Characteristics of the testing greenhouses.
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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.
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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 an internationally recognized surgical specialty rapidly changing with evolving advancements in technology. Specialists of this field care for patients with problems such as impacted teeth, facial pain, misaligned jaws, facial injuries, oral cancer, cysts, tumors, and patients requiring facial cosmetic surgery and dental implants. New texts are needed to keep practitioners up-to-date because advancements are being made world-wide on a daily basis. This book seeks to present advanced concepts on complex topics within the scope of this dynamic discipline.",isbn:null,printIsbn:"978-953-51-1146-7",pdfIsbn:"978-953-51-7166-9",doi:"10.5772/3316",price:169,priceEur:185,priceUsd:219,slug:"a-textbook-of-advanced-oral-and-maxillofacial-surgery",numberOfPages:872,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"b5d9f2be309d43646fca5ce2cd1e3d19",bookSignature:"Mohammad Hosein Kalantar Motamedi",publishedDate:"June 26th 2013",coverURL:"https://cdn.intechopen.com/books/images_new/2988.jpg",numberOfDownloads:191718,numberOfWosCitations:36,numberOfCrossrefCitations:22,numberOfCrossrefCitationsByBook:4,numberOfDimensionsCitations:66,numberOfDimensionsCitationsByBook:4,hasAltmetrics:1,numberOfTotalCitations:124,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 24th 2012",dateEndSecondStepPublish:"June 14th 2012",dateEndThirdStepPublish:"September 10th 2012",dateEndFourthStepPublish:"October 10th 2012",dateEndFifthStepPublish:"November 9th 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"90148",title:"Dr.",name:"Mohammad Hosein",middleName:"Kalantar",surname:"Motamedi",slug:"mohammad-hosein-motamedi",fullName:"Mohammad Hosein Motamedi",profilePictureURL:"https://mts.intechopen.com/storage/users/90148/images/system/90148.jpg",biography:"Dr. Mohammad Hosein Kalantar Motamedi is a Professor of Oral and Maxillofacial Surgery at the Trauma Research Center, BMSU, and attending faculty of OMF Surgery at the Azad University of Medical Sciences, Tehran. He received his American Diploma from Pennington High School, in Pennington, VA, USA (honor student) and his Iranian Diploma from Hurr High School in Tehran (honor student). After graduation he was accepted at the University of Houston, Texas, USA where he studied predent. Two years later he transferred to the University of Tehran College of dentistry where he received his Doctorate of Dental Surgery Degree (DDS). He completed his Residency in Oral and Maxillofacial Surgery at SBUMS from 1987-1991, and was valedictorian of the National Board Exams in 1991. In 1995 he completed his fellowship at the University of Basel, Switzerland; he obtained full Professorship in 2007. He has published 25 books, 61 international conferences proceedings, supervised 63 doctorate dissertations, and published 185 papers indexed in PUBMED with an h-index of 21. He is Editor-in-Chief of TRAUMA MONTHLY journal and one of the founders of the BMSU Trauma Research Center and more recently the Craniomaxillofacial Research Center. He was granted 1st place prize at the Bagher Alum National Research Festival in 2010 and awarded 2nd Place Prize at the 16th International Razi Research Festival on Medical Sciences. The Iranian Society of OMF Surgeons awarded him “Most Published” in 2009 and 2010, and he was chosen for membership in the National Elite Foundation. In 2013 he was accepted in the Academy of Medical Sciences and he is listed in the Marquis Book \\'Who’s Who in the World” (1999-2020).",institutionString:"BMSU and AUMS",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"7",totalChapterViews:"0",totalEditedBooks:"5",institution:{name:"Baqiyatallah University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1149",title:"Oral and Maxillofacial Surgery",slug:"oral-and-maxillofacial-surgery"}],chapters:[{id:"44949",title:"Complications Following Surgery of Impacted Teeth and Their Management",doi:"10.5772/53400",slug:"complications-following-surgery-of-impacted-teeth-and-their-management",totalDownloads:18721,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:1,abstract:null,signatures:"Çetin Kasapoğlu, Amila Brkić, Banu 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\r\n\r\n\tThe book will discuss integrated management of plant diseases and plant protection, with an emphasis on phytopathogenic fungi, biocontrol, plant genetics and resistance.
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He is a Winner of the 2011 Top Ethanol Award and Winner of the Top Science Award in 2008 and 2013.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"146372",title:"Dr.",name:"Fernando",middleName:"Cezar",surname:"Cezar Juliatti",slug:"fernando-cezar-juliatti",fullName:"Fernando Cezar Juliatti",profilePictureURL:"https://mts.intechopen.com/storage/users/146372/images/system/146372.jpg",biography:"Dr Fernando Cezar Juliatti is a professor and researcher, born in Ribeirão Vermelho Minas Gerais state, Brazil. He obtained a degree in Agronomy from the School of Agriculture of Lavras/UFLA (1981), a master's degree in Agronomy (Phytopathology) from the Federal University of Viçosa/UFV (1985) and a PhD in Agronomy (Plant Breeding) from Federal University of Lavras/UFLA (1994). He was head of the Department of Agronomy at Federal University of Uberlândia (UFU) in 1995 and 1996 . From 1998 to 2000, he presided over the implementation of the Graduate Program in Agronomy at Federal University of Uberlândia (master's and doctorate). He was director of the Institute of Agricultural Sciences at Federal University of Uberlândia from 2001 to 2004. He was president of the Brazilian Society of Phytopathology in the period from 2003 till 2004. Dr Juliatti is a member of the Brazilian Societies of Phytopathology, Plant Breeding and Brazilian Horticulture in addition to the Paulista Group of Phytopathology. He is technical consultant in the areas of corn and soybeans/ phytopathological problems in the Brazilian savannah. \nHe and the Plant Improvement team at Federal University of Uberlândia developed strains of soybean, tomato and beans with multiple resistance to phytopathogens. Together with this team, he launched 05 protected soybean cultivars for the Brazilian savannah with multiple resistance to important soybean pathosystems, including partial resistance to Soybean Asian rust. \nHe chaired the organizing committee of the Brazilian Congress on Agroenergy and the First International Symposium on Biofuel. He coordinated the Chamber of Agronomy of CREA-MG (in 2008 and 2009) and the National Coordination of Agronomy of the CREA / CONFEA system (in 2009). He is a coordinator of the Latin American Committee on studies on Sclerotinia (Sclerotinia International Working Group). Dr Juliatti is a Financial Director of SMEA (Minas Gerais Society´ of the Agronomy Engineers) and current member of the Fiscal Council of the same entity. He is also President of ABEAS (Brazilian Association of Higher Agricultural Education - Triennium 2011-2013 and 2013-2016). He is Winner of the 2011 Top Ethanol Award in the area of technological innovation in industrial energy offered by UNICA and Winner in 2008 and 2013 of the Top Science Award offered by the company Basf. S.A.\nDr Juliatti is a permanent professor in the Graduate Programs in Agronomy (UFU, Brazil - Master and Doctorate) and Biofuels (UFU-UFVJM - Master and Doctorate). He was the Technical Director for four terms at the Association of Agricultural Engineers of Triângulo Mineiro and Alto Paranaíba, Minas Gerais, Brazil. He was President of the Triângulo Mineiro and Alto Paranaíba Association (AGROTAP), from 2017 to 2018 . In 2017 he was President of 50 Brazilian Congress of Phytopathology (Golden Jubilee) and 16 International Workshop on Sclerotinia and II Brazilian Workshop on Soybean Rust. Currently he is coordinator of the UFU Agronomy Course at the Campus Uberlândia. He published more 200 articles in important journals from Brazil and other countries, 11 books and 12 chapters in the field of Agronomy.",institutionString:"Federal University of Uberlândia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"13",title:"Immunology and Microbiology",slug:"immunology-and-microbiology"}],chapters:[{id:"80375",title:"Trichoderma: A Biofertilizer and a Bio-Fungicide for Sustainable Crop Production",slug:"trichoderma-a-biofertilizer-and-a-bio-fungicide-for-sustainable-crop-production",totalDownloads:131,totalCrossrefCites:0,authors:[null]},{id:"81107",title:"Can Genus Trichoderma Manage Plant Diseases under Organic Agriculture?",slug:"can-genus-trichoderma-manage-plant-diseases-under-organic-agriculture",totalDownloads:85,totalCrossrefCites:0,authors:[null]},{id:"80357",title:"Trchoderma Spp.: Their Impact in Crops Diseases Management",slug:"trchoderma-spp-their-impact-in-crops-diseases-management",totalDownloads:77,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"418641",firstName:"Iva",lastName:"Ribic",middleName:null,title:"M.Sc.",imageUrl:"https://mts.intechopen.com/storage/users/418641/images/16830_n.png",email:"iva.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. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"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:"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"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.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:"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"}}]},chapter:{item:{type:"chapter",id:"49871",title:"Mitigating Greenhouse Gas Emissions from Winter Production of Agricultural Greenhouses",doi:"10.5772/62271",slug:"mitigating-greenhouse-gas-emissions-from-winter-production-of-agricultural-greenhouses",body:'Agricultural buildings, such as horticultural greenhouse, usually require additional heating during winter and cold days in high latitude regions of the Northern Hemisphere [1, 2]. In northern China and many European countries, coal-fired heating system (CFHs) and the natural gas-fired heating system (GFHs) are dominant heating methods in greenhouses [3, 4]. However, conventional fossil fuels, such as coal, natural gas, and oil, which are nonrenewable and are the major greenhouse gas (GHG) contributors, may lead to the global climate change, air pollution, and energy crisis [5-7].
Renewable and clean energy, such as solar, geothermal, and shallow geothermal energy (SGE), has been increasingly applied to replace fossil energy systems in heating agricultural buildings (especially in urban area) across the world [8-11]. The SGE is mainly the stored solar energy in groundwater and soil layers less than 200 m deep from the earth soil surface [12, 13]. It can be used as heat source or sink for air conditioning in residential, industrial, and agricultural buildings with the ground source heat pump system (GSHPs), also known as geothermal heat pumps (GHPs) [4, 14].
The GSHPs has been applied to heat agricultural greenhouse in many countries [15-18]. The GSHPs could be considered with zero GHG emissions if the electricity was the only energy source that could be consumed by the system. However, producing electricity in coal-fired power plant (CFPP) or gas-fired power plant (GFPP) would emit a large quantity of GHG (e.g., CO2). Besides, the refrigerant (e.g., R22 and R134a) used by the heat pump unit has been reported with the high risk of leaking in the year-round operation [19]. Therefore, assessing GHG emissions of the GSHPs should consider both direct and indirect sources.
In northern China (the area with altitude higher than 30° in the Northern Hemisphere) [20], there was mainly two kinds of horticultural greenhouses: the Chinese solar greenhouse (denoted as G1), which may or may not require assisted heating depending on the building design and the plants be cultivated, and the multispan greenhouses (denoted as G2), which require 100% assisted heating systems (primarily in the form of coal burning or gas burning) during winter time [21, 22]. The Chinese solar greenhouse, characterized with east-west orientation, transparent camber south roof, and solid north roof and east and west walls, usually has higher heat-preserving capacity than multispan greenhouse and requires less heating [23, 24]. However, the healthy growth of thermophilic vegetables, such as cucumber and tomato, and most flowers in Chinese solar greenhouse still requires assisted heating especially during cold winter nights or consecutive days of snowing or cloudy [3].
By the end of 2007, about 19,300 ha of greenhouses and tunnels had been constructed and used in Beijing, the capital city of China [25, 26], primarily for producing vegetables, flowers, and fruits. About 6000 ha Chinese solar greenhouses (the structure similar to G1) and 1000 ha multispan greenhouses (the structure similar to G2) may require assisted heating in winter with the systems of the CFHs and GFHs [27]. Therefore, quantifying the heating rate and GHG emission rate for the primary types of agricultural greenhouses with different heating systems and energy sources is important for developing the national or regional GHG emissions inventory of the greenhouse heating and mitigation strategies.
The objectives of this chapter are to (1) address the environmental concern on agricultural production over winter; (2) quantify the heating loads and the GHG emission rates for the two primary agricultural greenhouses (the G1-Chinese solar greenhouse and the G2-multispan greenhouses) in northern China; (3) assess the annual GHG emissions inventory of the greenhouse heating with different energy sources in Beijing, the capital city of the China; and (4) identify the difference between the shallow geothermal energy and the conventional fossil energy systems in GHG emissions of agricultural greenhouses heating.
A Chinese solar greenhouse (G1, Figure 1 and Table 1) and a multispan greenhouse (G2, Figure 2 and Table 1), two important types of greenhouse in Northern China, were equipped with the groundwater-type GSHPs (Figure 3 and Table 2) in Beijing (latitude 39°40′N) and tested for developing heating rate and GHG emission rate. Performances of GHSPs were compared to CFHs and GFHs. In addition, different electricity generation methods (e.g., coal and gas power plant) were considered for assessing the GHG emissions of the GSHPs.
The Chinese solar greenhouse (G1)
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Chinese solar greenhouse (G1) | \n\t\t||
(1) North wall | \n\t\t\t240 mm clay brick+100 mm polystyrene heat preservation layer+240 mm clay brick | \n\t\t\t150 | \n\t\t
(2) East and west end walls | \n\t\t\t240 mm clay brick+100 mm polystyrene heat preservation foam board+240 mm clay brick | \n\t\t\t36 | \n\t\t
(3) South roof | \n\t\t\t0.15 mm single layer transparent polyethylene (south roof was covered with 10 mm needled felt heat blanket at winter nights) | \n\t\t\t510 | \n\t\t
(4) North roof | \n\t\t\t50 mm steel plate+100 polystyrene heat preservation layer+50 mm steel plate | \n\t\t\t108 | \n\t\t
(5) Floor | \n\t\t\tBare soil (clay) | \n\t\t\t480 | \n\t\t
Multispan greenhouses (G2) | \n\t\t||
(1) North wall | \n\t\t\t5 mm coated steel sheet+100 mm polystyrene heat preservation layer+5 mm coated steel sheet | \n\t\t\t126 | \n\t\t
(2) East and west wall | \n\t\t\t20 mm double-layer glass | \n\t\t\t110 | \n\t\t
(3) South wall | \n\t\t\t20 mm double-layer glass | \n\t\t\t98 | \n\t\t
(4) North roof | \n\t\t\t20 mm double-layer glass | \n\t\t\t108 | \n\t\t
(5) Floor | \n\t\t\tBare soil for planting with concrete walkway (floor area was covered with aluminum film at horizontal height of 3.5 m at winter nights) | \n\t\t\t756 | \n\t\t
Characteristics of the testing greenhouses.
The multispan greenhouse.
The groundwater type of GSHPs. (I is the stage of SGE extraction, II is the stage of SGE promotion, and III is the stage of greenhouse heating.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
(1) Compressor | \n\t\t\tManufacturer: four Danfoss hermetic scroll compressor; rated power of electric motor driving: 16.08×4 kW; refrigerant: 58 kg R22. Rate of refrigeration capacity: 380 kW; rate of heating capacity: 450 kW. | \n\t\t
(2) Condenser | \n\t\t\tHorizontal shell–tube model | \n\t\t
(3) Evaporator | \n\t\t\tDry evaporator model | \n\t\t
(4) Throttle | \n\t\t\tCopper capillary tube | \n\t\t
(5) Fan-coil | \n\t\t\tIn G1: FP-136; rated input power: 56 W; number: 6. In G2: 42VM006; rated input power: 87 W; number: 45 | \n\t\t
(6) Pumps | \n\t\t\tFlux: 33.2 m3 h−1; rated input power: 11 kW; number: 4 | \n\t\t
(7) Control system | \n\t\t\tPLC touching screen controller | \n\t\t
Characteristics of the GSHPs.
The GSHPs started to heat greenhouses on October 15, 2007 and ended on February 4, 2008. Cucumbers and strawberries were grown in G1 and cotton was grown in G2 during heating tests. The fan coil units in two greenhouses were controlled by the T-type thermocouple controllers automatically. For G1, the indoor air temperature was controlled in the range of 18°C–20°C, and for G2, the indoor air temperature was controlled in the range of 18°C–22°C, considering the poor thermal stability caused by the larger volume in G2.
The heat provided by the GSHPs for heating greenhouses was quantified with the thermal flow meter (TFM) (Model DN35 and DN100, Beijing Jingyuan Liquid Apparatus Company, Beijing, China). A weather station (Qingsheng Electronic Science and Technology Co. Ltd., Handan, China) installed in the agriculture station was used to monitor outdoor environmental factors. Total electricity consumption of the GSHPs was recorded with watt-hour meter (Shanghai Huaxia Ammeters Manufactory, Shanghai, China).
There are six kinds of gases (Table 3) highlighted as the GHG in Intergovernmental Panel on Climate Change (IPCC 2006) [5]. For the greenhouse heating with the CFHs and GFHs, carbon dioxide (CO2) is the only GHG to be considered. But for the GSHPs greenhouse heating, most electricity consumed (in Beijing area) was generated in the CFPP or the GFPP, and the process of the power generation could emit large amount of CO2 [25]. Besides, it has been reported that the leaking fraction of the refrigerant (i.e., R22(HFC-22) in this study) used in the GSHPs is around 0.02 kg−1 (2%) per year [19]. The R22 was not listed as one of the six primary GHG in the IPCC (2006), but it was reported with 1.28 times of the global warming potential (GWP) of the R134a (HFC-134a) [28]. Therefore, the GWP of R22 was estimated to be 4902, 1830, and 557 based on 20a, 100a, and 500a, respectively, based on the relationship of the GWP with the R134a.
GHG | \n\t\t\t20a | \n\t\t\t100a | \n\t\t\t500a | \n\t\t
CO2\n\t\t\t | \n\t\t\t1 | \n\t\t\t1 | \n\t\t\t1 | \n\t\t
CH4\n\t\t\t | \n\t\t\t72 | \n\t\t\t25 | \n\t\t\t7.6 | \n\t\t
N2O | \n\t\t\t289 | \n\t\t\t298 | \n\t\t\t153 | \n\t\t
HFCs(HFC-134a) | \n\t\t\t3830 | \n\t\t\t1430 | \n\t\t\t435 | \n\t\t
PFCs (PFC-116) | \n\t\t\t8630 | \n\t\t\t22,800 | \n\t\t\t32,600 | \n\t\t
SF6 | \n\t\t\t16,300 | \n\t\t\t22,800 | \n\t\t\t32,600 | \n\t\t
GWP of different greenhouse gases
Note: Data were cited from IPCC 2006[5], expressed in CO2 eq.
The GHG emissions from heating G1 and G2 with the GSHPs can be quantified with Eq. 1.
where HEGSHPs,G, total energy provided by the GSHPs for heating G1 and G2 during whole winter (monitored with TFM), MJ; HEGSHPs,G
The carbon (C) or natural gas (assumed to be 100% as CH4 in calculating CO2 emissions) consumed to produce the electricity consumed by the GSHPs was estimated for the CFPP and GFPP based on Eq. 2.
where ELEGSHPs,G
The GHG emissions from the GSHPs were compared with two primary greenhouse heating systems used in northern China: the CFHs and the CFHs. The equivalent quantity of CO2 emissions from the GSHPs, CFHs, and GFHs was quantified based on the same heat energy provided by the GSHPs during whole winter for the G1 and G2 (Eqs. 3–6). In northern China, the heating efficiencies of CFHs and GFHs were considered as 0.6 and 0.8, respectively [27, 29].
where EMCFHs, CO2 emissions from greenhouse CFHs, kg CO2; EMGFHs, CO2 emissions from greenhouse GFHs, kg CO2;
Under the normal temperature and atmospheric pressure (288 K and 1 atm), burning a kilogram of the standard coal (C) and natural gas (CH4) in oxygen (O2) completely has potential to emit 3.67 and 2.75 kg CO2, respectively, with Eqs. 7 and 8 [30]. Meanwhile, the
Total GHG emissions from greenhouses heating in Beijing can be estimated based on the total area of greenhouses (similar to G1 or G2) and heat rate per square greenhouse floor (Eqs. 9–11):
Where,
Total heat energy provided by the GSHPs for G1 and G2 were 149270.4 and 640659.1 MJ during 2007–2008 winter at the electricity consumptions of 10826.1 and 44372.2 kWh, respectively. The electricity consumed by the GSHPs usually came from the power plants of CFPP and GFPP in northern China. Therefore, the difference between the CFPP and GFPP in producing GHG at different stages of the SGE in the GSHPs was compared (Table 4).
\n\t\t\t | \n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Electricity from CFPP | \n\t\t||||
G1 electricity consumption, kWh | \n\t\t\t2440.6 | \n\t\t\t7135.5 | \n\t\t\t1250.3 | \n\t\t\t10826.4 | \n\t\t
G1 standard coal consumption, kg C | \n\t\t\t1095.5 | \n\t\t\t3202.7 | \n\t\t\t561.2 | \n\t\t\t4859.4 | \n\t\t
G1 GHG emissions, kg CO2 eq. | \n\t\t\t4020.3 | \n\t\t\t11754.0 | \n\t\t\t2059.5 | \n\t\t\t17833.8 | \n\t\t
G2 electricity consumption, kWh | \n\t\t\t10003.0 | \n\t\t\t29245.0 | \n\t\t\t5124.2 | \n\t\t\t44372.2 | \n\t\t
G2 standard coal consumption, kg C | \n\t\t\t4489.8 | \n\t\t\t13126.4 | \n\t\t\t2300.0 | \n\t\t\t19916.2 | \n\t\t
G2 GHG emission, kg CO2 eq. | \n\t\t\t16477.4 | \n\t\t\t48174.0 | \n\t\t\t8440.9 | \n\t\t\t73092.3 | \n\t\t
Electricity from GFPP | \n\t\t||||
G1 electricity consumption, kWh | \n\t\t\t2440.6 | \n\t\t\t7135.5 | \n\t\t\t1250.3 | \n\t\t\t10826.4 | \n\t\t
G1 natural gas consumption, kg CH4\n\t\t\t | \n\t\t\t397.7 | \n\t\t\t1162.8 | \n\t\t\t203.7 | \n\t\t\t1764.2 | \n\t\t
G1 GHG emissions, kg CO2 eq. | \n\t\t\t1093.7 | \n\t\t\t3197.6 | \n\t\t\t560.3 | \n\t\t\t4851.6 | \n\t\t
G2 electricity consumption, kWh | \n\t\t\t10003.0 | \n\t\t\t29245.0 | \n\t\t\t5124.2 | \n\t\t\t44372.2 | \n\t\t
G2 natural gas consumption, kg CH4\n\t\t\t | \n\t\t\t1630.0 | \n\t\t\t4765.6 | \n\t\t\t835.0 | \n\t\t\t7230.7 | \n\t\t
G2 GHG emission, kg CO2 eq. | \n\t\t\t4482.6 | \n\t\t\t13105.4 | \n\t\t\t2296.3 | \n\t\t\t19884.3 | \n\t\t
GHG emission from G1 and G2 heating with GSHPs.
Note: (1) G1=480m2, heated with GSHPs for 146 days; G2=756 m2, heated with GSHPs for 111 days. (2) In calculating the CO2 emissions, the CH4 was assumed to be 100% chemical component of the natural gas.
In producing the amount of the electricity consumed by the GSHPs for G1 and G2, about 4.9 and 20 t of coal (C) were consumed in CFPP. If use the GFPP produced electricity, the total natural gas burned could be 1.8 and 7.2 t (CH4). During 2007–2008 winter heating, the GWP of the GHG emissions from G1 and G2 (Figure 4) was estimated to be 18.3 and 74.9 t CO2 eq. with the CFPP and 5.3 and 21.7 t CO2 eq. with the GFPP, respectively, over a 20-year time horizon. The GWP of 100a was 1.5%–1.6% lower than 20a for G1 and 5.3%–5.4 % lower than 20a for G2 due to the reduced GWP on R22. Similar to HCFC-22, R22 has shorter atmospheric lifetime [5]. Generally, the CO2 eq. contributed by the leak of R22 accounted for 2.4% and 8.4% in the scenarios of CFPP and GFPP, respectively.
GWP of the GHG emissions derived from the GHSPs heating in G1 and G2.
Among different stages of the SGE flow, most GHG emissions (66%) happened at the stage of SGE promotion due to the higher consumption of electricity in compressors. Therefore, improving the efficiency of the compressors has the potential to reduce the GHG emissions from the GHSPs heating.
Providing G1 and G2 with the same quantity of heat that the GSHPs has provided (i.e., 149270.4 and 640659.1 MJ) requires the CFHs to consume 8.49 and 36.40 t of standard coal and the GFHs to consume 3.55 mg (4964 m3 at 288 K and 1 atm) and 15.22 t (21,304 m3 at 288 K and 1 atm) of natural gas (CH4), respectively. Accordingly, the GHG emissions from heating G1 and G2 (Figure 5) were estimated to be 32.7 and 133.7 t CO2 eq. for the CFHs system and 9.8 and 41.8 t CO2 eq. for the GFHs system.
The GHG emissions from the GHSPs and the fossil energy systems.
The unit electricity consuming rate of the GSHPs was 0.15 and 0.53 kWh m−2 d−1 for heating G1 and G2 and which can be standardized as 1500 and 5300 kWh ha−1 d−1 in Beijing during 2007–2008 winter. The 20a and 100a GWPs of the GHG emissions from the GSHPs heating for G1 and G2 (Figure 6a) were 0.076–0.893 and 0.072–0.879 g CO2 eq. m−2 d−1, respectively. The GHG emission rate of G2 is 3.42 times of G1 because Chinese solar greenhouse has better heat-preserving capacity than multispan greenhouses.
Regarding to the CFHs and GFHs, the standardized GWPs of the GHG emissions (Figure 6b) were 0.142–1.214 CO2 eq. m−2 day−1, and there were no difference between 20a and 100a because the GWP of the CO2 will not change with the time.
Standardized GHG emissions (GWP) for different greenhouse types and energy sources.
According to the areas of the G1 and G2 in Beijing that require assisted heating (6000 ha Chinese solar greenhouses-G1 and 1000 ha multispan greenhouses-G2), the total GHG emissions from greenhouses heating with the CFHs or GFHs were quantified as 5238 or 2294 Mt CO2 eq. in Beijing, and there is no difference between 20a and 100a GWP (Figure 7).
Total GHG emissions from heating greenhouses with fossil energies in Beijing.(G1-CFHs, heating all the G1-type greenhouses in Beijing with CFHs; G2-CFHs, heating all the G2-type greenhouses in Beijing with CFHs; G1-GFHs, heating all the G1-type greenhouses in Beijing with GFHs; G2-GFHs, heating all the G2-type greenhouses in Beijing with GFHs).
The total GHG emissions from heating greenhouses in Beijing with the GSHPs were quantified as 1658 and 2909 Mt CO2 eq., based on 20a GWP or 1619 and 2839 Mt CO2 eq. based on 100a GWP (Figure 8). The GHG emissions from heating G1-type greenhouses are higher than heating G2-type of greenhouses due to the large area of the G1 built and used in Beijing during 2007–2008 winter.
Total GHG emissions from heating greenhouses with the GSHPs in Beijing (G1-CFPP, heating all the G1-type greenhouses in Beijing with the GSHPs be powered with the electricity generated in CFHs; G2-CFPP, heating all the G2-type greenhouses in Beijing with the GSHPs be powered with the electricity generated in CFHs; G1-GFPP, heating all the G1-type greenhouses in Beijing with the GSHPs be powered with the electricity generated in GFHs; G2-GFPP, heating all the G2-type greenhouses in Beijing with the GSHPs be powered with the electricity generated in GFHs).
Applying the GSHPs to heat G1 and G2 with the electricity from the CFPP, the equivalent CO2 emissions were 43% and 44% lower than directly burning coal with the CFHs but were 46.4% and 44.2% higher than the GFHs that burning natural gas. However, when using the GFPP generated electricity to run the GSHPs, the equivalent CO2 emissions would be 83.5% and 83.8% lower than directly burning coal with the CFHs and were 45.9% and 48.1% lower than the GFHs that burning natural gas.
It was assumed that all the solar greenhouse and multispan greenhouses with the same heating rate of G1 and G2 in this study, which would lead to errors due to the varying structures and materials in different greenhouses. For the solar greenhouses with improved wall materials and structures, the heat loss and heating rate would be lower [31]. Besides, heat-preserving technologies, such as multilayer aluminum foil heat reflecting materials, would have lower heat loss and heating rate than G2 in this study [3].
The shallow geothermal heat used in the GSHPs came from the groundwater (14°C), which has different GHG emissions from the borehole or U-tube-based HPs [19, 32]. Besides, the leaking factor of R22 was assumed to be 2% per year based on European studies, which may be changing with the change of the pump unit and maintenance of the system.
The GHG emissions calculated in this study are based on the real heating quantity required by G1 and G2 during winter heating, and the cycle of the SGE from extraction, enhance, and greenhouse heating was considered, the analysis can be considered as a partial life cycle assessment (LCA). However, a full LCA analysis could be applied to account the GHG emissions from greenhouse constructing with different materials, the transportation of the coal or natural gas for the location of the greenhouses, and the plants cultivated in the greenhouses [33-35].
In this study, we assumed that all the G1-type Chinese solar greenhouses would need additional heating in calculating the GHG emissions. However, novel structures and materials were applied for building Chinese solar greenhouses in Beijing in recent years [36], which improved the heat-preserving capacity of the greenhouse so that heating was not required in winter time. Therefore, the GHG emissions from heating Chinese solar greenhouse could be lower than the amount calculated in this study.
The unit electricity consuming rate of the GSHPs were 0.15 and 0.53 kWh m−2 d−1 for heating the Chinese solar greenhouse (G1) and multispan greenhouse (G2) or expressed as 1500 and 5300 kWh ha−1 d−1 in Beijing. The 20a and 100a GWPs of the GHG emissions from the GSHPs heating for G1 and G2 were 0.076–0.893 and 0.072–0.879 g CO2 eq. m−2 d–1, respectively.
The total GHG emissions from heating greenhouses in Beijing with the GSHPs were quantified as 1658–2909 Mt CO2 eq. Among different stages of the SGE flow, most GHG emissions (66%) happened at the stage of SGE promotion due to the higher consumption of electricity in compressors.
The total GHG emissions from greenhouses heating with the CFHs or GFHs were quantified as 5238 and 2294 Mt CO2 eq. in Beijing, respectively. Applying the GSHPs to heat G1 and G2 with the electricity from the CFPP, the equivalent CO2 emissions were 43% and 44% lower than directly burning coal with the CFHs but were 46.4% and 44.2% higher than the GFHs that burning natural gas. However, when using the GFPP-generated electricity to run the GSHPs, the equivalent CO2 emissions would be 83.5% and 83.8% lower than directly burning coal with the CFHs and were 45.9% and 48.1% lower than the GFHs that burning natural gas.
The glass-covered G2 consumed more heating energy than G1 during the heating period. This demonstrated that the Chinese solar greenhouse design had better heat preservation than the glass greenhouse. Besides, novel structures and materials applied for building Chinese solar greenhouses in Beijing could further reduce the GHG emissions from heating.
The study was sponsored by the “Beijing Natural Science Foundation (6132011),” “Young Researcher Foundation (QNJJ201212)” in Beijing Academy of Agricultural and Forestry Sciences, “Earmarked Fund for Modern Agro-industry Technology Research System (CARS-25-D-04),” and “Twelve-Five-Year National Science and Technology Support Program (2011BAD12B01).”
We also thank the support from the program of "Promotion network of research and application services on vegetables varieties (KJCX20140416)".
Civil engineering practitioners such as researchers, engineers, technicians and those interested in it have always favored the best concrete formulas based on classical or modern methods. The methods used were either experimental (laboratory tests), or empirical or semi-empirical method or analytical methods. The current results of concrete production always indicate that they are oriented towards modern methods which are mainly based on numerical modeling [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
Scientific research in the field of civil engineering, as in many other applied sciences and technologies, is a major use of its implementation according to modern tools, in particular calculation tools [1, 3]. The main objective of this study is to discover the best methods that help in the economic aspect, in particular the building materials when testing, in addition to saving time and effort lost in their realization without taking them into account. Thus, it was more interesting to use technology (electronics and computer) with analytical methods and concrete models to achieve experimental methods.
On the other hand, our main goal is to take advantage of recent studies [5, 6, 7, 8, 9, 10] that rely on granular distribution to quantify aggregate dosages. According to the parameters of the fractal dimension FD [1, 2, 3, 4] and the granular extent D/d [2].
On the other hand, we emphasize the importance of extending the determination of the granular distribution by using the fractal distribution as a new model to determine the granular mix class of concrete. Our experimental results, which we adopted in this study, allow us to determine a numerical value as one of the physical properties of the aggregate, which is the fractal dimension (FD). It facilitates the formulation of concrete by precisely specifying the components of the aggregate. Our objective in this study is to create a large database which helps us to save time and materials in experimental studies within the framework of concrete formulation methods to determine dosages of granular materials, which is certainly useful in the field of civil engineering. Initially, we rely on the study of a component to facilitate the process and start from the simplest operations. We took, for example, the study of the effect of the sand component [4]. It should be noted that this work is mainly based on the data, which takes the aggregate as the basic component in the production of concrete so that as it is known, 80% of the concrete is composed of aggregate, in order to obtain a good granular distribution (continuous granulometry). We emphasize that the appropriate selection of aggregates according to the desired concrete requirements allows us to achieve one of the most important characteristics that distinguish concrete, which is the compressive strength of concrete [3, 4]. In addition, the new definition of granularities by fractal dimension helps in choosing the classes of aggregate to be used in concrete. We confirm that one of the main objectives of this study is to highlight the optimal importance of the fractal dimension parameter and its results, which brings us to the possibility of knowing how to determine the reference particle size curve of granular mixtures at using the fractal model for granular concrete mixtures.
To obtain a concrete having the desired properties according to climatic and other requirements, and to use local materials for economic reasons and in order to know the proper method of concrete formulation, we have seen that it is necessary to mention some of the conventional methods widely used in the formulation of concrete.
Methods were adopted for the formulation of concrete whose first principles of physical relations emerged at the end of the 19th century, and these methods of formulation have varied depending on the materials available and our need for the required concrete quality.
René Féret [11] in 1892 was one of the first to research the law governing the prediction of the compressive strength of concrete
Its formula based on the strength of the cement (the true class), the nature of the aggregate, the cement/water dosage ratio and taking into account the volume of voids. But does not take into account neither the shape of the aggregate nor the granular distribution, nor the resistance to fragmentation of the aggregate. The latter is formulated using the following expression:
In 1892, Féret [11] to whom the first researches are attributed, worked on a principle of the mechanical resistance of concrete
Fuller and Thomson [12] in 1907 established their method based on the maximum compactness of the continuous granular mixture, and it depends mainly on the porosity of the granular mixture (2) and the granular expansion. However, it does not directly take into account the shape of the grains, nor the resistance to friability of the aggregate used, and its relation is written as soot:
Abrams [13] in 1918, Regardless of the European school, he empirically proposed an exponential equation to predict the compressive strength of concrete, still used in North America, which has two adjustable parameters [Popovics, 1995].
The cement/water ratio, and involves through a coefficient (improved K_Féret) which indirectly presents nature and shape of the aggregates.
We note the absence of a direct representation of the resistance to fragmentation of the aggregate and of the granular distribution, as is the case in the rest of the previous methods, its formula (3) is written:
Bolomey [14] in 1925 is based on a formula (4), (improved iron) to determine the dosages of cement and water. This formula for predicting the mechanical compressive strength of concrete, which depends on the shape of the aggregates as well as the consistency of the concrete, and the dosages of cement and water, and the volume of voids. But does not take the representation of resistance to aggregate fragmentation.
This formula, like that of Féret, is the product of three terms which share, in order of factors, the influence of aggregates, cement and concrete formulation. The difference, compared to the relation of Féret, relates exclusively to the third term, parabolic in Féret, linear in Bolomey. It has been shown that the Bolomey relation is a good approximation of that of Féret for the values of the E/C ratio between 0.40 and 0.70; within this range, the error is less than or equal to 3%.
The scientist Caquot [15] circulated his research during the year 1937, through which he sought to find the optimal aggregate distribution in which the porosity of the aggregate mixture is minimal, according to the basic hypothesis of compatibility between two aggregates classes without influence due to the presence of another aggregates class.
This basic idea was taken up by F. de Larrard [3], who had previously embarked on a vast process of developing other concrete formulation programs.
The relation is determined empirically by assuming that the volume of the voids depends on the dimensions of the small grains, then on the addition of grains, then on a constant determined empirically according to the relation of Caquot (5).
We find in the work of Faury [16] and Joisel [17] that they made modifications to the work of Caquot in 1942 and 1952, and Faury extends to the granular range up to 6.5 μm, incorporating the cement as a granular material and taking into account the effect of the wall. And Joisel gave a reference straight line (at a complex scale) taking into account the cement, water, voids, granulometry and the compactness of the granular classes. Here, we note an indirect representation of the granular distribution with the mechanical resistance of the aggregates [18].
The optimum grain size of a concrete is a mixture (in a certain proportion) of two kinds of grains of the aggregate.
The reference curve to be followed consists of two straight sections.
The first AB gives the granulometry of fine grains. The second straight line is that of coarse grains. The y coordinate of, called the break point, indicates the percentage by volume of the grains. Its value is given by the experimental formula (6).
He method of Dreux and Gorisse [19] is based on the optimal granularity which is still current for the design of the concrete formulation. This is an empirical approach according to an OAB granular reference curve (segments of two lines in a semi-logarithmic plot). Contrary to the moment, the cement is not part of the reference curve of the mixture, since its mass is determined separately. It is a method which takes into account a large number of parameters [18]. But it does not take into consideration the direct representation of the granular distribution of the aggregate, and indicates what the true class of cement represents, and the dosage of cement and water, type, shape, quality and dimensions. of aggregates, the smoothness, consistency and strength of concrete.
This method is fundamentally empirical in nature, unlike the Faury method which predates it [Faury, 1942] and which is based on Caquot’s theory of the granular optimum [Caquot, 1937]. Dreux carried out a large survey to collect data on satisfactory concretes [de Larrard, 2000]. On the basis of a statistical analysis of this large number of concretes and by combining the granular curves obtained, they were able to base an empirical approach to determine a reference granular curve.
It is also very easy to use since it only requires knowing the grain size curves of the aggregates used.
A test batch is necessary to be carried out in the laboratory in order to make any usage corrections.
“B” (on the ordinate 100%) corresponds to the dimension D of the largest aggregate.
“O” (at ordinate 0%) corresponds to the dimension d of the smallest aggregate.
The break “A” has the following coordinates:
- on the abscissa (from the dimension D of the sieve) on the ordinate.
Si: D ≤ 20 mm; the abscissa is D / 2.
If: D ≥ 20 mm; the abscissa is located in the middle of the “gravel segment” limited by the modulus 38 (5 mm) and the modulus corresponding to D.
in ordinates (7)
The method of Baron and Lesage [20] is based on a technique proposed in 1976 to improve the granular skeleton according to the principle of relating the minimum flow time specified by the LCL Maniabilimeter according to Standard 18–452 [21] with the quantity optimal granularity for constant cement and water ratios.
The principle is to measure the time taken for a concrete sample to flow under vibration to a certain mark. The optimum proportions of aggregate are assumed to give the minimum flow time, for a given amount of cement and water. Once the granular proportions have been identified, the water and cement dosages are adjusted experimentally, so that the mixture has the desired workability and resistance. It is assumed, in this method, that the optimum proportions of aggregates do not depend on the quantity of cement.
Baron and Olivier have developed a formulation method derived from the Dreux-Gorisse method with modifications to adapt to existing concrete in 1996. This method makes it possible to integrate a large number of the parameters listed, taking into account the additives and incorporating mineral additives.
Baron and Olivier have developed a concrete formulation method derived from the Dreux-Gorisse method with modifications to adapt to existing concrete in 1996. This method makes it possible to integrate a large number of listed parameters, taking into account the additives and incorporating mineral additions.
The method is due to Mr. BARON, from the experimental studies he carried out in the years 1970–1980 and which were subsequently optimized by using the directives of the NF P 18–305 standard, replaced by the European Standard. EN 206–1. This method is developed in a book co-written by Messrs. BARON and OLLIVIER “BETONS, Bases and data for their formulations” published by Eyrolles.
The experiments confirmed the work carried out by BOLOMEY and FAURY, certain formulas of which were adopted with regard to the binder and water dosages.
For the dosage in aggregates, it is, in part, the work of DREUX that was retained. The whole is completed by experimental results which make the method as affordable as the DREUX method without having the drawbacks of its limitation to the only common concretes.
The problem of the optimal dosage of concrete is not unique: there are actually two problems that can be addressed independently of each other:
Binding paste
We start the formulation from 2 main assumptions which are the target resistance and the optimal effective water quantity.
The target resistance
From these values, we can therefore determine the cement dosage. Corrections are made from a trial mess.
The granular skeleton
The granular skeleton retained by BARON is very close to that obtained by DREUX, however with a simpler approach and definition. This method was chosen not for its scientific basis, but because its results have been satisfactory over the past 25 years.
There are other innovative methods of concrete formulation, which can use numerical models such as René LCPC or BétonLab and BétonLab Pro2 [3], and other methods are analytical.
The first theories dealing with the maximum pressure of granular mixtures [Féret 1892, Caquot 1937] do not explicitly take into account the interactions and grain sizes between them. Through the compact stacking model, De Larrard [3] incorporates new concepts such as clamping, wall effect and thinning effect.
The fractal model for determining the granular distribution is a conclusion drawn from fractal analysis and is a new model for the mathematical description of everything used and found in nature, in which its truncated shapes reveal patterns similar to increasingly precise scales. And irregular and recurring shapes can be described using mathematical models. The term “fractal” is a mathematical term coined by Benoit Mandelbrot [22] from the Latin root fraction. It was originally used as an adjective (fractal line) and today is a noun meaning broken or irregular.
Sebsadji and Chouicha in 2012 [5, 6, 7, 8, 9, 10, 23] showed that Fractals can be defined as disordered systems that are self-similar independent of scale of observation. Their fundamental property is a non-integer dimension called fractal dimension, which can measure.
Its result builds on the findings of previous researches (Lecomte and Thomas, 1992; Chouicha, 2006), according to which ideal grading curves of concrete can be transformed into straight-lines power-law of the form given in Eq. (11).
Marmi [24], in 2019 he expressed the fractal dimension as a parameter exists in classical geometry, and is a line is a one-dimensional object a surface a two-dimensional object, a volume a dimensional object. We are therefore used to objects whose dimension (D) is an integer 1,2 or 3. But it is not specified, what would be the dimension of a series of points on a line, an irregular and plane curve, a surface full of convolutions. For this purpose, the term fractal dimension was introduced by B. Mandelbrot in 1975 the fractal dimension is therefore a number which measures the degree of irregularity or fragmentation of an object or which measures the roughness of a surface.
The fractal dimension is the fraction or an irrational number (; 1.23; etc.) or an integer.
This notion of fractal dimension applies to scale-invariant objects: there are parts which are similar to the object itself up to an expansion (enlargement).
When we change the observation scale of a scale invariant object, we keep the shapes.
The particle size distribution curves of the cumulative sieve percentages as a function of the grain dimensions can be transformed to a straight line representing cumulative numbers as a function of the grain dimensions.
We can do this by assuming that the shapes of the grains have the same oval shape, and this, if we adopt the same hypothesis proposed by Lecomte and Thomas [25] in his work, which first touched on the analysis fractal and through which he approached the application of the fractal dimension in the determination of three types of granular mixtures for high-performance concrete. And in 1992, he achieved his study results by applying fractal analysis to a granular mixture of concrete related to the definition of granular analysis of granular mixture of concrete, which consists of several granular types. These results indicate at the time that he adopted the hypothesis of the dimension of a spherical grain of aggregate of main and standard dimension G, and the relation (8) below summarizes the determination of the volume of the spherical grain.
We can estimate the mass of the grains, called
It is also possible to express the cumulative number Nc of aggregate grains whose dimension is greater than or equal to the size of the opening of the sieve G, and Relation No. (10) shows the determination of the cumulative number of grains of aggregate. Thus, Relation (11) allows us to express the number of grains of aggregate rejected in a sieve, in terms of the cumulative numbers of all the granular components.
The granulometric analysis of cement is done by laser, “Laser granulometry” this technique is based on the diffraction of light and was proposed by Fraunhofer under the application of their theory of Fraunhofer “. We have Table 1 below which shows by a sub-detail of the fractal analysis which will identify the particle size of an example of CPA cement by the fractal dimension (FD).
Sieve size (mm) | Particle size analysis | Fractal analysis | ||||||
---|---|---|---|---|---|---|---|---|
Refusal mass (g) | % Cumulated | Density (g/cm3) | Grain volume (cm3) | Number of grains | ||||
Partial | Cumulated | Refusal | Passing | Partial | Cumulated | |||
0.125 | 0.00 | 0.00 | 0 | 100 | 3,00 | 1.02E-06 | 0.00E+00 | 0.00E+00 |
0.1 | 2.40 | 2.40 | 1 | 99 | 5.23E-07 | 1.53E+06 | 1.53E+06 | |
0.08 | 9.60 | 12.00 | 5 | 95 | 2.68E-07 | 1.19E+07 | 1.35E+07 | |
0.063 | 12.00 | 24.00 | 10 | 90 | 1.31E-07 | 3.06E+07 | 4.40E+07 | |
0.05 | 12.00 | 36.00 | 15 | 85 | 6.54E-08 | 6.11E+07 | 1.05E+08 | |
0.04 | 12.00 | 48.00 | 20 | 80 | 3.35E-08 | 1.19E+08 | 2.25E+08 | |
0.0315 | 24.00 | 72.00 | 30 | 70 | 1.64E-08 | 4.89E+08 | 7.14E+08 | |
0.025 | 21.60 | 93.60 | 39 | 61 | 8.18E-09 | 8.81E+08 | 1.59E+09 | |
0.02 | 14.40 | 108.00 | 45 | 55 | 4.19E-09 | 1.15E+09 | 2.74E+09 | |
0.016 | 12.00 | 120.00 | 50 | 50 | 2.14E-09 | 1.87E+09 | 4.61E+09 | |
0.0125 | 24.00 | 144.00 | 60 | 40 | 1.02E-09 | 7.83E+09 | 1.24E+10 | |
0.01 | 12.00 | 156.00 | 65 | 35 | 5.23E-10 | 7.64E+09 | 2.01E+10 | |
0.008 | 7.20 | 163.20 | 68 | 32 | 2.68E-10 | 8.96E+09 | 2.90E+10 | |
0.0063 | 4.80 | 168.00 | 70 | 30 | 1.31E-10 | 1.22E+10 | 4.13E+10 | |
0.005 | 4.80 | 172.80 | 72 | 28 | 6.54E-11 | 2.45E+10 | 6.57E+10 | |
0.004 | 19.20 | 192.00 | 80 | 20 | 3.35E-11 | 1.91E+11 | 2.57E+11 | |
0.00315 | 9.60 | 201.60 | 84 | 16 | 1.65E-11 | 1.96E+11 | 4.52E+11 | |
0.0025 | 2.40 | 204.00 | 85 | 15 | 8.18E-12 | 9.78E+10 | 5.50E+11 | |
0.002 | 2.40 | 206.40 | 86 | 14 | 4.19E-12 | 1.91E+11 | 7.41E+11 | |
0.0016 | 9.60 | 216.00 | 90 | 10 | 2.14E-12 | 1.49E+12 | 2.23E+12 | |
0.00125 | 2.40 | 218.40 | 91 | 9 | 1.02E-12 | 7.83E+11 | 3.02E+12 | |
0.001 | 2.40 | 220.80 | 92 | 8 | 5.23E-13 | 1.53E+12 | 4.55E+12 |
Particle size analysis and fractal analysis of CPA.
FD fractal dimension is, therefore, an approximation of the granular distribution curve. If this approximation is good over almost the entire grain size measurement field, the granular distribution line is said to be the fractal or quasi-fractal dimension. If the curve obviously tends towards a limit when the dimension of the seeds tends towards zero, then this curve is said to be semi-fractal.
We show without difficulty that any physical measurement on a granular structure, even purely fractal, results in a granular curve on a logarithmic scale (quasi-fractal) due to the smaller dimension of d mm, an empirically necessary procedure. It turns out that only successive zooms, and logarithmic scale transformations (d, D), probably reveal (on the slope of the lower higher convergence line) the effective quasi-fractal drift of the studied process.
If this drift has several changes in the slope, then in some cases it will be referred to as “multi-fractal.”
Figure 1 Presented the granular distributions of four types of cement identified by the fractal line, the cements are:
Portland cement compound class 42.5 MPa CPJ 42.5.
Fractal lines of granular distributions of four types of cements alone.
Cement sulphate resistant class 42.5 MPa CRS 42.5.
Artificial Portland cement class 52.5 MPa CPA 52.5.
Portland cement compound class 42.5 MPa type P6 CPJ P 42.5.
We present in Figure 1 - the different fractal distribution with correlation coefficients of the fractal lines of the granular distributions, and the minimum correlation coefficient value is R2 = 0.96. Appears in the granular distribution between three closely related types of cement, and another is different.
We followed the same method according to the results Figure 2 of Lecomt [25] presented in the Figure 2 which has ideally defined an example of the granular mixture containing a spread granular for a high-performance concrete, as well as all the granular classes of this concrete including the active mineral additions were used.
Transformation from a particle size distribution to a fractal distribution for a concrete mixture and its components from the Lecompt [
All concrete formulation methods, old or new, are based on particle size for determining the different dosages of the granular constituents. The results obtained by Lecompte [25] and Chouicha [2], show that these methods which use a granular distribution, indirectly use a fractal distribution.
In Figure 3 of Chouicha [2], the particle size curves for different granular mixtures that he identified with a uniform particle size range, we can determine the particle distribution of the granular mixtures with a fractal dimension from FD = 0.5 to FD = 7, knowing that this field is for the granular mixtures in general, which is much larger than the field of the granular concrete mix, so it is outside the concrete field, because the fractal dimension FD does not exceed the value of 3 .
Particle size curves of the different granular mixes identified by FD Chouicha [
We applied one of the three high quality BHP concrete mixes that he adopted by Lecomt in his research (Figure 2), we clearly show through Figure 4 the curves of the granular distribution of the component classes of the concrete, as well as its curve of the granular mixture, and this gives some similarities between Lecomt’s work and what we got despite using different components in terms of density and type of aggregate ect, and what we got despite using different components in terms of density and origin of gravels, and this is due to our relying on the granule size assumption of the spherical-shaped relation (8) to obtain the fractal distribution of this granular mixture with its components as shown in Figure 4 (a), (b) which gives us the results of converting granular curves to fractal lines. Lines.
Transformation from a particle size distribution to a fractal distribution for a concrete mixture and its components (example BHP) a) particle size curves, b) fractal line.
The process of transforming the particle size curve into a fractal line has a direct relation to the granular variety of a granular class or of a granular mixture regardless of the mass taken for the granular variety. Figure 5 (a) shows that the grain size curve remains the same, regardless of the mass for the same grain class. Figure 5 (b) shows the transformation into a fractal line which gives us two lines of the fractal distribution, for each mass gives a fractal line for the same granular class, but with the same slope value, so it is the same value of the fractal dimension, and this is what he had confirmed by Chouicha [2] in his work in 2006.
Transformation of a particle size curve of a granular mixture 0,63/25 to a) fractal line particle size curves, b) fractal line.
First, on the one hand, we deal with the transformation of the granulometric curves of local materials concerning the granular classes of sand and then of gravel. On the other hand, we show the transformation of the particle curves for the cementitious materials of CPJ 42.5 and CRS 42.5.
The determination of the granular distribution is one of the important physical properties for the definition of aggregates and as is known in the context of concrete formulation methods, and it is important to control and determine the proportions of the appropriate aggregate components for concrete.
Figure 6 (a) is an example showing the grain size curve of dune sand with a grain range of 0/5, Figure 6 (b) is its transformation into a fractal line, and this sand is one of the 10 sand classes of dunes shown in Table 2 and its smallest, granular extent is 0.005/0.63 and it is a very fine dune sand. As for the granular extent of coarse dune sand is 0.005/5. Thus, a quarry sand with its granular extent is 0.063/63.
Transformation of a grain size curve from a sand from Tilouline dune to a fractal line (example) [
Sand dune site | ES | MF | STg (mm2) | d/D | DF | R2 |
---|---|---|---|---|---|---|
Ouinna site n°01 - Adrar (SD) | 82,43 | 1,78 | 8,58E+14 | 0,05/1,6 | 0,91 | |
Tinerkouk - Timimoun (SD) | 78,08 | 1,06 | 3,45E+14 | 0,05/2,5 | 0,89 | |
Tilouline - Z. Kounta (SD) | 82,17 | 1,42 | 2,34E+12 | 0,063/5 | 0,96 | |
Ouinna site n°02 - Adrar (SD) | 74,20 | 1,78 | 6,39E+11 | 0,063/0,8 | 0,89 | |
Bordj Badji Mokhtar (SD) | 73,18 | 1,64 | 3,38E+12 | 0,05/0,63 | 0,82 | |
Tsabit - Adrar site n°01 (SD) | 74,32 | 2,22 | 6,63E+13 | 0,063/2,5 | 0,93 | |
Tsabit - Adrar site n°02 (SD) | 74,32 | 2,22 | 1,50E+13 | 0,063/1,25 | 0,93 | |
Tsabit - Adrar site n°03 (SD) | 74,32 | 2,22 | 1,69E+15 | 0,05/1 | 0,93 | |
Mimoun Adrar (SD) | 72,00 | 1,21 | 1,10E+14 | 0,063/1,25 | 0,88 | |
Cherouine -Timimoun (SD) | 75,00 | 1,82 | 5,27E+14 | 0,05/5,00 | 0,98 | |
Ouinna -Adrar (SC) | 77,90 | 1,67 | 4,84E+12 | 0,063/6.3 | 0,80 |
Conventional and unconventional parameters for certain sands of the Adrar-Algeria sites [1].
The identification results shown in Table 2 were obtained by conducting laboratory experiments to determine the varieties of sand dunes at the Building Materials Laboratory of the University of Adrar in cooperation with the Regional Workshop Laboratory. On desert techniques (ARTS) [26]. The sand studied is sand from sites approved for the use of dune sands from different sites in the Saharan region in Adrar, Algeria.
The graphical fit of the fractal distribution is a linear fit by the equation y = b + ax. And constant “a “, it is the slope which represents the fractal dimension. Figure 6 is an example showing the methodology for determining fractal dimensions of all granular classes, including dune sand.
In this case, the slope is 2.69 and the correlation coefficient is R2 = 0.96. The slope obtained by cumulative number of grains according to grain dimensions is the fractal dimension DF, which determines the class of the distribution of grains of a granular class or of the granular mixture.
Table 2 summarizes the conventional physical properties of dune sands in the Adrar region, namely granular extent, fineness modulus and sand equivalent. And the unconventional properties are the fractal dimension of the granular distribution, whose value varies from 1.14 to 4.20 with a correlation coefficient whose value varies from 0.82 to 0.98, which is a value close to one therefore is accepted. In addition to the total area of the grains of sand calculated by the fractal dimension.
Note: Physically, the value of the fractal dimension does not exceed the value of 3, because the study is within the framework of the three dimensions. But mathematically, that is to say according to the calculations of the hypothesis adopted, certain granular varieties can exceed the value of their fractal dimension the value 3.
But keep the optimal value that he adopted by chouicha [2], in his work, which does not exceed the value of 3 until the contrary is physically proven.
In Figure 7 (a) below, we show the grain size curves for all classes of dune sands as well as the quarry sand which we presented in Table 2, Figure 7 (b) we show the resulting fractal lines fractal distributions after transformation of their grain size curves for the different grain classes of dune sands. We note that the grain distribution of these sands is different and also gives us a different fractal distribution, which is proved by the obtained values of the fractal dimension.
Transformation of the particle size curves of Adrar Dune Sands to fractal lines a) particle size curves, b) fractal line.
In this study, we adapted 12 concrete formulation tests for 12 projects carried out at the wilaya in the State of Adrar which are detailed in Table 3. Example of the particle sizes of the Granular Mixtures based on the particle sizes of different granular classes, Figure 8 with cement and Figure 8 without cement.
Granular mixtures of concrete | Cement class | Concrete mix | RC28 | ||
---|---|---|---|---|---|
DFC | R2 | DFb | R2 | MPa | |
Mixture 01- [ARTS 22], CRS 42.5, Ouinna DS, Koussane Gr | 2,85 | 0,98 | 2,66 | 0,99 | 22,80 |
Mixture 02- [ARTS 22], CPJ 42.5, Ouinna DS, Koussane Gr | 2,75 | 0,99 | 2,83 | 0,99 | 20,60 |
Mixture 03- [LECT 24], CRS 42.5, Tinerkouk DS, Koussane Gr | 2,94 | 0,98 | 2,61 | 0,98 | 17,90 |
Mixture 04- [LECT 24], CRS 42.5, Tillouline DS, Koussane Gr | 2,94 | 0,98 | 2,76 | 0,99 | 18,90 |
Mixture 05- [LECT 24], CRS 42.5, Ouinna DS, Koussane Gr | 2,94 | 0,98 | 2,85 | 0,99 | 18,40 |
Mixture 06- [LECT 24], CRS 42.5, DS Gr from B.B.M | 2,94 | 0,98 | 2,87 | 0,99 | 19,10 |
Mixture 07- [LECT 24], CRS 42.5, Ouinna CS, Cherouine Gr | 2,94 | 0,98 | 2,86 | 0,98 | 20,20 |
Mixture 08- [LECT 24], CRS 42.5, Brinkane DS, Cherouine Gr | 2,94 | 0,98 | 2,83 | 0,99 | 29,50 |
Mixture 09- [LECT 24], CRS 42.5, Brinkane DS, Koussane Gr | 2,94 | 0,98 | 2,76 | 0,99 | 20,50 |
Mixture 10- [LECT 24], CRS 42.5, Brinkane DS, Cherouine Gr | 2,94 | 0,98 | 20,30 | ||
Mixture 11- [LECT 24], CRS 42.5, Brinkane DS, El Menia Gr | 2,94 | 0,98 | 23,40 | ||
Mixture 12- [LAMCO 23], CPJ 42.5, Mimoun DS, Koussane Gr | 2,75 | 0,99 | 18,80 |
Identification by DF for granular mixtures of concrete at Adrar [1].
Transformation of a grain size curve of the granular mixture of Oued zine to a fractal line (mixture 01) a) particle size curves, b) fractal line.
As for the concrete studies, we adopted the studies using dune sand that we have already studied, knowing that the concrete compositions included in this study are part of an executive study for projects scheduled to be implemented in arid regions.
Thus, the concrete compositions were studied in coordination between the building materials laboratory of the University of Adrar and the local technical laboratories, so that the study was carried out with the laboratory assigned to monitoring and control at the site. of the project. This within the framework of cooperation and the exchange of experiences between the university and the technical operator. These laboratories include the ARTS Soil Analysis Laboratory, Regional workshop of Saharan techniques, the LECT Technical Studies and Controls Laboratory and the LAMCO Building Materials analysis laboratory [26, 27, 28].
As for the aggregate materials used in these concrete structures, in addition to the sand dunes which were under study, the aggregates used locally come from quarries (Ouainna, Koussane, Cherouine, etc.), with two classes of CPJ cement. 42.5 and CRS 42.5.
In Figure 8 we apply the hypothesis of fractal analysis and show through it an example of the granular mixture with its components, which is dedicated to the first concrete project, and through it we give a typical idea of granular mixtures applied in the field, giving us a clear picture of the conversion of the granular distribution into a fractal distribution from which we derive the fractal dimension of the concrete mixture, which is DF = 2.66 with a correlation coefficient of R2 = 0.99. This granular mixture used, contains four granular classes, which are 03 classes of gravel and one of dune sand in addition to the cement class, which we have considered as a granular class in the dry concrete mix.
The variable component in our approved concretes is the variety of dune sands used, and we present in Table 3 below a summary of the identification they concrete used through their dry granular mixtures used. The fractal dimension of concretes used in executive projects on site and not limited to laboratory research only. We draw attention to a necessary parameter, namely that we have taken into account the effect of cement as a granular material which participates in the granular mixture by filling the pores with particles, while maintaining its main role in concrete, which is the main binder.
We have presented the values the fractal dimension of the cement used. Since we only used three classes of cement in the concrete compositions listed in Table 2, their values the fractal dimension of these classes, respectively, are as follows:
CPj 42.5 DF = 2.75,
CRS 42.5 DF = 2.85 Type 1,
CRS 42.5 DF = 2.94 Type 1.
This is in accordance with what is indicated in the three cement classes mentioned in Table 3.
But it seems very clear that the difference in the values the fractal dimension of the granular concrete mixtures is due to all the different granular classes used in the concrete, which have shown their effect on the granular distribution in the granular mixture. The smallest value of the fractal dimension of concrete is DFb = 2.61 and the highest value obtained from the fractal dimension of concrete is DFb = 2.87. This confirms the previous results of researcher Chouicha [2], which indicates that the optimal value of the fractal dimension does not exceed the value 3.
Table 1 shows details of how to transform a grain size curve into a fractal line for a single type of cement, and analytically we draw the fractal line of the cumulative grain sizes according to the grain dimensions with a scale logarithmic. But the granular mixture is made up of several different constituents, which makes it somewhat difficult to calculate the different arithmetic operations involved in fractal analysis.
The different required steps on which the proposed software is based can be addressed through two main sections.
First: enter the necessary data relating to each component of the mixture, namely the granular extent, the density and the standardized dimensions of the sieves, as well as the experimental data for the particle size analysis of each granular class.
Second: this software would draw the particle size curve of the granular mixture composed of several granular constituents, then display the transformed fractal line, and plot the linear fit and give its linear relationship followed by the correlation coefficient R2.
We proposed to name the program GranuFract in relation to the transformation of the particle size curve into a fractal line.
Figure 9 shows the image designed in the GranuFract software to capture basic information (density, cumulative sieve, and dimensions of sieve openings) to obtain the fractal distribution.
Main window for entering basic information in the GranuFract software.
Concerning the transformation of the particle size curves to a fractal line, we adopted the method of fractal analysis carried out in the previous works [1, 29, 30, 31], as shown in Figure 8.
The GranuFract program allows us to easily determine the granular distribution of granular mixtures. This also helps us in the possibility of processing discontinu ous grain size curves, that is to say by knowing the coordinates of each grain. It also helps to infer the variance of the coordinates to correct for items that have missing items (Figure 10).
Application of the transformation of a particle size distribution to a fractal distribution [
It is necessary to verify the data obtained from the program GranuFract, we have adopted ... the necessary procedures to compare the results that they obtained from the proposed software GranuFract and those that they obtained in the usual way, that is ie a graphical analysis using one of the mathematical programs “Origlab” and after a series of calculations The repeated values for each component of the granular mixture shown in Figure 11 (a) and (b) [1] we give the adopted values of the fractal dimension resulting from the two methods, they obtained values with an ideal correlation coefficient, and the standard deviation is ±0.05. The values obtained by verification are:
Example of the validation of the transformation of a particle size curve to a fractal line [
According to ‘GranuFract’: DF = 2.82; R2 = 0.99.
According to ‘Originlab’: DF = 2.83; R2 = 0.99.
We draw the attention of those interested in such a study represented in the use of mathematical models to find solutions to the framework of the search for concrete materials, that our main objective of this work is to highlight what we have achieved through to laboratory research and modeling in the application of the fractal analysis represented in the fractal dimension parameter and its positive results which have helped us to determine the granular distribution of the granular components of concrete through the granular mixture - the fractal line.
As for the proposed program, we look forward to improving and generalizing it by expanding a database that depends on a lot of experimental data for different components, including mineral additives and adjuvants, through which we hope to achieve a program to complete which will allow us to generalize it to all types of concrete, including innovative concrete.
This work allowed us to open a new window on the methods of concrete formulation using the granular distribution of granular mixtures to determine important properties in the definition of concrete, and we knew the efficiency of using modeling fractal to determine these granular mixtures of dry concrete for different concrete. It is now easy to know these granular mixtures according to their fractal dimension and their granular extent (FD, d/D).
Through this study, we show the contribution of an additional parameter for the formulation of concrete. It has already been mentioned that all concrete formulation methods depend on the granulation curve of the gravel mixture (sand gravel) to determine the gravel doses using the OAB grain reference curve, and the fractal model. Allow to determine the latter as a reference curve in the fractal dimension.
We initially proposed the “GranuFract” program, which converts the measurement curve of the grains of granular mixtures into a fractal line. This same program facilitates calculations and determination of the fractal distribution.
First of all, this work must be followed by careful empirical study to control the determination of granular aggregates with a concrete fractal line, in order to determine the dosages of the components of the concrete.
Secondly, we also aspire to extend this work by developing the “GranuFract” software in order to build an electronic database based on fractal modeling and obtained from particle size analysis in general.
C | Cement dosage |
E | Effective water dosage |
d | Minimum grain size |
D | Maximum grain size |
d/D | Granular extent |
E/C | Cement on water dosage report |
G/S | Gravel on sand dosage report |
N | Numbers of grains |
Nc | Cumulative numbers of grains |
EC | Cumulative workforce |
ES | Sand equivalent |
MF | Fineness modulus |
STg | Total area of grains (mm2) |
FD | Fractal dimension |
R2 | Correlation coefficient |
SC | Crushed sand |
SD | Dune sand |
FDc | Fractal dimension of cement |
FDb | Fractal dimension of concrete mix |
FD | Fractal dimension |
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Lakka and Chandrasekar Kuppan",authors:[{id:"304950",title:"Prof.",name:"Chandrasekar",middleName:null,surname:"Kuppan",slug:"chandrasekar-kuppan",fullName:"Chandrasekar Kuppan"},{id:"309984",title:"Mr.",name:"Narasimha S",middleName:null,surname:"Lakka",slug:"narasimha-s-lakka",fullName:"Narasimha S Lakka"}]},{id:"33046",title:"Affinity Chromatography: Principles and Applications",slug:"affinity-chromatography-principles-and-applications",totalDownloads:48609,totalCrossrefCites:8,totalDimensionsCites:21,abstract:null,book:{id:"1490",slug:"affinity-chromatography",title:"Affinity Chromatography",fullTitle:"Affinity Chromatography"},signatures:"Sameh Magdeldin and Annette Moser",authors:[{id:"123648",title:"Dr.",name:"Sameh",middleName:null,surname:"Magdeldin",slug:"sameh-magdeldin",fullName:"Sameh Magdeldin"},{id:"136483",title:"Dr.",name:"Annette",middleName:"C.",surname:"Moser",slug:"annette-moser",fullName:"Annette Moser"}]},{id:"50574",title:"Bioinformatics for RNA‐Seq Data Analysis",slug:"bioinformatics-for-rna-seq-data-analysis",totalDownloads:5930,totalCrossrefCites:6,totalDimensionsCites:7,abstract:"While RNA sequencing (RNA‐seq) has become increasingly popular for transcriptome profiling, the analysis of the massive amount of data generated by large‐scale RNA‐seq still remains a challenge. RNA‐seq data analyses typically consist of (1) accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events; (2) quantifying expression levels of genes, transcripts, and exons; (3) differential analysis of gene expression among different biological conditions; and (4) biological interpretation of differentially expressed genes. Despite the fact that multiple algorithms pertinent to basic analyses have been developed, there are still a variety of unresolved questions. In this chapter, we review the main tools and algorithms currently available for RNA‐seq data analyses, and our goal is to help RNA‐seq data analysts to make an informed choice of tools in practical RNA‐seq data analysis. In the meantime, RNA‐seq is evolving rapidly, and newer sequencing technologies are briefly introduced, including stranded RNA‐seq, targeted RNA‐seq, and single‐cell RNA‐seq.",book:{id:"5160",slug:"bioinformatics-updated-features-and-applications",title:"Bioinformatics",fullTitle:"Bioinformatics - Updated Features and Applications"},signatures:"Shanrong Zhao, Baohong Zhang, Ying Zhang, William Gordon,\nSarah Du, Theresa Paradis, Michael Vincent and David von Schack",authors:[{id:"176364",title:"Dr.",name:"Shanrong",middleName:null,surname:"Zhao",slug:"shanrong-zhao",fullName:"Shanrong Zhao"}]},{id:"49873",title:"An Introduction to Actinobacteria",slug:"an-introduction-to-actinobacteria",totalDownloads:8089,totalCrossrefCites:29,totalDimensionsCites:101,abstract:"Actinobacteria, which share the characteristics of both bacteria and fungi, are widely distributed in both terrestrial and aquatic ecosystems, mainly in soil, where they play an essential role in recycling refractory biomaterials by decomposing complex mixtures of polymers in dead plants and animals and fungal materials. They are considered as the biotechnologically valuable bacteria that are exploited for its secondary metabolite production. Approximately, 10,000 bioactive metabolites are produced by Actinobacteria, which is 45% of all bioactive microbial metabolites discovered. Especially Streptomyces species produce industrially important microorganisms as they are a rich source of several useful bioactive natural products with potential applications. Though it has various applications, some Actinobacteria have its own negative effect against plants, animals, and humans. On this context, this chapter summarizes the general characteristics of Actinobacteria, its habitat, systematic classification, various biotechnological applications, and negative impact on plants and animals.",book:{id:"5056",slug:"actinobacteria-basics-and-biotechnological-applications",title:"Actinobacteria",fullTitle:"Actinobacteria - Basics and Biotechnological Applications"},signatures:"Ranjani Anandan, Dhanasekaran Dharumadurai and Gopinath\nPonnusamy Manogaran",authors:[{id:"48914",title:"Dr.",name:"Dharumadurai",middleName:null,surname:"Dhanasekaran",slug:"dharumadurai-dhanasekaran",fullName:"Dharumadurai Dhanasekaran"}]},{id:"72074",title:"The Chemistry Behind Plant DNA Isolation Protocols",slug:"the-chemistry-behind-plant-dna-isolation-protocols",totalDownloads:3691,totalCrossrefCites:3,totalDimensionsCites:5,abstract:"Various plant species are biochemically heterogeneous in nature, a single deoxyribose nucleic acid (DNA) isolation protocol may not be suitable. There have been continuous modification and standardization in DNA isolation protocols. Most of the plant DNA isolation protocols used today are modified versions of hexadecyltrimethyl-ammonium bromide (CTAB) extraction procedure. Modification is usually performed in the concentration of chemicals used during the extraction procedure according to the plant species and plant part used. Thus, understanding the role of each chemical (viz. CTAB, NaCl, PVP, ethanol, and isopropanol) used during the DNA extraction procedure will benefit to set or modify protocols for more precisions. A review of the chemicals used in the CTAB method of DNA extraction and their probable functions on the highly evolved yet complex to students and researchers has been summarized.",book:{id:"8912",slug:"biochemical-analysis-tools-methods-for-bio-molecules-studies",title:"Biochemical Analysis Tools",fullTitle:"Biochemical Analysis Tools - Methods for Bio-Molecules Studies"},signatures:"Jina Heikrujam, Rajkumar Kishor and Pranab Behari Mazumder",authors:[{id:"74521",title:"Dr.",name:"Rajkumar",middleName:null,surname:"Kishor",slug:"rajkumar-kishor",fullName:"Rajkumar Kishor"},{id:"309357",title:"Prof.",name:"Pranab Behari",middleName:null,surname:"Mazumder",slug:"pranab-behari-mazumder",fullName:"Pranab Behari Mazumder"},{id:"318351",title:"Ph.D. Student",name:"Jina",middleName:null,surname:"Heikrujam",slug:"jina-heikrujam",fullName:"Jina Heikrujam"}]}],onlineFirstChaptersFilter:{topicId:"6",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82195",title:"Endoplasmic Reticulum: A Hub in Lipid Homeostasis",slug:"endoplasmic-reticulum-a-hub-in-lipid-homeostasis",totalDownloads:1,totalDimensionsCites:null,doi:"10.5772/intechopen.105450",abstract:"Endoplasmic Reticulum (ER) is the largest and one of the most complex cellular structures, indicating its widespread importance and variety of functions, including synthesis of membrane and secreted proteins, protein folding, calcium storage, and membrane lipid biogenesis. Moreover, the ER is implicated in cholesterol, plasmalogen, phospholipid, and sphingomyelin biosynthesis. Furthermore, the ER is in contact with most cellular organelles, such as mitochondria, peroxisomes, Golgi apparatus, lipid droplets, plasma membrane, etc. Peroxisomes are synthesized from a specific ER section, and they are related to very-long-chain fatty acid metabolism. Similarly, lipid droplets are vital structures in lipid homeostasis that are formed from the ER membrane. Additionally, there is a specific region between the ER-mitochondria interface called Mitochondria-Associated Membranes (MAMs). This small cytosolic gap plays a key role in several crucial mechanisms from autophagosome synthesis to phospholipid transfer. Due to the importance of the ER in a variety of biological processes, alterations in its functionality have relevant implications for multiple diseases. Nowadays, a plethora of pathologies like non-alcoholic steatohepatitis (NASH), cancer, and neurological alterations have been associated with ER malfunctions.",book:{id:"11674",title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg"},signatures:"Raúl Ventura and María Isabel Hernández-Alvarez"},{id:"82409",title:"Purinergic Signaling in Covid-19 Disease",slug:"purinergic-signaling-in-covid-19-disease",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105008",abstract:"SARS-CoV-2 virus infection causes the Covid-19 disease pandemic. Purinergic signaling is a form of extracellular signaling. Purinergic signaling plays significant role in the pathology of Covid-19. Purinergic system includes extracellular nucleotides, nucleosides, ectonucleotidases, and purinergic receptors. ATP, ADP, and adenosine are the main nucleotides, nucleosides. CD39 and CD73 are the main ectonucleotidases. There are two classes of purinergic receptors, P1 and P2. Each of them can be further divided, P1 into A1, A2A, A2B, and A3, P2 into P2X, and P2Y. In Covid-19, the purinergic system is disordered. SARS-CoV-2 viruses invading leads to extracellular ATP and ADP accumulation, purinergic receptor abnormally activation, tissue homeostasis balance is broken, which lead to inflammation even hyperinflammation with cytokine storm and thrombosis et al. symptoms. Currently, Covid-19 therapeutic medicine is still in shortage. Target purinergic system components is a promising way to treat Covid-19, which will help inhibit inflammation and prevent thrombosis. Currently, many relevant preclinical and clinical trials are ongoing. Some are very promising.",book:{id:"10801",title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg"},signatures:"Hailian Shen"},{id:"81708",title:"High Throughput Methods to Transfer DNA in Cells and Perspectives",slug:"high-throughput-methods-to-transfer-dna-in-cells-and-perspectives",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.104542",abstract:"Genome sequencing led to thousands of genes to study and their molecular cloning to provide ORF collection plasmids. The main approach to study their function involves analysis of the biological consequences of their expression or knockdown, in a cellular context. Given that, the starting point of such experiments is the delivery of the exogenous material, including plasmid DNA in cells. During the last decades, efforts were made to develop efficient methods and protocols to achieve this goal. The present chapter will first give a rapid overview of the main DNA transfer methods described so far: physical, chemical, and biological. Secondly, it will focus on the different methods having reached high-throughput nowadays. Finally, it will discuss the perspectives of this field in terms of future enhancements.",book:{id:"11356",title:"Molecular Cloning",coverURL:"https://cdn.intechopen.com/books/images_new/11356.jpg"},signatures:"Colin Béatrice and Couturier Cyril"},{id:"82374",title:"The Potential of the Purinergic System as a Therapeutic Target of Natural Compounds in Cutaneous Melanoma",slug:"the-potential-of-the-purinergic-system-as-a-therapeutic-target-of-natural-compounds-in-cutaneous-mel",totalDownloads:7,totalDimensionsCites:0,doi:"10.5772/intechopen.105457",abstract:"Cutaneous melanoma is an aggressive and difficult-to-treat disease that has rapidly grown worldwide. The pharmacotherapy available in so many cases results in low response and undesirable side effects, which impair the life quality of those affected. Several studies have been shown that the purinergic system is involved in cancer context, such as in cutaneous melanoma. With technological advances, several bioactive compounds from nature are studied and presented as promising adjuvant therapies against cancer, as phenolic compounds and related action by purinergic system modulations. Thus, phenolic compounds such as rosmarinic acid, resveratrol, tannic acid, as well as vitamin D may be promising substances in a therapeutic perspective to treat cutaneous melanoma via purinergic system pathway. More research needs to be done to open up new horizons in the treatment of melanoma by the purinergic signaling.",book:{id:"10801",title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg"},signatures:"Gilnei Bruno da Silva, Daiane Manica, Marcelo Moreno and Margarete Dulce Bagatini"},{id:"82338",title:"Advantages of Noncoding RNAs in Molecular Diagnosis",slug:"advantages-of-noncoding-rnas-in-molecular-diagnosis",totalDownloads:7,totalDimensionsCites:0,doi:"10.5772/intechopen.105525",abstract:"Noncoding RNAs contribute to physiological processes by regulating many intracellular molecules participating in the life-supporting mechanisms of development, differentiation, and regeneration as well as by disrupting various signaling mechanisms such as disease development and progression and tumor growth. Because microRNAs (miRNAs) target and regulate the functions of key proteins, it is very useful to identify specific miRNAs that contribute to cellular functions and to clarify the roles of their target molecules as diagnostic and therapeutic strategies for cancer prognosis and treatment. In this section, the roles of miRNAs in various cancers and the processes leading to the identification of their target molecules are described, and the latest diagnostic strategies using miRNAs are discussed with specific examples.",book:{id:"11353",title:"Recent Advances in Non-Coding RNAs",coverURL:"https://cdn.intechopen.com/books/images_new/11353.jpg"},signatures:"Tomomi Fujii, Tomoko Uchiyama and Maiko Takeda"},{id:"82298",title:"Predicting SNPs in Mature MicroRNAs Dysregulated in Breast Cancer",slug:"predicting-snps-in-mature-micrornas-dysregulated-in-breast-cancer",totalDownloads:8,totalDimensionsCites:0,doi:"10.5772/intechopen.105514",abstract:"Breast cancer (BC) is the leading type of cancer among women. Findings have revolutionized current knowledge of microRNA (miRNA) in breast tumorigenesis. The seed region of miRNA regulates the process of gene expression negatively. The presence of SNPs in the seed regions of miRNA dramatically alters the mature miRNA function. Additionally, SNPs in the out-seed region of miRNAs have a significant impact on miRNA targeting. This study focuses on the in silico analysis procedure of mature miRNA SNPs and their impact on BC risk. The database annotated SNPs on mature miRNAs was used. Also, target gene alterations, miRNAs function in BC, and the interaction of miRNAs with targets were predicted. A list of 101 SNPs in 100 miRNAs with functional targets in BC was indicated. Under the SNPs allele variation, 10 miRNAs changed function, 6 miRNAs lost targets, 15 miRNAs gained targets, 48 onco-miRNAs remained unchanged, and 21 tumor suppressor miRNAs remained unchanged. At last, a list of 89 SNPs, which alter miRNA function and miRNA-mRNA interaction, were shown to be potentially associated with BC risk. This research theoretically generated a list of possible causative SNPs in the mature miRNA gene that might be used in future BC management studies.",book:{id:"11353",title:"Recent Advances in Non-Coding RNAs",coverURL:"https://cdn.intechopen.com/books/images_new/11353.jpg"},signatures:"Thanh Thi Ngoc Nguyen, Thu Huynh Ngoc Nguyen, Luan Huu Huynh, Hoang Ngo Phan and Hue Thi Nguyen"}],onlineFirstChaptersTotal:59},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"3",title:"Dentistry",doi:"10.5772/intechopen.71199",issn:"2631-6218",scope:"\r\n\tThis book series will offer a comprehensive overview of recent research trends as well as clinical applications within different specialties of dentistry. Topics will include overviews of the health of the oral cavity, from prevention and care to different treatments for the rehabilitation of problems that may affect the organs and/or tissues present. The different areas of dentistry will be explored, with the aim of disseminating knowledge and providing readers with new tools for the comprehensive treatment of their patients with greater safety and with current techniques. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This series of books will focus on various aspects of the properties and results obtained by the various treatments available, whether preventive or curative.
",coverUrl:"https://cdn.intechopen.com/series/covers/3.jpg",latestPublicationDate:"May 13th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:8,editor:{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. 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",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:2,paginationItems:[{id:"1",title:"Oral Health",coverUrl:"https://cdn.intechopen.com/series_topics/covers/1.jpg",isOpenForSubmission:!0,annualVolume:11397,editor:{id:"173955",title:"Prof.",name:"Sandra",middleName:null,surname:"Marinho",slug:"sandra-marinho",fullName:"Sandra Marinho",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRGYMQA4/Profile_Picture_2022-06-01T13:22:41.png",biography:"Dr. Sandra A. Marinho is an Associate Professor and Brazilian researcher at the State University of Paraíba (Universidade Estadual da Paraíba- UEPB), Campus VIII, located in Araruna, state of Paraíba since 2011. She holds a degree in Dentistry from the Federal University of Alfenas (UNIFAL), while her specialization and professional improvement in Stomatology took place at Hospital Heliopolis (São Paulo, SP). Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). Dr. Marinho's experience in Dentistry mainly covers the following subjects: oral diagnosis, oral radiology; oral medicine; lesions and oral infections; oral pathology, laser therapy and epidemiological studies.",institutionString:null,institution:{name:"State University of Paraíba",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null},{id:"2",title:"Prosthodontics and Implant Dentistry",coverUrl:"https://cdn.intechopen.com/series_topics/covers/2.jpg",isOpenForSubmission:!0,annualVolume:11398,editor:{id:"179568",title:"Associate Prof.",name:"Wen Lin",middleName:null,surname:"Chai",slug:"wen-lin-chai",fullName:"Wen Lin Chai",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHGAQA4/Profile_Picture_2022-05-23T14:31:12.png",biography:"Professor Dr. Chai Wen Lin is currently a lecturer at the Department of Restorative Dentistry, Faculty of Dentistry of the University of Malaya. She obtained a Master of Dental Science in 2006 and a Ph.D. in 2011. Her Ph.D. research work on the soft tissue-implant interface at the University of Sheffield has yielded several important publications in the key implant journals. She was awarded an Excellent Exchange Award by the University of Sheffield which gave her the opportunity to work at the famous Faculty of Dentistry of the University of Gothenburg, Sweden, under the tutelage of Prof. Peter Thomsen. In 2016, she was appointed as a visiting scholar at UCLA, USA, with attachment in Hospital Dentistry, and involvement in research work related to zirconia implant. In 2016, her contribution to dentistry was recognized by the Royal College of Surgeon of Edinburgh with her being awarded a Fellowship in Dental Surgery. She has authored numerous papers published both in local and international journals. She was the Editor of the Malaysian Dental Journal for several years. Her main research interests are implant-soft tissue interface, zirconia implant, photofunctionalization, 3D-oral mucosal model and pulpal regeneration.",institutionString:null,institution:{name:"University of Malaya",institutionURL:null,country:{name:"Malaysia"}}},editorTwo:{id:"479686",title:"Dr.",name:"Ghee Seong",middleName:null,surname:"Lim",slug:"ghee-seong-lim",fullName:"Ghee Seong Lim",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003ScjLZQAZ/Profile_Picture_2022-06-08T14:17:06.png",biography:"Assoc. Prof Dr. Lim Ghee Seong graduated with a Bachelor of Dental Surgery from University of Malaya, Kuala Lumpur in 2008. He then pursued his Master in Clinical Dentistry, specializing in Restorative Dentistry at Newcastle University, Newcastle, UK, where he graduated with distinction. He has also been awarded the International Training Fellowship (Restorative Dentistry) from the Royal College of Surgeons. His passion for teaching then led him to join the faculty of dentistry at University Malaya and he has since became a valuable lecturer and clinical specialist in the Department of Restorative Dentistry. He is currently the removable prosthodontic undergraduate year 3 coordinator, head of the undergraduate module on occlusion and a member of the multidisciplinary team for the TMD clinic. He has previous membership in the British Society for Restorative Dentistry, the Malaysian Association of Aesthetic Dentistry and he is currently a lifetime member of the Malaysian Association for Prosthodontics. Currently, he is also the examiner for the Restorative Specialty Membership Examinations, Royal College of Surgeons, England. He has authored and co-authored handful of both local and international journal articles. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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