Three basic types of relationships in Decomposition1.
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"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:"5195",leadTitle:null,fullTitle:"Magnetic Materials",title:"Magnetic Materials",subtitle:null,reviewType:"peer-reviewed",abstract:"This book reports on the recent progresses in theory, application, and characterization of magnetic materials. It covers a broad spectrum of topics on magnetic materials with different shapes and morphologies such as transition metals, cylindrical and 2D ferromagnetic nanowires, core-shell nanowires, monoatomic-layered nanostructures, and nanocrystals. This book addresses diverse groups of readers with general background in physics and material science and also covers topics for the specialists in the field of magnetism. It is believed that this book will be interesting for the readers and will provide a solid foundation about the topic for the students, scientists, and engineers working in the field of material science and condensed matter physics.",isbn:"978-953-51-2428-3",printIsbn:"978-953-51-2427-6",pdfIsbn:"978-953-51-6670-2",doi:"10.5772/61497",price:119,priceEur:129,priceUsd:155,slug:"magnetic-materials",numberOfPages:278,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"4f04cfbb54e455378de5fc7725e36a0c",bookSignature:"Khan Maaz",publishedDate:"August 24th 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5195.jpg",numberOfDownloads:17941,numberOfWosCitations:9,numberOfCrossrefCitations:2,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:5,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:16,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 14th 2015",dateEndSecondStepPublish:"November 4th 2015",dateEndThirdStepPublish:"February 8th 2016",dateEndFourthStepPublish:"May 8th 2016",dateEndFifthStepPublish:"June 7th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"107765",title:"Dr.",name:"Maaz",middleName:null,surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan",profilePictureURL:"https://mts.intechopen.com/storage/users/107765/images/system/107765.png",biography:"Dr. Maaz Khan is working as Deputy Chief Scientist (Professor) at PINSTECH, Pakistan. He has done Ph.D. and post doctorate in the field of Material Science (Nanoscience). His research interests include fabrication of nanomaterials and their structural, optical, magnetic, and electrical characterizations. He has authored more than 100 research articles and published 10 books. Presently, he is the Editor-in-Chief of ‘Journal of Materials, Processing and Design\\' and \\'The Nucleus\\'. He is also the Executive Editor of \\'International Journal of Nano Studies and Technology\\'. Dr. Maaz also serves as the editorial board member of several journals of Material Science.",institutionString:"Pakistan Institute of Nuclear Science and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"9",institution:{name:"Pakistan Institute of Nuclear Science and Technology",institutionURL:null,country:{name:"Pakistan"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"946",title:"Nanotechnology",slug:"metals-and-nonmetals-nanotechnology"}],chapters:[{id:"51314",title:"Scaling in Magnetic Materials",doi:"10.5772/63285",slug:"scaling-in-magnetic-materials",totalDownloads:1772,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The chapter presents applications of the scaling in several problems of magnetic materials. Soft magnetic materials (SMMs) and soft magnetic composites (SMCs) are considered. Application of scaling in investigations of problems, such as power losses, losses separation, data collapse of the losses characteristics and modelling of the magnetic hysteresis, is presented. The symmetry group generated by scaling and gauge transformations enables us to introduce the classification of the hysteresis loops with respect to the equivalence classes. SMC materials require special treatment in the production process. Therefore, algorithms for optimization of the power losses are created. The algorithm for optimization processes is based on the scaling and the notion of the pseudo-equation of state. The scaling makes modelling and calculations easy; however, the data must obey the scaling. Checking procedure of statistical data to this respect is presented.",signatures:"Krzysztof Z. Sokalski, Barbara Ślusarek and Jan Szczygłowski",downloadPdfUrl:"/chapter/pdf-download/51314",previewPdfUrl:"/chapter/pdf-preview/51314",authors:[{id:"179335",title:"Prof.",name:"Krzysztof",surname:"Sokalski",slug:"krzysztof-sokalski",fullName:"Krzysztof Sokalski"},{id:"181153",title:"Prof.",name:"Barbara",surname:"Slusarek",slug:"barbara-slusarek",fullName:"Barbara Slusarek"},{id:"181154",title:"Prof.",name:"Jan",surname:"Szczyglowski",slug:"jan-szczyglowski",fullName:"Jan Szczyglowski"}],corrections:null},{id:"51443",title:"How to Characterize Cylindrical Magnetic Nanowires",doi:"10.5772/63482",slug:"how-to-characterize-cylindrical-magnetic-nanowires",totalDownloads:1692,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Cylindrical magnetic nanowires made through the help of nanoporous alumina templates are being fabricated and characterized since the beginning of 2000. They are still actively investigated nowadays, mainly due to their various promising applications, ranging from high-density magnetic recording to high-frequency devices, passing by sensors, and biomedical applications. They also represent suitable systems in order to study the dimensionality effects on a given material. With time, the development in fabrication techniques allowed to increase the obtained nanowire complexity (controlled crystallinity, modulated composition and/or geometry, range of materials, etc.), while the improvements in nanomanipulation permitted to fabricate system based either on arrays or on single nanowires. On the other side, their increased complexity requires specific physical characterization methods, due to their particular features such as high anisotropy, small magnetic volume, dipolar interaction field between them, and interesting electronic properties. The aim of this chapter was to offer an ample overview of the magnetic, electric, and physical characterization techniques that are suitable for cylindrical magnetic nanowire investigation, of what is the specific care that one needs to take into account and which information will be extracted, with typical and varied examples.",signatures:"Fanny Béron, Marcos V. Puydinger dos Santos, Peterson G. de\nCarvalho, Karoline O. Moura, Luis C.C. Arzuza and Kleber R. Pirota",downloadPdfUrl:"/chapter/pdf-download/51443",previewPdfUrl:"/chapter/pdf-preview/51443",authors:[{id:"180153",title:"Prof.",name:"Fanny",surname:"Béron",slug:"fanny-beron",fullName:"Fanny Béron"},{id:"180848",title:"MSc.",name:"Peterson",surname:"Grandini De Carvalho",slug:"peterson-grandini-de-carvalho",fullName:"Peterson Grandini De Carvalho"},{id:"180849",title:"MSc.",name:"Luis Carlos",surname:"Costa Arzuza",slug:"luis-carlos-costa-arzuza",fullName:"Luis Carlos Costa Arzuza"},{id:"180850",title:"MSc.",name:"Karoline",surname:"Moura",slug:"karoline-moura",fullName:"Karoline Moura"},{id:"180851",title:"Dr.",name:"Marcos",surname:"Puydinger Dos Santos",slug:"marcos-puydinger-dos-santos",fullName:"Marcos Puydinger Dos Santos"},{id:"180853",title:"Prof.",name:"Kleber Roberto",surname:"Pirota",slug:"kleber-roberto-pirota",fullName:"Kleber Roberto Pirota"}],corrections:null},{id:"51213",title:"Magnetization Dynamics–Induced Charge and Spin Transport on the Surface of a Topological Insulator Subjected to Magnetism",doi:"10.5772/62531",slug:"magnetization-dynamics-induced-charge-and-spin-transport-on-the-surface-of-a-topological-insulator-s",totalDownloads:1349,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"We theoretically show spin and charge transport on the disordered surface of a three‐dimensional topological insulator with a magnetic insulator when localized spin of the magnetic insulator depends on time and space. To ascertain the transports, we use a low‐energy effective Hamiltonian on the surface of a topological insulator using the exchange interaction and calculate analytically using Green's function techniques within the linear response to the exchange interaction. As a result, the time‐dependent localized spin induces the charge and spin current. These currents are detected from change in the half‐width value of the ferromagnetic resonance of the localized spin when the magnetic resonance of the localized spin is realized in the attached magnetic insulator. We also show spin and charge current generation in a three‐dimensional Weyl–Dirac semimetal, which has massless Dirac fermions with helicity degrees of freedoms. The time‐dependent localized spin drives the charge and spin current in the system. The charge current as well as the spin current in the Weyl–Dirac system are slightly different from those on the surface of the topological insulator.",signatures:"Katsuhisa Taguchi",downloadPdfUrl:"/chapter/pdf-download/51213",previewPdfUrl:"/chapter/pdf-preview/51213",authors:[{id:"181168",title:"Dr.",name:"Katsuhisa",surname:"Taguchi",slug:"katsuhisa-taguchi",fullName:"Katsuhisa Taguchi"}],corrections:null},{id:"51474",title:"Metamaterial Properties of 2D Ferromagnetic Nanostructures: From Continuous Ferromagnetic Films to Magnonic Crystals",doi:"10.5772/64070",slug:"metamaterial-properties-of-2d-ferromagnetic-nanostructures-from-continuous-ferromagnetic-films-to-ma",totalDownloads:1506,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In recent years the study of low-dimensional magnetic systems has become topical not only for its several technological applications but also for achieving a deep understanding of the underlying physics of magnetic nanostructures. These efforts have considerably advanced the field of magnetism both theoretically and from an experimental point of view. Very recently, for their challenging features, great attention has been given to the investigation of the static and dynamical properties of magnetic nanostructures with special regard to magnonic crystals, a class of periodic magnetic systems. As shown by micromagnetic and analytical methods, the ferromagnetic materials composing magnonic crystals can be regarded as metamaterials since they exhibit effective properties directly linked, for instance, to the definition of an effective magnetization, an effective permeability, and an effective wavelength. Hence, the aim of this chapter is to give an overview of the recent results obtained on the study of metamaterial properties of two-dimensional ferromagnetic nanostructures ranging from those of thin films to the ones of two-dimensional magnonic crystals. Some possible applications based on the effective properties for tailoring new magnetic devices are suggested.",signatures:"Roberto Zivieri",downloadPdfUrl:"/chapter/pdf-download/51474",previewPdfUrl:"/chapter/pdf-preview/51474",authors:[{id:"181334",title:"Prof.",name:"Roberto",surname:"Zivieri",slug:"roberto-zivieri",fullName:"Roberto Zivieri"}],corrections:null},{id:"51707",title:"Molecular Magnetism Modeling with Applications in Spin Crossover Compounds",doi:"10.5772/64281",slug:"molecular-magnetism-modeling-with-applications-in-spin-crossover-compounds",totalDownloads:2101,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Molecular magnetic materials have become flourishing fields for research and technological developments due to their novel behavior compared to classical magnetic materials. Molecular magnetism modeling has reached a certain degree of maturity, although several experimental findings are still open problems. This chapter is aimed at providing a general introduction to physical modeling in molecular materials with a special emphasis placed on spin crossover compounds. This presentation includes Ising-type models and their generalizations, such as Wajnflasz and Pick, Bousseksou et al., Zimmermann and König, Sorai and Seki, and Nasser et al., along with their applications to the characterization of phase transition, hysteresis behavior, and thermal relaxations in spin crossover compounds. Recent experimental findings are explained in this context and the relevance of theoretical results for technological applications is also discussed.",signatures:"Mihai Dimian and Aurelian Rotaru",downloadPdfUrl:"/chapter/pdf-download/51707",previewPdfUrl:"/chapter/pdf-preview/51707",authors:[{id:"179954",title:"Dr.",name:"Mihai",surname:"Dimian",slug:"mihai-dimian",fullName:"Mihai Dimian"},{id:"181093",title:"Dr.",name:"Aurelian",surname:"Rotaru",slug:"aurelian-rotaru",fullName:"Aurelian Rotaru"}],corrections:null},{id:"50157",title:"Proteresis of Core-Shell Nanocrystals: Investigation through Theoretical Simulation and Experimental Analysis",doi:"10.5772/62398",slug:"proteresis-of-core-shell-nanocrystals-investigation-through-theoretical-simulation-and-experimental-",totalDownloads:1589,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"A study of proteresis (inverted hysteresis) in core-shell nanocrystals is presented. A core-shell anisotropic energy (CSAE) model is established to describe the observed proteretic behavior in Ni/NiO core-shell nanocrystals. The magnetic compositions of core-shell nanocrystals can be selected for ferromagnetic, antiferromagnetic, or paramagnetic materials where the exchange intercoupling between them results in both a large effective anisotropic energy and intercoupling energy. Simulation of the magnetization of core-shell nanocrystals reveals the existence of an exchange in the intercoupling energy between the interface of the core and shell moments that, surprisingly, is tuneable in both hysteresis and proteresis. Observations have shown a distinct proteresis, which is related to the spin-flip and exchange intercoupling energy between Ni and NiO. Our approach shows that the processing-dependent technology plays an important role when the grain size decreases to the order of nanometers and when the magnets are reduced from the single domain to core-shell domain. Integrated studies of process-dependent, theoretical modeling and core-shell nanocrystal fabrication technology will lead to more encouraging development in the overunity industry.",signatures:"Jhong-Yi Ji and Sheng Yun Wu",downloadPdfUrl:"/chapter/pdf-download/50157",previewPdfUrl:"/chapter/pdf-preview/50157",authors:[{id:"7156",title:"Prof.",name:"Sheng Yun",surname:"Wu",slug:"sheng-yun-wu",fullName:"Sheng Yun Wu"},{id:"184417",title:"Dr.",name:"Jhong-Yi",surname:"Ji",slug:"jhong-yi-ji",fullName:"Jhong-Yi Ji"}],corrections:null},{id:"50909",title:"Radiation and Propagation of Waves in Magnetic Materials with Helicoidal Magnetic Structure",doi:"10.5772/64014",slug:"radiation-and-propagation-of-waves-in-magnetic-materials-with-helicoidal-magnetic-structure",totalDownloads:1618,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we are shortly reviewing some problems of electromagnetic and acoustic wave propagation and radiation in the magnets with helicoidal spin structure. We show the band structure of the coupled wave spectrum in the materials. The band gap width depends on the spiral angle (or, equivalently, on external magnetic field value). Interaction of spin and electromagnetic waves leads to opening the gap in spin-electromagnetic dispersion. This gap leads to opacity window in reflection spectrum of spiral magnet plate. The opacity window closes at phase transition into collinear ferromagnetic state and reaches a maximum at simple spiral state. At the frequencies near band gap boundaries, the rotation of polarization plane of propagating electromagnetic wave is observed. Account of interaction of spin and electromagnetic waves with acoustic subsystem leads to opening the gap in spin-acoustic spectrum. This gap leads to some features in electromagnetic reflectance spectrum and to rotation of acoustic wave polarization plane, i.e. to acoustic Faraday effect. We also show the possibility of acoustic and electromagnetic wave radiation by helicoidal magnets at phase transition into collinear ferromagnetic state. Some features of electromagnetic waves generation by spiral magnets placed in homogeneous magnetic field with harmonical time-dependence are also discussed.",signatures:"Igor V. Bychkov, Dmitry A. Kuzmin and Vladimir G. Shavrov",downloadPdfUrl:"/chapter/pdf-download/50909",previewPdfUrl:"/chapter/pdf-preview/50909",authors:[{id:"178503",title:"Prof.",name:"Igor",surname:"Bychkov",slug:"igor-bychkov",fullName:"Igor Bychkov"},{id:"179168",title:"Dr.",name:"Dmitry",surname:"Kuzmin",slug:"dmitry-kuzmin",fullName:"Dmitry Kuzmin"},{id:"179169",title:"Prof.",name:"Vladimir",surname:"Shavrov",slug:"vladimir-shavrov",fullName:"Vladimir Shavrov"}],corrections:null},{id:"50527",title:"Giant Magnetoimpedance Effect and AC Magnetic Susceptibility in Amorphous Alloys System of FeCoNbBSiCu",doi:"10.5772/63024",slug:"giant-magnetoimpedance-effect-and-ac-magnetic-susceptibility-in-amorphous-alloys-system-of-feconbbsi",totalDownloads:1457,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The study of Giant Magnetoimpedance (GMI) effect of the amorphous alloys system of Fe72-xCoxNb6B10Si11Cu1 (for x = 35 and x = 40 at. percent Co) and AC magnetic susceptibility for the amorphous alloy of Fe37Co35Nb6B11Si10Cu1 composition are presented in this chapter. The importance of GMI effect for the improvement of technological applications in sensor devices in amorphous magnetic Fe- and Co-based alloys is introduced; then it is described as the experimental procedure of magnetoimpedance and AC magnetic susceptibility measurements. The obtained results are discussed and finally the conclusions are presented.",signatures:"Zulia Isabel Caamaño De Ávila, Amilkar José Orozco Galán and\nAndrés Rosales-Rivera",downloadPdfUrl:"/chapter/pdf-download/50527",previewPdfUrl:"/chapter/pdf-preview/50527",authors:[{id:"181146",title:"Dr.",name:"Zulia",surname:"Caamaño",slug:"zulia-caamano",fullName:"Zulia Caamaño"}],corrections:null},{id:"51160",title:"Magnetization Statics and Ultrafast Photoinduced Dynamics in Co/garnet Heterostructures",doi:"10.5772/62542",slug:"magnetization-statics-and-ultrafast-photoinduced-dynamics-in-co-garnet-heterostructures",totalDownloads:1513,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"We demonstrate experimental studies of the magnetization behavior from statics to ultrafast photoinduced dynamics with high temporal resolution in ultrathin Co/garnet heterostructures with a sub-nanometer roughness at the interface. We report on modulation of spin precession in Co/garnet heterostructures with distinct frequencies and show that the excitation efficiency of these precessions strongly depends on the amplitude and the direction of external magnetic field. Furthermore, it is shown that the magnetization precession in the garnet film can be manipulated by the strong magnetostatic coupling between Co and garnet layers. These findings could provide new possibilities in all-optical excitation and local spin manipulation by polarized femtosecond pulses for the application in nanodevices with high-speed switching.",signatures:"Andrzej Stupakiewicz",downloadPdfUrl:"/chapter/pdf-download/51160",previewPdfUrl:"/chapter/pdf-preview/51160",authors:[{id:"41112",title:"Dr.",name:"Andrzej",surname:"Stupakiewicz",slug:"andrzej-stupakiewicz",fullName:"Andrzej Stupakiewicz"}],corrections:null},{id:"51644",title:"Magnetic Micro-Origami",doi:"10.5772/64293",slug:"magnetic-micro-origami",totalDownloads:1670,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Microscopic origami figures can be created from thin film patterns using surface tension of liquids or residual stresses in thin films. The curvature of the structures, direction of bending, twisting, and folding of the patterns can be controlled by their shape, thickness, and elastic properties and by the strength of the residual stresses. Magnetic materials used for micro- and nano-origami structures play an essential role in many applications. Magnetic force due to applied magnetic field can be used for remote actuation of microrobots. It can also be used in targeted drug delivery to direct cages loaded with drugs or microswimmers to transport drugs to specific organs. Magnetoelastic properties of free-standing micro-origami patterns can serve for stress or magnetic field sensing. Also, the stress-induced anisotropy and magnetic shape anisotropy provide a convenient method of tuning magnetic properties by designing a shape of the micro-origami figures instead of varying the composition of the films. Micro-origami figures can also serve as building blocks for two- and three-dimensional meta-materials with unique properties such as negative index of refraction. Micro-origami techniques provide a powerful method of self-assembly of magnetic circuits and integrating them with microelectro-mechanical systems or other functional devices.",signatures:"Leszek Malkinski and Rahmatollah Eskandari",downloadPdfUrl:"/chapter/pdf-download/51644",previewPdfUrl:"/chapter/pdf-preview/51644",authors:[{id:"115596",title:"Dr.",name:"Leszek",surname:"Malkinski",slug:"leszek-malkinski",fullName:"Leszek Malkinski"},{id:"187129",title:"MSc.",name:"Rahmatollah",surname:"Eskandari",slug:"rahmatollah-eskandari",fullName:"Rahmatollah Eskandari"}],corrections:null},{id:"51165",title:"Magnetic Properties of Gadolinium-Doped ZnO Films and Nanostructures",doi:"10.5772/63320",slug:"magnetic-properties-of-gadolinium-doped-zno-films-and-nanostructures",totalDownloads:1674,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The magnetic properties of Gd-doped ZnO films and nanostructures are important to the development of next-generation spintronic devices. Here, we elucidate the significant role played by Gd-oxygen-deficiency defects in mediating/inducing ferromagnetic coupling in in situ Gd-doped ZnO thin films deposited at low oxygen pressure by pulsed laser deposition (PLD). Samples deposited at higher oxygen pressures exhibited diamagnetic responses. Vacuum annealing was used on these diamagnetic samples (grown at a relatively high oxygen pressures) to create oxygen-deficiency defects with the aim of demonstrating reproducibility of room-temperature ferromagnetism (RTFM). Samples annealed at oxygen environment exhibited superparamagnetism and blocking-temperature effects. The samples possessed secondary phases; Gd segregation led to superparamagnetism. Theoretical studies showed a shift of the 4f level of Gd to the conduction band minimum (CBM) in Gd-doped ZnO nanowires, which led to an overlap with the Fermi level, resulting in strong exchange coupling and consequently RTFM.",signatures:"Iman S. Roqan, S. Assa Aravindh and Singaravelu Venkatesh",downloadPdfUrl:"/chapter/pdf-download/51165",previewPdfUrl:"/chapter/pdf-preview/51165",authors:[{id:"181087",title:"Dr.",name:"Iman",surname:"Roqan",slug:"iman-roqan",fullName:"Iman Roqan"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6552",title:"Silver Nanoparticles",subtitle:"Fabrication, Characterization and Applications",isOpenForSubmission:!1,hash:"fa35924b88365602189440c335634a77",slug:"silver-nanoparticles-fabrication-characterization-and-applications",bookSignature:"Khan Maaz",coverURL:"https://cdn.intechopen.com/books/images_new/6552.jpg",editedByType:"Edited by",editors:[{id:"107765",title:"Dr.",name:"Maaz",surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited 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by"}}},ofsBook:{item:{type:"book",id:"11678",leadTitle:null,title:"Cytotoxicity",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tMedical devices and pharmaceutical agents must undergo rigorous testing to determine their biocompatibility when they have contact with the body, regardless of their mechanical, physical, and chemical properties. All new drugs and medical devices must undergo biocompatibility tests of cytotoxicity, sensitization, intradermal irritation, acute systemic toxicity, and a series of tests before entering a clinical environment to ensure safe and effective use for humans.
\r\n\tCell viability is defined as the number of healthy cells in a sample and proliferation of cells is a vital indicator for understanding the mechanisms inaction of certain genes, proteins, and pathways involved in cell survival or death after exposure to toxic agents. The methods used to determine viability are also common for the detection of cell proliferation. A cell viability assay is performed based on the ratio of live and dead cells. This assay is based on an analysis of cell viability in cell culture for evaluating in vitro drug effects in cell-mediated cytotoxicity assays for monitoring cell proliferation. Various methods are involved in performing a cell viability assay, including the dilution method, surface viable count, roll tube technique, nalidixic acid method, fluorogenic dye assay, and the Trypan Blue Cell Viability Assay. The cell viability assays can determine the effect of drug candidates on cells and be used to optimize the cell culture conditions. The parameters that define cell viability can be as diverse as the redox potential of the cell population, the integrity of cell membranes, or the activity of cellular enzymes.
\r\n\tCytotoxicity is the degree to which a substance can cause damage to a cell. Cytotoxicity assays measure the ability of cytotoxic compounds to cause cell damage or cell death. Cytotoxicity assays are widely used in fundamental research and drug discovery to screen libraries for toxic compounds. The cell cytotoxicity and proliferation assays are mainly used for drug screening to detect whether the test molecules have effects on cell proliferation or display direct cytotoxic effects. In a cell-based assay, it is important to know how many viable cells are remaining at the end of the experiment. There are a variety of assay methods based on various cell functions such as enzyme activity, cell membrane permeability, cell adherence, ATP production, co-enzyme production, and nucleotide uptake activity. These methods could be classified in to different categories: (I) dye exclusion methods such as trypan blue dye exclusion assay, (II) methods based on metabolic activity, (III) ATP assay, (IV) sulforhodamine B assay, (V) protease viability marker assay, (VI) clonogenic cell survival assay, (VII) DNA synthesis cell proliferation assays and (V) Raman micro-spectroscopy.
\r\n\tMedical devices have been widely used in various clinical disciplines and these devices have direct contact with the tissues and cells of the body, they should have good physical and chemical properties as well as good biocompatibility. Biocompatibility testing assesses the compatibility of medical devices with a biological system. It studies the interaction between the device and the various types of living tissues and cells exposed to the device when it comes into contact with patients.
\r\n\t
\r\n\tThe book will cover original studies, reviews, all aspects of Cell Viability and Cytotoxicity assays, methods, Biocompatibility of studies of biomedical devices, and related topics.
There are vast opportunities to improve energy use efficiency by eliminating waste through process optimization. Applying today’s computing and control equipment and techniques is one of the most cost-effective and significant opportunities for larger energy users to reduce their energy costs and improve profits. An Energy Management Information System (EMIS) is an important element of a comprehensive energy management program. It provides relevant information to key individuals and departments that enable them to improve energy performance. Today it is normal for companies, particularly in process sectors, to collect huge amounts of real-time data from automated control systems, including Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA), etc. The captured data is shared and analysed in an orderly and precise way that identifies problem areas and provides solutions, this mass of data is merely information overload. Advances in information technology (IT), defined here as the use of computers to collect, analyse, control and distribute data, have developed rapidly. It is now common for managers and operators to have access to powerful computers and software. Today there are a number of techniques to analyse the factors that affect efficiency, and models are automatically generated based on “what if” scenarios in order to improve decisions to be taken.
The paper shows a very advanced technology for handling automatically more than 200 digital and analogue (i/p and o/p) parameters via intelligent monitoring and controlling system.
However, load management is the process of scheduling the loads to reduce the electric energy consumption and or the maximum demand. It is basically optimizing the processes/loads to improve the system load factor. Load-management procedures involve changes to equipment and/or consumption patterns on the customer side. There are many methods of load management which can be followed by an industry or a utility, such as load shedding and restoring, load shifting, installing energy-efficient processes and equipment, energy storage devices, co-generation, non-conventional sources of energy, and reactive power control [1]-[3]. Meeting the peak demand is one of the major problems now facing the electric utilities. With the existing generating capacity being unmanageable, authorities are forced to implement load shedding in various sectors during most of the seasons. Load shifting will be a better option for most industries. Load shifting basically means scheduling the load in such a way that loads are diverted from peak period to off-peak periods, thereby shaving the peak and filling the valley of the load curve, so improving the load factor[4]-[6].
To encourage load shifting in industries, and thereby to reduce peak demand automatically, a new technology such as introduced here will be extended.
Also, power quality is of major concern to all types of industries, especially those operating with critical machinery and equipments. Poor quality of power leads to major problems like break-downs, production interruptions, excess energy consumption etc. Modern industries require automation of their operation enabling them to produce quality products and also for mass production. The conventional systems are being replaced by modern Power Electronic systems, bringing a variety of advantages to the users. Classic examples are DC & AC Drives, UPS, soft starters, etc. Power Quality Alarming and Analysis provides a comprehensive view into a facility\'s electrical distribution system. Power Quality can be monitored at the electrical mains or at any critical feeder branch in the distribution system such as described here. Devices in this category typically provide all of the parameters found in basic devices, plus advanced analysis capabilities [7]-[8]. These advanced analysis capabilities include using waveform capture to collect and view waveform shape and magnitude, providing harmonic analysis graphs, collection and storage of events and data, and recording single or multiple cycle waveforms based on triggers such as overvoltage or transients. With the ever-increasing use of sophisticated controls and equipment in industrial, commercial, and governmental facilities, the continuity, reliability, and quality of electrical service has become extremely crucial to many power users. Electrical systems are subject to a wide variety of power quality problems which can interrupt production processes, affect sensitive equipment, and cause downtime, scrap, and capacity losses. Momentary voltage fluctuations can disastrously impact production [7]-[8].
The proposed modified intelligent monitoring and controlling system will introduce monitoring, alarming, controlling, and power quality mitigation based on data collected and analyzed from the system. The original system can afford the following features:
Complete information about the plant (circuit breakers status, source of feeding, and level of the consumed power).
Information about the operating values of the voltage, operating values of the transformers, operating values of the medium voltage, load feeders, operating values of the generators. These values will assist in getting any action to return the plant to its normal operation by minimum costs.
Protective information such as the insulation of cables, temperatures of the generators. These parameters are used as a back up for the main protection.
Information about the quality of the system (harmonics, current, voltages, power factors, flickers, etc.). These values will be very essential in case of future correction.
Recorded information such case voltage spikes, reducing the voltage on the medium or current interruption.
The hardware configuration of the original intelligent monitoring and controlling system is divided into two levels. The first level includes two workstations -1 and -2 with two different software programs are used for data handling and monitoring purpose. The second level includes the PLC for data collected that constituted from 10 digital meters and some smart sensors to cover many points in the system. Some digital meters are fed directly to the workstation-2 using different software for data handling. All other parameters such as breaker status, temperature, controllers, and cable insulators are fed through the PLC. Fig. 1 shows the overall structure of original intelligent monitoring and controlling system achieved at the Eastern Company in Egypt. The intelligent monitoring and controlling system uses the most recent technology of Profibus in data transferring. Workstaton-1 used the Wincc flexible software program for data handling received from the MV, Transformers and Generators. Workstation-2 used the Sicaro Q manger software program for data handling from the loads. Both workstations are linked through Ethernet network. One programmable logic controller S7-300 associated with 10 power meters for monitoring the MV, Transformers and Generators, Insulation relays, Temperature transducers for generators, and Circuit Breakers auxiliary points for all loads have been applied to workstation 1 through Profibus network-1. Workstation-2 associated with 12 power quality meters for monitoring all loads (Compressors, Pumps, Motors, Processes, etc.) via Profibus network-2. All system parameters are communicated using the Profibus technology. The output system is limited by given alarming and recommendation to the operator without doing any automatic actions for the system. The system components used in the system are produced from Siemens and can be described as:
PROFIBUS is the powerful, open and rugged bus system for process and field communication in cell networks with few stations and for data communication. Automation devices such as PLCs, PCs, HMI devices, sensors or actuators can communicate via this bus system. PROFIBUS is part of totally integrated automation, the uniform, integrated product and system range from Siemens for efficient automation of the entire production process for all sectors of industry. PROFIBUS can be used, for example, for the following applications: Factory automation, Process automation and Building automation. Different PROFIBUS versions are available for the various fields of application:
Process or field communication (PROFIBUS DP) (for fast, cyclic data exchange with field devices). PROFIBUS PA (for intrinsic safety applications in process automation)
Data communication (PROFIBUS FMS) (for data communication between programmable controllers and field devices).
Power Quality devices are installed at various measuring points in order to record a series of measurements of the required values for an analysis of the network quality. The devise can be installed on the load. In addition to all relevant measured variables, the meter can also record system disturbances, always when an upward or downward limit value violation has occurred. The recorded values can be called up and evaluated using a PC. Power Quality is available in 3 device versions with the following communication interfaces: RS232, RS485, and PROFlBUS-DP. Furthermore the device version Power Quality with PROFIBUS-DP interface opens up another area of application. Together with programmable control systems (PLCs), it can be used as a “sensor” for electrical measured variables. In the system achieved, the PROFIBUS-DP technology is used.
The Power Quality PAR parameterization software is executable under the Windows 2000/XP operating systems. The software allows you to define the device address, so that each device is uniquely identified and to configure the power quality for the communication protocol to be used (PROFIBUS DP) in order to prepare it for the measurement task. Fig. 2 shows the display of the currently transmitted measured values.
Power Meter is a power meter for panel mounting, with big graphic display and background illumination. The major application area is power monitoring and recording at MV and LV level. The major information types are measured values, alarms and status information. Power monitoring systems with Power Meter, a permanently installed system, enables continuous logging of energy-related data and provides information on operational characteristics of electrical systems. Power Meter helps identify sources of energy consumption and time of peak consumption. This knowledge allows you to allocate and reduce energy costs. Measured values include r.m.s values of voltages (phase-to-phase and/or phase-to-ground), currents, active, reactive and apparent power and energy, frequency, power factor, phase angle per phase, symmetry factor, harmonics of currents and voltages, total harmonic distortion. Ten maters are installed on the system and arranged on the incoming feeders, transformers and also on the generators. PROFIBUS-DP and Power Meter are connected in a master-slave operation mode. The communication parameters are loaded to the master station using the GSD file. The Power Meter supports data transmission rates from 9.6 kbit/s to 12 Mbit/s. The Measured values can be: Voltage, Current, Active power, Reactive power, Apparent power, Power factor, Active power factor, Phase angle, Frequency, Active energy demand, Active energy supply, Active energy total, Active energy total, Reactive energy, inductive, Reactive energy, capacitive, Reactive energy total, Apparent energy, Unbalance voltage, Unbalance current, THD voltage, THD current, Harmonic voltage, and Harmonic current.
SIMATIC S7-300 PLC: S7-300 programmable controller is made up of the following components:
Power supply (PS)
Signal modules (SM)
Function modules (FM)
Communication processor (CP).
Several S7-300s can communicate together and with other SIMATIC S7 PLCs via PROFIBUS bus cables. Fig. 3 shows the components of the PLC. The Runtime application of the WinCC basic software offers all essential functions of a powerful SCADA-System. Using WinCC User Administrator, One can assign and control users access rights for configuration and runtime.
The application functions of the data collection and monitoring are all performed via two workstations, PLC and two different software programmes. The program can include data exchange communication protocol between the communication system and PLC, through digital power meters, breakers’ status (On/Off), power quality monitoring, threshold for alarming. Figs. 4 and 5 show part of the system operation for monitoring feeder-2 and transformer-2 parameters for the system installed at Eastern Company in Egypt. Fig. 6 shows one of the event messages produced from the system. Fig. 7 shows the block diagram that demonstrates the various function components of IMCS. All of these components are programmed as functions of the system. Some of the system functionality can be described as;
Sensors and power meters communicate measurements and status information from the plant to the monitoring modules of the IMCS.
IMCS offers a wide range of options for monitoring the plant. Information can be monitored locally and centrally. Access to the IMCS is protected and the users must login to gain access to functionality. Information received from the plant can be monitored in different forms from data or trends mode. The monitoring includes; Alarms, Trends, Recommendations Status, Configuration utilities, Event messages, etc.
The control of the system is limited while the system is based on monitoring purposes and given recommendation messages for the operator.
The IMCS has the feature of data logging for some selected parameters such as (switching procedure during week; temperature, breaker status, transients, etc.) for further analysis.
The main feature of the IMCS is producing the alarms, recommendation and event reporting functionality. The alarms, recommendation and event reporting are based on customers basis.
Predictive maintenance is an essential part of the IMCS. The system gives all information about the power quality of the plant and cable insulation. This can be achieved through reports and alarms message produced from the system.
The overall structure of the original intelligent monitoring and controlling system.
The display of the currently transmitted measured values of Quality Meter used in the system.
The main components of the PLC.
Part of the monitored data on fedder-2 at Eastern Company.
Part of the monitored data on Transfwer-2 at Eastern Company.
One of the output event messages produced from the system at Eastern Company.
Block diagram of the IMCS components installed at Eastern Company.
The original system is used mainly for monitoring purposes with some recommendation messages produced from it. The paper introduces an extension for the system for producing many digital and analogue output signals from the PLC to control loads based on load management programs and power quality mitigation procedure. The proposed modified system can accept load management schemes load shedding during peak period, cycling on/off load control, and direct load control. The modified IMCS associated with load management and power quality schemes gives the customer the possibility of load reduction or control during the peak periods of the day, moreover, gives more information about the power quality of the system.
The modified IMCS with Load management can include:
Load shedding during peak
Cycling on/off load control,
Direct load control.
The modified IMCS with power quality monitoring can include:
Monitoring overvoltage or transients
Monitoring harmonic graphs for feeders and loads
Monitoring power failure
Monitoring High frequency noise
Monitoring Spikes
Monitoring ground faults and deterioration insulation in cables
The IMCS can also monitor all the breakers status and temperatures of the stand-by generators. The following subsections explain some function components embedded in the modified IMCS
The new generation of the power monitoring device provides accurate knowledge of the systems characteristics with maximum, minimum and average values for voltage, current, power values, frequency, power factor, symmetry and THD. The SENTRON PAC4200 detects the values for active, reactive and apparent energy – both for high and low tariff. It measures ratings and power values via the four quadrants, i.e. power import and export are measured separately. The SENTRON PAC4200 also facilitates the detection of a measuring period’s average values for active and reactive power. These values can be further processed into load curves in a power management system. Typically, 15-minute intervals are used for this purpose. PAC4200 also detects uneven harmonics from the 3rd to the 31st for voltage and current, the distortion current strength (Id), the phase angle and the asymmetry for voltage and current with reference to the amplitude and phase. For further processing of the measured data, the devices can be very easily integrated in superior automation and power management systems in the proposed system the meters are interfaced with SIMATIC PCS 7 powerrate and SIMATIC WinCC powerrate software packages. The Wincc powerrate software packages can handle very complicated schemes for load management.
The purpose of the control module in the modified IMCS is to provide the control of the necessary parameters for each point in the plant. It provides the functionality required to control the load in case of peak period or in case of exceeding the threshold boundary. It offers control functionality, e.g. load shedding, on/off load control, direct load control, power quality control. It also provides the functionality required to control devices such as pumps, motors, compressors, on/off breakers, interlocking, power quality mitigation. Operation can be configured to be automatic. The modified system offers the facility for adjusting control parameters (e.g., set points, output quantity, tolerances, time delay) in order to achieve the desired condition for each program. Fig. 8 shows the new added function blocks for the modified intelligent monitoring and controlling system. The figure shows three main components; workstation-2 with control scheme, Control output module, new generation of the power quality meters compatible with Wincc powerrate software program. As given in Fig. 9, the proposed modified system uses one additional PLC interfaced with the new power quality meters located on the loads. The data is shared through Profibus network-2. Workstation-2 uses Wincc powerrate software program for programming and controlling purposes. Workstation-2 manipulates different load management programs through collected data received from the PLC. Many controlled output signals are produce from the PLC o/p modules. The proposed modified system avoids many of original system limitations by replacing the power quality meters interfaced directly with workstation-2. The proposed modified IMCS can manipulate the following programs;
Block diagram of the modified IMCS function components.
The overall structure of the proposed modified intelligent monitoring and controlling system.
A company\'s electric bill, in most countries, consists of two major components: demand charge and energy consumption charge. Demand charges are reset monthly and are based on the highest rate at which electricity is consumed during periods that are peak utility service hours. Demand charges are measured in kilowatts and, depending on the utility service provider, the highest consumption rate is measured in 15- or 30-minute intervals during peak hours or contracted value. Demand charges form a significant portion of a company\'s monthly electric bill. Peak load management strategies that lower a facility\'s demand during times when the peak demand is measured can result in significant facility cost savings, especially for commercial, industrial and governmental sectors. Fig. 10 shows the procedure of the load management that can be achieved automatically in the proposed modified IMCS. The scheme of load management can be built and programmed according to previous scenario using energy auditing. In such a case the customer can avoid any penalty from the utility and can save money as well. The procedure of the control can be run through workstation-2 and the readings of the loads received from PLC with power quality meters. The program can be run under Wincc powerrate software program. Firstly, the system will check the required load capacity to be shaved during peak value and then select the minimum diesel power generated that coves the required loads. The system will produce output signal through the PLC output module to the suitable generator/generators. In case of the load reduction the system disconnects the generator/-s sequentially according to the required load and get back the loads to the normal operation on their feeders.
The load management control for avoiding the peak period.
Flow generating equipment like fans, pumps and compressors are often used without speed control. In stead, flow is traditionally controlled by throttling or using a valve or damper. When flow is controlled without regulating the motor speed, it runs continuously full speed. Because HVAC systems rarely required maximum flow, a system operating without speed control wastes significant energy over most of its operating time. Using VFD to control the motor speed can save up to 70% of the energy (Central line of Honeywell). HVAC system consumes a large percentage of the total energy utilized by the organization. The original IMCS can not do that. The extended system can include VFD interfaced with the PLC and can run according to the given parameters and atmospheric boundary conditions in the side control module. The devices such as chillers, motors (pumps and fans) and AHU are controlled using VFD through the proposed modified IMCS.
The demands to the power reliability and the power quality become stricter due to the popular application of variable-frequency and variable-speed drives, robots, automated production lines, accurate digital-control machines, programmable logic controllers, information manage systems in computers and so on. These devices and computer systems are very sensitive to the power-supply ripple and various disturbances. Any power quality problems may result in the reduction of product quality or confusion of management order which means great economic loss. Power Quality can be measured with power quality meters and analyzed with software in the control module. Power Quality events usually are infrequent, making them hard to detect and store without specialized equipment. Over a certain period, there may only be a few Power Quality events. The system is able to collect and analyze events on the basis for identifying the Power Quality problem. Analyzed data is the starting point for improvement. Data identifies potential sources of problems based on the timing of the events. This information can show the cause of the problem. Once the source is identified, improvement begins based on corrective actions. Corrective actions may include: changing motor starting procedures, replacing faulty switches or relays, filtering harmonic producing loads, or changing switching schedules for power factor correcting capacitors, changing control of the power filter. Once the corrections are in place, further monitoring will verify that the corrective action worked. The applied system such as IMCS can line with this specifications for power quality mitigation.
The application of good plant quality and demand control concepts requires an understanding of utility rates, auditing, and metering in addition to a basic knowledge of the process and load being controlled or shed. This process is professionally achieved in Eastern Company with 10MW capacity and 11kV/380V plant before installing the original intelligent monitoring and controlling system. The paper introduced an overview explanation for the original IMCS achieved at the Eastern Company. The paper also proposed modified intelligent monitoring and controlling system that has the feature of high speed data manipulation through the technology of Profibus and new generation of digital meters. The proposed modified system has the function of demand monitoring/load shedding scheme that operated in automatic mode. Also, Power Quality can be measured with power quality meters and analyzed with software in the control module. Data identifies potential sources of problems based on the timing of the events.
The feature of the energy consumption and power quality mitigation are significantly enhancing the power system operation. The proposed system has the features of
Accurate load management during peak periods designed based on the plant requirements,
Real time monitoring of the plant performance,
Intelligent alarming capabilities for early power quality problems
Predictive maintenance planning for cables and standby generators
Energy management
Power system efficiency
Save money by avoiding any penalty from the utility
Keep the voltage tolerance with the allowed limits
Continuous monitoring for the power quality
Load control and management with proper methodology
Keep the consumption with the contracted limit
Recoding information that can assist for any future development for the electrical network
Catching any transient vents happened in the system
Mitigation the plant through continuous monitoring of the power quality
Fault diagnosis and alarming
Identify and fix the causes of power disturbances to avoid recurrences
Improved system efficiency
The authors are going to implement the modified components of the IMCS.
Social science has made great strides over the last half-century, with some of the most significant gains made in micro-level studies. With advances in computing and communication technology, researchers can now collect, share, and process statistical data on millions of households and individuals. The combination of increased computing power and the expanded availability, size, and complexity of social science datasets has resulted in a “data gold rush,” with the gold digging powerfully aided by remarkable advances in novel statistical methods [1]. With these methods, researchers can now explore in great detail the mix of factors shaping individual behaviors.
These methods can offer powerful insights into individual behavior. Yet, scientists and planners are primarily interested in societal rather than individual outcomes. For example, rather than how individual characteristics --such as education level-- shape an individual’s likelihood of smoking, we might also want to see the big picture: how would an educational expansion at the national level shape changes in smoking rates? While the former, microlevel question can be robustly addressed with existing micro-regressions, the latter cannot. This is unfortunate when the interest is in national-level outcomes.
Answering such societal questions is tricky on two fronts. First, as Robinson [2] argued 70 years ago, is the risk of ecological fallacy when one draws macro-level conclusions from micro-level analyses, or vice-versa. Thus, a researcher may find highly educated individuals to be less likely to smoke, but it does not follow that highly educated
Researchers thus need alternative approaches to link micro-data and macro-issues, building on the detail and robustness of microlevel statistics as they aggregate them to inform macro-level questions. Decomposition methods, a broad set of tools that emerged from the field of demography, present a useful set of options [5, 6, 7, 8, 9, 10, 11]. Researchers interested in studying social transformations (spontaneous or induced changes in the structure or performance of a large community or state) can leverage various decomposition approaches to examine issues where the unit of analysis is not the individual but the community.
These methods can be used when the study outcome meets three critical criteria; namely, it is 1) quantifiable, 2) aggregate, and 3) the result of a gradual change. A
Decomposition methods are accounting tools. Thus, while excellent at describing the processes, sectors, or groups driving change, they say little about causation. Thus, in examining changes in smoking rates, decomposition methods could reveal the extent to which different education groups (i.e., no schooling, primary, secondary, higher) contributed to declines in smoking rates. Still, they cannot say
Additionally, decomposition methods have four key advantages over other forms of data analysis: they are 1) easy to interpret; 2) transparent; 3) compatible with other research methods, and 4) efficient. The findings from decomposition methods are easily applied and interpreted because they use basic analyses that do not rely on complex statistics or software. For instance, one standard output from decomposition is to show the percentage of social change accounted for by a given process or group. Such an output (a percentage) is easily digestible compared to outputs from regression analyses such as beta coefficients or odds ratios. The method is transparent because the results of a decomposition analysis are easy to replicate, and the accuracy of the results is easy to check: the sum of all groups’ contributions is 100%. The efficiency of decomposition methods reflects its flexibility and the tradeoff between data requirement, analytical technicity, and the type of findings. Its basic forms can be modified and support more complex combinations to suit the researcher’s individual needs. Also, decomposition methods are not very data intensive. In the field of global development, for instance, one can conduct insightful decomposition analyses by relying on tabulations and data sources that are widely available online.
As the examples in this contribution will show, the method and results are quite transparent. First, the input data and sources can be easily checked online by other scholars. Second, unlike multivariate statistics, where results can vary heavily depending on the model specification and individual coding details, decomposition results do not depend on the vagaries of individual modeling choices. Finally, the output from complex regression analysis often seems to be spewed from an impenetrable black box, and it must be taken at face value, with the reader often unable to detect an odd result from, say, a programming mistake. Such is not the case with decomposition: the decomposition findings are presented in a way that allows the reader to immediately assess the internal coherence, credibility, and accuracy of the results.
A third strength is that the decomposition approach can be leveraged and combined with other methods of analysis. In our previous work, we combined a micro-level regression examining the associations between the number of siblings a child has and their educational outcomes, with a decomposition analysis [12]. By combining these two approaches, one can aggregate the robust micro-level findings and answer the macro-level question of how fertility transitions (a country level phenomenon) impact educational attainment at the national level. This integration of methods bypassed the standard limitations of cross-country regression, while also drawing upon the robust micro-level regression findings. The decomposition method is also compatible with many other methods, including qualitative analysis. By quantifying the key behavioral and compositional changes and stratifying across groups and processes, decomposition methods can serve to direct the qualitative research. Essentially, this series of methods does not replace or compete with other methods but, rather, it complements them and expands our toolkit in innovative ways.
Last but not least, decomposition methods efficiently leverage existing information. While some parts of the world are indeed experiencing a “data gold rush,” researchers and policymakers in many parts of the global South often operate with a paucity of data [1, 13]. Despite recommendations by the International Monetary Fund’s (IMF) General Data Dissemination System (GDDS) that countries conduct censuses every ten years, 66 countries currently fail to meet the standard [14]. Many researchers in these settings turn to publicly available nationally representative living standards (e.g., the Living Standards Measurement Surveys) or health surveys (e.g., the Demographic Health Surveys or Multiple Indicator Cluster Surveys). These sources can generate robust micro-level data, but they do not occur at regular intervals and are spotty or unavailable for numerous countries. For instance, from 2002 to 2011, 57 countries had either zero or a single poverty estimate [15]. Thus, decomposition methods are an ideal tool that allows scholars to leverage limited data in creative ways.
Decomposition is not a single method but a set of related methods. It is used across different fields, but its variants are insufficiently integrated. Most researchers know only the variants that directly apply to their issues of interest, such as life expectancy [8, 16], job discrimination [6, 17], and poverty or inequality [18, 19]. Yet all these variants can fit into the general taxonomy presented here. Their commonality is in using accounting-based approaches to describe patterns of change. However, they vary in the functional relationship between independent and dependent variables, as shown in Table 1.
Relationship between X & Y | Example | Nature of the dependent variable | Description | Formula |
---|---|---|---|---|
The total smoking rate in a country (Y) is a function of the educational composition of the population (wj) and the average smoking behavior within each education group (yj). | Nominal or ordinal (e.g., country region, age group, ethnicity, marital status, educational levels). | The macro-level outcome (Y) is a weighted (by demographic weight, wj) average of prevailing values in the various subpopulations of the country (yj) | Y = ∑(yj, wj) | |
An individual’s expected earnings (Y) as a function of the model intercept (α), the “payoff of each additional year of education (βt) and the average level of schooling (Xt). | Quantitative (e.g., a person’s years of education, the number of siblings, or income in dollars). | A linear regression relationship between the dependent and independent variables (Y and X, respectively) | Yt = αt + βtXt | |
GDP per capita (Y), which is a function of a country’s GDP (G) and its total population (P). | A quantitative variable (i.e dollars spent per pupil) | The dependent and independent variables are linked by a simple mathematical relationship, which typically involves a quotient, sum, product, or log. | Y = G/P |
Three basic types of relationships in Decomposition1.
Notations used in this table and text are as follows:
X indicates the independent variable.
Y indicates dependent variable for the entire country.
w refers to the demographic weight of an individual subgroup.
yj = value of the dependent variable for group j,
xj = value of the independent variable for group j.
Δ = indicates the historical change.
For each of these relationships, decomposition analysis allows us to examine how a change in the dependent variable is driven by changes in each independent variable (reflecting a group or a process).
In a demographic decomposition, the main accounting question is about the contribution of ‘composition’ or group-specific size versus ‘group-specific behavior.’ Thus, a researcher might be interested in documenting how much the change in the national rate of smoking between 2000 and 2020 was driven by changes in the distribution of the national population across different education levels (compositional effect) versus changes in the smoking rates of each education group (behavioral effect).
Formally, the national rate of smoking rate (Y) is expressed as a weighted average (by wj) of smoking rates in subpopulations groups defined by educational categories (yj).
In this formula, a national change in the smoking rate can be broken down into two components:
Formula (2) thus allows the analyst to apportion change into two conceptually compelling components- the compositional (
Simple regression analysis tends to model an outcome (Y) as a function of a baseline outcome or intercept (α), a regression coefficient (β), and the value of a predictor variable (X). For instance, one may predict the performance of a well-trained athlete (Y) by knowing the basic performance of a ‘couch-potato’ who has never set foot in a gym (α) and the payoff from each hour of being at the gym (β) multiplied by the number of hours one spends at the gym (X). In that case, if you randomly picked two people in the world, say one from Senegal and the other from Turkey, the difference in their athletic performance (ΔY) can entirely be explained by three factors including:
the difference in the basic performance of a Senegalese couch-potato vs. a Turkish couch-potato (Δα)
the difference in the payoff of gym work in Senegal vs. Turkey (Δβ)
the difference in the number of gym hours for our Senegalese vs. Turkish person (ΔX)
Similarly, a researcher might use a regression decomposition to document how much of the variation in men and women earnings was driven either by the differences in education levels or by the differences in returns to education, suggesting that the gap is driven by discrimination processes. In other words, the researcher would like to know the contribution of (1) the male-female difference in the average level of education, (2) the male-female difference in return to education to male–female wage differences, and (3) the differences in baseline wages between men and women. These three possibilities are explored below. The formal analysis consists of writing the earning equations for males (m) and females (f) and then taking the difference between these two equations.
The decomposition seeks to explain the difference in wages based on the change in the various parameters of the regression equation. This change is expressed as follows
In Eq. (5), the term with upper bar represents the average
Eq. (5) expresses how much of the pay gap is driven by differences in base salaries (Δα) versus differences in the levels of schooling (ΔX) and differences in return to education (Δβ).
Note that the same procedure can apply to both cross-sectional analysis (the difference between two groups in a given year) and longitudinal analysis (the change experienced by one group between years). The approach is the same; only the interpretations differ.
In some cases, an outcome of interest is a function of a set of other variables. For instance, a country’s GDP per capita (Y) is measured as the size of the economy divided by the total population. Thus, change in GDP (∆Y) between two time periods can be decomposed into two pieces-the amount of change driven by the growth or contraction in the economy (∆G) and the change in the total population size (∆P) or precisely change in the inverse of the population (∆(1/P)).
This first decomposition is not very informative. However, one can transform the initial equation into a formula that is slightly longer but conceptually richer:
With this transformation, G/A represents the economic productivity of the adult population (or π). This is a conceptually important factor in theories of economic growth. The same is true for the new term A/P (or α), which reflects the ratio of the working-age population to the total population, a core variable in the analysis of demographic dividends. Thus, we now have two theoretically interesting variables (π and α) and can decompose national income in terms of these two variables.
Now, any historical change in GDP (∆Y) can be decomposed into the change in productivity (π) versus the change in the share of the working population (α).
While offering readers a review of different decomposition approaches, we wanted to focus in some detail on demographic decomposition. As noted above, demographic decomposition applies to national outcomes (Y) that are an aggregated result of the outcomes of several subpopulations (yj), each weighted by its relative size (wj). Formally:
For example, the mortality rate of a country (
Exhaustiveness simply means that each member of the population belongs to one, and only one, of the categories within the independent variable. The set of categories must cover the entire population, and the categories have to be mutually exclusive. Distribution refers to the concept that the number of categories cannot be too few (>2) nor too many. If there are too few categories, as in a variable like a dichotomy, the analysis will not be detailed enough to be informative. Yet, with too many categories (i.e., age in single years), the data will end up spread too thinly.
It is also crucial that the independent variable change over time, i.e., the size of the individual categories comprising the independent variable (the wj) must fluctuate over time. Without such variation, the compositional effect in the decomposition analysis will always remain zero. Because of this, an independent variable like gender is often not ideal, as sex-ratios in a national population rarely change dramatically over time. Similarly, annual income with set threshold cut-offs (i.e., less than $20,000; $20,000–$40,000; $40,000- $60,000; more than $60,000) could be appropriate, but income quartiles would not, since, by definition, there is no change in the size of each quartile over time.
Lastly, a good independent classification variable should be conceptually relevant to the outcome of interest. Thus, for many phenomena, a strong classification variable might be “region of country” if there has been 1) changes in the size of the population within each region and 2) variation in the Y variable by region. This would be especially appropriate if the variation in Y reflects a scenario where programs and policies are designed at the regional level.
Demographic decomposition allows the analyst to document how much of a particular change is driven by 1) changes in the behavior of different social groups vs. 2) changes in the demographic size of these social groups.
Both compositional and behavioral factors can drive a range of important changes in national social, economic, and health outcomes. Figure 1 below illustrates a basic decomposition using the example of infant mortality. This topic is especially relevant for sub-Saharan African policymakers working to achieve Target 3.2 of the Sustainable Development Goals (SDGs), aiming to reduce the levels of child mortality to no more than 25 deaths per 1,000 live births by 2030 [20]. In 2019, Sub-Saharan Africa had the highest neonatal mortality rate at 27 deaths per 1,000 live births [21]. In their first month of life, a child born in sub-Saharan Africa is ten times more likely to die than a child born in a high-income country [21]. However, this fact masks considerable variation within African countries, including severe differences by parental wealth and maternal education [22]. Having a better understanding of the internal variation in these trends can be critical for developing effective policies.
Patterns of change: Vertical versus horizontal convergence in infant mortality.
Figure 1 provides a visual description of how the same national-level change can stem from very different subnational patterns. On the left-hand side of Figure 1 (Frame A: Macro-Level Change) are five squares that represent the trend in child mortality for the country as a whole, with darker colors indicating lower levels of mortality. The chart shows a steady regression at the national level from lower levels of child mortality (the white square on the far left) to higher mortality levels (the black square on the far right). Given these trends, one crucial question is how this evolution occurred, specifically, how various socioeconomic classes contributed to it.
On the right side of the diagram (Frame B) are two possible, and fundamentally opposite, scenarios of how this change can unfold. The first, (B1), highlight a case of horizontal change, where the child mortality rates decline at the same rate for all income groups. Conversely, in Frame B2, we see a vertical change, with the mortality declines beginning with the country’s highest income group before gradually spreading to the rest. In year 2, it was only the highest SES group that was experiencing any decline in child mortality—the other income groups remained unchanged. If we were to ask about the groups that drive the change, we would get different answers from the two scenarios: in the first case, all groups evolved simultaneously, while in the second case, the change occurred first in the higher SES groups. Understanding how this national trend unfolds has key implications for resource allocation and policy targeting.
The example above shows how the same national-level change in mortality can emerge from a single group (i.e., entirely driven by mortality declines among the highest SES group) or be something that occurs as the result of a widespread change throughout the population (i.e., all groups experience a decline, but the declines are quantitatively smaller for each group). However, a full decomposition allows the analyst to identify the groups driving the change (referred to here as the compositional effect) and
Figure 2 presents a hypothetical case where a researcher is trying to understand changes in average monthly income. The figure’s left portion shows Time 1, where the average monthly income for all individuals is $142.50. The classification variable is economic class, ranging from richest to poorest1, and income is the weighted average of incomes across all of the economic classes making up the national population. On the right-hand side of the figure are two different scenarios, with the average monthly income rising to $159.20 in both cases. Yet while the two scenarios reflect an identical aggregate change, they are qualitatively quite different.
Decomposing change in child mortality, Cameroon 1991–2011.
In scenario 1, the average income of each economic group remains exactly the same between time 1 and time 2. The only factor that changes is the percentage of the population in each economic class. The percentage of the population in the second-highest economic group rose from 15% to 20%, while the poor’s share of the population declined. Thus, the change in scenario one is entirely
This example presents an extreme contract, where social change entirely (100%) stems from a change in either composition (Scenario 1) or behavior (Scenario 2). While useful for pedological purposes, it’s perhaps unsurprising that reality is usually less extreme. In most cases, change is driven by some combination of the two effects. For example, compositional change might explain 30% of the change, while behavior accounts for the rest. Demographic decomposition allows the analyst to piece apart these different drivers of change (Figure 3).
Compositional vs. behavioral change: An extreme case.
In this first case, we focus on a national average; Y will be expressed as a weighted average (by wj) of the values of individual subpopulations (yj).
In this formula, a national change can be broken down into two components:
As noted above, decomposition methods apportion change into two key components. The compositional effect captures the amount of change driven by changes in each population subgroup’s relative size. As seen in the last example, national income may go up simply because the number of individuals in the highest economic class increases, thus increasing the group’s demographic weight. Conversely, the behavioral effect captures change that was driven by an actual change in the group’s behavior. In our last example, this was reflected by actual gains in income among the lowest economic groups.
In the case of child mortality, declines can similarly be driven by compositional and behavioral effects. Mortality declines could result from an economic contraction whereby the relative size of the lower economic groups, already characterized by higher mortality levels, expands (i.e., their population weight,
Regardless of the topic, a researcher using demographic decomposition needs to attend to four key tasks, as follows:
For our example, the analyst is trying to understand the extent to which changes in the composition of the population (here, changes in the relative size of each economic class) versus behavioral changes of economic groups (here, changes in the mean mortality rate of each economic group) drove the declining rates of infant mortality in Cameroon.
Column 1 highlights the social class categories used (Highest, Second Highest, Average, Second Lowest, Lowest). Columns 2–5 are where the analyst must insert their data (gathered elsewhere) on group size (
The last step is then to explain this 25.9 unit decline, using formula 10. As reflected in Column 6, the researcher can see that 17% of the decline was driven by compositional change. This is unsurprising, as the class composition of the population changed slightly during this period. The proportion of children living in low-income families declined (from 18% to 15%) while there was a slight rise in the proportion of children living in rich families (19 to 21%). As poorer families exhibit higher infant mortality rates, mortality rates will mechanically go down if their representation in the population shrinks. Column 7 displays the amount of change due to behavioral changes, which was the larger contributor to change, at 83%. A glance at Columns 2 and 5 (showing mortality rates by economic class in 1991 and 2011) confirms that mortality rates did indeed decline across all social classes during this period, including the poorest.
As is evident from Figure 2, the sum of all contributions in a decomposition reflects the total change- thus, the values in columns 6 and 7 sum to −25.9, the total decline in mortality. As a percentage, the sum of all contributions must equal 100%. One important note is that some contributions can be negative (less than 0%) or greater than 100%. Conceptually, a negative value reflects a contribution that worked in the opposite direction of the observed change. In our case, a negative percentage would reflect a change in either the composition or behavior of a group that worked to increase mortality. Percentages larger than 100% signal that the overall change would have been even greater, if it was not hampered by the effects of opposite influences from other groups.
While relatively easy to digest, decomposition findings are easy to present to non-technical audiences as a graph rather than a table. Pie charts, such as the one on the left side of Figure 2, can cleanly show how much of the total change was driven by compositional vs. behavioral factors. Similarly, stacked histograms can efficiently summarize the results and identify the dominant social groups driving change.
Even if decomposition is not a causal method, it can guide policy. For instance, if a planner observes that much of recent national change is derived from compositional changes, then s/he gains insights into future changes. In a country where education levels are rising, having a positive compositional effect (having more educated people leads to progress), then one can anticipate further gains if national levels of education continue to grow. Likewise, if child mortality is declining mostly via a compositional effect (fewer high-parity births), then one expects child mortality to continue to decline if fertility does. In some cases, the planner may have reason to expect the composition changes to continue largely on their own, as is the case of mortality, educational, fertility transitions. In some instances, s/he must proactively induce compositional change through policy.
If mortality is driven by a behavioral effect, the appropriate response is to target either the leading group or the lagging group. One would target the leading groups if one is not worried about inequalities and one does not expect further growth in this top group to be curtailed by a ceiling effect. One has further justification for investing in this leading group if one expects the example set by this vanguard group to trickle down and promote change among the following groups. Some development theorists might argue that in the early stages of development, it can make sense to build up some pioneers who would set the pace and pull the rest of the population [23]. On the other hand, one may favor the lagging group if one assumes that leading already has a momentum on their own and will continue to progress even if unaided. One may further favor the lagging groups out of concern for inequality.
This application of decomposition to policy requires a nuanced theoretical understanding of processes of change and the diffusion of innovations in the general population. In diffusion theory, change may accelerate among the lagging groups after reaching a critical mass in preceding groups [24, 25]. In other ways, change proceeds in a domino pattern and, to the extent that decomposition analysis helps identify the next group in that domino line, it can speed the process.
Students of social change need robust methods to inform policy reliably. With the significant advances in data and computing technology achieved over the last forty years, they are in a better position to study micro-level processes, including the causes of individual behavior. However, this microlevel expertise is not sufficient to account for social change, where the focus is on aggregate (not individual) outcomes. Applying the existing micro-methods methods to understand aggregate social problems amounts to “barking up the wrong tree” or—to stay with this canine metaphor- “letting the methodological tail wag the dog.” Decomposition methods can help address this micro–macro conundrum by making it possible to aggregate evidence from smaller units to understand the big picture.
Given that the big picture, rather than individual-level detail, is the focus of most socioeconomic planning, decomposition methods are quite relevant to policy. The methods are ideal for studying many of the social transformations underway across the globe. In particular, they can inform the study of critical components in the United Nations’ Sustainable Development Goals (SDG), including poverty, health, inequality, and schooling. Many countries are working hard to achieve these goals face with severely limited data and resources. A fuller understanding of the drivers of socioeconomic change and the unevenness of change in these rapidly changing and diverse societies can allow policymakers to target policies more effectively. To this end, decomposition methods can help.
The authors would like to acknowledge support from the Hewlett Foundation and the Minerva Program in the US Department of Defense. We also thank colleagues and students in our Demographic Methods course at Cornell (DSoc 4080/PAM 6060) for their valuable comments.
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\n\nIf you are interested in publishing your book with IntechOpen, please submit your book proposal by completing the Publishing Proposal Form.
\n\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com.
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