Nodulation phenotypes of F3 seeds.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"89",leadTitle:null,fullTitle:"Search Algorithms and Applications",title:"Search Algorithms and Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"Search algorithms aim to find solutions or objects with specified properties and constraints in a large solution search space or among a collection of objects. A solution can be a set of value assignments to variables that will satisfy the constraints or a sub-structure of a given discrete structure. In addition, there are search algorithms, mostly probabilistic, that are designed for the prospective quantum computer.\nThis book demonstrates the wide applicability of search algorithms for the purpose of developing useful and practical solutions to problems that arise in a variety of problem domains. Although it is targeted to a wide group of readers: researchers, graduate students, and practitioners, it does not offer an exhaustive coverage of search algorithms and applications. The chapters are organized into three parts: Population-based and quantum search algorithms, Search algorithms for image and video processing, and Search algorithms for engineering applications.",isbn:null,printIsbn:"978-953-307-156-5",pdfIsbn:"978-953-51-6005-2",doi:"10.5772/620",price:159,priceEur:175,priceUsd:205,slug:"search-algorithms-and-applications",numberOfPages:506,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"724aa705bd006317eaa0062212fd8b05",bookSignature:"Nashat Mansour",publishedDate:"April 26th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/89.jpg",numberOfDownloads:53098,numberOfWosCitations:33,numberOfCrossrefCitations:31,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:39,numberOfDimensionsCitationsByBook:4,hasAltmetrics:0,numberOfTotalCitations:103,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 19th 2010",dateEndSecondStepPublish:"June 16th 2010",dateEndThirdStepPublish:"October 21st 2010",dateEndFourthStepPublish:"November 20th 2010",dateEndFifthStepPublish:"January 19th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"22053",title:"Prof.",name:"Nashat",middleName:null,surname:"Mansour",slug:"nashat-mansour",fullName:"Nashat Mansour",profilePictureURL:"https://mts.intechopen.com/storage/users/22053/images/1632_n.jpg",biography:"Nashat Mansour is a Professor of Computer Science at the Lebanese American University. 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Identification of SCC occurred between 1930s and 1950s, the mechanism of SCC was explained between 1960s and 1970s and its application and development began at 1980s [1]. In fact, the industry owners did not find the austerity of the problems connected to the stress corrosion cracking until the tragic rupture of a digester at Pine Hill, Alabama, in 1980 [2]. After the accident, considerable investigations have been taken on SCC phenomenon. In this type of failure, the crack commencement and growth is the consequence of reciprocal action of sensitizes material, tensile stress, and corrosive environment.
\nVarious types of SCC have been distinguished. Chloride SCC appears in austenitic stainless steels under tensile mechanical stress in the existence of chloride ion, oxygen, and a high temperature condition [3]. Cracking of stainless steels under caustic environments in the presence of a high hydrogen concentration is known as caustic embrittlement [4]. SCC cracking of steels in hydrogen sulfide environment is confronted in oil industries [3]. Cracking of brass in ammonia environments is another type of SCC, which is known as sessional cracking.
\nDuring SCC phenomenon, the material is essentially unattacked over most of its surface, while fine and branch cracks develop into the bulk of material [5]. This cracking phenomenon has serious consequences since it can occur under stress levels much lower than the designer intended and cause the equipment and structural elements to catastrophic fractures [6, 7, 8, 9, 10].
\nIn this chapter, the feature of SCC is first introduced. Then, the requirements of the stress corrosion cracking are expressed. In next step, the mechanisms attributed to this corrosion phenomenon are discussed in detail. Designing of structural elements and mechanical equipment based on the tensile properties may be led to false results. Accordingly, the science of fracture mechanics applies in situation where SCC can be occurred. Fracture mechanic presented criteria that considered the role of imperfection on the fracture of materials. Next, based on the corrosion science and empirical data, the methods of preventions are presented. Finally, case studies are introduced to better understand of the issue.
\nIn macroscopic scale, SCC failures appear to be brittle even if the material is ductile, and stress level is lower than design stress. The cracks in SCC are not mechanical but are caused by corrosion. A typical growth and developments of cracking by cause of the stress corrosion cracking phenomenon in AISI316L stainless steel are shown in \nFigure 1a\n.
\nMacroscopic and microscopic feature of the stress corrosion cracking: (a) Cross-section of a failed tube and formation of macro branching cracks on it. (b) Optical micrograph of a typical SCC in AISI316L stainless steel exposed to chlorides [
The branches developed, intergranular and transgranular cracks are the character of stress corrosion cracking in microscopic levels, \nFigure 1b\n. In intergranular mode, the cracks proceed along grain boundaries while transgranular cracks grow across the grains [3]. The mode of crack proceeds depends on the material microstructure and environment.
\nThree key elements are essential for initiation and growth of the stress corrosion cracking: a sensitized material, a specific environment, and adequate tensile stress. It is shown in \nFigure 2\n.
\nEssential parameters for occurring SCC.
When austenitic stainless steel grades contain more than 0.03 wt% that are exposed to temperature from 415 to 850°C, their microstructure becomes sentient to precipitation of chromium carbides (M23C) along grain boundaries called as sensitization [11, 12]. Constitution of Cr enrichment carbides along with grain boundaries may severely evacuate the area aside to the grain boundaries from free chromium and render them sensitive to rapid corrosion in presence of aggressive agents such as chloride, caustic, and polythionic acid [4, 12, 13]. Grain boundary dissolution aside tensile tress lead the component made of these ductile alloys to brittle fracture. An example of the SCC failure in an austenitic stainless steel is shown in \nFigure 1\n.
\nCopper is alloyed with zinc, tin, nickel, aluminum, and silicon to produce various types of brass, bronze, cupronickel, aluminum bronze, and silicon bronze, respectively. Most of SCC failure mechanisms for copper-based alloys have been predominantly investigated for ammonia-brass systems [14]. A classic example of SCC for brass systems is season cracking. It specifies the stress corrosion cracking of brass cartridge cases of British forces in India [11]. Two mechanism have been found to be attributed to SCC of copper-based alloys: (a) the passive film rupture and (b) de-alloying [15]. According to first theory, intergranular SCC is to occur due to passive-film rupture and transient dissolution mechanism in copper-zinc alloys. Chen et al. reported that aluminum bronze (Al-Cu-Zn) underwent SCC in fluoride-containing solutions through the film-rupture mechanism [16]. According to de-alloying, that is, selective segregation of the alloying elements, for example, Zn in Cu-Zn systems, is the primary factor in the stress corrosion cracking phenomenon. For example, in the case of Cu-Zn alloy systems, preferential segregation of Zn take place in the crack tip region, or along the grain boundaries. Domiaty and Alhajji found that cupronickel alloy Cu-Ni (90–10) underwent SCC in seawater polluted with sulfide ions due to selective dissolution of copper [17]. Chen et al. also reported that de-alloying resulted in SCC of aluminum brass (Al-Cu-Zn) in fluoride-containing aqueous solutions [16]. The Pourbaix diagrams for pure metals can be helpful in anticipating the sensitivity to de-alloying and in appraising the tendency of selective dissolution and hence the SCC behavior [16].
\nStress corrosion cracking of carbon steels has been identified as one of the main reasons leading to leakage and rupture events of pipe lines and steam generator boilers with catastrophic consequences [18, 19]. Carbon steels have shown SCC in conditions that there is a desire to form a protective passive layer or oxide film [13, 20, 21, 22]. In fact, the environments where carbon steel is susceptible to SCC are carbonates, strong caustic solutions, nitrates, phosphates, and high-temperature water [13, 23, 24, 25]. The problems associated with SCC in carbon steels are influential for both economic and safety reasons, because of widespread use of these alloys in various industries.
\nAluminum and its alloys are extensively used in military, aerospace, and structural applications. These groups of engineering material under specific environment and sufficient magnitude of stress are susceptible to SCC [26, 27]. Determinant factor on the stress corrosion cracking behavior in Al alloy systems is the chemical composition. Alloying elements have strong influences on the formation and stability of the protecting layer on the surface of alloy and also may show effects on the strength, grain size, grain boundary precipitations, and magnitude of residual stress within the material [28, 29]. In between them, series of 2xxx, 7xxx, and 5xxx (alloys containing magnesium) are sensitive to SCC [28]. Failures associated with SCC in aircraft constituents made of 7075-T6, 7079-T6, and 2024-T3 Al-alloys were reported between 1960 and 1970 [27].
\nAnother important factor on SCC behavior of aluminum alloys is type of heat treatment and quenching rates [26, 30]. Heat treatment shows a crucial role in formation and organization of the constituent particles in order to attain a high strength aluminum alloys. It has been reported that aluminum alloy series 7xxx are sensitive to cooling rate and many researchers investigated the effect of cooling rates on the stress corrosion cracking behavior of the alloys. Researches have displayed that a decline in the cooling rate results in a rise in the size and inter particle distance of the GBP forward with an increase in width of the PFZ. Unfortunately, this leads to a decrease of copper contents in the grain boundary of precipitates and reduction of resistance to SCC [28, 29, 30].
\nThere is a significant discrepancy in the texts on the exact mechanism of stress corrosion cracking in Al alloys. Evidence shows that the proposed mechanism of SCC pertains on the alloy system in question: anodic dissolution is suggested for the 2xxx alloy systems (AI-Cu and Al-Cu-Li), while hydrogen-induced cracking or HIC is presented by most investigators in the 5xxx and 7xxx alloys. Another mechanism may be properly explained SCC in some Al alloy systems is the rupture passive film. Thus, while SCC phenomenon of Al alloys is well documented, the actual mechanisms are still challenged [29].
\nTwo methods are suggested improving SCC resistance in Al alloys. The first method is to break down the continuity of the GBPs and the second is to decline the galvanic potential nonconformity between the grain boundaries and matrix.
\nTitanium alloys are broadly utilized in aerospace industry because of their high corrosion resistance and specific strength. Two widely used grades of Ti are Ti-6Al-4 V known as Ti-64 alloy and Ti-8Al-1Mo1V known as Ti-811. The latter is specified by its lower density and surpassing stiffness, but unfortunately is susceptible to SCC.
\nThe significant corrosion resistance of Ti alloys under oxidizing media is a result of formation of the protective passive TiO2 film on the surface. For Ti-811 grade, instinctive passivation arises in 3.5% sodium chloride HCl solution [31]. However, the passive protective layer will be destroyed if load applied is over the yield stress of the oxide film, and corrosive environment will then expose to the fresh metal and lead to SCC [32]. In Ti alloys systems, oxygen and aluminum additions enhance SCC sensitivity and change the slip condition to a planar slip [33]. Slip plan growth of SCC cracks in this grade of Ti alloys is shown in \nFigure 3\n.
\nIPF map of the SCC crack growth in a wrought Ti-811 specimen. Crack propagation extended along planar slip (0001) [
SCC can be occurred in polymers, when the components made of these materials are exposed to specific aggressive agents like acids and alkalis. Similar to metals combination of polymer and environment which lead to SCC is specific. For instance, polycarbonate is susceptible to SCC in alkalis, but not by acids. On the other hand, polyesters are prone to SCC when exposed to acids [34]. Another form of SCC in polymeric materials is ozone attack [35]. Small traces of the gas in the air will attack polymer bonds and lead them to degradation. Natural rubber, nitrile butadiene rubber, and styrene-butadiene rubber were found to be most sensitive to this form of failure. Ozone cracks are very dangerous in fuel pipes because the cracks can grow from external surfaces into the bore of the pipe, and so fuel leakage and fire will be its consequences [36, 37, 38]. Investigations have shown a critical amount of deformation and ozone concentration require for SCC crack growth in rubbers [35].
\nThe stress corrosion cracking is less common in ceramic materials that are more resistant to chemical attacks. However, phase transformation under stress is usual in the ceramic substances such as zirconium dioxide. This phenomenon led them to toughening [39]. Silica or SiO2 is extensively used in microelectromechanical systems and in integrated circuits as sacrificial release layers and dielectric layers. Silica is sensitive to SCC in air because of the reaction between Si-O bonds with H2O molecules under stress [40].
\nThe environment prone to stress corrosion cracking is special since not all environments provide condition for cracking. The corrosive condition may be a permanent service environment, such as processes water for a heat exchanger, or temporary environments produced by an operational errors, like deposits form on the equipment during shut downs in chemical plants. Moreover, environments that lead the components to SCC are often aqueous in nature and can be either a thin layer of condensed moisture or a volume solution [41]. Generally, the stress corrosion cracking occurs in aqueous medium, but it also occurs in certain metals, fused salts, and inorganic liquids [11]. The aggressive environment to passive film of stainless steels is chlorides, caustic, and polythionic acid. The austenitic stainless steel series 300 like AISI304 and AISI316 are more sensitive in environment consisting of chlorides. It was reported that the SCC in austenitic stainless steels containing chlorides proceeds transgranulary and often develops at a temperature over 70°C [42, 43]. Caustic embrittlement or the SCC induced by caustic is another severe problem in the equipment made of austenitic stainless steels and led to many explosions in the steam boilers and super heaters [4, 44, 45]. It was also seen that the existence of sulfur in feed gas in petrochemical plants causes forming of polythionic acid (H2SxO, x = 2 to 5), which in presence of moisture also induced intergranular SCC in austenitic stainless steels [46, 47].
\nCarbon steels are found to be sensitive to SCC in nitrate, NaOH, acidic H2S, and seawater solutions [48]. Soil environment is responsible for SCC cracking of underground pipeline carbon steels where cathodic protection is utilized to preserve lines against general corrosion [49, 50]. Cathodic protection of buried pipelines can result in the production of alkali solutions at the pipe surface where the coating is disbanded. The formed alkalis consist of sodium hydroxide, carbonate, and bicarbonate solution. Investigations have shown that in the presence of these compounds and some range of potential protection carbon steel pipes fail due to the stress corrosion cracking. For instance, carbonate solution and a corrosion potential [between −0.31 and −0.46 V (SHE)] developed the stress corrosion cracking in a high strength carbon steel pipeline [49, 50].
\nEnvironment conditions that lead the copper alloys to SCC are ammonia, amines, and water vapor [48]. The source of ammonia may be from decomposition of organic materials [11]. In ammonia production plans, ammonia comes from leakage of the equipment and piping systems, when these components are not completely leak-proof.
\nIn aluminum and aluminum alloy, pure aluminum is safe to SCC. On the other hand, duralumin alloy refers to aluminum-copper alloys, under tension stress aside of moisture, and it may fail due to cracking along the grain boundaries. Aluminum alloys are also susceptible in SCC in NaCl solution [50].
\nThe stress corrosion cracking of pure titanium was observed by Kiefer and Harple in red fuming nitric acid for the first time [51]. Titanium alloys are also susceptible to SCC when is exposed to sea water and methanol-HCl [52].
\nThe stresses that cause SCC are directly applied stress, thermal operational stress, residual stress or combination of all [3]. The operational stress consists of operational applied stress and thermal stress. The operational applied stress is considered by designers and usually calculated less than the yield strength. The thermal stress is generated due to thermal cooling and heating of component during services and shut downs. The thermal stress is calculated from thermal strains that are expressed by a temperature-dependent differential expansion coefficient [13]:
\nin which, \n
The main source of stress attributed to stress corrosion cracking comes from the residual stress. The well-known sources of the residual stress are welding and fabrication processes. Residual stress due to welding plays an imperative role in the stress corrosion cracking of metal alloys. In this area, two factors are determined. First, the welding residual stress by the cause of nonuniform temperature changes during welding, which can be computed by Eq. (1). Second, in some of the steel grades, the solid-state austenite to martensite transformation in the time of cooling generates a significant value of the residual stresses [53]. In carbon and low alloy steels, martensite is formed by quenching of the austenite containing carbon atoms at such a fast cooling rate that carbon atoms do not have a chance to diffuse out from the crystal structure and form cementite. The trapped carbon atoms in octahedral interstitial sites of iron atoms, generating a body-centered tetragonal (bct) structure that is in super saturation, relate to the ferrite with body-centered cubic (bcc) structure [54]. Therefore, during this phase transformation, the volume of metal increases, and a significant residual stress is also produced [54]. In austenitic stainless steel, there are no phase transformation processes, and so this mechanism does not attribute in their welding residual stress. An example of SCC in the weld area of an austenitic stainless steel component is shown in \nFigure 4a\n.
\nTwo sources of stress in the SCC: (a) welding residual stress and (b) mechanical residual stress [
The product manufacturing systems that generate considerable residual stresses include casting, rolling, forging, drilling, machining, heat treatments, cooling, carburization, and straightening [55]. In many of the material manufacturing processing, there is a rebalancing of the generated residual stresses during or after the production process, and the extent of the final residual stresses is often less than half of the yield stress or σy. Some material processing, such as heat treatment with slow cooling rate, may release the stresses generated by previous treatments. But processes like rapid quenching or machining, which create localized yielding at the surface, may leave residual stress in magnitude of the yield stresses at surface or down surface of the material [55]. A typical SCC in heavy machining area of an austenitic stainless steel component is presented in \nFigure 4b\n.
\nFinally, the stress must be tensile form, and comprehensive stresses do not cause the equipment to failure due to SCC.
\nThe mechanism of SCC depends on the type of material and environment. Many models have been presented describing SCC phenomenon. Each of these models has its own restraints in that it can be utilized to describe SCC in limited number of metal-environment systems. In fact, there is no unified mechanism attributed in SCC for all metal environment combination. A few presented models are given in following.
\nAccording to this model, there are pre-existing regions in an alloy microstructure that become sensitive to anodic dissolution. For instance, a precipitate in grain boundary may be anodic with respect to the grain boundary and provide an active path for localized corrosion. In the same way, if a nobler phase is precipitated at grain boundaries, adjacent to precipitates provide an active track for localized corrosion. A classic example of this mechanism can be seen in austenitic stainless in which precipitates of chromium carbides along grain boundaries depletes adjacent areas from chromium and provides a path for localized corrosion to be occurred [1].
\nThe film rupture mechanism implies for the alloys which passive layer is formed on their surface. In this mechanism, plastic deformation plays the main role. The plastic strain causes film disruption on the surface of metal. After the film disruption, the bare metal is disclosed to environment, and a localized attack at the area of disruption occurs. The processes of disruption strain and film formation are repeated, and cracking growth continues. This mechanism was originally considered for caustic cracking of boilers. It has also been utilized for description of SCC of alpha brass in ammonia environments [1].
\nThe adsorbing model is based on the process of embrittles of the material in the vicinity of a corroding area. First, the model was used to study on failure of high strength martensitic stainless steels in chloride media. According to this model, the adsorption of environmental agents drops the interatomic bond strength and the stress need for a brittle fracture. The fracture mechanic theory properly explains the issue. According to this theory, the theoretical fracture stress required to take apart two layers of inter atomics spacing b is given by:
\nin which, E, γ, and b are Young’s modulus, surface energy, and spacing between atom layers, respectively. According to this model, in corroded environments, the aggressive agents are adsorbed at the crack tip, surface energy effectively decreased, and fracture take place in a stress level much lower than the normal condition [1].
\nInvestigation has shown that the maximum operational stresses and the maximum defect sizes imparted during production processing could limit the life time of materials [56]. Stress produced due applied, thermal or residual, usually below the yield strength of a material. However, surface defects that either preexist or are produced during services by cause of corrosion, wear, or other processes (\nFigure 5\n) may provide stress concentration and so condition for fracture [41]. Hence, the role of imperfections should take into account. Fracture mechanic introduced another material property named fracture toughness or \n
The sources of SCC sites (a) machining marks, (b) pre-existing crack, (c) intergranular corrosion, and (d) pit [
in which, \n
According to this relation, there is a critical size of flaw which led a component to brittle fracture. Investigation has shown that for the structure expose to corrosive environments, the scenario is quite different [58]. The corrosive environments cause a significant drop in this property of the materials [59]. This typically is shown in \nFigure 6\n. As it is seen from the graph, the KIc in presence of aggressive agents shown by KIscc is much lower than its value in normal conditions. This means in corrosive environments prone to SCC, a smaller flaw size led the components to catastrophic brittle fractures. Susceptibility of existing cracks to environmentally assisted propagation is a critical consideration for designers while selecting proper materials for environments prone to this form of corrosion [58].
\nKI vs. Tf of AISI4340 steel in 3.5% NaCl at ambient temperature [
\n
If we assume that the applied load is on the magnitude of yield stress (Y.S), the critical crack depth, \n
In dry condition: \n
In 3.5% NaCl condition: \n
The results show that the \n
Since the mechanism of SCC is not fully understood, methods of preventing are based on empirical experiences. One or more application of the following methods can be worked out in reduction or prevention of the SCC [11]:
Lowering the stress levels. This may be done by annealing treatment in the case of residual stress. Stress relief temperature for plain carbon steels are at a range of 1100–1200°F and for austenitic stainless steels are frequently at temperatures ranging from 1500 to 1700°F.
Eliminating aggressive species in the environment. For instance, in case of austenitic stainless steels, reduction of chloride under 10 ppm reduces significantly the probability of the stress corrosion cracking to occur [44].
Changing the material in one plane if neither environment condition nor stress level can be changed. For example, in case of AISI304, stainless steel utilizing a high nickel alloy content (e.g., Inconel) can be useful.
Applying cathodic protection by impressed current or sacrificial anode. In cathodic protection, note that the failure is due to SCC, not hydrogen embrittlement. In cathodic protection, the released hydrogen due to cathodic reaction can accelerate the hydrogen embrittlement.
Adding inhibitors such as phosphate and other organic and inorganic to the system can reduce the stress corrosion cracking effects in mildly corrosive media.
Coating is sometimes effective.
Shot-peening generates residual compressive stress in surface of the component and prevent the stress corrosion cracking to be occurred.
The problem associated with ozone stress cracking can be prevented utilizing anti-ozonants to the rubber before vulcanization treatment.
In April 2004, an oil transportation pipeline in north part of Iran failed and caused oil leaking. Visual inspections revealed macrocracks and shallow pits on the external surface of the corroded area (\nFigure 7\n). Optical observation indicated that cracks were started from the bottom of the pits and extended into the wall thickness as shown in \nFigure 8\n. There was a main singular crack and some branched cracks at the end. The crack features are characteristics of a stress corrosion cracking [60].
\nMacroscopic observation of the cracking area.
(a) Initiating of cracks from pitting on the surface of the carbon steel pipe and its branched development (unetched), (b) intergranular crack growth in the microstructure (etched) [
The oil transportation pipe line is made of API 5 L X52 carbon steel that is coated by polyethylene tape coating. The utilized polyethylene coating on the external surface of the carbon steel pipe gets loose and separated in some areas. As a consequence, the surface of the underground steel pipeline was uncovered to the wet soil environment. Because of the chemical reactions and the formation of carbonate-bicarbonate solution and arise of underweight stress due to rain fall and land sliding, condition was provided for SCC. Further to SCC, sulfate reducing bacteria (SRB) activities around the pipelines have accelerated corrosion and the failure process.
\nTwo types of SCC on the external surface of underground pipelines have been diagnosticated: classical or high-pH SCC and near neutral or low-pH SCC. In high-pH SCC case, the external cracks more often initiate and progress intergranularly, and in low-pH SCC, cracks extend transgranularly. In this case, as a result of the formation of a high pH carbonate-bicarbonate solution, the cracks have been developed intergranularly (\nFigure 7\n). By propagation and so increasing the crack depth, chemical composition in the crack’s tip changed toward low pH, and the cracks propagated transgranularly (\nFigure 7\n). In a critical length of crack and the presence of inclusions such as MnS, atomic hydrogen will be formed in crack’s tip and penetrates into the microstructure. Formation of hydrogen was due to application of negative cathodic potential, more than 1.2 V with respect to Cu/CuSO4 reference electrode. Investigations have been shown that atomic hydrogen commonly tends to concentrate in the stress concentration points, microstructure interfaces like inclusion and matrix, microvoids, and other defects. The increase in the amount of atomic hydrogen in these sites and combination together decreased the interface cohesively strength and make atomic disbanding. These led the crack growth and fracture toward a cleavage and brittle mode.
\nUtilizing a high level of negative potential (1.2 V Cu/CuSO4) in cathodic protection provides condition for production of carbonate and bicarbonate which induced SCC. Therefore, controlling of the potential can be effective in reduction of these failures.
\nA vertical hydro-processing reactor failed only after 1 month operation. Penetrant testing (PT) revealed some cracks on the inner surface of the component. The cracks are mostly found to be initiated from the heat affected zone or HAZ of the weldments. The cracks were extended in perpendicular to the weld in the head side (\nFigure 9\n). Metallographic investigation showed that all the cracks had propagated essentially in transgranular mode, and they had the appearance of branching as it is shown in \nFigure 10\n. These features indicate occurrence of the stress corrosion cracking on the reactor [44].
\nCracks in heat affected zone of the weld toe.
The stress corrosion cracking: (a) heat affected zone and (b) the weld line.
The reactor shell is made of AISI 316L stainless steel and operates at a pressure of 4.5 MPa. The operating temperature is 200°C. The processing environment consists of some kind of neutral organic compound and hydrogen. The base metal and corrosion products were analyzed by scanning electron microscope (SEM) and energy dispersive X-ray (EDAX) analyzer. The results indicated that the reactor was made of AISI316L stainless steel. The analysis also revealed the presence of chlorides in corrosion products. The equipment history showed that the reactor was not annealed after weld though the thickness of the reactor was 45 mm. Therefore, remaining residual stress in services arise to a level of ultimate strength.
\nThe reactor has a thickness of 45 mm. Welding was used in manufacturing processes. A high magnitude of the welding residual stress is created (in magnitude of the ultimate strength) during welding operation. The chloride ions came from the catalysts. For the utilized catalyst, the chloride concentration is over 60 ppm. The operating temperature of the equipment is 200°C. Investigations have shown a chloride concentration of 10 ppm is enough to motive SCC in an austenitic stainless steel grade if the metal temperature is over 60°C [2]. Therefore, chloride SCC is responsible for the failure of the reactor.
\n\n
The main cause of failures is the presence of high concentration of chlorides. Therefore, decreasing the concentration of this aggressive agent in the catalyst can be an effective approach. Experience on the other reactor used in the same operational conditions shows that the concentration of chloride in the catalyst should be less than 60 ppm for the AISI 316L material.
Selecting more resistant materials to SCC. Since AISI 316L cannot tolerate such a high concentration of chloride, more corrosion-resistant alloys, such as duplex stainless steel, should be utilized if the concentration of the chloride in the catalyst cannot decrease to a proper level.
Decreasing the magnitude of stress. As welding residual stress is a main source of stress attributed to the stress corrosion cracking, decreasing its order should decline the possibility of SCC. It means that the reactor should be stress relived after welding.
Copper alloy C12200 tubes of a refrigerant failed in bend area (\nFigure 11\n). Failures were occurred only after approximately 6 months of services. The cracked area is shown in \nFigure 12\n. SEM image of fractured areas revealed the branch cracked through the microstructure, \nFigure 13\n, which is characteristic of a stress corrosion cracking phenomenon [61].
\nNew and old design of refrigerating system.
Macroscopic image cracked copper tube.
SEM image of the intergranular crack growth in the copper tube.
Chemical analysis of the tube materials proved the C12200 copper alloy composition. Analysis of the black foam insulation utilized with the subject copper tubing revealed the presence of ammonia by reason of the fabrication process. The stress comes from the cold-working of copper tubes during manufacturing processes.
\nThe phosphorized copper alloy tube failed due to SCC induced by a moist ammonia environment related the black foam insulation. Although the resistance of copper alloy under dry ammonia environment is notable, gathering of moisture ease by the bent tube geometry provokes the tubing to be exposed to wet ammonia, which is substantially more. The sensitivity of the copper tubing to SCC was enhanced in consequence of the cold work operation for bending the tube at an angle of 180°. Stress cycling and/or repeated loading increased the sensitivity to ammonia stress corrosion cracking as a result of the service-induced oscillational loading from the other components linked to the tube.
\nCold work operation generated high magnitude of residual stress which is responsible for the tube failure. Hence, it is suggested that the copper tubing be annealed after the bending operation in order to eliminate the adverse impacts of the cold working and also decrease the residual stresses available for ammonia stress corrosion cracking. Another recommendation is to avoid the installation of the tube under stress condition.
\nStress corrosion cracking failure is because of combined interaction of sensitizes material, tensile mechanical stress, and corrosive environment. Chloride stress corrosion cracking of austenitic steels and seasonal cracking of copper alloys are classical example of SCC. During stress corrosion cracking, the metal or alloy is basically uncorroded over most of its surface, while fine and branch cracks growth into the bulk of material and lead the components to unpredictable premature failures. The main source of stress attributed to the stress corrosion cracking is come from the residual stress. The well-known sources of the residual stress are welding and fabrication processes such as machining and bending. The stress must be in tensile form, and comprehensive stresses can be utilized to restrict SCC. Surface defects that either preexist or are created during service due to corrosion, wear, or other processes accelerate the stress corrosion cracking phenomenon. Therefore, the role of imperfections must be taken into account. Fracture mechanics introduced a parameter named fracture toughness or \n
If one of three SCC elements does not exist, this kind of failure will not develop. Accordingly, the solving methods can be one or more of these factors:
Lowering the stress levels
Eliminating aggressive species in the environment
Changing the material is one possible
Applying cathodic protection
Coating that is sometimes effective
Shot-peening that produces residual compressive stress
Soybean (
As a leguminous plant, soybean roots bear nodules formed as a result of infection by nodulating rhizobia, which perform symbiotic nitrogen fixation, and the plant acquires atmospheric nitrogen in the form of ammonia through these root nodules. Major soybean-nodulating rhizobia include
The compatibility and preference for nodulation by bradyrhizobial strains of soybean cultivars and varieties exhibiting the
In this chapter, we describe breeding and selection processes, shoot growth, yield components, and infection tendency of useful bradyrhizobia of
To select
Theoretically, all F1 lines obtained by crossing “Bonminori” and “Fukuyutaka” should present the
Appearance of nodulation following the inoculation of
Phenotypes of F3 seeds | Number of soybean lines |
---|---|
non-nodulation | 8 |
non-nodulation or nodulation | 63 |
nodulation | 82 |
not tested | 4 |
Total | 157 |
Nodulation phenotypes of F3 seeds.
Among the five seeds sown for selection, lines that did not form root nodule on all plants were classified as “non-nodulation,” lines that formed root nodule more than one of the five plants were classified as “non-nodulation or nodulation,” and lines that formed root nodule all plants classified as “nodulation.”
A 2-year field trial was conducted in 2016 and 2017 in the experimental field of the Agricultural Science Section, Education and Research Center for Biological Resources, Faculty of Life and Environmental Science, Shimane University, Japan (35°30′55″N, 133°06′36″E). The experimental sites were located at 35°30′60″N, 133°06′35″E in 2016 and 35°31′02″N, 133°06′40″E in 2017. Both experimental fields had gray lowland soil (paddy conversion fields). Soil pH (H2O) and electrical conductivity (mS cm−1) were respectively 6.42 and 0.10 in 2016 and respectively 6.72 and 0.06 in 2017. Before sowing, nitrogen, potassium, and phosphorus were applied at doses of 40, 100, and 100 kg ha−1, respectively. To correct soil pH, magnesium lime was applied at the dose of 1000 kg ha−1. The experiment followed the split-plot design with three replicates. Three soybean cultivars, namely “Enrei,” “Sachiyutaka,” and “Fukuyutaka,” as well as F10 or F11 plants of three
Soybean growth was evaluated during the flowering and harvest periods. Samples were collected by from 10 consecutive plots per replicate. During the flowering period, plant height, node number, branch number, stem and leaf dry weight, and main culm dry weight (2017 only) were measured. During the harvest period, plant height, node number, shoot dry weight, pod number, seed number, 100-seed weight, and yield were measured. Plant dry weight was measured after drying at 70°C for over 72 h in a drying apparatus. All statistical analyses were performed using R version 4.0.3 [49]. Soybean growth parameters during the flowering period were analyzed using Tukey’s honestly significant difference (HSD) test for multiple comparisons using the R package “multcomp.” Soybean yield components were subjected to two-way analysis of variance using anovakun version 4.8.5 [50]. Meteorological data during soybean cultivation were collected from past information provided by the Japan Weather Association (Table 2).
Year | month | Temperature (°C) | Precipitation Sunshine | |||
---|---|---|---|---|---|---|
minimum | maximum | mean | (mm) | duration (h) | ||
2016 | Jun. | 19.0 | 26.8 | 22.3 | 166.0 | 4.9 |
Jul. | 23.5 | 30.8 | 26.6 | 77.0 | 5.7 | |
Aug. | 23.6 | 32.2 | 27.2 | 140.5 | 7.7 | |
Sep. | 20.7 | 26.7 | 23.3 | 293.0 | 2.8 | |
Oct. | 15.3 | 22.3 | 18.5 | 103.5 | 3.5 | |
Nov. | 8.8 | 16.2 | 12.2 | 120.0 | 3.2 | |
2017 | Jun. | 16.5 | 25.9 | 20.9 | 86.5 | 7.3 |
Jul. | 24.8 | 31.5 | 27.6 | 168.5 | 5.6 | |
Aug. | 24.4 | 31.5 | 27.4 | 141.5 | 6.8 | |
Sep. | 18.5 | 26.3 | 22.1 | 214.5 | 4.9 | |
Oct. | 14.3 | 21.0 | 17.5 | 358.0 | 3.6 | |
Nov. | 6.7 | 15.7 | 11.1 | 93.0 | 4.6 |
Meteorological data during soybean cultivation in 2016 and 2017.
Values indicate monthly averages, and each value was calculated based on meteorological data provided by the Japan Weather Association.
The results of soybean growth during the flowering period in 2017 are presented in Table 3. The measurements during the flowering period were conducted on August 10, 2017, for B × F − E and “Enrei”; August 17, 2017, for B × F − M and “Sachiyutaka”; and August 26, 2017, for B × F − L and “Fukuyutaka.” The plant height of
Cultivar | Plant height (cm plant−1) | Node number (No. plant−1) | Branching number (No. plant−1) | Main culm dry weight (g m−2) | Stem and leaf dry weight (g m−2) | Shoot dry weight (g m−2) |
---|---|---|---|---|---|---|
BxF–E | 66.2 ab | 13.5 | 6.1 ab | 13.0 | 23.1 | 36.1 |
BxF–M | 67.9 ab | 13.1 | 6.5 ab | 15.3 | 25.1 | 40.4 |
BxF–L | 74.9 b | 13.6 | 7.2 bc | 16.7 | 22.4 | 39.0 |
Enrei | 53.7 a | 11.5 | 4.5 a | 12.2 | 27.0 | 39.2 |
Sachiyutaka | 57.3 a | 12.3 | 6.4 ab | 12.7 | 22.6 | 35.4 |
Fukuyutaka | 64.5 ab | 12.2 | 9.2 c | 17.4 | 23.8 | 41.2 |
ANOVA | * | ns | *** | ns | ns | ns |
Growth of soybean cultivars during the flowering period in 2017.
Values are presented as the means of three replicates. *
The results of yield components of soybean cultivars during the harvest period in 2016 and 2017 are presented in Table 4. In ANOVA, all yield components, except 100-seed weight, significantly differed between years and among cultivars. Specifically, pod and seed number and yield were significantly higher in 2016 than in 2017. Conversely, plant height, node number, and shoot dry weight were significantly higher in 2017 than in 2016. Based on the average values of the 2 years, pod and seed number in B × F − M was significantly higher than that in the other cultivars. Moreover, the yield of B × F − M and “Sachiyutaka” was significantly higher than that of B × F − E, B × F − L, and “Enrei.” Furthermore, 100-seed weight of “Sachiyutaka” was significantly higher than that of the other cultivars, except “Fukuyutaka.” Plant height and shoot dry weight of B × F − L tended to be higher than those of the other cultivars. The interaction between year and cultivar was detected for all test parameters, except seed number and yield. Therefore, multiple comparison analysis was performed among 12 cohorts for each test item, and the results are shown in Figure 2. Briefly, pod and seed number and yield were lower in all soybean cultivars in 2017 than in 2016. Furthermore, pod and seed number of B × F − E, B × F − M, “Enrei,” and “Sachiyutaka” decreased significantly. While the yield of “Sachiyutaka” decreased significantly, that of B × F − E, B × F − M, B × F − L, “Enrei,” and “Fukuyutaka” tended to decrease, albeit without significant differences. “Enrei,” “Sachiyutaka,” and “Fukuyutaka” are soybean cultivars that are suitable or possible to cultivate in the Chugoku region of Japan, including Shimane prefecture, where the cultivation test was conducted in the present study [51, 52]. Additionally, pod and seed numbers are the most important soybean yield components, which are primarily determined during the period from before and after flowering to pod set, including the beginning of the seed filling period [53]. However, increasing temperature during the growing season can negatively affect soybean leaf photosynthesis, growth, flowering, pod and seed number, and yield [54, 55]. In the present study, the monthly mean maximum temperature in August during the flowering period of soybean was 32.2°C in 2016 and 31.5°C in 2017 (Table 2). Specifically, in early August of 2017, when B × F − E and “Enrei” were flowering, the temperature remained above 35°C for 3 consecutive days. Furthermore, in late August of 2017, the temperature remained above 32°C for 4 consecutive days. Additionally, in October 2017, nearly 3.5 times the amount of precipitation in 2016 was recorded (Table 2). Soybean pod and seed number and yield in 2017 were significantly lower than the values in 2016 due to the effects of these meteorological factors (Table 4). Moreover, the 100-seed weight of B × F − M was lower than that of “Sachiyutaka” and “Fukuyutaka” (Table 4). Therefore, backcrossing with these cultivars is expected to produce soybean cultivars with larger seeds and higher yield.
Year | Cultivar | Plant height (cm plant−1) | Node number (No. plant−1) | Shoot dry weight (g m−2) | Pod number (No. m−2) | seed number (No. m−2) | 100-seed weight (g) | Yield (g m−2) |
---|---|---|---|---|---|---|---|---|
2016 | BxF–E | 50.3 | 11.1 | 251.6 | 1426.7 | 937.0 | 23.9 | 214.4 |
BxF–M | 51.1 | 11.3 | 298.5 | 1927.0 | 2276.8 | 22.9 | 514.5 | |
BxF–L | 71.1 | 12.1 | 336.6 | 1036.0 | 1372.9 | 23.5 | 325.8 | |
Enrei | 34.3 | 9.5 | 215.7 | 542.6 | 421.4 | 22.6 | 96.1 | |
Sachiyutaka | 46.1 | 12.1 | 300.3 | 1531.1 | 1742.9 | 33.8 | 584.6 | |
Fukuyutaka | 56.3 | 12.1 | 267.1 | 1057.3 | 1377.3 | 29.8 | 407.4 | |
2017 | BxF–E | 67.4 | 13.4 | 251.8 | 318.8 | 218.9 | 24.6 | 53.8 |
BxF–M | 69.4 | 13.8 | 432.8 | 1180.7 | 1477.4 | 23.6 | 348.2 | |
BxF–L | 71.0 | 13.3 | 424.1 | 784.0 | 1037.7 | 20.7 | 214.6 | |
Enrei | 56.2 | 12.8 | 340.0 | 101.0 | 56.2 | 29.7 | 16.7 | |
Sachiyutaka | 55.2 | 12.1 | 344.2 | 704.1 | 974.4 | 29.3 | 285.8 | |
Fukuyutaka | 70.5 | 16.1 | 467.9 | 934.1 | 1245.6 | 25.0 | 311.7 | |
2016 | 51.5 | 11.4 | 278.3 | 1253.4 | 1354.7 | 26.1 | 357.1 | |
2017 | 64.9 | 13.6 | 376.8 | 670.4 | 835.0 | 25.5 | 205.1 | |
BxF–E | 58.8 bc | 12.2 a | 251.7 a | 872.7 b | 578.0 a | 24.2 a | 134.1 a | |
BxF–M | 60.3 bc | 12.5 ab | 365.7 b | 1553.8 c | 1877.1 c | 23.2 a | 431.4 c | |
BxF–L | 71.0 d | 12.7 ab | 380.4 b | 910.0 b | 1205.3 b | 22.1 a | 270.2 b | |
Enrei | 45.2 a | 11.1 a | 277.8 a | 321.8 a | 238.8 a | 26.1 a | 56.4 a | |
Sachiyutaka | 50.6 ab | 12.1 a | 322.3 ab | 1117.6 b | 1358.6 b | 31.6 b | 435.2 c | |
Fukuyutaka | 63.4 cd | 14.1 b | 367.5 b | 995.7 b | 1311.5 b | 27.4 ab | 359.6 bc | |
ANOVA | Year (Y) | *** | *** | *** | *** | *** | ns | *** |
Cultivar (C) | *** | *** | *** | *** | *** | *** | *** | |
Y x C | * | ** | ** | ** | ns | ** | ns |
Yield components of soybean cultivars in 2016 and 2017.
Values are presented as the means of three replicates. *
Yield components for each soybean cultivar in 2016 and 2017 growing seasons. Values are presented as the mean ± SE of three replicates. Different letters indicate significant differences (Tukey’s HSD test) at
The nodules used in the present experiment were collected from soybean roots at the flowering stage in 2017, as described in Section 3.2. The nodules were surface-sterilized with 70% ethanol for 3 min and diluted sodium hypochlorite solution (0.25% available chlorine) for 30 min, followed by washing with sterile distilled water. After washing, 24 nodules were randomly collected and transferred to 1.5 mL microcentrifuge tubes. Each nodule was homogenized in sterile distilled water and streaked onto a yeast extract–mannitol agar (YMA) plate [56]; to isolate a single colony per nodule, the plates were incubated for 5–7 days in the dark at 28°C. A total of 144 isolates were obtained from six soybean plants and used for PCR-restriction fragment length polymorphism (RFLP) analysis of the 16S–23S rRNA gene internal transcribed spacer (ITS) region.
For DNA extraction, each isolate was cultured in 1.5 mL HM medium supplemented with 0.1% l-arabinose for 5–7 days in the dark at 28°C. Total DNA for use as the PCR template was extracted from the isolates in BL extraction buffer, as described previously [42] based on the method reported by Hiraishi
The 16S–23S rRNA gene ITS region was PCR-amplified using
Eighty-seven indigenous soybean-nodulating bradyrhizobial isolates belonging to the cluster
The occupancy rate of indigenous bradyrhizobia infecting each soybean cultivar is presented in Table 5. Based on the fragment patterns obtained from PCR–RFLP analysis, the indigenous isolates with the similar patterns as the five reference strains, namely
Cultivar | Bj6 | Bd110 | Be46 | Be76 | Be94 | ||
---|---|---|---|---|---|---|---|
BxF–E | 12.5 | 8.3 | 62.5 | 12.5 | 0.0 | 0.0 | 4.2 |
BxF–M | 70.8 | 0.0 | 16.7 | 12.5 | 0.0 | 0.0 | 0.0 |
BxF–L | 0.0 | 8.3 | 45.8 | 25.0 | 4.2 | 16.7 | 0.0 |
Enrei | 83.4 | 0.0 | 8.3 | 8.3 | 0.0 | 0.0 | 0.0 |
Sachiyutaka | 13.0 | 8.7 | 34.8 | 39.1 | 0.0 | 4.4 | 0.0 |
Fukuyutaka | 25.0 | 20.8 | 16.7 | 37.5 | 0.0 | 0.0 | 0.0 |
Occupancy rate (%) of indigenous soybean-nodulating bradyrhizobia in each soybean cultivar in 2017.
Bj6, Bd110, Be46, Be76, and Be94 showed RFLP patterns similar to those of
Correlation analysis was used to evaluate the association between the occupancy rate of indigenous bradyrhizobial strains and yield components. Correlation coefficients were computed based on data obtained from the measurement of yield components and occupancy rate of indigenous soybean-nodulating bradyrhizobia. The R package “psych” was used to compute and plot the correlations. Additionally, the significance of the correlations was tested using the “cor.test” function in R.
The results of correlation analysis between the occupancy rate of indigenous bradyrhizobial strains and yield components of soybean are presented in Figure 3. The correlation coefficients of the occupancy rate of Bj6 isolates with plant height, node number, shoot dry weight, pod number, seed number, 100-seed weight, and yield were − 0.30, −0.04, 0.09, −0.13, −0.15, 0.41, and − 0.16, respectively. The correlation coefficients of the occupancy rate of Bd110 isolates exhibiting
Correlation coefficient between the occupancy rate of Bd110 isolates carrying the
Correlation between the occupancy rate of
In the present chapter, we described the breeding and selection processes, shoot growth, yield components, and infection tendency of useful bradyrhizobial strains carrying the
First, we selected eight lines exhibiting the characteristics of the
Finally, to evaluate the association between the occupancy rate of indigenous bradyrhizobial strains and yield components of soybean, correlation analysis was performed. Correlation coefficients of the occupancy rate of Bd110 isolates exhibiting
The authors thank the members of the laboratories of Shimane University and University of Miyazaki involved in the present study. Additionally, the authors thank the technical staff of the Agricultural Science Section, Education and Research Center for Biological Resources, Faculty of Life and Environmental Science, Shimane University for their support in managing soybean cultivation. The authors also thank the Faculty of Life and Environmental Sciences at Shimane University for financial support to publish this chapter.
The authors declare no conflict of interest.
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Magnetic materials absorb greatly microwaves. The more magnetic, the more microwaves are absorbed. The aim of this chapter is to present the fundamental physics of the absorption of microwave power (energy per unit time) by ferrimagnetic and ferromagnetic matter in the nano and micro size scale. The magnetic moments and their collective modes are the basic microscopic absorbers under in-resonance and out-of-resonance conditions. Experimental setups and measurement techniques are described. The profiles of microwave absorption are described and connected to the micromagnetic environment that elicits such absorption. Section by section and the overall microwave power absorption profiles are related to the micromagnetic structures. Emphasis is made on nano- and micromagnets. 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The radiofrequency electromagnetic waves (RFW) emitted by different smart phones was measured by using a TriField meter. Chick fertilized eggs were placed in an egg incubator, divided into control and exposed groups. In the exposed group, a mobile phone was placed inside an incubator in call receiving mode, while in the control group, the mobile phone was not used. Studies were conducted at low and high exposure (dose) of RFW. Chick embryos were sacrificed at day 10 and day 15, and embryos were examined for mortality, gross malformation, weight, and length. Histology, electron microscopy, and Hsp 70 of liver were done for the high dose group. No mortality was observed in the low dose group; however, in the high dose group, the mortality was 14%, and deformities of the limbs and skin abnormalities were observed. Weight and length in the exposed groups were significantly lower than the control at higher dose. Histology and ultrastructure of liver revealed fatty infiltration, increase number of mitochondria, deformation, and disappearance of its cristae. 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Also, the effect of various parameters such as current, gain, frequency bandwidth, optical to terahertz conversion efficiency, etc. are studied to study the importance of IPCAs.",book:{id:"10206",title:"Terahertz Technology",coverURL:"https://cdn.intechopen.com/books/images_new/10206.jpg"},signatures:"Shyamal Mondal, Nisha Flora Boby Edwin and Vaisshale Rathinasamy"},{id:"80114",title:"Terahertz Sensing Based on Photonic Crystal Fibers",slug:"terahertz-sensing-based-on-photonic-crystal-fibers",totalDownloads:113,totalDimensionsCites:0,doi:"10.5772/intechopen.101732",abstract:"Photonic-crystal-fiber (PCF) based sensors in the terahertz spectrum have been immensely studied and implemented due to their unique advantages and high sensitivity. At an early stage, conventional and hybrid structured porous core PCF-based sensors were proposed, but the sensitivity was not so high. With the advancement of PCF fabrication technology, hybrid structured hollow-core PCFs have been reported and offer superior sensing characteristics than the previous types. In this chapter, both porous core and hollow-core PCF-based THz sensors are analyzed and the propagation characteristics are explained using terahertz spectrum. Finally, some promising terahertz sensors are studied and compared at the end of this chapter.",book:{id:"10206",title:"Terahertz Technology",coverURL:"https://cdn.intechopen.com/books/images_new/10206.jpg"},signatures:"Md. Ahasan Habib, Md. Shamim Anower and Md. Nazmul Islam"},{id:"79181",title:"Terahertz Nano-Imaging with s-SNOM",slug:"terahertz-nano-imaging-with-s-snom",totalDownloads:159,totalDimensionsCites:1,doi:"10.5772/intechopen.99102",abstract:"Spectroscopy and imaging with terahertz radiation propagating in free space suffer from the poor spatial resolution which is a consequence of the comparatively large wavelength of the radiation (300 μm at 1 THz in vacuum) in combination with the Abbe diffraction limit of focusing. A way to overcome this limitation is the application of near-field techniques. In this chapter, we focus on one of them, scattering-type Scanning Near-field Optical Microscopy (s-SNOM) which − due to its versatility − has come to prominence in recent years. This technique enables a spatial resolution on the sub-100-nm length scale independent of the wavelength. We provide an overview of the state-of-the-art of this imaging and spectroscopy modality, and describe a few selected application examples in more detail.",book:{id:"10206",title:"Terahertz Technology",coverURL:"https://cdn.intechopen.com/books/images_new/10206.jpg"},signatures:"Matthias M. Wiecha, Amin Soltani and Hartmut G. Roskos"},{id:"77821",title:"Optical Heterodyne Measurement of Terahertz Wave",slug:"optical-heterodyne-measurement-of-terahertz-wave",totalDownloads:146,totalDimensionsCites:0,doi:"10.5772/intechopen.99168",abstract:"One of the most notable frequency regions in terms of research currently lies in the ‘frequency gap’ region between microwaves and infrared: terahertz wave. Although new methods for generating and detecting terahertz wave have been developed, few detectors operating at room temperature are able to capture low-energy terahertz beams. Here we introduce the optical heterodyne measurement (nonlinear frequency up-conversion detection) of terahertz wave using parametric wavelength conversion in a nonlinear crystal; this has better sensitivity than many commonly used thermal detectors such as pyroelectric detectors. Additionally, optical heterodyne techniques allow the beams of terahertz wave to be visualized and their frequency and intensity determined directly as visible light. These are very promising for extending applied researches into the terahertz region, and we expect that these will open new research fields such as wireless information communications or non-destructive inspection in the terahertz region.",book:{id:"10206",title:"Terahertz Technology",coverURL:"https://cdn.intechopen.com/books/images_new/10206.jpg"},signatures:"Shin’ichiro Hayashi and Norihiko Sekine"},{id:"77432",title:"A Novel Approach for Room-Temperature Intersubband Transition in GaN HEMT for Terahertz Applications",slug:"a-novel-approach-for-room-temperature-intersubband-transition-in-gan-hemt-for-terahertz-applications",totalDownloads:154,totalDimensionsCites:1,doi:"10.5772/intechopen.98435",abstract:"Terahertz (THz) technology has attracted tremendous attention recently due to its promising applications in various domains such as medical, biological, industrial imaging, broadband, safety, communication, radar, space science, and so on. Due to non-availability of powerful sources and highly sensitive and efficient detectors, the so-called THz gap remains largely unfilled. Despite seamless efforts from electronics and photonics technology researchers, the desired level of technology development to fill the THz gap still remains a challenge. GaN-based HEMT structures have been investigated as potential THz sources and detectors by a number of researchers. This chapter presents a very new and versatile mechanism for electrical tuning of intersubband transitions (ISBT) GaN high electron mobility transition (HEMT) devices. ISBT phenomena are usually demonstrated in photonic devices like a quantum cascade laser (QCL). Here we explore ISBT in an electronic GaN HEMT device. Conventional photonic devices like a QCL are operated at cryogenic temperature to minimize thermal effect. Tuning the conduction band through external gate bias is an advantage of an HEMT device for room temperature (RT) THz applications. This chapter demonstrates the theoretical and experimental novel ISBT phenomenon in GaN HEMT is for potential ambient applications in the THz range.",book:{id:"10206",title:"Terahertz Technology",coverURL:"https://cdn.intechopen.com/books/images_new/10206.jpg"},signatures:"Rakesh Kaneriya, Gunjan Rastogi, Palash Basu, Rajesh Upadhyay and Apurba Bhattacharya"}],onlineFirstChaptersTotal:9},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:124,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:12,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. In brief, we hope that books in this series will provide accessible references for those interested or working in this field and encourage learning in a range of different topics.",coverUrl:"https://cdn.intechopen.com/series/covers/13.jpg",latestPublicationDate:"August 7th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:11,editor:{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"19",title:"Animal Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/19.jpg",isOpenForSubmission:!0,editor:{id:"259298",title:"Dr.",name:"Edward",middleName:null,surname:"Narayan",slug:"edward-narayan",fullName:"Edward Narayan",profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",biography:"Dr. Edward Narayan graduated with Ph.D. degree in Biology from the University of the South Pacific and pioneered non-invasive reproductive and stress endocrinology tools for amphibians - the novel development and validation of non-invasive enzyme immunoassays for the evaluation of reproductive hormonal cycle and stress hormone responses to environmental stressors. \nDr. Narayan leads the Stress Lab (Comparative Physiology and Endocrinology) at the University of Queensland. A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},{id:"20",title:"Animal Nutrition",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",isOpenForSubmission:!0,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"28",title:"Animal Reproductive Biology and Technology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",isOpenForSubmission:!0,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). She is the head of the Reproduction and Embryology Laboratories and was lecturer of Reproduction and Reproductive Biotechnologies at Veterinary Medicine Faculty. She has over 25 years of experience working in reproductive biology and biotechnology areas with a special emphasis on embryo and gamete cryopreservation, for research and animal genetic resources conservation, leading research projects with several peer-reviewed papers. Rosa Pereira is member of the ERFP-FAO Ex situ Working Group and of the Management Commission of the Portuguese Animal Germplasm Bank.",institutionString:"The National Institute for Agricultural and Veterinary Research. Portugal",institution:null},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:20,paginationItems:[{id:"82991",title:"Diseases of the Canine Prostate Gland",doi:"10.5772/intechopen.105835",signatures:"Sabine Schäfer-Somi",slug:"diseases-of-the-canine-prostate-gland",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"82956",title:"Potential Substitutes of Antibiotics for Swine and Poultry Production",doi:"10.5772/intechopen.106081",signatures:"Ho Trung Thong, Le Nu Anh Thu and Ho Viet Duc",slug:"potential-substitutes-of-antibiotics-for-swine-and-poultry-production",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Antibiotics and Probiotics in Animal Food - Impact and Regulation",coverURL:"https://cdn.intechopen.com/books/images_new/11578.jpg",subseries:{id:"20",title:"Animal Nutrition"}}},{id:"82905",title:"A Review of Application Strategies and Efficacy of Probiotics in Pet Food",doi:"10.5772/intechopen.105829",signatures:"Heather Acuff and Charles G. 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She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}}]},{type:"book",id:"7144",title:"Veterinary Anatomy and Physiology",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7144.jpg",slug:"veterinary-anatomy-and-physiology",publishedDate:"March 13th 2019",editedByType:"Edited by",bookSignature:"Catrin Sian Rutland and Valentina Kubale",hash:"75cdacb570e0e6d15a5f6e69640d87c9",volumeInSeries:2,fullTitle:"Veterinary Anatomy and Physiology",editors:[{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}}]},{type:"book",id:"8524",title:"Lactation in Farm Animals",subtitle:"Biology, Physiological Basis, Nutritional Requirements, and Modelization",coverURL:"https://cdn.intechopen.com/books/images_new/8524.jpg",slug:"lactation-in-farm-animals-biology-physiological-basis-nutritional-requirements-and-modelization",publishedDate:"January 22nd 2020",editedByType:"Edited by",bookSignature:"Naceur M'Hamdi",hash:"2aa2a9a0ec13040bbf0455e34625504e",volumeInSeries:3,fullTitle:"Lactation in Farm Animals - Biology, Physiological Basis, Nutritional Requirements, and Modelization",editors:[{id:"73376",title:"Dr.",name:"Naceur",middleName:null,surname:"M'Hamdi",slug:"naceur-m'hamdi",fullName:"Naceur M'Hamdi",profilePictureURL:"https://mts.intechopen.com/storage/users/73376/images/system/73376.jpg",biography:"Naceur M’HAMDI is Associate Professor at the National Agronomic Institute of Tunisia, University of Carthage. He is also Member of the Laboratory of genetic, animal and feed resource and member of Animal science Department of INAT. He graduated from Higher School of Agriculture of Mateur, University of Carthage, in 2002 and completed his masters in 2006. Dr. M’HAMDI completed his PhD thesis in Genetic welfare indicators of dairy cattle at Higher Institute of Agronomy of Chott-Meriem, University of Sousse, in 2011. He worked as assistant Professor of Genetic, biostatistics and animal biotechnology at INAT since 2013.",institutionString:null,institution:null}]},{type:"book",id:"8460",title:"Reproductive Biology and Technology in Animals",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/8460.jpg",slug:"reproductive-biology-and-technology-in-animals",publishedDate:"April 15th 2020",editedByType:"Edited by",bookSignature:"Juan Carlos Gardón Poggi and Katy Satué Ambrojo",hash:"32ef5fe73998dd723d308225d756fa1e",volumeInSeries:4,fullTitle:"Reproductive Biology and Technology in Animals",editors:[{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón Poggi",slug:"juan-carlos-gardon-poggi",fullName:"Juan Carlos Gardón Poggi",profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:null,institution:{name:"Valencia Catholic University Saint Vincent Martyr",institutionURL:null,country:{name:"Spain"}}}]}]},openForSubmissionBooks:{paginationCount:2,paginationItems:[{id:"11673",title:"Stem Cell Research",coverURL:"https://cdn.intechopen.com/books/images_new/11673.jpg",hash:"13092df328080c762dd9157be18ca38c",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"July 13th 2022",isOpenForSubmission:!0,editors:[{id:"203598",title:"Ph.D.",name:"Diana",surname:"Kitala",slug:"diana-kitala",fullName:"Diana Kitala"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"12215",title:"Cell Death and Disease",coverURL:"https://cdn.intechopen.com/books/images_new/12215.jpg",hash:"dfd456a29478fccf4ebd3294137eb1e3",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"July 29th 2022",isOpenForSubmission:!0,editors:[{id:"59529",title:"Dr.",name:"Ke",surname:"Xu",slug:"ke-xu",fullName:"Ke Xu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:21,paginationItems:[{id:"83000",title:"Purine and Pyrimidine Pathways as Antimalarial Targets",doi:"10.5772/intechopen.106468",signatures:"Yacoba V.T. 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Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. 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He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"18",type:"subseries",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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