The comparative relative hydration properties (%) [13].
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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It is a conventional renewable energy source for generating electricity in small- and large-scale production. Due to its important utilization and future prospects, various interesting topics of research related to hydroelectric power generation are covered in this book. This book is the result of significant contributions from several researchers and experts worldwide. It is hoped that the book will become a useful source of information and basis for extended research for researchers, academics, policy makers, and practitioners in the area of renewable hydropower technologies.",isbn:"978-953-51-3382-7",printIsbn:"978-953-51-3381-0",pdfIsbn:"978-953-51-4723-7",doi:"10.5772/63684",price:119,priceEur:129,priceUsd:155,slug:"renewable-hydropower-technologies",numberOfPages:108,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"15ea891d96b6c9f2d3f28d5a21c09203",bookSignature:"Basel I. 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Dr. Ismail was the leading research investigator in a collaborative project with Goldcorp-Musselwhite Canada Ltd. and Engineering, Lakehead University from 2007 to 2010. This innovative project was state-of-the-art in geothermal heat pump technology applied in Northwestern Ontario, Canada. 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Analysis consisted of the determination of the pressure on the blade surface using Computational Fluid Dynamics, and the calculation of the stress distribution in the blade due to hydrodynamic, inertial and gravitational loads using the finite element methods. The results indicate that the blade undergoes significant vibration and deflection during the operation, and the centrifugal and hydrodynamic loads considerably affect the structural response of the blade; however, the stresses produced in all of the analysed models did not exceed the safe working stresses of the materials used to manufacture the blade. Modal analysis was conducted to calculate first significant natural frequencies. Results were studied in depth against operating frequency of the turbine. After carrying out the modal analysis, harmonic analysis was also done to see the response of the turbine under dynamic loading. It was observed that the turbine is safe in its entire operating range as far as phenomenon of resonance is concerned. Additionally, it was observed that maximum harmonic response of the turbine on the application of dynamic loading is far lesser than its failure limit within the specified operating range.",signatures:"Edwin Chica and Ainhoa Rubio-Clemente",downloadPdfUrl:"/chapter/pdf-download/53621",previewPdfUrl:"/chapter/pdf-preview/53621",authors:[{id:"189040",title:"Prof.",name:"Ainhoa",surname:"Rubio Clemente",slug:"ainhoa-rubio-clemente",fullName:"Ainhoa Rubio Clemente"},{id:"193744",title:"Prof.",name:"Edwin",surname:"Chica",slug:"edwin-chica",fullName:"Edwin Chica"}],corrections:null},{id:"53620",title:"Planning Hydropower Production of Small Reservoirs Under Resources and System Knowledge Uncertainty",doi:"10.5772/66912",slug:"planning-hydropower-production-of-small-reservoirs-under-resources-and-system-knowledge-uncertainty",totalDownloads:1546,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Available energy from water varies widely from season to season, depending on precipitation and streamflows, especially in small catchments. In addition, the reservoir operation problem is associated with the inability of operators to formulate crisp boundary conditions, due to uncertainty in knowledge. In this chapter, an approach for planning the operation of small multipurpose reservoir systems for hydropower generation and flood control under consideration of the stochastic nature of inflows and initial storage levels and allowed formulation of constraints with some range of uncertainty will be presented. The approach is based on joint chance constrained and fuzzy programming, which addresses the problem of including risk directly in the optimization. Therefore, the stochastic nature of inputs is incorporated directly in the model through the use of convolution of random variables. Furthermore, probabilistic/vague constraints and preassigned tolerance levels are used to transform the stochastic optimization problem into its deterministic equivalent. The approach searches for a control strategy, which maximizes the benefits acquired from hydropower generation and minimizes the economic losses incurred due to not meeting the required reliability levels from the various purposes served by the reservoir system. Besides the optimal reservoir release strategy, this approach also determines the optimal reliabilities of satisfying hydropower demand and flood control storage requirements. Therefore, this tool has some advantages in planning the operations of reservoirs in extreme hydrological events such as floods and droughts. 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Since 1911, 500 kW power generation at Pharping, now reached 782.45 MW production in 2016. Nepal government has planned to increase its current 67.3% access in electricity to 1426 MW (87%), by 2022. Globally, 16.6% generation of hydroelectricity, 1,079 GW production, in 2015 will be increased to 1,473 GW by 2040 as projected. Although, hydropower is considered as a renewable clean energy, dam closure, influence within the downstream river and connected ecosystems have consequent impacts on hydropower production. Nepal’s topography offered more RoR types of hydropower and has more risk of landslide, flooding, GLOFs, LDOFs, and flash floods. Despite, Nepal contributes 0.027% of total global Green House Gas (GHG) emissions; Nepal has focused on renewable energy, hydropower production, targeting 12000 MW by 2030 to fulfill its growing demand of 11,500 MW. Consequent development of clean energy, GHG reduction, single Bhotekoshi hydropower can reduce 160092 tons CO2/year. 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\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state of the technology Blockchain for Digital Twins by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in industrial sectors. Our book envisages how blockchain can reshape and transform Digital Twins as technology/tools to bring about secure manufacturing that guarantees traceability, compliance, authenticity, quality, and safety. The scope includes several benefits of employing blockchain taxonomies in Digital Twins in the literature based on key parameters and levels, design phases, industrial use cases, key objectives, enabling technologies, and core applications. Our team provides insights into ongoing progress made towards Digital Twins as technology/tools by presenting recent synergies and case studies. Finally, the content should cover open challenges that serve as future research directions. This book is presented news authors' ideas of blockchain joint/plus Digital Twins as technology/tools.
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Dr. Vardan Mkrttchian taught for undergraduate and graduate student’s courses of control system, information sciences and technology, at the Astrakhan State University (Russian Federation), where he was is the Professor of the Information Systems (www.aspu.ru ) six years. Now he is full professor CAD&Economy departments of Penza State University (www.pnzgu.ru ). He is currently chief executive of HHH University, Australia and team leader of the international academics (www.hhhuniversity.com ). He also serves as executive director of the HHH Technology Incorporation. Professor Vardan Mkrttchian has authored over 400 refereed publications. He is the author of over twenty books published of IGI Global, included ten books indexed of SCOPUS in IT, Control System, Digital Economy, and Education Technology, and 43 works in SCOPUS and 47 in WoS. He also is Editor-in-Chief of International Journal of Applied Research in Bioinformatics (IJARB). For additional information please see https://www.igi-global.com/affiliate/vardan-mkrttchian/272027.",institutionString:"HHH University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"184402",firstName:"Romina",lastName:"Rovan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/184402/images/4747_n.jpg",email:"romina.r@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"872",title:"Organic Pollutants Ten Years After the Stockholm Convention",subtitle:"Environmental and Analytical Update",isOpenForSubmission:!1,hash:"f01dc7077e1d23f3d8f5454985cafa0a",slug:"organic-pollutants-ten-years-after-the-stockholm-convention-environmental-and-analytical-update",bookSignature:"Tomasz Puzyn and Aleksandra Mostrag-Szlichtyng",coverURL:"https://cdn.intechopen.com/books/images_new/872.jpg",editedByType:"Edited by",editors:[{id:"84887",title:"Dr.",name:"Tomasz",surname:"Puzyn",slug:"tomasz-puzyn",fullName:"Tomasz Puzyn"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3569",title:"Biodegradation",subtitle:"Life of Science",isOpenForSubmission:!1,hash:"bb737eb528a53e5106c7e218d5f12ec6",slug:"biodegradation-life-of-science",bookSignature:"Rolando Chamy and Francisca Rosenkranz",coverURL:"https://cdn.intechopen.com/books/images_new/3569.jpg",editedByType:"Edited by",editors:[{id:"165784",title:"Dr.",name:"Rolando",surname:"Chamy",slug:"rolando-chamy",fullName:"Rolando Chamy"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"78047",title:"Mineral-Bonded Wood Composites: An Alternative Building Materials",doi:"10.5772/intechopen.98988",slug:"mineral-bonded-wood-composites-an-alternative-building-materials",body:'Wood is one of the first raw materials for construction purposes. Its usage has continuously increased since human beings. However, as a result of excessive wood utilization, natural forests have become depleted at scarcity value. Therefore, consumers have become more aware of the destruction of the natural forests for wood supply. After technological developments and intensive studies, many valuable constructional elements have been developed from lignocellulosic in recent years [1, 2, 3]. In this context, numerous alternative biomass sources such as; agricultural and forest residues, low-value woody materials, annual plants have been considered to use as wood substitute alone or in combination with synthetic binders to manufacture construction materials [4, 5, 6, 7].
One of the interesting materials has been invented which using inorganic minerals as the bonding agent, called mineral-bonded wood composites. These products were first produced by an Austrian carpenter using wood shavings and gypsum together in 1914. However, cement-bonded wood composites called Wood Wool Cement Board (WWCB) were also produced in Austria in the 1920s and several others in Europe followed. Moreover, Cement Bonded Wood Chip Boards called Durisol were invented and commercially produced in the 1930s. After that, the cement-bonded coarse wood particleboards called Velox boards were produced in the 1950s. The first Cement Bonded Particle Board (CBPB) called Duripanel was produced in 1970. Since the first invention of these products in Europe, these materials have developed further with calling different names in the market, spread to the rest of the world. These days, numerous mils have been built throughout the world, mostly in manufacturing panel form [8].
Due to variables and wide range of properties, mineral bonded wood composite materials could be broadly divided into two distinct groups;
Composite materials in which woody materials (i.e. fibers, sawdust, chips) are incorporated as an aggregate in the mineral matrix,
Composite materials in which the mineral binder acts purely as a binder, (i.e. wood wool cement board, particleboard, or fiberboard).
However, the three most common mineral-bonded composite products could be found in the market. These are;
Cement-bonded composites,
Gypsum-bonded composites,
Magnesia cement-bonded composites,
All these mineral-based binders have been used to manufacture low-medium density (360 to 800 kg/m3), and medium-high density (800 to 1.400 kg/m3) products.
Cement-bonded low-density products are usually called Excelsior and high-density products are called cement-bonded particleboard or fiberboard. However, Portland cement is the most common mineral binders while gypsum and magnesia cement are sensitive to moisture, and their useability is generally restricted to interior applications. Therefore, the panel materials bonded with Portland cement are considered to be more durable than others. These make cement-bonded products are useful material in both interior and exterior applications [2, 3, 9]. Although, synthetic resin-bonded panels (fiberboards and particle boards) are produced in much higher volumes due to the low cost and wider application areas, especially wood-cement bonded panels present several advantageous properties that make them more competitive for some special outdoor applications.
The general classification of mineral bonded wood composite materials and their commercialized names are briefly given in Figure 1. However, much valuable information and some excellent results have been provided on these products could be found elsewhere [2, 3, 8, 9, 10, 11, 12].
The general classification of mineral-bonded composites [
The setting of inorganic binders is the result of a multi-complex chemical reaction causing a succession of crystallization stages. However, the hydration of mineral binder is an exothermic process which is possible to trace the compatibility by monitoring temperature changes. Typically, lignocellulosic have a cellular structure with various inhibitory substances (cellulose, hemicelluloses, lignin, extractives), and some of them dissolve in water and could be disturbed in the mineral binder crystallization [8, 9, 10, 11]. Thereby, species compatibility varies with the type of binder and chemical constituents. But the term compatibility usually refers to ‘the degree of binder setting after mixing with water and with a given wood in a fragmented form’. The inhibited reactions are generally characterized by;
Hydration temperature,
Hydration time,
Retarding the cure of binders.
As briefly explain introduction section, the woody material can act both as an aggregate and as a reinforcing element in a mineral binder-based matrix system. In either case, the interaction between the binder and the element is very important. Because the particle-matrix interface is the diffusion zone, the matrix phases are connected either chemically or mechanically [8, 9, 10, 11, 12]. However, this diffusion region mostly influences the mechanical properties because the interfacial adhesion between particles and matrix characterizes composite materials. During hydration of cement, it created crystals in the contact layer with variable dimensions. But the crystals in the middle layer should be appropriate in the transition layer and resemble should be well bonded to each other. Because of inhibiting constituent presence, the form and dimension of the crystals could be modified. Thereby, some layers of altered crystals cannot be distinguished [9, 13].
However, cement is more sensitive to wood chemical constituents than either gypsum or magnesia cement, in most cases, the hydration times are the longest. Moreover, relative hydration times of gypsum, as also affected by selected wood species while sugars and extractives do not have as much effect on the curing and bonding of magnesia cement. The general comparative hydration properties of selected species with the inorganic binder are shown in Table 1.
Wood or chemical | Gypsum | Magnesia cement | Portland cement |
---|---|---|---|
1.0 | 1.0 | 1.0 | |
1.20 | 1.20 | Inhibited | |
1.33 | 1.35 | 1.35 | |
1.46 | 1.53 | 2.55 |
The comparative relative hydration properties (%) [13].
The compatibility of wood with cement can be strongly influenced by
Cutting and storage time of woods,
Water to binder ratio of paste,
Wood particle size of paste
But the content and type of sugars present in wood have been previously identified as the most critical compounds causing incompatibility, especially in softwoods [9, 13, 14]. Hence, it is very important to supply woody elements with homogeneous physical and chemical properties for the standardization of the manufacturing process.
However, the hydration of mineral binders can be improved by treating the particles or by using some additives. In the most cases, the pre-treatment is necessary, allowing to compatibility with mineral binders to obtain more suitable production [9, 14]. The aging or seasoning of wood and some degree use of chemical agents (i.e. CO2, CaCl2 and MgCl2) found to be increase certain wood’s compatibility with cement [9, 11, 12, 13, 14]. The similar approaches could be useful for both gypsum and magnesia cement as well.
Portland cement is the most common type of binder in mineral based wood composite products. However, it reacts with water in a process called hydration and eventually solidifies into a hard mass. In general, the major ingredients of cement are three complex mixture of tricalcium silicate, dicalcium silicate, and tricalcium aluminate, which comprise more than 87% of the total weight [9, 13].
The advantages associated with wood elements in cement matrix system, include wide variety of species available, low density, high tensile strength, relatively low cost and well-developed technology to supply raw materials from renewable sources [9, 10, 11]. However, the use of cement in wood composites is faced with some limitations. One of the major drawback is the vulnerability of natural fibers to decompose in the alkaline environment of cement. In addition, some woods might exhibit incompatibility with cement due to specific chemical structures (sugars and extractives) that retard the cure of cement that impermeable hydrates are formed around unhydrated cement grains, which delay the setting and affect the final strength of the products [8, 9, 10, 11, 12, 13, 14]. Therefore, species selection can be important for the effective manufacturing process.
These drawbacks could be solved by several techniques which are effective in removing the detrimental components from wood. These are [9, 10, 11, 12, 13, 14];
Hot water extraction,
Leaching in cold water,
Chemical extractions,
The use of cement curing accelerators like CaCl2, MgCl2 and CaCO3,
CO2 treatment,
Addition of pozzolans such as volcanic ash, fly ash, rice husk ash and condensed silica fume.
Gypsum is a sedimentary mineral and composed of calcium sulfate (CaSO4) and water (H2O), naturally in a crystalline form known as calcium sulfate dihydrate (CaSO4•2H2O). However, gypsum boards are typically made from a slurry of gypsum, water, and lignocellulosic fibers. Its structure is composed of interconnected needle-like calcium sulfate dihydrate crystals which entangle and rehydrated during the binder curing process (calcination), hardening to create a gypsum network [15]. Because hydrate crystals form in a gypsum-natural fiber network, wood chemical constituents especially sugars or some extractives may retard the hydration of the binder and alter crystalline structures. Typically, gypsum crystals are relatively long and have a hexagonal form, but the retarder chemicals could be influenced, the form and dimension of the crystals are altered [13, 14].
Manufacturing of wood-based gypsum boards required a higher binder (gypsum) than that needed in the bonding of composites with thermosetting resins. However, one of the main drawbacks of gypsum as a building material is its heaviness and brittleness. Hence, these boards do not have strong impact resistance for some building applications. These situations can be partly overcome by combining gypsum with various types of natural fibers (waste paper, agriculture waste fibers) to impinging better mechanical performance [15, 16]. Some advantages and disadvantages of gypsum-based composite materials are given in Table 2.
Magnesia cement-based boards are formed by a chemical reaction between MgO and MgCl2, typically in a weight ratio of MgO/MgCl2 (1.0/2.5–3.5 by weight). This product is quite similar to Sorel cement but has both organic additions (sawdust, wood flour) and inorganic fillers (sand, lime, or volcanic ash) [17]. However, the hydrated product is hard and strong, but the product decomposes over time by contact with water or air at high relative humidity (RH) [17, 18].
The first industrially made inorganic bonded wood composites were magnesia-bonded wood wool boards called the Heraklith boards in Europe and Tectum boards in the USA [13, 17, 18]. Recent studies show that half calcined dolomite can be partially substituted for magnesia. However, it was proposed that wood composites can also be made using a mixture of heavy magnesia and ground dolomite in combination with a solution of ammonium polyphosphate as the binder. The process is further simplified by using caustic calcined magnesia or half calcined dolomite in combination with a sparely soluble ammonium polyphosphate [13].
Fewer boards bonded with magnesia cement have been produced than portland-cement bonded panels, mainly because of cost. However, magnesia cement does offer some manufacturing advantages over portland cement. These are;
The extractives and chemical constituents in lignocellulosic do not have as much effect on the curing and bonding,
The magnesia cement is more tolerant of high water content during production.
These open up possibilities to use lignocellulosic not amenable to cement composites, without leaching or other modification, and to use alternative manufacturing processes and products.
In the production of this panel product, wood wool (excelsior) is laid out in a low-density mat. The mat is then sprayed with an aqueous solution of magnesia cement, pressed, and cut into panels. The cure of magnesia binders can be readily accelerated by the addition of heat. Wood-based boards made with this material are therefore compressed in a heated press. As with resin-bonded wood composites, total press time can be reduced by rapidly transferring heat to the center of the board. Steam injection pressing, a process whereby saturated steam is forced into a mat during pressing, is being successfully used to raise the center-line temperature of resin-bonded boards to curing temperatures in less than a second [13, 18, 19].
However, the addition of fluorine anhydrite has been caused by the modification of wood–magnesia bonding mechanisms that affect the stabilization of creeping deformations of the products. The fluorine anhydrite intensifies the processes of caustic magnesia solidification and causes the formation of thick structure in the wood–magnesia panel products [19].
Typically, there are two types of water (free and chemically bound) in mineral bonded boards. This is important because it contributes to the fire resisting behavior. Hence, when exposed to fire, these materials undergo reactions in which the water is gradually driven off at temperatures above 100°C. However, considerable high-level heat energy is required to evaporate the free water and for the chemical reaction to release the water in the crystal structure. Moreover, those have also usually low heat transfer coefficients and are capable of quick release of the humidity.
However, wood-cement bonded composites have become environmentally benign sustainable materials for the constructions due to reducing material costs by combining a lower cost material [9, 20]. Hence, these products have very good dimensional stability, high fire resistance properties, and impart additional energy absorbing capacity to the matrix system. The wood cement composites typically show improved ductility, flexibility, and crack resistance when compared to neat cement concrete [9, 10, 11, 12]. In addition, besides being high strength properties, these products could be provided well protection against decay and insect attacks as well [21, 22].
The mechanical properties of gypsum base panels are closely correlated with panel porosity, water/gypsum ratio, network structure, intercrystalline interaction, crystal sizes, and aging time. Although the hydrated gypsum and magnesia bonded boards are hard and strong, the product decomposes over time by atmospheric effects at high relative humidity (RH) [17]. Hence, the gypsum and magnesia bonded boards are not recommended as sheathing in exterior facades or any other application where the boards are in contact with a moist climate. In contrast, the cement-bonded boards showed excellent dimensional stability and only a slight reduction in mechanical properties after outdoor exposure for years, greatly outperforming other wood-based panels.
The modified magnesium polyphosphate-bonded particle boards are some similar properties to those obtained with magnesium sulfate while shrinkage decreases due to carbonization of wood–magnesia matrix, and the density, strength, and water resistance increases [13, 19].
There are numerous studies for determining the suitable board configurations on the end-use applications. But many properties such as; strengths, fire resistance, sound absorption, and insulation behaviors of panels, are primarily influenced by the density of the product and the binder/wood ratio. Tables 3 and 4 show the general and physical comparative properties of mineral-based wood composite materials.
Advantages | Disadvantages |
---|---|
Ease of workability and adhesively attached to many substrates | Dry construction material that sensitive to water or moisture. |
Acoustics properties that can be used partitions and floor/ceiling systems for control sound. | Very short period of setting times |
Fire resistance material that gypsum could not support combustion | It has very brittle matrixes. |
Cost effective manufacturing process and lower CO2 emission compared to other construction materials (i.e. portland cement) | Water mold damage possible |
Lightweight material | Environmentally not sound |
The gypsum plaster supply chain has low energy consumption and | Harmful for health produce toxic SO2 gas |
Gypsum board is used to construct strong, high quality walls and ceilings | High wastage in use and manufacturing |
Some advantages and drawbacks of gypsum bonded composites.
Gypsum boards | Wood-cement boards | Magnesia boards | |
---|---|---|---|
+ | +++ | + | |
+++ | +++ | +++ | |
++ | +++ | ++ | |
+++ | ++ | ++ | |
+++ | + | + | |
+ | +++ | +++ | |
+++ | ++ | ++ | |
+ | +++ | ++ |
Wood composites | Density (kg/m3) | Binder/wood ratio | MOR (MPa) | IB (MPa) |
---|---|---|---|---|
1000–1350 | 2.9 | 6.0–15 | 0.4–0.6 | |
1000–1350 | 10 | 12–20 | 0.8 | |
1000–1200 | 4.0 | 6.0–9.0 | 0.3–0.6 | |
900–1000 | 5.0 to 6.0 | 4.0–7.0 | 0.3–0.5 | |
900–1250 | 1.5 | 7.0–14 | 0.4–0.6 | |
700 to 1100 | 5.0 | 8.0 to 10 | — |
Comparative physical Propoerties of mineral bonded boards boards [23].
The cement-cellulosic substrate matrix is a complex system that can be given different properties and the resulting products can be used for a broad variety of applications. However, cellulosic fibers are well bonding ability to each other. Especially well-fibrillated fibers are more flexible and have a higher area available for bonding. This is possible by using refiners which are breaking the primary wall and the fibrils from the secondary wall will stick out. This will increase the surface area for bonding and therefore increase composite strength. The results presented in Table 4 support this hypothesis.
An acceptable property from mineral bonded panels is dependent on both the type of the binder and wood properties. All these materials are considered non-toxic yet and commonly referred to as being virtually incombustible. Due to very high dimensional stability and physical properties, the cement-bonded products could be useful for many external applications including; exterior siding, agricultural buildings, pre-fabricated structures, mobile buildings, roofing, flooring, industrial and exterior domestic cladding, tunnel linings, highway sound-barriers, fire-barriers and paving tiles. However, the low-density cement-bonded boards (Excelsior) could be used for high-performance applications and improved acoustic and damping properties such as; fire-resistant, sound-absorbing walls, ceilings, and thermal insulation panels [24]. But it is important to note that the utilization of cement-bonded composites is highly dependent on construction techniques, esthetics, safety and energy regulations, and all the other underlying factors which determine public acceptance of a product. The vast literature on cement bonded-wood-based composites, their properties, and manufacturing variables could be found elsewhere [9, 20]. Table 5 shows comparative use ability and Figure 2 shows some examples of mineral bonded composites.
Cement bonded composites | Gypsum bonded composites | Magnesia bonded composite | |
---|---|---|---|
+++ | + | + | |
+++ | +++ | ++ | |
++ | +++ | ++ | |
++ | +++ | ++ | |
+++ | + | ++ | |
+++ | + | ++ | |
++ | ++ | ++ | |
++ | ++ | ++ | |
+++ | +++ | +++ |
General uses of mineral bonded composites (+: low level; ++: medium level; +++: high level).
The uses of mineral-bonded composites in some applications.
The gypsum-based composites are a well-known low-cost material and frequently used to finish interior wall and ceiling surfaces that are often called drywall, wallboard, or plasterboard [20, 25]. Thereby, these materials could be useful for both residential and non-residential construction applications. However, the paper-faced gypsum boards have been widely used since the 1950s for the interior lining of walls and ceilings which are appropriate to fire ratings [20]. The paper-faced gypsum boards also find use areas as exterior wall sheathings. The facings of drywall and gypsum sheathing panels are adhered to the gypsum core, providing the panels with impact resistance, and bending strength, and stiffness. An alternative to adhered facings is to incorporate lignocellulosic fiber (typically recycled paper fiber) in the gypsum core to make what is termed fiber-reinforced gypsum panels. Moreover, the gypsum sheathing panels are primarily used in commercial construction, usually over steel studding, and are distinguished from gypsum drywall by their water repellent additives in the paper facings and gypsum core [8, 15, 20]. It has already well established that natural fiber-reinforced gypsum panels (wood fibers) are typically stronger and more resistant to abrasion and indentation than paper-faced drywall panels and also have a moderate fastener-holding capability [8, 20, 25]. Although gypsum-based boards have usually been marketed for use as interior finish panels (drywall), some hydrophobic additives can provide a certain level of water resistance, for use as sheathing panels, floor, or roof underlayment, or tile-backer boards. In addition, the gypsum-based panels have a low thermal coefficient and low solid contents that these properties are good for insulating against heat and sound while the mechanical strength of the gypsum-based composites is still retained [8, 9, 16, 20].
Although composites bonded with magnesia cement are considered water sensitive, they are much less so than gypsum-bonded composites. One successful application of magnesia cement is a low-density panel made for interior ceiling and wall applications. However, the gypsum and magnesia-based wood composites have also presented high dimensional stability and resistance against biodegradation while well fire resistance and some level sound insulation properties. This is attributed to the lower content of organic matter and the crystal water in the binder. These special properties make these products could be useful for a wide variety of purposes in construction applications. Thereby, the low-density products could be useful as interior ceiling and wall panels while high-density panels could be used as complete wall and roof decking systems. Moreover, exterior-type panels are coated with stucco, and the interior is a gypsum board. These are also useful for decorative and sound barrier purposes in constructions [22, 26].
However, high-density magnesia and/or Portland cement-bonded boards can be used as flooring, roof sheathing, fire doors, and load-bearing walls. But complex shapes, such as decorative roofing tiles or non-pressure pipes, can be molded or extruded as well. The largest volume of cement-bonded wood-based composite materials manufactured in North America is fiber (pulp)-cement siding [27]. Moreover, cement-bonded panels can be used as low-cost housing systems in developing countries such as; rural prefabricated structures, mobile homes, structural insulation panels so on [9].
The Magnesia and gypsum boards might also be used outdoors but must be protected from direct exposure to the weather because of sensitivity to moisture.
The markets for inorganic-bonded wood composites vary throughout the world. However, there is a great potential for the use of wood species to make mineral-bonded composites. Substantial markets for these panels have been developed for various construction end-uses (i.e. sheathing and siding) with insulation partitions. These products may provide an option for using lignocellulosic residues for improved properties like fire and sound insulation characteristics and hence can be used as wall covering and filling material in the constructions. Moreover, inorganic-bonded boards could be adapted to the wood frame construction techniques used for residential housing.
The use of mineral-bonded composites is highly dependent on building codes, safety and fire regulations, construction techniques, esthetics, availability of materials, and all the other underlying factors which determine public acceptance of a product. However, cement-bonded wood composites (WBC) have been taken as very stable dimensionally when subjected to outdoor applications. However, gypsum boards are commonly used as a lining material in walls, ceilings, and wall partitions.
Traffic problems such as frequent traffic congestion, serious traffic accidents, and long commuting times have seriously reduced the travel experience of passengers and the efficiency of traffic operations [1]. To cope with these problems, researchers work on improving the traffic control strategies based on prediction of future traffic stratus [2]. Traffic flow is one of important road conditions to access [3]. Based on prompt and accuracy perdition, better and fast-adjusted traffic control and guidance could be applied. Therefore, reliable traffic flow prediction is also one of the key factors to upgrade the traffic system from “passive adjust” to “active control in advance”; even prediction of future short-term traffic status of road sections is quite useful to prevent congestion deteriorate. For traffic management departments, early detection of traffic instability and abnormal potential risks based on reliable prediction data can improve a large number of existing traffic management control applications, such as traffic calming, signal control, etc.; for road users, real-time route updates and adjustments based on dynamic traffic prediction results can adjust travel time and routes before congestion develops, thus providing vehicles to plan a driving path to avoid congested road sections and congested intersections or to plan a path with the shortest driving time for vehicles to improve traffic efficiency.
The current traffic prediction also faces the following challenges, as shown in Figure 1: (1) analyzing the spatial correlation of the road network: some roads are adjacent to each other and have different degrees of influence on upstream and downstream traffic volumes, so the traffic flow in this part is spatially correlated, and it is a challenge to consider the spatial location relationship to correlate the neighboring traffic flow characteristics [4]; (2) unlike the regular network layout, the traffic map structure is irregular; (3) the nonlinear retention of medium and long time prediction models: the traffic flow changes drastically at the peak time, which is difficult to predict, especially as the prediction time increases, the nonlinear retention ability of the model decreases and the time series signal gradually decays, so how to better correlate the time series relationship of traffic flow to maintain the steady-state time series prediction is also a long-term challenging task [5].
Challenges in traffic flow prediction; (a) road relevance from the web (
Traffic flow forecasting is based on historical traffic flow data to predict future traffic flow, which is a typical regression problem of traffic network time series [6]. In order to solve the traffic flow forecasting problem, factors such as traffic patterns, data types, spatial locations, and time periods need to be considered. Nowadays, many computational forecasting methods have been widely used in traffic flow prediction and have achieved good research results. As shown in Figure 2, common traffic flow prediction methods can be divided into three major categories [7]. ① early traffic flow prediction methods; ② machine-learning-based traffic flow prediction methods; ③ deep-learning-based traffic flow prediction methods.
Classification of time-line based traffic flow prediction methods.
Early traffic flow prediction methods mainly model the relationship between traffic flow, speed, and density and regress the traffic flow data as well as optimize the parameters to achieve the fitting prediction of traffic data, mainly including statistical models and traffic simulation.
Miska et al. [8] proposed cellular automata (CA) to simulate each participant of different flows and their interaction phenomena.
Ngoduy et al. [9] proposed the use of static and dynamic assignment methods to allocate traffic on a simulated road network.
Stephanedes et al. [10] have applied the historical mean model (HA) in urban traffic control systems in 1984.
Kumer et al. [11] used the Autoregressive Integrated Moving Average (ARIMA) model to represent the predicted traffic flow in the form of a mathematical model.
Models such as statistical mathematical models and traffic simulations can describe this traffic flow prediction as a time series problem approximately. However, simulation systems and simulation tools still need to consume a lot of computational power and skilled parameter settings to reach a steady state, and it is more difficult to get accurate prediction results from this prediction model due to the complexity of traffic scenarios. Besides, these methods based on statistics are only applicable to linear data, while traffic flow data are nonlinear and complex; thus, such methods are not capable of handling complex nonlinear traffic data.
With the demand for high accuracy in intelligent traffic scenarios, the shortcomings of traditional prediction methods that cannot model the complex state of traffic flow become more and more prominent, and machine learning methods gradually take an important place in traffic flow prediction tasks.
YS et al. [12] proposed a traffic flow prediction method using support vector machine regression. The idea of using support vector machine method is to map the low-dimensional nonlinear traffic data to a high-dimensional space by introducing a kernel function before linear classification.
Zhu et al. [13] predict the path traffic volume and roadway flow by building a Bayesian network model.
Qi et al. [14] proposed a Hidden Markov Model (HMM) for short-term highway traffic prediction.
Machine learning methods can better model the stochastic processes and nonlinear properties of traffic flows and have mostly better performance compared with traditional models. However, such methods do not consider the spatial and temporal correlation of traffic flow data and require extensive feature engineering. Therefore, it is difficult to solve complex traffic flow prediction problems.
Deep learning has been very successful in the fields of computer vision, speech recognition, and natural language processing, and more and more scholars are applying deep neural networks (DNNs) to various real-world scenario tasks. In traffic flow prediction, the models can be classified into road section prediction and area prediction according to their prediction range characteristics.
Chen et al. [15] proposed a convolutional neural network (CNN)-based traffic flow prediction method using time series folding for multi-scale learning.
Lv et al. [16] proposed a heap-based autoencoder (SAE) method for traffic flow prediction considering spatiotemporal relationships.
Yu et al. [17] proposed a Long-Short Term Memory (LSTM)-based method for traffic flow prediction on road networks during peak periods.
Cho K et al. [18] proposed a gated recurrent unit that can establish links for traffic data at adjacent moments and preserve the memory by gating and other means to learn long-term dependencies of traffic flow sequences.
Yu et al. [19] proposed a spatiotemporal graph convolutional network (STGCN) for traffic flow velocity prediction on a multi-scale traffic network.
The ST-ResNet [20] model restricts the input to grid data rather than graph structure in the traffic prediction problem, which makes it difficult to make predictions on complex highway data.
Geom-GCN [21] proposed a network to update the node representation, but it could not capture the distance dependence between nodes.
The DCRNN model (2018ICLR) [22] models spatial correlation as a diffusion process on directed graphs to model the translation of traffic flows and proposes diffusion convolutional recurrent neural networks capable of capturing spatial and temporal dependencies between time series using the seq2seq framework.
The GMAN model (2020AAAI) [23] uses an attention mechanism to model dynamic spatial and nonlinear temporal relationships, respectively.
ASTGCN [24] considers only low-order neighborhood relationships between nodes and ignores correlations between different historical time periods.
The core of the traffic prediction problem lies in how to effectively capture the spatiotemporal dimensional features and correlations of the data. Traditional convolutional neural networks can effectively extract local features of data, but can only work on standard grid data. The graph convolution can directly extract features from graph structured data and automatically mine the spatial patterns of traffic data. The convolution operation along the time axis can extract the temporal patterns of traffic data. Therefore, this paper focuses on the deep learning model based on graph convolutional network to capture the spatial and temporal characteristics of traffic data and effectively solve the traffic flow prediction problem.
GCN has become a mainstream method in the field of traffic flow prediction, but it started late, and its theoretical foundation and research depth are far from enough. At present, it still faces many problems that need to be solved. There are three main directions as follows.
Dynamic graph modeling: Most graph structures processed by GCN networks are static graphs, and there are fewer models involving dynamic graph structures. The graph structure of static graphs is static and unchanging, while the vertices and edges of dynamic graphs change randomly or even disappear, making it difficult to follow the rules.
Heterogeneous graph modeling: Homogeneous graph means that nodes and edges are only one type, and this kind of data is easier to handle. The heterogeneous graph refers to the type of nodes and edges, the same node and different node connections will show different properties, the same edge and different node connections will also show different relationships, heterogeneous graph structure is relatively complex to deal with. However, the heterogeneous graph is the most relevant scenario to the actual problem.
Deepening the model structure: One of the inherent advantages of GCN is that it smoothes the graph signal, but as its layers keep deepening, its training results are highly susceptible to over smoothing. Since graph convolution is a special form of Laplacian smoothing, the smoothing operation makes the signal more consistent at the feature level as the graph convolution aggregates the features of neighboring nodes, thereby causing the signal to lose its diversity and leading to a sharp performance degradation in the relevant prediction task, a phenomenon that is more pronounced on small data sets. Therefore, GCNs cannot be stacked continuously and deeply like general convolutional models, but shallow neural networks suffer from limited perceptual field and feature extraction capabilities.
This section introduces the principles and techniques related to traffic flow prediction based on graph neural networks. First, an overview of graph neural networks is given, and the graph convolutional networks (GCNs) [25] used to capture the spatial dependence of traffic flows in the road network are introduced separately in this paper. Secondly, the transformation of graph structure into actual road traffic graph structure modeling method is introduced; finally, this paper models the GCN on urban road networks and uses the topology of the GCN capture graph to handle the spatiotemporal traffic prediction task, and the application scenarios of traffic flow prediction are added at the end of the paper.
Graph is a common data structure that is an important object of study in the field of computer and data science [26]. A graph usually consists of two elements, Vertex and Edge, where the vertices correspond to an abstract representation of the object of study and the edges represent the interconnection between two of the objects. Graphs are often used to represent things and specific relationships between things; in fact, graphs can represent any system with binary relationships. Graphs have a very wide range of applications in real life; social networks of human life, citation systems, urban transportation networks, and biochemical molecules can be effectively represented by graph structures.
In graph theory, a graph is usually represented as a set of vertices and edges [27], denoted as
Basic types of common diagrams.
As a common data structure, graphs have many kinds of algebraic representations, and common storage representations include adjacency matrices [29], adjacency tables, and association matrices. Among them, adjacency matrices are widely used in graph representation learning because they can represent the constructional properties of graphs well and are easy to combine with matrix operations to understand the structural features of graphs.
If two vertices of an edge in a graph are
The degree of
The degree matrix is a matrix of the degrees of the vertices, so that the elements at the main diagonal positions are the vertex degrees and the remaining elements are 0. Accordingly, the directed graph has an entry degree matrix and an exit degree matrix. The adjacency matrix is a matrix used to represent the relationship between vertices. For graph
The core idea of the adjacency table of a graph is to have a neighbor table for each vertex of the vertex set. The association matrix is used to represent the direct association of nodes and edges and is defined as:
The Laplace matrix [30] is a special matrix that is often used in graph theory to study the structural properties of graphs. The Laplace matrix is defined as
Matrix representation of the graph; (a) graph structure; (b) degree matrix; (c) adjacency matrix; (d) Laplace matrix.
Previous classical convolutional networks based on deep learning mostly consider regular data in Euclidean space in processing data. When inputting ordered data with fixed dimensions (e.g., images, speech, video, etc.), the convolutional operation and the capture and compression of the pooling layer make the network fitting effect remarkable. However, when faced with sequentially disordered road network traffic data with variable dimensions, the suitability of the traditional convolution operation decreases. However, graph neural networks (GNNs) can handle the abovementioned irregular graphs by passing node features into the neural network during iteration and outputting the node states. The original GNNs converge the hidden state to a fixed point based on the “immobile point” theory, which is ineffective for extracting edge information, and in the specific scenario represented by the graph, some feature information is shared among nodes due to the fixed convergence, making the actual information obtained scarce. Therefore, two types of Graph Convolutional Network (GCN) based on frequency domain and null domain are generated. Two types of GCN models: null domain convolution is the same as the traditional convolution method, which can convolve directly at the pixel point of the picture; frequency domain convolution needs to start from the graph signal processing, treating the kernel in the convolution as a filter and the learned features as signals for weighted summation.
As shown in Figure 5, the common network framework for graph convolution is illustrated. First, the neighboring nodes of the input graph structure are updated with a layer of convolution operation, and then a layer of ReLU activation function is added to obtain the basic convolution layer plus activation function structure. The above structure is stacked sequentially until the number of stacked layers reaches the prediction of the model, and the output part transforms the node features into labels for the relevant tasks. Unlike GNN circular iterative parameter sharing, GCN is a multilayer stack and the parameters are different for each layer.
General framework of graph convolution.
Further, it mainly includes graph convolution based on the spectral domain (frequency domain) and the null domain. The spectral domain approach is to construct CNN simulations into the spectral domain by considering the localization of graph convolution through spectral analysis, such as Spectral Graph Convolution (SGC), which mainly focuses on the continuous derivation and improvement of the core formulations of spectral graph theory to reduce the computational power of the model from the perspective of optimization parameters. Empty domain methods perform convolution filters directly on the nodes of the graph and their neighborhoods, such as Diffusion Graph Convolution (DGC).
Spectral domain approach [31]: The absence of graph translation invariance poses difficulties in defining convolutional neural networks in the nodal domain. The spectral domain approach uses the convolution theorem to define the graph convolution from the spectral domain. The spectral domain graph convolution network is proposed based on graph signal processing, where the convolution layer of the graph neural network is defined as a filter, i.e., the filter removes the noise signal to obtain the result of the input signal. In practical applications, it can only be used to process graph structures that are undirected and have no information on the edges. The Fourier transform of the signal f(x) and its inverse transform are:
where
where
where
Using the Fourier transform and the inverse transform on the graph, the graph convolution operation can be implemented as follows.
where
To solve the excessive computation of Laplace eigenvalues and eigenvectors, Defferrar et al. [32] proposed ChebNet based on Chebyshev polynomials. The eigenvalue matrix is approximated by Chebyshev polynomials, and the Chebyshev polynomials are as follows.
where
where
After that, Kipf et al. used first-order Chebyshev polynomials and simplified the spectral graph convolution by restricting the parameters in order to make ChebNet have better local connectivity properties. Let
Where:
However, since
The spatial domain approach: spatial-based graph convolutional networks were first proposed in Neural Network for Graphs (NN4G), which is different from the spectral domain graph convolutional neural network from signal processing theory, the spatial domain graph convolutional neural network starts from the nodes in the graph, designs the aggregation function to gather the features of neighboring nodes, adopts the message propagation mechanism, and thinks about how to accurately and efficiently use the features of neighboring nodes of the central node to update the features of the central node. The essence of CNN is weighted summation, and the spatial domain graph convolutional neural network is based on the basic construction process of CNN to accomplish the purpose of GNN aggregation of neighboring nodes from the perspective of summation. Since the nodes in the graph are unordered and the number of neighboring nodes is uncertain, one idea of the spatial domain graph convolutional neural network is (1) to fix the number of neighboring nodes and (2) to sort the neighboring nodes. If the above two tasks are completed, the non-Euclidean structured data becomes ordinary Euclidean structured data, and naturally the traditional algorithm can be completely migrated to the graph. Among them, step (1) also facilitates the application of GNN to graphs with many nodes.
Currently, GCN has become a fundamental model for traffic flow prediction research and a benchmark method for experiments. Although neither the air-domain graph convolution network nor the frequency-domain graph convolution network is proposed for the traffic flow prediction problem, the natural graph structure property of traffic data makes GCN show high efficiency and accuracy in the field of traffic flow prediction than the traditional methods.
This section will first give a specific definition of the traffic flow prediction problem and then give the flow of the traffic flow prediction model based on spatiotemporal characteristics.
Traffic prediction is a typical time series prediction problem [33], and its road network traffic flow data exhibits a high degree of periodicity, which provides a great deal of potential for traffic prediction. Figure 6 shows the traffic data for the first week of December for individual toll stations on the Shaanxi Provincial Freeway, demonstrating a high degree of periodicity.
Periodicity of traffic data.
Given the first
where
Spatiotemporal correlations. (a) Stations in a road network. (b) Dynamic spatial correlations.
Traffic flow prediction by deep learning requires a large amount of data support, that is, real-world road traffic speed data. With the continuous improvement of traffic facilities, the amount of traffic data has also produced an explosive growth. Traffic flow prediction is precisely based on huge traffic data, so understanding the current common traffic data is the basis for achieving traffic flow prediction. The sources of traffic data mainly include road fixed-point detectors, vehicle GPS records, bus IC cards, license plate recognition, cell phone data, etc. We have made the common traffic data used for traffic flow prediction as Table 1.
Data set classification | Data set name | Data fields | Sampling period |
---|---|---|---|
Expressway | PeMS | Timestamp, Station ID, Region. Highway ID, direction, trip | 5 min |
METR-LA | Vehicle speed | 5 min | |
SEATTLE LOOP | Vehicle speed | 5 min | |
Madird Traces | Vehicle track | 0.5 s | |
Los-Loop | Vehicle speed | 5 min | |
Cabs | NYC Taxi | Boarding and alighting times, location, distance traveled, fare, payment type | / |
TaxiBJ21 | GPS data and weather data | 30 min | |
SH-Speed | Vehicle ID, location, operation status, speed | 10 min | |
CRAWDAD | Vehicle ID, time, coordinates | 7 s | |
SZ-taxi | Vehicle speed | 15 min | |
T-Drive | Vehicle track | / | |
Internet taxi | Didi-GAIA-Open-Data | Vehicle speed | / |
Rail Transit | SHMetro HZMetro | People flow | 15 min |
City Road Network | VTC (vant-trace-creteil) | Time, lane, vehicle angle, speed, and vehicle ID | 1 s |
0d_bologna Koln.Tr | Coordinates, speed, vehicle ID | 1 s | |
NYC-Bike | Vehicle ID, coordinates, time | / |
Common data sets for traffic flow prediction models.
Traffic flow data mainly detects parameters such as speed, flow rate, time, etc. The data collection process may result in detection equipment failure, instrument error, software failure, communication interference, environmental noise, etc., and even sudden road failure may have a great impact on the data, resulting in real-time data may be missing or abnormal, so the overall process of validity processing of this type of traffic data according to its type is shown in Figure 8.
Abnormal data processing
Overall flow of data preprocessing.
The preprocessing methods of abnormal data can be divided into two categories: Data rejection. Data rejection can be used when there is less erroneous data in the traffic data. The rejection of individual erroneous data will not affect the integrity and trend of the data, but if the proportion of erroneous data is large, the rejection method cannot be adopted because too much rejection of erroneous data will destroy the integrity of the data and its trend. Peak denoising. Since traffic data is highly nonlinear and the traffic data at peak hours can be very significant, i.e., the noise oscillation region during peak hours, peak denoising is needed. Commonly used methods such as empirical mode decomposition (EMD), i.e., fluctuation decomposition in the local oscillation part of the trend change.
Missing data processing [34]
Missing data is caused by hardware and software factors that do not detect data at the detection end or packet loss during data communication. In road traffic, this can be due to excessive vehicle density and inaccurate data collection by traffic flow detection instruments, data failures in transmission, and many other reasons for gaps in the collected data, such as missing data at a point in time, a certain period, or several periods of time. Typically, there are two classical missing patterns in time series data as shown in Figure 9 below. Figure 9(a) indicates that the exported toll records have randomly lost observations at a single toll station, and the white circles indicate the missing values. Figure 9(b) indicates that there are several consecutive time points in the records of multiple toll stations with no observed values, which is a more common pattern of missing spatiotemporal traffic data. The green curve in the green panel represents the observed values and the gray curve represents the missing values. This situation requires correlating and processing the missing data, and then repairing the data using interpolation and smoothing algorithms, prior to dimensionlessizing the data using initialization operators to consider the fact that the units and orders of magnitude of the characteristic series of influencing factors are not uniform.
Data normalization/normalization
Example of missing pattern of spatiotemporal data (traffic data as an example).
Generally, the obtained traffic data are scattered, and the distribution characteristic curve presented by the data is fuzzy, and the distribution cannot be determined. Therefore, the data do not satisfy the normal distribution and need to be normalized to regularize the data and improve the comparability between the data to facilitate the subsequent model prediction. The data are z-core normalized to approximately satisfy the normal distribution, so that the weights are more evenly distributed in the subsequent model training, i.e.,
where
To solve the problem of non-Euclidean structure of traffic network data, graph neural networks are often used to model spatial dependencies in traffic networks, and then convolution is used to fundamentally improve the efficiency of graph analysis and network construction from frequency and spatial domains, i.e., Graph Convolutional Network (GCN). Graph Convolution extends traditional convolution to graph-structured data, and powerful methods such as graph convolutional networks and their variants are widely used for these spatiotemporal network data prediction tasks with good performance. Most existing graph convolutional traffic flow forecasts are spatiotemporal in nature, since most traffic data sets have both spatial and temporal attributes. The development of traffic flow prediction models based on graph convolutional networks is presented as in Figure 10. In this paper, five of the most typical and most referenced models will be selected for illustration.
Traffic flow prediction model based on graph convolution.
Architecture of spatiotemporal graph convolutional networks.
The STGCN model proposed by Yu et al. [19] (2018AAAI) for the first time uses graph structures to model traffic networks while using graph convolution to model spatiotemporal sequences and uses pure convolutional structures to extract spatiotemporal features from the graph structures simultaneously.
STGCN is composed of several spatiotemporal convolutional blocks, each of which is formed as a “sandwich” structure with two gated sequential convolution layers and one spatial graph convolution layer in between. The framework STGCN consists of two spatiotemporal convolutional blocks (ST-Conv blocks) and a fully-connected output layer in the end. Each ST-Conv block contains two temporal gated convolution layers and one spatial graph convolution layer in the middle. The residual connection and bottleneck strategy are applied inside each block.
The model, although using convolution instead of LSTM-like patterns, does speed up training, but it also leads to missing historical data information and can only achieve short-term prediction, not long-term prediction, and graph convolution captures information between different nodes to model spatial models, which does not seem to make good use of the potential relationships between different regions.
The DCRNN model proposed by Li et al. [22] (2018ICLR) models spatial correlation as a diffusion process on directed graphs, thus modeling the transformation of traffic flow, and proposes diffusion convolution recurrent neural networks that can capture the spatial and temporal dependence between time series using a framework of seq2seq. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic prediction that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph and the temporal dependency using the encoder-decoder architecture with scheduled sampling.
The GMAN model (2020AAAI) proposed by Zheng et al. [23] uses a spatiotemporal attention mechanism to model dynamic spatial relationships and nonlinear temporal relationships separately, while using a gating mechanism to adaptively fuse the information extracted by the spatiotemporal attention mechanism.
Because the whole traffic is a network, the error of one node is amplified by other nodes, which affects the final prediction results. To solve the above problem, GMAN adopts an encoder-decoder architecture, where encoder is used to extract features and decoder to predict. A transformed attention layer is applied in between these two to transform the encoded traffic features to generate a sequential representation of future time steps as the input to the decoder. Here Encoder and Docoder are composed of ST-attention block. Then the authors use an STE block to combine the spatial and temporal information and then input into the ST-ATTENTION block to solve the problem of complex time–space correlation. Finally, the experimental results of the article on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN.
Wu et al. [35] propose in this paper a novel graph neural network architecture, Graph WaveNet, for spatial–temporal graph modeling. The model uses the idea of diffusion convolution in extracting spatial features of road networks and adds a novel adaptive connection matrix to make up for the deficiency of fixed topology in extracting spatial features and employs dilated causal convolution and gate mechanism on time series without the traditional RNNs cycle, which is validated by METR-LA and PEMS-BAY data sets, GWN in terms of training effect and time good results were achieved.
Guo et al. [24] propose a novel attention-based spatial–temporal graph convolutional network (ASTGCN) model to solve traffic flow prediction problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic, and weekly-periodic dependencies. More specifically, each component contains two major parts: 1) the spatial–temporal attention mechanism to effectively capture the dynamic spatial–temporal correlations in traffic data; 2) the spatial–temporal convolution, which simultaneously employs graph convolutions to capture the spatial patterns and common standard convolutions to describe the temporal features. The output of the three components is weighted fused to generate the final prediction results.
These five typical traffic flow prediction models above are compared in Table 2.
System architecture for the diffusion convolutional recurrent neural network designed for spatiotemporal traffic prediction.
Models | Model’s characteristics |
---|---|
STGCN (2018) | (1) Compared with traditional spatio-temporal models (RNN, LSTM) based on recurrent neural networks, the STGCN model combines graph convolution and gated time convolution, and for the first time uses pure convolutional layers to extract time and space information at the same time. (2) The STGCN model uses one-dimensional convolution to learn information in the time dimension and is not limited by the prediction data at the previous time point, so that the model can better capture the drastic changes in the data (such as traffic flow data during peak hours). (3) Due to the characteristics of the convolutional structure, STGCN model is parallelized at the input, with fewer parameters and faster training speed, allowing the model to process large-scale networks with higher efficiency. |
DCRNN (2018) | (1) In view of the dynamic characteristics of traffic flow, DCRNN model introduces diffusion convolution when modeling spatial dependence and considers forward propagation and back propagation and is more suitable for traffic networks with a directed graph structure. (2) When the DCRNN model models the time dependence, the matrix multiplication in the GRU model is changed to diffusion convolution, and the diffusion convolution-gated recurrent unit (DCGRU) is obtained. |
ASTGCN (2019) | (1) The ASTGCN model effectively learns the dynamic spatiotemporal correlation of traffic data through the spatiotemporal attention mechanism. (2) The ASTGCN model designed a multichannel network structure from multiple time periods, combining global time and space information to improve prediction accuracy |
GWN (2019) | (1) In terms of spatial dependency acquisition, The GWN model constructs an adaptive dependency matrix that can retain the implicit spatial relationship of the road network. (2) In the acquisition of time dependence, The GWN model adopts stacked dilated 1D convolution and does not need to consider the problem of information disappearance too long ago and can extract longer time dependence than RNN-based cyclic convolutional networks. |
GMAN (2020) | (1) The GMAN model refines the complexity of time and space. It is divided into dynamic spatial correlations and Non-linear temporal correlations, and the attention mechanism is introduced. (2) The GMAN model solves the cumulative error problem of stepwise prediction. A transformation attention layer is added between the encoder and the decoder, so that historical and predicted traffic characteristics can be converted. |
Characteristics of typical models.
Many researchers have already applied the proposed traffic flow prediction models to various traffic scenarios and achieved excellent results. For example, by predicting the traffic flow of a roadway in advance, it can provide drivers with more advanced travel routes. In addition, it can also provide prerequisites for traffic light optimization, etc. In this paper, three scenarios are chosen to illustrate the application of traffic flow prediction in the context of highways. These three scenarios are the work that has been done by our team so far, and the reason for choosing the highway is that the work in this area is more mature.
Traffic conditions of truck flow is one of the critical factors influencing transportation safety and efficiency, which is directly related to traffic accidents, maintenance scheduling, traffic flow interruption, risk control, and management. The estimation of the truck flow of various types could be better to identify the irregular flow variation introduced by various trucks and quantitatively assessed the corresponding road risks.
Jin et al. [36] first improved on the gated recursive unit (GRU) based on a deep learning approach to estimate various types of truck traffic. Then a multiple logistic regression method was proposed to classify the road risk into three classes: safe, risky, and dangerous. According to the CSV trend, road risks are classified into three categories as shown in Figure 16. Different risk classes can guide traffic control and management and broadcast traffic information to drivers to help them choose their travel routes.
The overall framework of GMAN model. (a) the framework of GMAN. (b) Spatiotemporal embedding. (c) the ST-attention block.
Finally, the road risk calculated by the predicted truck traffic is shown in Figure 17, from which the road risk status can be obtained at every moment.
The framework of graph WaveNet.
In China, the relatively heavy queues at freeway toll booths and service areas during peak hours, coupled with the saturation of manpower scheduled during off-peak hours, are undoubtedly a huge obstacle to efficient and cost-effective freeway operations. Therefore, it needs an intelligent manpower planning strategy to simultaneously ensure the efficiency of highway transportation management and road user satisfaction.
Jin et al. [37] addressed a high-precision prediction of vehicle flow based on historical multisource traffic data. Based on the prediction results, an improved manpower planning strategy is proposed to schedule the work accordingly. And the method was tested on a randomly selected toll station as an example, as Figure 18 shows the daily traffic pattern of the highway Hechizhai toll station.
The framework of ASTGCN. SAtt: Spatial attention.
Coefficient of speed variation (CSV) of passenger cars.
Road risk assessed by predicted truck flow in April 12,018.
Daily traffic pattern of the Hechizhai toll gate on the freeway.
Reversible toll lane configuration suggestion. The red lines refer to the number of toll lanes designed in each direction. The blue and brown gradient lines indicate the change in the number of exit and entrance lanes per hour, respectively. The black dash lines depict the reversible toll lane change period.
Comparison of duplex lane simulation process.
The results show from Figure 19 that the upper part and the lower part show the lane opening at the entrance and exit of toll gates in one week, respectively. Two narrow black dotted lines indicate the morning and evening peak hours. During the morning peak, it is obvious that two entry lanes are not used while the number of toll lanes of the exit has reached its upper threshold. The opposite phenomenon is seen in the evening peak. Therefore, we suggest that one or two of the entrance lanes can be set as a reversible lane so that the traffic pressure can be released in peak hours. Note that the usage condition of this suggestion is that the entrance and exit of the toll gate must be adjacent such as Hechizhai toll gate.
Yuan et al. [38] used the results of traffic prediction to analyze the capacity of a toll station and used different queuing models to describe the capacity of typical lanes and compared the delay time and queue length of each model and obtained that the single-way model is more efficient in a typical system. The traffic index of the multiplex lane is also simulated, and the specific simulation process is shown in Figure 20, and the capacity of the multiplex lane is obtained to be larger than that of the typical MTC lane, which can relieve the traffic pressure during the peak hours.
Traffic is the main driving force of urban development, and real-time and accurate traffic flow prediction is the key to the application of intelligent transportation system. Graph convolutional neural network is an efficient model for processing graph data and has received a lot of attention from researchers in the past few years. This section attempts to summarize the recent graph convolutional neural network models and their applications to traffic flow prediction.
This section summarizes the GCN-based traffic flow prediction model. Starting from the basic definition of graph convolution, the basic principles of GCN are introduced with the focus on frequency-domain graph convolution and space-domain graph convolution. Then, the representative models are clarified, and the structure and characteristics of different prediction models are further categorized and reviewed.
This section provides the traffic prediction problem with constructed traffic graph structure. Some public traffic data sets that are widely used in scientific research worldwide are introduced for traffic prediction experiments, including their data sources, data contents, and data acquisition addresses, and the whole data processing process is analyzed.
The application scenarios of traffic flow prediction are discussed. There are two applications are provided: 1) with prediction of the traffic flow of truck, the transportation safety and efficiency could further assess. 2) The work schedule arrangement could also improve based on the prediction of traffic flow to avoid manpower waste and allow more passing gate to open in the peak hours. Other than those applications, there still many aspects worth to explore.
Thanks to the following researchers for their great support in the writing of this book, especially Wenbang Hao, Erlong Tan, Wanrong Xu, Zhen Jia, Yiwen Gao, Yajie Zhang, etc., who have put a lot of energy into many formulas and illustrations.
This work was supported in part by the National Key R&D Program of China (2020YFB1600400), Key Research and Development Program of Shaanxi Province (No.2020GY-020), National Natural Science Foundation of China (Grant No. 51505037) and Supported by the Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102320305).
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He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",country:{name:"Spain"}}},{id:"342152",title:"Dr.",name:"Santo",middleName:null,surname:"Grace Umesh",slug:"santo-grace-umesh",fullName:"Santo Grace Umesh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/342152/images/16311_n.jpg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"333647",title:"Dr.",name:"Shreya",middleName:null,surname:"Kishore",slug:"shreya-kishore",fullName:"Shreya Kishore",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333647/images/14701_n.jpg",biography:"Dr. Shreya Kishore completed her Bachelor in Dental Surgery in Chettinad Dental College and Research Institute, Chennai, and her Master of Dental Surgery (Orthodontics) in Saveetha Dental College, Chennai. She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\r\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\r\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Orthodontist, Assoc Prof in the Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"344229",title:"Dr.",name:"Sankeshan",middleName:null,surname:"Padayachee",slug:"sankeshan-padayachee",fullName:"Sankeshan Padayachee",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"315727",title:"Ms.",name:"Kelebogile A.",middleName:null,surname:"Mothupi",slug:"kelebogile-a.-mothupi",fullName:"Kelebogile A. Mothupi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"337613",title:"Mrs.",name:"Tshakane",middleName:null,surname:"R.M.D. Ralephenya",slug:"tshakane-r.m.d.-ralephenya",fullName:"Tshakane R.M.D. Ralephenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}}]}},subseries:{item:{id:"28",type:"subseries",title:"Animal Reproductive Biology and Technology",keywords:"Animal Reproduction, Artificial Insemination, Embryos, Cryopreservation, Conservation, Breeding, Epigenetics",scope:"The advances of knowledge on animal reproductive biology and technologies revolutionized livestock production. Artificial insemination, for example, was the first technology applied on a large scale, initially in dairy cattle and afterward applied to other species. Nowadays, embryo production and transfer are used commercially along with other technologies to modulate epigenetic regulation. Gene editing is also emerging as an innovative tool. This topic will discuss the potential use of these techniques, novel strategies, and lines of research in progress in the fields mentioned above.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11417,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). She is the head of the Reproduction and Embryology Laboratories and was lecturer of Reproduction and Reproductive Biotechnologies at Veterinary Medicine Faculty. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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