Thin Films and the Influential Factors",doi:"10.5772/63302",slug:"hardness-of-thin-films-and-the-influential-factors",totalDownloads:1982,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Hardness is one of the most significant mechanical characteristics of a material. Hard materials are known for their durability. Currently, diamond is the hardest substance known in the world. Researchers have substantially worked on the production of this expensive material. Coating this material as a thin film was also the topic of a separate research. At the same time, hardness measurement, including the measurement of the hardness of a thin film was a topic of research as well. In this section, we will examine what researchers are doing for the measurement of the hardness, especially for bulk materials.",signatures:"İshak Afşin Kariper",downloadPdfUrl:"/chapter/pdf-download/50602",previewPdfUrl:"/chapter/pdf-preview/50602",authors:[{id:"178506",title:"Dr.",name:"Ishak Afşin",surname:"Kariper",slug:"ishak-afsin-kariper",fullName:"Ishak Afşin Kariper"}],corrections:null},{id:"51244",title:"Graphene-Based Composites and Hybrids for Water Purification Applications",doi:"10.5772/63666",slug:"graphene-based-composites-and-hybrids-for-water-purification-applications",totalDownloads:2598,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:1,abstract:"The increasing water demand and the worldwide shortage of clean water call for new technologies for wastewater treatment, of which sorption is recognized as simplest and efficient method for all major water pollutants, including heavy metal ions, organic dyes and organic solvents. In this context, graphene, an atomically thin two-dimensional hexagonal arrays of carbon atoms, and its analogs have been visualized as ultimate materials for the separation of pollutants from water due to properties such as superhydrophobicity, superoleophilicity, chemical-environmental-thermal stability, chemical manipulability, controllable morphology, texture, porosity and accessible surface area, facile processing, low density, biocompatibility and low-cost synthesis from a variety of abundant carbonaceous precursors. This chapter begins with an introduction of water purification, related processes and fundamentals of adsorption and absorption phenomenon. The next section emphasizes the importance and advantages of graphene as sorbent, brief about its synthesis, types of graphene-based composites/hybrid systems and their synthesis strategies. The last section throws light on the performance of graphene-based foams, aerogels and composites/hybrids for the separation of metal ions, organic dyes and various oils/organic solvents, supported by comprehensive literature account. Finally, the chapter concludes with the comments on the current status, major challenges and future scope in the direction.",signatures:"Rahul Sharma and Parveen Saini",downloadPdfUrl:"/chapter/pdf-download/51244",previewPdfUrl:"/chapter/pdf-preview/51244",authors:[{id:"149897",title:"Dr.",name:"Parveen",surname:"Saini",slug:"parveen-saini",fullName:"Parveen Saini"}],corrections:null},{id:"51099",title:"The Application of Nanodiamond in Biotechnology and Tissue Engineering",doi:"10.5772/63549",slug:"the-application-of-nanodiamond-in-biotechnology-and-tissue-engineering",totalDownloads:2715,totalCrossrefCites:4,totalDimensionsCites:14,hasAltmetrics:0,abstract:"Diamond in the allotrope form consists of carbon atoms arranged in a cubic crystal structure covalently bonded in sp3 hybridization. Diamond has emerged as a very promising material for various biomedical applications due to its excellent mechanical properties (hardness, low friction coefficient, good adhesiveness to the underlying substrate, good interlayer cohesion), optical properties (the ability to emit intrinsic luminescence), electrical properties (good insulator in the pristine state and semiconductor after doping), chemical resistance (low chemical reactivity and resistance to wet etching) and biocompatibility (little if any toxicity and immunogenicity). For advanced biomedical applications, diamond is promising particularly in its nanostructured forms, namely nanoparticles, nanostructured diamond films and composite scaffolds in which diamond nanoparticles are dispersed in a matrix (mainly nanodiamond-loaded nanofibrous scaffolds). This chapter summarizes both our long-term experience and that of other research groups in studies focusing on the interaction of cells (particularly bone-derived cells) with nanodiamonds as nanoparticles, thin films and composites with synthetic polymers. Their potential applications in bioimaging, biosensing, drug delivery, biomaterial coating and tissue engineering are also reviewed.",signatures:"Lucie Bacakova, Antonin Broz, Jana Liskova, Lubica Stankova,\nStepan Potocky and Alexander Kromka",downloadPdfUrl:"/chapter/pdf-download/51099",previewPdfUrl:"/chapter/pdf-preview/51099",authors:[{id:"129320",title:"Dr.",name:"Alexander",surname:"Kromka",slug:"alexander-kromka",fullName:"Alexander Kromka"},{id:"179175",title:"Dr.",name:"Lucie",surname:"Bacakova",slug:"lucie-bacakova",fullName:"Lucie Bacakova"},{id:"184741",title:"MSc.",name:"Antonin",surname:"Broz",slug:"antonin-broz",fullName:"Antonin Broz"},{id:"184742",title:"Dr.",name:"Jana",surname:"Liskova",slug:"jana-liskova",fullName:"Jana Liskova"},{id:"184744",title:"Dr.",name:"Lubica",surname:"Stankova",slug:"lubica-stankova",fullName:"Lubica Stankova"},{id:"184745",title:"Dr.",name:"Stepan",surname:"Potocky",slug:"stepan-potocky",fullName:"Stepan Potocky"}],corrections:null},{id:"50955",title:"Laser-Assisted Machining of Difficult to Cut Materials",doi:"10.5772/63417",slug:"laser-assisted-machining-of-difficult-to-cut-materials",totalDownloads:1732,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The chapter presents the results of experimental investigation of the machinability of difficult to cut materials, e.g. silicon carbide particulate, aluminium metal matrix composite, cemented carbides layers, technical ceramics (Si3N4). The work focuses on the study of machinability during conventional and laser-assisted turning. Nowadays, the difficult to cut materials have a great application in different fields of industry, e.g. automobile or aerospace. Metal matrix composites are known as the difficult to machine materials, because of the hardness and abrasive nature element—in this case—SiC.",signatures:"Damian Przestacki",downloadPdfUrl:"/chapter/pdf-download/50955",previewPdfUrl:"/chapter/pdf-preview/50955",authors:[{id:"179387",title:"Dr.",name:"Damian",surname:"Przestacki",slug:"damian-przestacki",fullName:"Damian Przestacki"}],corrections:null},{id:"50703",title:"DLC Thin Films and Carbon Nanocomposite Growth by Thermionic Vacuum Arc (TVA) Technology",doi:"10.5772/63367",slug:"dlc-thin-films-and-carbon-nanocomposite-growth-by-thermionic-vacuum-arc-tva-technology",totalDownloads:1446,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The aim of this chapter is to report the results on synthesis DLC thin films and carbon nanocomposites by the versatile nanofabrication method based on plasma entitled thermionic vacuum Arc (TVA). TVA technology is based on the localized ignition of the arc plasma in vacuum conditions. Among thin film coating methods by vacuum deposition techniques with high purity, low roughness, and good adhesion on the substrates, TVA is one of the major suitable methods to become a powerful coating technology. Two or three different TVA discharges can be ignited simultaneously in the same chamber for multi-material processing using TVA and separate power supplies. These TVA discharges are localized and do not interfere with each other. Simultaneous two or three TVA discharges were already used for the production of alloy/composite of various materials. This is due to the high versatility concerning the configuration of experimental arrangements, taking into account the number of electron guns, symmetry of the electrodes, relative position of the anode versus cathode, and also the huge opportunity to combine the materials to be deposited: bi- and multi-layers, nanocomposites, or alloys in order to have specific applications. This chapter presents the comparative results concerning the surface-free energy information processing, the reflective index, the hardness, and the morphology to provide a coherent description of the diamond-like carbon films and carbon nanocomposites synthesized by thermionic vacuum arc (TVA) and related configurations where Me = Ag, Al, Cu, Ni, and Ti: binary composites (C-Me, C-Si) and ternary composites (C+Si+Me). The results include reports on the distribution in size, surface, geometry, and dispersion of the nanosized constituents, tailoring and understanding the role of interfaces between structurally or chemically dissimilar phases on bulk properties, as well as the study of physical properties of nanocomposites (structural, chemical, mechanical, tribological). The results presented here could have a great impact on the development of advanced materials and many manufacturing industries, as well as expanding the technologically important field of interface science where the control of the film-substrate interface would be critical.",signatures:"Rodica Vladoiu, Corneliu Porosnicu, Aurelia Mandes, Ionut Jepu,\nVirginia Dinca, Aurelian Marcu, Mihail Lungu, Gabriel Prodan and\nLiga Avotina",downloadPdfUrl:"/chapter/pdf-download/50703",previewPdfUrl:"/chapter/pdf-preview/50703",authors:[{id:"181440",title:"Dr.",name:"Rodica",surname:"Vladoiu",slug:"rodica-vladoiu",fullName:"Rodica Vladoiu"},{id:"185213",title:"Dr.",name:"Corneliu",surname:"Porosnicu",slug:"corneliu-porosnicu",fullName:"Corneliu Porosnicu"},{id:"185214",title:"Dr.",name:"Aurelia",surname:"Mandes",slug:"aurelia-mandes",fullName:"Aurelia Mandes"},{id:"185215",title:"Dr.",name:"Ionut",surname:"Jepu",slug:"ionut-jepu",fullName:"Ionut Jepu"},{id:"185216",title:"Dr.",name:"Virginia",surname:"Dinca",slug:"virginia-dinca",fullName:"Virginia Dinca"},{id:"185217",title:"Dr.",name:"Aurelian",surname:"Marcu",slug:"aurelian-marcu",fullName:"Aurelian Marcu"},{id:"185218",title:"Mr.",name:"Mihail",surname:"Lungu",slug:"mihail-lungu",fullName:"Mihail Lungu"},{id:"185219",title:"M.Sc.",name:"Liga",surname:"Avotina",slug:"liga-avotina",fullName:"Liga Avotina"},{id:"185220",title:"Dr.",name:"Gabriel",surname:"Prodan",slug:"gabriel-prodan",fullName:"Gabriel Prodan"}],corrections:null},{id:"51064",title:"Carbon Nanotube (CNT)-Reinforced Metal Matrix Bulk Composites: Manufacturing and Evaluation",doi:"10.5772/63886",slug:"carbon-nanotube-cnt-reinforced-metal-matrix-bulk-composites-manufacturing-and-evaluation",totalDownloads:2817,totalCrossrefCites:3,totalDimensionsCites:11,hasAltmetrics:0,abstract:"This chapter deals with the blending and processing methods of CNT-reinforced metal matrix bulk composites (Al/CNT, Cu/CNT and Ni/CNT) in terms of solid-state processing, referring mainly to the research works of the last ten years in this research field. The main methods are depicted in a brief way, and the pros and cons of each method are discussed. Furthermore, a tabular summary of the research work of the mentioned three systems is given, including the blending methods, sintering methods, the used amount of CNTs and the finally achieved relative density of the composite. Finally, a brief discussion of each system is attached, which deals with the distribution and interaction of the CNTs with the matrix material.",signatures:"Sebastian Suárez, Leander Reinert and Frank Mücklich",downloadPdfUrl:"/chapter/pdf-download/51064",previewPdfUrl:"/chapter/pdf-preview/51064",authors:[{id:"180146",title:"Dr.Ing.",name:"Sebastian",surname:"Suarez",slug:"sebastian-suarez",fullName:"Sebastian Suarez"},{id:"184293",title:"Dr.",name:"Leander",surname:"Reinert",slug:"leander-reinert",fullName:"Leander Reinert"},{id:"184294",title:"Dr.",name:"Frank",surname:"Mücklich",slug:"frank-mucklich",fullName:"Frank Mücklich"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"3817",title:"Developments in Corrosion Protection",subtitle:null,isOpenForSubmission:!1,hash:"8ff86fac7ac8bce142fdc3c0e5a79f30",slug:"developments-in-corrosion-protection",bookSignature:"M. 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It is central to the neurodevelopmental disorders, autism, learning disability, bipolar disorder and schizophrenia as well as closely associated conditions like psychopathy and personality disorder. Indeed, treating empathy deficits is central to psychiatry and psychology. In the conditions described above, there is so much overlap and empathy deficits of various kinds are critical to understanding these conditions. Because of the serious long-term outcomes, early treatment is critical, even if there is a need for a great deal of extra research to be done in this area.
\nThe relationship between empathy, autism and psychopathy is insufficiently appreciated. While it is very complex, this chapter tries to explain the relationship. It reviews the relationship between these concepts in the psychiatric literature and also builds on the author’s 45 years of clinical experience, dealing with these patients.
\nFundamental criticism of the American Psychiatric Association [1] DSM 5. Now psychology and neuropsychiatry are intimately entwined. This chapter aims to elucidate the neuropsychology and neuropsychiatry of empathy, autism and psychopathy.
\nEmpathy is a spectrum concept. It is an overarching phenomenon. Batson [2] describes many, “phenomena” in relation to empathy, ranging from (1) “knowing another person’s internal state, including his or her thoughts and feelings”; (2) “adopting the posture or matching the neural response of an observed other”; (3) “coming to feel as another person feels”; (4) “intuiting or projecting oneself into another’s situation,” (a simulation phenomenon); (5), “imagining how another is thinking and feeling,” (a theory phenomenon); (6), “imagining how one would think and feel in the other’s place,” (a simulation phenomenon). Erickson [3], points out that to simulate is to put oneself in the shoes of another and that a simulation theory is sometimes called empathy theory; (7), “feeling distress at witnessing another person’s suffering”; (8), “feeling for another person who is suffering” [2]. There will be further elaborations of these phenomena in the years to come, with further research on empathy. Chialant et al. [4], defines empathy as, (a), “an affective state that is (b) “isomorphic to another person’s affective state” and (c) “is elicited by observing or imagining another person’s affective state,” and (d) “is experienced while remaining cognisant that the other person’s affective state is the source of one’s own affective state.” Chialant et al. [4], also note that, “our capacity to distinguish whether the source of an affective experience is triggered by another or lies within ourselves is part of the broader capacity for perspective taking.” This chapter will start with a discussion of neuropsychology.
\nSwan and Riley [5] note that “how we understand another person’s mind and reflect on our own mental states, or, ‘mentalize’, [6], is the basis of empathy.” There are two different views on the mechanism that puts us in the “shoes,” (the mind) of another person: (a) “thinking or mind-reading, and (b) feeling or empathy.” One way to provide empathy is to share another person’s feelings in an emotive manner, defined as, “affective response more appropriate to someone else’s situation than to one’s own” [7]. Various alternative forms, each based on cognitive theories infer the state of another mind through theory of mind, [8]. This definition [5] involves, “the ability of humans and some animals to ascribe unique mental states to others and to use those mental state attributes during social interactions.” Swan and Riley [5], also note the, “theory-theory of [9], which means that, “we attribute mental states to others on the basis of a theory of mind that is constructed in early infancy and subsequently revised and modified or else is the result of maturation of innate mind-reading modules” [10]. Assimilation theory of the mind, [11, 12], is described by Swan and Riley [5], as stating that, “simulation theorists deny that our understanding of others is theoretical and maintain that we use our mind as a model when understanding the mind of others.” Finally, Swan and Riley note that mind-reading, [13, 14], note that, “ascribed to the target is ascribed as a result of the attributors instantiating, undergoing or experience that very state.” Mind-reading involves attributing a mental state to another. “It is the ability to detect the intentions and predict the behaviours of other individuals.” Swan and Riley ([5], page 13), point out that, “it has been argued that empathy is extended to include mentalizing. Mentalization embodies the capacity to include a sense of the actions of oneself and other people on the basis of desires, feelings and beliefs.” It is a big issue in autism and Decety and Michalskak Lahey [15] points out that, “empathic perspective taking also partially differs from mentalizing and theory of mind functions, which involve taking another person’s perspective and attributing to them particular cognitive states, in that it is more involved in attributing emotional states.”
\nResearch suggests a sharp distinction between autism and psychopathy. Persons with autism are described as having problems with theory of mind and persons with psychopathy having intact theory of mind attributes [16], but when one is dealing with neuropsychology and the brain, the situation is rarely so clear cut, so black and white. There is continuous heterogeneity and variability. In the clinical world, these issues are almost always on a spectrum with greater or lesser theory of mind problems. Indeed, some high-functioning autism persons can pass theory of mind tests [10, 17]. Blair [18] points out that “cognitive empathy or theory of mind is profoundly impaired in individuals with autism.” These theories have been very seriously undermined by research on high-functioning autism and Scheeren et al. [17], points out that, “counter to what theory of mind theory of ASD would predict, school age children and adolescents with high-functioning ASD seem to be able to master the theoretical principals of advanced mental state reasoning.” This is a warning to absolutism in neuropsychology and neuropsychiatry. In short, only some persons with autism have theory of mind problems.
\nFrom a clinical perspective, the author, having diagnosed over 4500 persons with autism sees these overlapping features all the time. It is not rare to see children with autism and callous, unemotional traits.
\nThere is massive variability in the, “real world,” that is clinical work with routine clinical patients and this is most clearly seen in those who have criminal autistic psychopathy, [19]. The empathy spectrum, the autism spectrum and psychopathy spectrum are almost infinite in terms of severity and variability, [20]. There is an empathy spectrum from high empathy to no empathy. In actual fact, the idea of zero empathy, [21], is something that is a theoretical concept and that does not occur in the real world. We need to think in dimensional perspective, not categorical perspectives. We need to think in terms of non-specificity rather than absolutely specificity. In the vast majority of these situations we are dealing with multiple genes of small effect interacting with the environment. This gives us almost unlimited variability and heterogeneity in the concept of empathy, autism and psychopathy.
\nIn relation to diagnosis, there is massive overlap between psychiatric diagnostic categories, [22], and there is equally massive overlap between degrees of empathy in many diagnostic categories. Indeed, an over-arching neurodevelopmental category makes more sense than individual diagnostic categories because there is so much overlap in neurodevelopmental psychiatry, [20]. According to Scull [23], Steven Hyman, the former director of NIMH stated that DSM 5, “was totally wrong in the way its authors could not have imagined. So in fact, what they produced was an absolute scientific nightmare. Many people who got one diagnosis got five diagnoses, but they didn’t have five diseases – they have one underlying condition.” Insel [23, 24], who was also the director of the NIMH stated that DSM 5 showed, “a lack of validity … as long as the research community takes DSM 5 to be a bible, we will never make progress. People think that everything has to match DSM 5 criteria, but what you know … biology never read the book, and he went on to point out that in future the NIMH would be, re-orientating into research away from DSM 5 categories … patients with mental illness deserve better.” Clearly, Hyman and Insel were absolutely correct. He, Insel [24], proposed Research Domain Criteria to collect, “genomic, cellular, imaging, social and behavioural information,” and he also recommended focusing on the brain and, “connectopathies.” Thomas Insel noted that psychiatrists, “actually believe, (that their diagnoses) are real, but there’s no reality. They are just constructs.” The first step is to analyse the huge spectrum of empathy and diagnosis. This chapter elucidates the divergence between “laboratory” (research) findings using rarefied, (autism diagnostic interview), instruments [25]. In the clinical world, research findings have to be modified when they do not take into account the complex, “real,” clinical world. The autism diagnosis interview misses out three quarters of persons with autism spectrum disorders.
\nThe vast majority of persons with ASD show a failure to orientate to other’s distress, have difficulty understanding intentions and attributing thoughts to others and appear to respond emotionally, “in a limited way,” according to Rogers et al. [26]. They have problems with social relationships, reduced eye contact, problems understanding social know-how, problems sharing thoughts, problems seeing things from other people’s perspectives, preservation of sameness, narrow interests and sensory issues. Most are highly moral, but many can show aggression in a non-lethal way and then there are those who are highly dangerous with criminal autistic psychopathy, [19]. Indeed, aggression was well recognised by Asperger [27], Frith [28]. Asperger [27] described Fritz V. as, “aggressive and lashed out with anything he could get hold of, (once a hammer).” Asperger [27] also described autistic acts of malice, sadistic traits, “typically calculated,” which suggested an understanding of other minds and suggesting the overlap here between autism and psychopathic traits. One boy, according to Asperger [27], stated, “mummy, I shall take a knife one day and put it into your heart, then blood will spurt out and this will cause a great stir.” This suggests a sadistic pleasure characteristic of both psychopathy and autism, (criminal autistic psychopathy) again showing the overlap. The author of this chapter is suggesting that we bring back the diagnosis of autistic psychopathy for those with autism and Asperger syndrome, who engage in criminal activities with a new diagnosis of criminal autistic psychopathy [19]. These persons have the dual features of autism and psychopathy. There are overlapping features, but also differences from psychopathy.
\nIn relation to the school shooting at Sandy Hook School, Solomon [29], noted that from his conversation with Peter Lanza, the father of Adam who shot 26 people at this school, that he showed poor eye contact, problems with social relationships, preservation of sameness, narrow interests, poor communication skills and sensory issues. These are all classic features of Asperger syndrome, DSM 4, or of the sub-group of Asperger syndrome, called criminal autistic psychopathy, [19]. He had major empathy deficits in relation to other people. According to Solomon [29], when his mother asked Adam whether he would feel sad if anything happened to her, he replied “no.” He found it much easier to communicate online. It’s online that the true state of affairs of people with dangerous thoughts and fantasy, particularly those with Asperger syndrome, are best seen. The potential for criminal autistic psychopathy can be noticed in very early life with callous traits. Adam felt hostile to people, particularly females and he wrote online, “why females are inherently selfish.” Solomon [29], pointed out that, “misogynism is very common in these conditions.” His father [29] stated that “Adam would have killed me in a heartbeat, if he’d had the chance.” He shot his mother “four times,” Solomon [29].
\nThe school shooter Eric Harris at Columbine also had criminal autistic psychopathy, [19]. Cullen [30], described him as, “painfully shy,” and that he was hypersensitive to criticism or rejection. Cullen [30], pointed out that he stated that, “I hate almost everyone,” and, “I wanna rip his head off, and eat it,” in a flat voice. Cullen [30], stated that Eric, “described going to some random, downtown area … and blowing up and shooting up everything he could, and that, he would feel no remorse, no shame. He would make them pay”. Cullen [30], went on to point out that he was, “egotistical, empathy-free” and, “egocentric with appalling failure of empathy.” Cullen [30], also described him as being, “callous and cunning.” Here again, we see the overlap between psychopathy and autism. In the real world, people do not fit into neat categories or boxes. There is a massive overlap between diagnostic categories.
\nBlair [16] points out that callous, unemotional people show a lack of empathy and remorse, despite perfectly good understanding of thoughts and feelings. He emphasises “lack of empathy, poor processing or distress cues in other people, lack of remorse, but good mentalizing.” Blair [16] points out that, “the naming of emotional expressions recruits the neural architectures involved in the processing of these, (emotional), expressions.” Blair [16] also pointed out that these persons, “do not present with impaired responding to angry, happy or surprised facial or vocal expressions.” Herpers et al. [31], point out that, “deficits in emotional recognition are thought to play an important role in impaired, empathic function in psychopathy. It has been suggested that impaired functioning of the amygdala leads to impaired recognition of facial expressions of distress, specifically fear.” Blair [18] notes out that, “there is selective impairment for the processing of fearful and to a lesser extent, sad expressions in individuals with psychopathy.” Clinically, the author sees these features commonly in persons with autism. The impairment of processing of fearful expressions was most seriously undermined when Dadds et al. [32]; Dadds and Rhodes [33], suggested that, “facial fear recognition may not be impaired when participants are instructed to look at the eyes.” Empathy deficits and theory of mind deficits are partly caused by reduced eye contact, which is evident in clinical practice with persons with autism every day. This reduces their capacity to understand how people, “tick.” One cannot read a face emotionally if one does not look at it.
\nBlair [16], points out that, “individuals with autism show indications of an aversive response to the distress of others.” The problem is that the author sees this clinically all the time in patients with autism and it is not specific to psychopathy. There is huge variability and heterogeneity in autism and this does not take into account the phrase that persons with autism show an aversive response to the distress of others. Indeed, I see many persons with autism who show no aversive response to others. Of course, there is a large gap between what happens in rarefied research studies and how people present in the real world. This is mostly seen in this discussion of empathy, autism and psychopathy. Blair [18], points out that, “it is uncertain whether there is impairment in processing emotional expressions in individuals with autism.” Working with persons with autism, as the author does every day, shows this clearly, that there are huge problems in processing emotional expressions in persons with autism. The findings in the research lab do not always mirror the “real-life” situation of clinical practice.
\nBernhardt and Singer [34], pointed out that, “alexithymia is sub-clinical phenomena related to difficulties in identifying and describing feelings and in distinguishing feelings from bodily sensations,” [35]. Bernhardt and Singer [34], also point out that there was a modulation observed, “by the degree of alexithymia in controls and individuals with ASD, [36]. Indeed, the greater the participant’s deficits in understanding their own emotions, regardless of whether they were control subjects or patients, the less activation they showed in anterior insula, while empathising with people present in the same room undergoing painful experiences. These results confirmed the hypothesis that representations in anterior insula underly representations of our own feeling states, which in turn form the basis for understanding the feelings of others. Thus, understanding your own feeling states may be a prerequisite to engage in vicarious simulation for a better understanding of other people’s states.” This means self-empathy is necessary for other empathy. If one cannot identify one’s own emotions, one will not be able to identify other’s emotions. “This is necessary for authentic simulation,” [37].
\nBlair et al. [18], noted that children with autism show, “relatively preserved moral judgement, as long as the judgement does not require representation of the interest of the perpetrator.” Blair [18] also points out that, “empathic responding is necessary for successful moral judgement,” but again, there is massive variability. It’s not so much that there’s a double hit, there may be 100 genetic hits and more impacting on interacting on autism and psychopathy. Rogers et al. [26], pointed out that, “boys with co-occurring ASD and CU tendencies share some of the behaviours and aspects of cognitive profile with boys who have psychopathic tendencies alone. Callous/psychopathic acts in a small number of individuals with ASD probably reflect a ‘double hit’, involving an additional impairment of empathic response to distress cues, which is not part and parcel of ASD itself.” This so called double hit should be hundreds of hits.
\nIn relation to cognitive empathy and theory of mind, Blair [18] stated that, “cognitive empathy or theory of mind is intact in individuals with psychopathy.”
\nThese ideas have been very seriously undermined by Drayton et al. [38] in relation to automatic perspective taking. Previous research did not take the complexity of cognitive empathy into account and this led to serious misunderstandings of cognitive empathy. Drayton et al. [38] points out that automatic theory of mind processes are engaged when an individual unintentionally represents the perspective of another person, also called, “altercentric interference.” Drayton et al. [38], suggest that “psychopathic individuals have a diminished propensity to automatically think from another’s perspective, which may be the cognitive root of their deficits in social functioning and moral behaviour.” Drayton et al. [38] raise for this author, the possible failure of previous research on theory of mind and psychopathy, failing, “to tap into a critical component of normal theory of mind processing; or tendency to take other’s perspective automatically.” Drayton et al. [38], defined, “automatic theory of mind processes,” as an individual representing, “the thoughts and feelings of another person without intending to do so.” They also point out that psychopathic individuals have a previously unobserved cognitive deficit that might explain their patterns of destructive and antisocial behaviour, that is … failure, “to automatically take the perspective of others, but can deliberately (controlled), take the perspective of others.” These findings suggest that psychopathic individuals have the ability to take the perspective of others, but lack the propensity to do so. It seems they can pass theory of mind tasks in the research situation but fail to do so in the real world situation. This is one of the endless problems of laboratory research not translating into the, “real world,” that is the clinical world. This lack of generalisation can be a serious flaw in academic psychological research. Drayton et al. [38], notes that, “psychopathic individuals do show deficits in their ability to understand what others are feeling but this capacity to represent other feelings appears to be distinct from capacity to represent what others see and believe.” They also point out that, “psychopathic individuals appear to represent other’s perspective in a relatively typical manner when doing so. It is goal-conducive and yet, are able to ignore other’s perspective when it is not conducive.” This means that all previous theory of mind research on psychopathy missed the fundamental point of the deficit of automatic perspective of others. Drayton et al. [38], point out that, “this combination of relatively intact deliberative theory of mind but impaired spontaneous theory of mind may allow psychopathic individuals to use information about others’ mental states to achieve their own ends, while at the same avoid the, ‘cost’, of automatically representing other’s mental states, results in callous and chronic criminal behaviour.” They have no empathic interest in other minds, except getting their own egocentric desires met.
\nOf course, there has been debate about altercentric interference. Marshal et al. [39], noted that, “considerable debate has focused on whether adults possess an implicit system for representing others’ mental states. Some argue that people automatically represent the perspective of others using evidence from altercentric interference - cases in which another agent’s perspective affects the speed with which one can report one’s own perspective. Others have argued that altercentric interference is not always specific to social stimuli and thus, may represent a simpler process such as submentalizing.” Marshal et al. [39] conclude that, “participants experience both egocentric and altercentric interference, and these effects emerge equally in social and non-social conditions.”
\nRogers et al. [26], pointed out that ASD plus callous, unemotional traits, “shared some behaviours and aspects of cognitive profile with boys who have psychopathic tendencies alone,” and that, “anti-social personality tapped the same latent construct in children with ASD as in previous samples” [40].
\nFearlessness is not rare in autism, indeed, is a major concern for children with autism in schools and is also seen in psychopathy. They both have emotional processing deficits and indeed, they both, particularly criminal autistic psychopathy can show moral deficits. Of course, many persons with autism have very high moral standards. Shame and embarrassment can be absent in both conditions. Autism is a most contradictory condition.
\nBlair [16], is correct to point out that, “some forms of empathic responding occur independently of attachment style.” The causality of attachment research is often deeply flawed. Indeed, Bowlby [41], discussed, “inappropriate mothering,” as a cause of autism. Indeed, the causality here is in the opposite direction. Blaming the mother, suggesting that mothers caused autism or psychopathy was totally inaccurate.
\nChialant et al. [4] pointed out that deficiencies in the prefrontal cortex and limbic system are associated with both violent behaviour and empathy. They also pointed out that empathy comes out of a, “brainstem-mediated mimicry,” and, “mirror neuron mediated emotional resonance which emerges in the very first months of life.” Shamay-Tsoory et al. [42], point out that, “recent evidence suggests that there are two possible systems of empathy: a basic emotional contagion system and a more advanced cognitive perspective-taking system. It is not clear whether these two systems are part of a single, interacting empathy system or whether they are independent. Additionally, the neuroanatomical bases of these systems are largely unknown.” They found nevertheless that, “a remarkable behavioural and anatomic double dissociation between deficits in cognitive empathy (ventromedial prefrontal), and emotional empathy (inferior frontal gyrus). Furthermore, precise anatomical mapping of lesions revealed Brodmann area 44 to be critical for emotional empathy, while areas 11 and 10 were found necessary for cognitive empathy.” This supports the notion that the inferior frontal gyrus is involved in emotional empathy and the ventromedial prefrontal, in cognitive empathy, and that, “these two neural networks with two core components which are triggered and operate independently.”
\nHerpers et al. [31], note that, “youths with CU traits show lower levels of prosocial reasoning,” and, “lower emotional responsibility,” with, “reduced response of the amygdala and a weaker functional connectivity between the amygdala and the ventromedial prefrontal cortex.” Glenn and Raine [43], point out that, “poor autonomic fear conditioning - the ability to learn associations between neutral cues and aversive stimuli – is another well replicated correlate of adult criminal and psychopathic adult offending, conduct disorder in children and adolescents, and juvenile offending. A review of forty-six human brain imaging studies suggests that deficits in fear conditioning may reflect abnormalities in a common core fear network, that consists of the amygdala, insula and anterior cingulate.” Indeed, as Linden [44], points out, the brain is a, “rather weird agglomeration of adhoc solutions that have been piled on through millions of years of evolutionary history.” No wonder there is so much overlap.
\nBlair [16], points out that, “moral socialisation is better achieved through the use of induction,” (reasoning that draws children to the effects of their misdemeanour on others). Blair [16], also stated that children with, “emotional difficulties of a lack of guilt/remorse linked to psychopathy,” then parental socialisation has no effect, i.e., “no statistical bearing.” This is a recipe for hopelessness and abandonment of these children. Indeed, new research does not support that view. Clearly, these children from a very young age, even before the second year, need help in relation to empathy. I see these children from about one and a half years upwards, and they can be helped, as I will show shortly. Waller et al. [45], points out that, “heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous/unemotional behaviours. Positive parenting can buffer these risky pathways,” and that, “mother positive parenting moderated the fearlessness to callous-unemotional behaviour pathway.”
\nTaubner et al. [46], pointed out that, “psychopathic traits alone only partially explain aggression in adolescents. Mentalization may serve as a protective factor to prevent the emergence of proactive aggression in spite of psychopathic traits and may provide a crucial target for intervention.” Viding [47], points out that, “callous/unemotional traits are malleable,” “respond to warm parenting” and that Dadds et al. [40] showed that they might benefit from training in emotional literacy and emotional recognition.
\nEmpathy effects a whole variety of psychiatric disorders to a greater or lesser degree. There is particular emphasis on psychopathy here, but psychopathy and autism are on a neurodevelopmental mental spectrum and they do overlap. A whole range of neurodevelopmental disorders are on this neurodevelopmental spectrum including learning disability, schizophrenia, bipolar disorder, ADHD, autism, psychopathy. It was an error in DSM 5 [1] to separate neurodevelopmental disorders from schizophrenia. In clinical practice, patients are often on more than one points of this neurodevelopmental spectrum and each aspect of the spectrum that they present with needs treatment.
\nIn the field of business, one of the most important constituents of a business system, and one which can give a business a cutting-edge over the others, is the supply chain (SC) of the business, and its effective operation and management.
A fundamental strategy associated with supply chains is that of a ‘Responsive Chain’. A responsive chain focuses on its ability to respond quickly to demand changes and meet them within the shortest possible time. A responsive chain, by its very definition, has
Thus, we are led naturally to the use of system-dynamic methods and concepts of control theory for effective control of a supply chain. In the following sections we explore the dynamic modeling and analysis concepts in effectively controlling a supply chain that would yield good performance characteristics.
Supply chain dynamics essentially deals with the dynamic behavior and temporal variation of the inventories and flows in the system over time when subjected to demand disturbances.
We now look at a simple three stage serial supply chain, a schematic diagram of which is given below in Figure 1. Each stage represents an inventory storage facility in the chain. Stage 1 represents the raw material input storage to a manufacturing plant, and stage 2 the finished goods inventory storage at the manufacturing plant. Stage 3 represents the finished goods warehouse (W/H) at the downstream end of the chain which meets the demand for the finished product, and from which material is shipped out to customers. The inventory levels at each stage are the state variables of the system, and the material flows between the stages the control variables.
A three-stage serial supply chain.
In constructing a dynamic model of a supply chain system, we adopt the standard schematic diagram (Figure 1), system notation, and model variables and equations below, as commonly found in control theory literature [1, 2, 3, 4, 5, 6, 7, 8, 9].
We use a discrete time representation of the SC system, as this is more in keeping with the prevailing practices in the SC industry, wherein the inventory levels (state variables) are recorded at the end of each period or day. In most cases the state variable records are updated at the end of each day. Hence, we take our period to be a single day. However, we need to also emphasize that this need not always be so and would be dependent entirely on the convenience of the SC practitioners. And hence the state variables are recorded at epochs corresponding to the end of each period. The flow variables however are aggregated and taken to occur within and up to the end of each period.
The inventory and flow variables are subscripted to indicate the stages in the chain. We now give the detailed representation below:
with: i = 1, representing (the raw material) the upstream end of the production facility.
i = 2 representing (the finished goods) the downstream end of the production facility.
i = 3 representing (the finished goods) the warehouse.
The dynamic equations of the system are written using deviation variables, as under:
where,
Under consistent units (in equivalent units of finished goods) for all inventory and flows in the system, the dynamic equations of the system are written as:
where for i = 3,
Now, since the demand is a stochastic variable, it can be split into two components, viz., a mean demand component, and a stochastic component which represents the random variations over and above the mean demand. The mean demand is what is predicted using forecasting techniques and is the planned offtake and the demand that the system is designed to meet in the normal course. Since prediction can never be exact, the residual variation is the stochastic component.
In such stochastic systems, the demand has an additional stochastic term, which is a white noise term, given by
The standard initial conditions for the system are:
The demand disturbance could be of the following Types:
a sudden shock demand increase, represented by a Dirac delta input function.
a sudden and sustained increase in demand, represented by a step input.
a demand with an increasing trend, represented by a ramp input function.
a demand with second order (quadratic), third order (cubic), or higher-order trends represented by higher-order polynomial input functions.
a demand with seasonality, represented by a sinusoidal input function.
A random component which is represented by a White Noise process.
The first five components pertain to and define
The demand can have any combination of, or even all of the above components in the mean demand, and, of course, the additional stochastic component represented by a White Noise process. Such demands are often seen in supply chain warehouses.
Thus, a demand disturbance at the downstream end (at the warehouse) provides the perturbation to the system. The system is controlled through regulation/control of the replenishment flows which are the control variables in the system.
Now from the above, we can see that the system response will have two components, the
The performance metrics of a supply chain that would be of interest to us from a dynamic performance point of view and control system design are explained below.
Since the demand input to the system suddenly increases, we can expect the system response to lag the demand and to fluctuate, as the system scrambles to catch up with the increased demand. Accordingly, the key performance indicators that would be of importance and interest to us from the point of view of both design and operation are the following [1, 2, 3, 4, 5, 6, 7, 8, 9]:
Permanent depletion of the inventory level (the offset), if any.
The trough value or the lowest dip in the inventory levels (the undershoot).
The amplitude of fluctuations of the inventory.
The center-line about which fluctuations occur.
The fraction of time the inventory level stays depleted (in the negative region).
The limiting inventory variance.
The first indicator shows whether the system is able to catch up with the demand and whether it is ultimately restored to its original level, or not. The second indicator, the trough value, represents the lowest point or value that the inventory level is likely to touch, and impacts the
The two most common triggers for initiation of replenishment control action in the system are [1, 2, 3, 4, 5, 6, 7, 8, 9]:
The inventory levels at the W/H, as found in logistic systems and warehouses,
The demand variations at the W/H, as in electronic-data-interchange systems.
Thus, the control flows into the W/H are set to a function of the latest available inventory deviations and latest available Demand deviations. Thus, we have,
We discuss these points in more detail for a single stage system first.
To differentiate between the standard abbreviations in conventional control theory, we adopt the following notation used to indicate the type of control used.
P, PI, PID would represent Proportional, Proportional-integral, and Proportional-integral-derivative control as usual. Additionally, MA denotes a ‘Moving Average’ type of control (explained in detail subsequently).
An ‘I’ within parenthesis would represent ‘inventory-triggered’ control, while ‘D’ within parenthesis would denote a ‘Demand-triggered’ control. Also, ‘ID’ within parenthesis would denote a control which would have both inventory-triggered and Demand-triggered components. We give examples below.
Thus P(D) (pronounced as “P of D”) denotes Demand-triggered Proportional control, PI(I) denotes Inventory-triggered PI control. Similarly, PID(ID) (pronounced as “PID of ID”) denotes PID control with inventory-triggered and Demand-triggered components.
We also could have cases of multiple inventory triggers and multiple-demand triggers. These will be denoted as follows:
We now first look at a single stage system below which we take to be the warehouse end of a SC.
Since the demand has both a deterministic component as well as a stochastic component, the response of the system can also be broken down into two components, viz., a deterministic part which is
From the above discussion we can see the close parallel with conventional feedback control theory. Here the information about the warehouse inventory (the state variable) is fed back to the system for initiating replenishment flow control action. Additionally, we can also have controls wherein the disturbance is also directly fed back to the system for initiation of control action.
In close parallel with classical feedback control theory, the control flows are functions of the state variables and the demand or input perturbation to the system. The types of functions used also closely parallel classical control theory. And hence we have P, PI, and PID controls.
Additionally, we also have Moving Average (MA) controls, wherein the control flow is set to a weighted Moving Average of the latest available inventory deviations of up to ‘r’ periods back. The parameter ‘r’ is termed the ‘Order’ of the Moving Average.
We first look at Proportional Controls.
Now, for Proportional Controls of the P(I) type, the control flows into the warehouse in stage 3 of the chain would be given by:
where
The development herein corresponds to the ‘Regulator Problem’ with ‘set point’ of zero. And hence control of a Responsive Chain parallels the regulator problem in conventional control theory.
In conventional modeling of SCs, the lag is taken as the number of periods strictly between the period of order initiation and the period of arrival of the consignment, not including the period of arrival of the consignment. Thus, the lag is taken to be zero if the replenishment consignment arrives in the period immediately succeeding the period of order initiation. Instantaneous replenishment is not envisaged and is very rare in SC contexts. Arrival of the consignment within the same period of order initiation is also not envisaged and is very rare in such contexts. The earliest arrival of an ordered consignment is taken to be the immediately succeeding period, for which we take the lag as zero.
This convention is based on what is normally followed in the industry and practice, as well as the literature on dynamic modeling of SCs.
Hence in our further development of dynamic models of SCs we take the lag as zero if an order initiated in period k i.e., in the interval (k – 1, k] arrives in period (k + 1), i.e., in the interval (k, k + 1].
For a transportation lag of ‘l’ periods, an order placed in the interval (k – 1, k] would arrive in the interval (k + l, k + l + 1].
Hence for the warehouse under
Now it is to be noted that the order is initiated in period k i.e., in the interval (k – 1, k] based on the
Thus, warehouse records would be updated at the closure of the day’s operations and would show the
This is the convention that we will follow in the further development of the models.
We next look at the next type of control, which is the PI(I) control.
For the Proportional-Integral (PI(I) type) control case with
where the second term is the integral term in our discrete-time system, and Kc is the proportionality constant (gain term) factor of the integral of the error.
Next we have the PID(I) control wherein the control flows into the warehouse under
where the last term represents the derivative term in our discrete-time system, and Kd is the proportionality constant factor (gain term) of the derivative component of the control.
Next we have the Moving Average (MA) type of control (MA(I) type), for which the control flows into the warehouse under
where the r is the order of the moving average, the Ks are the control parameters (the weights) of the MA terms. Thus, the control flow is set to a weighted moving average of the latest available fully observed inventory levels up to r period back.
The above controls discussed above are the conventional inventory-triggered schemes. In all these types of controls, we could additionally have demand-triggered terms also, like the PI(ID), PID(ID), MA(ID) etc.
We discuss some of them below for single stage systems.
Firstly, we note herein that in solving for the response of a supply chain system, we will not use the Z-transform nor block diagrams and transfer functions as in conventional control theory. Rather we will work directly on the system difference equation in the time domain itself. And instead, we will use direct Operator methods to obtain the system solution and response (rather than the transformed equations and inverse transforms). This is one of the advantages of this modeling paradigm.
Another advantage of the type of discrete time modeling taken up here is that
We now take up the simplest form of control which is the P(I) control and illustrate the formulation of the system equation and its solution method.
The control is an inventory-triggered Proportional control. And we take the Replenishment Lag = 0 in the simplest case.
The flow balance equation for the warehouse is given by:
The control flows into the warehouse are given by:
It is to be noted that the value of
Thus, we can clearly see that the controls are of the ‘feedback’ type, and they seek to keep the inventory deviation at zero level, which is just the’ Linear Regulator with zero set-point’ in standard control theory.
Thus, substituting for the control flow into the flow balance eqn. Above, yields the system-dynamic eqn. For the warehouse as:
The above is the deterministic part of the system equation. Since demand is a stochastic variable with a stochastic component, the complete system equation is given by:
which has both parts. To solve the system equation completely, we split it into its two components and solve for each of the components separately. We hence solve the following two equations, one each for the deterministic part and the stochastic part.
The first is a deterministic Linear Difference Equation, and the second, a Stochastic Linear Difference Equation (SDE).
Both are second-order Linear Difference Equations (LDEs) in the state variable
An excellent treatment of difference calculus and solution methods for LDEs is given in [10]. We follow the methods given therein.
In order to solve the LDE, we first introduce the Forward Shift Operator E as under:
Now using the forward Shift Operator E, we can write the LDE in Operator form as:
We first look at the deterministic part of the solution below, which will yield the
Now this is an LDE of order two (the order being the highest power of the Operator E).
We write the Characteristic Equation of the LDE as [10]:
to determine the characteristic roots of the LHS Operator.
The stability of the system is entirely controlled by the roots of the LHS Operator. And elementary analysis leads to the following stability conditions:
Now we take up the solution of the system LDE for a unit step increase in demand, i.e.,
Substituting for the demand disturbance in the system equation yields the LDE:
which we can call the “Original Non-Homogeneous Eqn.” (O-NHE).
We first look at the solution of the homogeneous LDE (i.e., with RHS = 0). The homogeneous LDE is:
which upon factoring the LHS Operator can be written as: (
which has the solution as:
For a repeated root, the solution is given by:
Now that we have the solution of the homogeneous LDE, we next look for a particular solution of the Original Non-Homogeneous LDE (O-NHE).
A standard method of solution of the Non-homogeneous eqn. is by the ‘Annihilator Method’ [10].
We look for the Operator that annihilates the RHS terms of the O-NHE, say A(E). Then operating by the Annihilator on both sides of the O-NHE yields:
which is a homogeneous LDE albeit of a higher order, but which can be solved by factorizing the LHS Operator. As an example, in our case of a unit step disturbance,
And the Annihilator is given by:
And hence the equivalent Homogeneous LDE is given by:
which has the solution:
Now we note that the O-NHE being of order two, will admit only two undetermined constants. The above solution, however, has three undetermined constants. The third was introduced by us due to the Annihilator. Hence to determine the extra constant D in the solution, we substitute the solution into the O-NHE to determine D. Thus, and noting that the terms involving the roots of the LHS operator of the O-NHE are precisely the homogeneous solution terms of the O-NHE, we only need substitute the extra terms introduced by the Annihilator into the O-NHE. Hence, we have:
Noting that E(D) = D itself (
Thus, the full solution is given by:
Now, for stable solutions, we will have
The Damping rate, which is the rate at which the fluctuations decay to zero are given by the magnitudes of the roots of the LHS Operator
We take the case of distinct roots first, say for a value of
Hence, we have the system equation as:
The roots of the LHS Operator are readily obtained as:
which shows a Damping Rate of the Order of
P(I) control.
We next take a value of
which shows a Damping Rate of the Order of
Thus, while we would like the oscillations to be damped out rapidly, this would compromise on the Offset value, which would impact the base stock requirements of the system.
Thus, for the practitioner, the implications are quite clear:
We next obtain the undetermined constants in the general solutions above, using the Initial Conditions (ICs) of the system, as under:
The standard ICs of the system are as:
Now the system LDE:
And hence the ICs for our warehouse system can be obtained from the system equation itself using the standard system ICs and yields:
We additionally examine the case for
which can be simplified to.
where H(.) is the unit Heaviside step function and yields a sinusoidal pattern with a center-line of – 1 and constant amplitude of
This last case is that of
The responses for the three cases above are plotted in Figure 2.
We next look at the determination of the limiting inventory variance, which is a measure of the variation that we could expect even after the system (mean inventory level) has been restored to its original value.
Also, for the behavior of the mean response as well as our inferences from it to be meaningful, it is necessary that the limiting inventory variance be bounded and finite. We can then expect the inventory levels to be within the band given by:
In order to determine the limiting inventory variance, we make use of the stochastic component of the system equation and determine the stochastic part of the response. For our system the Stochastic LDE (or SDE) is as under:
where the term on the RHS of the SDE is the random variation represented by a White Noise Process, with
We can note that the LHS Operator is again the same as in the deterministic part of the system LDE that we have solved for above. Hence the roots of the LHS Operator remain unaltered in the SDE also.
Now following the method used in [11], we can note that if
where the
And hence the stochastic part of the solution of the system eqn. can be written as an infinite linear combination of the white noise disturbance terms.
Now since the individual white noise terms are Uncorrelated and Normal, i.e., with
In order to solve for the weighting terms, the βs, we substitute the solution into the system SDE above, as under:
which is
Another and more convenient way to write the SDE is to use the Backward Shift Operator
The SDE for the βs becomes:
which is
Now comparing coefficients of ε(k) for each k, yields the system of equations as below:
LDE term | Coefft of ε(k) | Coefft of ε(k-1) | Coefft of ε(k-2) | Coefft of ε(k-3) | Coefft of ε(k-4) | Coefft of ε(k-5) | …. | Coefft of ε(k) |
---|---|---|---|---|---|---|---|---|
1 | ….. | |||||||
- | 0 | ….. | ||||||
0 | 0 | ….. | ||||||
RHS | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
The above set of equations yields:
We can note that the LHS Operator is the same as for the system LDE, and hence has the same characteristics and form of solution.
We first illustrate the computation for the stable case
To obtain the limiting inventory variance, we firstly note that:
And hence,
Hence, we have:
We next illustrate the computation for the marginally stable case
And we can see from the above that the βs oscillate in value, from −1 to 0 to +1 infinitely often, i.e., the sequence {…0, −1, −1, 0, +1,+1, 0…..} repeats infinitely often. And hence the series
Hence in this case the limiting inventory variance is
It can similarly be shown that the limiting inventory variance is not bounded for unstable solutions also.
Thus, the limiting inventory variance will be bounded only for stable cases.
We can also note from the above discussion and work up that we have also obtained the stochastic part of the solution, as:
where
The above representation proves useful for simulation purposes.
We next take up the P(ID) control. We discuss only the deterministic system LDE hereafter, since the stochastic part of the solution and the limiting inventory can be obtained by methods similar to that discussed above in all cases to follow.
In this type of control an additional demand-triggered component is also added to the control thereby making it more proactive. The control initiates corrective replenishment action no sooner than a demand deviation is observed. It does not wait for an inventory deviation to take place before initiating replenishment action though it does have an inventory-triggered component also.
The replenishment control flow is given by:
where the first term is the inventory-triggered component and the second the demand-triggered component. Substituting for the control flow into the system equation yields:
which can be written as
We can note that the addition of the demand-triggered component has left the LHS of the LDE unaltered. Thus, the LHS Operator of the LDE remains the same and is unaffected by addition of demand-triggered components to the control. The system eqn. can hence be written in Operator form as:
Since
The ICs for the LDE have been obtained from the system Eq. (55) above using the standard system ICs;
Since the LHS Operator is the same as for the earlier P(I) control, the stability analysis remains the same as earlier, as also the roots of the LHS Operator for various values of the inventory-trigger parameter discussed earlier, i.e.,
The solutions are the same as given earlier in Eqs. (25) and (26).
And substituting the solution back into the O-NHE yields the value of the extra constant D as
which hence yields the solutions for the two cases as:
where the offset term is now given by
The important point to note in the above solution is that the offset can be made zero by choice of the demand-trigger parameter as
And hence we can observe the enhanced response of the P(ID) control over the earlier P(I) control, in that the Offset can now be controlled by us by choice of
In fact, we can also achieve a (+)ve value of the offset by choosing
We can now obtain the full solutions for the three cases above, using the LDE ICs
The solution curves are plotted in Figure 3, from which we can see that the response in all cases has zero offset.
P(ID) control.
We can similarly extend the modeling and analysis to PI(I), PID(I), and MA(ID) controls.
We could also have different types of input disturbances as indicated earlier in Section 3.1. Additionally, the third dimension of our analysis could be to have non-zero lags, i.e., lags of one, two periods, and so on. Cases with non-zero and higher lags will result in higher order system LDEs, and the LHS Operator would be of a higher order.
Further details of the above can be obtained in [1, 2, 3, 4, 5, 6, 7, 8, 9].
We look at the serial supply chain system as given in Figure 1.
We can see from Figure 1 that the immediately succeeding downstream stage in a supply chain will provide the “demand perturbation” for the immediately preceding stage. Thus, the demand perturbation at the warehouse at the downstream end will successively be felt up the chain. And the single-stage analysis described above can be used in turn for each stage of the chain.
For non-serial supply chains, the arguments are similar and single-stage analysis can be used as described above.
Details of some of these analyses can be found in [1, 2, 3, 4, 5, 6, 7, 8, 9].
This chapter has presented the application of control concepts to the control of supply chains. The state variables have been taken to be the inventory levels, while the control variables are the replenishment flows into the various stages of the system. The conventional P, PI, PID controls have been discussed, as also some newer forms of control which are especially applicable to supply chains and warehouses. The performance of P(I) and P(ID) controls have been derived in detail, and their performance analyzed.
A significant feature of this chapter is that the conventional block diagrams and transfer functions of conventional control theory have not been used. Rather direct Operator Methods have been used to good advantage to solve the system equations.
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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. 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He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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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. 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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. 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