\r\n\t \r\n\tThe output uncertainty in mathematical models can be reduced by using uncertainty and sensitivity analysis. \r\n\tThis book intends to provide the reader with a comprehensive overview of uncertainty quantification and sensitivity analysis, their implementations and possible applications in different fields of science and technology.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"850fbcfc8555e1f90f5788fe6beb0454",bookSignature:"Dr. Raheem Gul and Dr. Aamir Shahzad",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9326.jpg",keywords:"Uncertainty analysis, Uncertainty quantification, Sensitivity analysis, Physical systems, Cardiovascular system, Parametric Uncertainty, Measurement Uncertainty, Monte Carlo Simulations, Local Sensitivity Analysis, Global Sensitivity Analysis, Variance Decomposition Methods, Design Experiments",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 15th 2019",dateEndSecondStepPublish:"November 12th 2019",dateEndThirdStepPublish:"January 11th 2020",dateEndFourthStepPublish:"March 31st 2020",dateEndFifthStepPublish:"May 30th 2020",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"209417",title:"Dr.",name:"Raheem",middleName:null,surname:"Gul",slug:"raheem-gul",fullName:"Raheem Gul",profilePictureURL:"https://mts.intechopen.com/storage/users/209417/images/system/209417.jfif",biography:"Dr Gul is an Assistant Professor at COMSATS University Islamabad, Abbottabad Campus, Pakistan. He received a PhD in Mathematics from Free University Berlin, Germany. His research is in he area of cardiovascular modeling, uncertainty quantification and sensitivity analysis. He published 12 research articles in well reputed journals and international conferences. Additionally, he supervised 6 MS students in COMSATS University Islamabad, Abbottabad Campus, Pakistan.",institutionString:"COMSATS University Islamabad",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:{id:"304588",title:"Dr.",name:"Aamir",middleName:null,surname:"Shahzad",slug:"aamir-shahzad",fullName:"Aamir Shahzad",profilePictureURL:"https://mts.intechopen.com/storage/users/304588/images/system/304588.jfif",biography:"Dr. Shahzad is a Lecturer at the COMSATS University Islamabad, Abbottabad Campus, Pakistan. He holds a PhD in Mathematics from COMSATS University Islamabad, Islamabad Campus, Pakistan. 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1. Introduction
Both natural and man-made acidic habitats are widely distributed in global land and ocean ecosystems, such as acidic sulfur-rich thermal springs, marine volcanic vents, and acid mine drainage (AMD) [1]. However, these unique habitats harbor the active acidophilic organisms that are well adapted to the acidic environments. Undoubtedly, acidophiles are distributed randomly throughout the tree of life and prevalent in the acidity or extreme acidity habitats, archaea and bacteria in particular, and they represent an extreme life-forms [2, 3, 4]. Generally, acidophilic archaea and bacteria mainly include members of phylum Euryarchaeota, Crenarchaeota, Proteobacteria, Acidobacteria, Nitrospira, Firmicutes, Actinobacteria and Aquificae such as Ferroplasma, Acidiplasma, Sulfolobus, Acidianus, Acidiphilum, Acidithiobacillus, Acidihalobacter, Ferrovum, Acidiferrobacter, Acidobacterium, Leptospirillum, Sulfobacillus, Acidibacillus, Acidimicrobium, and Hydrogenobaculum [5, 6, 7]. More importantly, acidophiles, as an important taxa of microorganisms, are closely related to the biogeochemistry cycles, eco-environment and human development, such as driving the elemental sulfur and iron cycles [8], the water and soil polluted by acidic effluents [9], biomining-bioleaching techniques and bioremediation technologies [9, 10, 11]. Thus, a comprehensive understanding of the acid-resistance networks and modules of acidophiles would be helpful for the understanding of the evolutionary processes, ecological behaviors and industry applications of acidophiles.
Acidophiles thrive at an extremely low pH and maintain a relatively neutral cytoplasm pH [12], namely maintenance several orders of magnitude difference in proton concentrations in cell; thus, one of the main challenges to these microorganisms living in acidic habitats is the extremely acidic stress environments. Acidophiles have evolved a large number of mechanisms to withstand the deleterious effects of fluctuations in proton concentration (Figure 1), due to the fact that acidophiles face the challenge of maintaining a near neutral intracellular pH. Currently, the mechanisms of growth and acid tolerance of typical extreme acidophiles in extremely low pH environments have been widely studied [13, 14, 15]. Herein, we, specifically focusing on acid-tolerant mechanisms, strategies, functions, and modules instead of species types, reviewed and summarized the current knowledge of the acid-resistance networks adopted by acidophiles for coping with acid or extreme acid environments. In addition, owing to space constraints and complexity of acidophiles types, we limit our discussion of the acid-tolerant adaptation mechanisms to typical acidophiles (archaea and bacteria) that populate acidic habitats.
Figure 1.
The active and passive acid-resistance mechanisms in acidophiles. (a) Proton pump: F1Fo–ATPase complex pump protons out of the cells though ATP hydrolysis. (b) Proton consumption modules: GadB-GadC system can consume excess intracellular protons. (c) Reversed transmembrane electrical potential (Δψ) modules: Generating a reversed Δψ is by positive ions transport (e.g. K+ transport). (d) Membranes system: The highly impermeable cell membranes structure. (e) Macromolecules protection modules: A larger proportion of DNA and protein repair systems such as Dps, GrpE, MolR, and DnaK proteins. (f) Escaping system: QS system, biofilm, chemotaxis and cell motility modules. (g) Other modules: Some possible mechanisms of imperfect classification, including iron “rivet”, degradation proteins of organic acids, surface proteins of high pI values, and outer membrane porin.
2. Acid-resistant mechanisms of acidophiles
2.1 Active support of acidophiles pH homeostasis
Microorganisms tend to maintain a high proton motive force (PMF) and a near-neutral pH in cytoplasm. The transmembrane electrical potential (Δψ) and transmembrane pH gradient (ΔpH) could vary as a function of the external pH. The immediately available energy source for acidophilic cell is this pre-existing transmembrane proton gradient, due to the external environments are frequently in the pH range of 1.0–3.0, while the typical pH of cytoplasms are close to 6.5 (that is, the differential proton concentrations of 4–6 orders of magnitude). The ΔpH across the membrane is a major part of the PMF, and the ΔpH is linked to cellular bioenergetics. Acidophiles, such as Acidithiobacillus ferrooxidans and Acidithiobacillus caldus, are capable of using the ΔpH to generate a large quantity of ATP [16, 17]. However, this processes would lead to the rapid acidification of the cytoplasm of alive cells. Because a high level of protons concentration would destroy essential molecules in cell, such as DNA and protein, acidophiles have evolved the capability to pump protons out of their cells at a relatively high rate. The F1Fo–ATPase consists of a hydrophilic part (F1) composed of α, β, γ, δ, and ε subunits and a hydrophobic membrane channel (Fo) composed of a, b, and c subunits; among them, the F1 catalyzes ATP hydrolysis or synthesis and the Fo translocates protons. This mechanism pumps out protons from cells by hydrolyzing ATP (Figure 1), thereby efficiently protecting cells from the acidic environments. In several microorganisms, transcriptional level of the atp operon upregulated by exposure to the acidic environments, including A. caldus, Acidithiobacillus thiooxidans, and Lactobacillus acidophilus [18, 19, 20], suggesting its critical role in acid resistance of cell. Several proton efflux proteins have also been identified in the sequenced genomes of A. ferrooxidans, A. thiooxidans, A. caldus, Ferroplasma acidarmanus, and Leptospirillum group II [21, 22]. The H+-ATPase activity and NAD+/NADH ratio were upregulated in A. thiooxidans under the acid stress [19]. The cells actively pump out protons by a respiratory chain from cell. For example, under the acid stress, the A. caldus increases its expression of respiratory chain complexes that can pump protons out of the cells [20]. Meanwhile, NAD+ involved in glycolysis as the coenzyme of dehydrogenase, generating large amount of ATP and contributing to pump protons out of the cells though ATP hydrolysis.
Among the active mechanisms, the proton consumption systems are necessary to remove excess intracellular protons. Once protons enter the cytoplasm, some mechanisms and patterns are required to mitigate effects caused by a high concentration of proton in cells. Under the acidic conditions, there is increased expression of amino acid decarboxylases enzymes (such as Glutamate decarboxylase-β (GadB)) that could consume the cytoplasmic protons by the catalytic reactions [23]. GadB, coupling with a glutamate/gamma-aminobutyrate antiporter (GadC), catalyzed glutamate to γ-aminobutyric acid (GABA) and exchanged with glutamate substrate to achieve continued decarboxylation reactions (Figure 1) [24]. It consumed a proton during the decarboxylation reactions and thus supported the intracellular pH homeostasis. And, it would contribute to a reversed Δψ in most bacteria. Similarly, the gadB gene was found in Ferroplasma spp., and the gene transcription was upregulated under acid shock conditions in A. caldus [20, 22]. Therefore, in order to maintain pH homeostasis of cell, acidophiles need to be able to consume excess protons from the cytoplasm.
A second major strategy for the active mechanisms used by acidophiles to reduce the influx of protons is the generation of an inside positive Δψ that generated by a Donnan potential of positively charged ions. A positive inside transmembrane potential was contributed to a reversed Δψ that could prevent protons leakage into the cells. The acidophiles might use the same strategies to generate a reversed membrane potential to resist the inward flow of protons, Na+/K+ transporters in particular (Figure 1) [25]. Previous data showed that some genomes of acidophiles (A. thiooxidans, F. acidarmanus, Sulfolobus solfataricus, etc.) contain a high number of cation transporters genes and these transporters were probably involved in the generation of Donnan potential to inhibit the protons influx [21, 22, 25, 26]. The genome of Picrophilus torridus also encodes large number of proton-driven secondary transporters which represents adaptation to the more extremely acidic environment [27]. Furthermore, we found that the maintenance of Δψ in A. thiooxidanswas directly related to the uptake of cations, especially the influx of potassium ions [25]. Further evidence of chemiosmotic gradient created by a Donnan potential to support acid resistance is the Donnan potential created by a passive mechanism, that is, a small residual inside positive Δψ and ΔpH are maintained in inactive cells of A. caldus, A. ferrooxidans, Acidiphilium acidophilum, and Thermoplasma acidophilum [28, 29, 30]. The residual Δψ and ΔpH studies have been criticized because of measurement methods [31]. However, subsequent data showed that the energy-dependent cation pumps played an important role in generating an inside positive Δψ. In addition, acidophilic bacteria are highly tolerant to cations and are more sensitive to anions. In summary, the inside positive Δψ is a ubiquitous and significant strategy in maintaining the cellular pH homeostasis.
Although improving the efflux and consumption of protons and increasing the expression of secondary transporters are a common strategy, the most effective strategy is also to reduce the proton permeability of cell membrane [32, 33]. Acidophiles can synthesize a highly impermeable membrane to respond to proton attack (Figure 1). These physiological adaptations membranes are composed of the high levels of iso/anteiso-BCFAs (branched chain fatty acids), both saturated and mono-unsaturated fatty acids, β-hydroxy, ω-cyclohexyl and cyclopropane fatty acids (CFAs) [34]. It was found that cell membrane resisted the acid stress by increasing the proportion of unsaturated fatty acid and CFAs in some bacteria, such as A. ferrooxidans and Escherichia coli [35, 36, 37]. Although the cytoplasmic membrane is the main barrier to protons influx, the destruction of the membrane caused by protons may cause this barrier to break down. The key component of membranes preventing acid damage seems to be CFAs, which contributes to the formation of cell membrane compactness. Supporting this mechanism is that E. coli with a mutation in the cfa gene became quite sensitive to low pH and can overcome this sensitivity by providing the exogenous cfa gene [36]. Meanwhile, the transcription of cfa gene was upregulated under the acid stress in A. caldus [20], and it suggests that changing the fatty acid content of the cell membrane is an adaptive response to acid stress. In brief, the CFAs is important for maintaining membrane integrity and compactness under the acid conditions.
To maintain the pH homeostasis of cells, acidophilic archaea cells have a highly impermeable cell membrane to restrict proton influx into the cytoplasm. One of the key characteristics of acidophilic archaea is the monolayer membrane typically composed of large amount of GDGTs, which are extremely impermeable to protons [38, 39, 40]. Although acidophilic bacteria have a variety of acid-resistant adaptation strategies, compared with acidophilic archaea, it has not been found that these bacteria would exhibit excellent growth ability below pH 1. The special tetraether lipid is closely related to acid-tolerance capability, because the ether linkages are less sensitive to acid hydrolysis than ester linkages [41]. And, the results of studies on acidophilic archaea indicated that tetraether lipids may be more resistant to acid than previously thought [42]. Therefore, the contribution of tetraether lipids to adaptation of archaea to extremely low pH is enormous. To a certain extent, it also supports the reason why dominance of archaea under extremely acidic environments. Similarly, the extreme acid tolerance of archaea can be attributed to cyclopentane rings and the vast methyl-branches [43]. In addition, it was found that the less phosphorus in the lipoprotein layer of acidophilus cell can contribute to higher hydrophobicity, which was beneficial for resisting extreme acid shock [13]. Irrespective of the basic composition of cell membranes, bacteria and archaea have extensively reshaped their membrane components to overcome the extremely low acid environments. In summary, the impermeable of acidophilic cell membrane is an important strategy for the pH homeostasis of acidophiles formed by restricting the influx of protons into the cells.
3. Passive strategies for acidophiles living
When the cells are attacked or stressed by higher concentrations of protons, the passive mechanisms of pH homeostasis would support the active mechanism. If protons penetrate the acidophilic cell membrane, a range of intracellular repair systems would help to repair the damage of macromolecules [13]. The DNA and protein repair systems play a central role in coping with acid stress of cells (Figure 1). Because DNA carries genetic information of cell life and protein plays an important role in the physiological activities of cells, DNA or protein damage caused by protons would bring irreversible harm to cells. When the cells are exposed to a high concentration of proton environments or protons influx into the cells, a great number of DNA repair proteins and chaperones (such as Dps, GrpE, MolR, and DnaK protein) would repair the damaged DNA and protein [19, 44, 45]. Previously reported study showed that a great number of DNA and protein repaired genes presence in wide range of extreme acidophiles genomes might be related to the acid resistance, for example, a large number of the DNA repaired proteins genes in P. torridus genome [27, 46]. Indeed, the transcription and expression of these repair systems were upregulated under the extreme acid stress, for example, the transcription of molecular chaperones repair system-molR and DnaK were enhanced in A. thiooxidans [19]. In addition, the GrpE and DnaK proteins expression were significantly improved in Acetobacter pasteurianus for coping with acetic acid stress [47]. Similarly, the molecular chaperones involved in protein refolding were largely expressed in L. ferriphilum under the AMD biofilm communities [48]. And, the chaperones were also highly expressed in F. acidarmanus during aerobic culture [49].
Quorum sensing (QS) system is a ubiquitous phenomenon that establishes the cell to cell communication in a population through the production, secretion and detection of signal molecules. In addition, The QS system is also widely involved in various physiological processes in cell such as biofilm formation, exopolysaccharides, motility, and bacterial virulence [50, 51, 52]. Moreover, the QS system can contribute to bacteria tolerating extreme environmental conditions by regulated biofilm formation. For example, bacteria showed the strong resistance to extremely low pH, due to these bacteria grown in a biofilm environment [53]. In case of acidophiles, QS system has been reported in A. ferrooxidans by producing the stable acylated homoserine lactones (AHLs) signal molecules under acidic conditions and overexpression strains promoted cell growth by regulated genes expression [54, 55].
Flagella is an important cell structure for the motility and chemotaxis in most bacteria, and is also involved in the biofilm formation [56]. Flagella-mediated chemotaxis is essential for cells to respond to environmental stimuli (pH, temperature, osmotic pressure, etc.) and find nutrients for growth. The chemotaxis and motility of cells is a complex physiological behavior regulated by the diverse transcription factors, such as RpoF (σ28 or FliA) of the σ factors and ferric uptake regulator (Fur) of the global regulator, and has strictly spatiotemporal characteristics [20, 56]. For example, the mutant strain of A. caldus fur gene significantly upregulated some genes (cheY, cheV, flhF, flhA, fliP, fliG, etc.) related to cell chemotaxis and motility under the acid shock conditions [20]. Similarly, F. acidarmanus was capable of motility and biofilm formation [57]. This indicates that although the chemotaxis and cell motility ability of acidophiles cannot directly involve in acid resistance and maintain cell pH homeostasis, they have the ability to avoid extremely unfavorable acid environments to improve cells survival. Altogether, we suggest that the QS system and chemotaxis and cell motility are essential part of escaping the extremely acidic environments in passive mechanisms (Figure 1).
It could be seen from the classification description above that there are a variety of mechanisms and strategies by which acidophiles can tolerate or resist the acidic or extremely acidic environments. However, some possible mechanisms have been imperfectly understood or classified, for example, the distinctive structural and functional characteristics of extremely acidophilic microorganisms (Figure 1) [13, 15]. First, iron may act as a “rivet” at low pH, which plays an important role in maintaining proteins activity, for example, the high proportion of iron proteins in F. acidiphilum. And, it has been found that the removal of iron from proteins can result in the loss of proteins activity [58, 59]. Secondly, the strategy of cell surface charges. The surface proteins of acidophiles have a high pI values (a positive surface charges), which can act as a transient proton repellent on the cell surface. For example, the isoelectric point (pI) of the OmpA-like protein in the outer membrane of the A. ferrooxidans is 9.4, whereas that of E. coli OmpA is 6.2 [60]. It may be the functional requirements that the possession of positive surface charges could reduce the permeability of A. ferrooxidans cells to protons. Then, adjustment of pore size of membrane channels is also used to minimize inward proton leakage under acid stress. For example, under the acid shock, the expression of outer membrane porin (Omp40) of A. ferrooxidans was upregulated [61], which could control the size and ion selectivity of the entrance to the pore. Ultimately, since organic acids could diffuse into the cells in the form of protonation at low pH environments and then the proton dissociation quickly acidify the cytoplasm, the degradation of organic acids might be a potential mechanism for maintaining pH homeostasis, especially heterotrophic acidophiles. Although the genes that degrade organic acids in some acidophile (such as F. acidarmanus, P. torridus) have been identified, it is unclear whether the degradation of organic acids would contribute pH homeostasis [27, 62]. In summary, these possible mechanisms remain to be confirmed but these genes of existence and identification could be a mechanism associated with low pH tolerance.
4. Evolution of low pH fitness of acidophiles
In the past few decades, studies have confirmed that acidophilic microorganisms are widely present in the three domains of bacteria, archaea and eukarya, indicating that acidophiles have gradually developed in the evolution of life on earth, rather than from a single adaptation events. Although the extremely acidic environments are toxic to most organisms, there are still large number of indigenous microorganisms that can thrive in these habitats. The generally accepted view is that acidophiles can be divided into moderate acidophiles that have pH optima of between 3 and 5, extreme acidophiles that have pH optima for growth at pH < 3, and hyperacidophiles that have pH optima for growth below pH 1 [1]. Generally, with the acidity becomes more extreme, biodiversity also gradually decreases. Accordingly, as would be anticipated, the most extremely acidic environments hold the less biodiversity, for example, hyperacidophiles includes the relatively few species (e.g. F. acidarmanus and Picrophilus oshimae) [1]. Acidophiles can survive in the acidic or extremely environments and are the source of acidity environment [1, 63, 64]; thus, they have the ability to resist the acidic environments that evolved during evolution.
Acidic hydrothermal ecosystems, such as Tengchong hot springs, Crater Lake, and Yellowstone National Park, are dominated by archaea [40, 65], and suggesting that the acidophilic archaea evolved in the extremely acidic hydrothermal environments after the emergence of oxygenic photosynthesis [66]. Based on the niche similarity and physiological adaptation among archaea, it showed that the long-term acidity stress is the main selection pressure to control the evolution of archaea and leads to the co-evolution of acid-resistant modules [66]. Although the species diversity decreases significantly as the pH decreases, the high abundance of acidophilic taxa, such as Gammaproteobacteria and Nitrospira, was detected in acid habitats. Indeed, for the dominant lineages such as Acidithiobacillus spp. and Leptospirillum spp., this pH-specific niche partitioning was obvious [67]. Consistent with this, Ferrovum is more acid-sensitive than A. ferrooxidans and L. ferrooxidans, and prefers to grow under the near-moderate pH [68]. Interestingly, the majority of acidophiles growing at extremely acidic (i.e. pH < 1) are heterotrophic acidophiles that are capable of utilizing organic matter for growth such as T. acidophilum and P. torridus. In addition, although the Acidiplasma spp. and Ferroplasma spp. can oxidize ferrous iron in biomining, organic carbon can also be used for growth, and their relative abundance would increase with the mortality of other bioleaching microorganisms [69, 70]. Therefore, they can be regarded as scavengers of the dead microorganisms and help the material and energy cycle in acidic habitats. To sum up, coexisting species may occupy different niches that could be affected by the pH changes, resulting in the changes in their distribution patterns.
The reasons for the dominance of these particular microorganisms in acidic ecosystems are presumed to their adaptive capabilities. Adaptations to acid stress dictate the ecology and evolution of the acidophiles. Acidic ecosystems are a unique ecological niche for acid-adapted microorganisms. These relatively low-complexity ecosystems offer a special opportunity for the evolutionary processes and ecological behaviors analyses of acidophilic microorganisms. In the last decade, the use of high-throughput sequencing technology and post-genomic methods have significantly promoted our understanding of microbial diversity and evolution in acidic environments [68]. At present, metagenomics studies have revealed various acidophilic microorganisms from environments such as the AMD and acidic geothermal areas, and found that these microorganisms play an important role in acid generation and adaptability to the environments [71, 72]. For example, because the comparative metagenomics and metatranscriptomics directly recover and reveal microbial genome information from the environments, it has the potential to provide insights into acid-resistance mechanisms of the uncultivated bacteria, such as clpX, clpP, and sqhC genes for resistance against acid stress. In addition, metatranscriptomics and metaproteomics analyses further uncovered the major metabolic and adaptive capabilities in situ [71], indicating the mechanisms of response and adaptation to the extremely acid environments.
The continuous exploration of acidic habitats and acidophilic microorganisms is the basis for comprehending the evolution of acidophilic microbial acid-tolerant modules, strategies, and networks. First, methods based on transcriptomics and proteomics are the key to understanding the global acid-tolerant network of individuals under acid stress [19, 73]. Secondly, comparative genomics plays a vital role in exploring the acid adaptation mechanism of acidophiles and studying the evolution of acidophiles genomes [74]. Ultimately, the emerging metagenomics technologies play an important role in evaluating and predicting microbial communities and their adaptability to acidic environments [75]. Moreover, metagenomics approaches could also provide a large amount of knowledge and functional module analysis on the acid tolerance of acidophiles to fully develop their potential in the evolution of acid tolerance [76]. With the publication of large number of metagenomics data, the evolution of the acid-tolerant components of these extremophiles would be better illustrated in the future.
5. Conclusions
Understanding the maintenance of pH homeostasis in acidophiles is of great significance to comprehend the mechanisms of cells growth and survival, as well as to the eco-remediation and application of biotechnology; thus, it is essential to fully understand the acid-tolerant networks and strategies of acidophilic microorganisms. The aims of this chapter presents the acid-resistant modules and strategies of acidophiles in more detail, including the proton efflux and consumption, reversed membrane potential, impermeable cell membrane, DNA and protein repair systems, and QS system (Figure 1). However, at present, several of the pH homeostatic mechanisms still lack clear and rigorous experimental evidence to support their functions from my point of view. In addition, we also discussed the evolution of acidophiles and its acid-resistant modules. In brief, the true purpose of acidophilic microorganisms evolving these mechanisms is to tolerate the extremely acidic environments or reduce its harmful effects for cell survival.
Acidophiles are known for their remarkable acid resistance. Over the last decades, the combination of molecular and biochemical analysis of acidophiles with genome, transcriptome, and proteome have provided new insights into the acid-resistant mechanisms and evolution of the individual acidophiles at present. Using these genome sequences in a functional context through the application of high throughput transcriptomic and proteomic tools to scrutinize acid stress might elucidate further potential pH homeostasis mechanisms. However, the disadvantages of genomics, transcriptomics, and proteomics are that the data are descriptive and analogous and more work is required to verify the hypotheses such as the mutational analyses and genetic markers. One of the main obstacles to the current research on acid tolerance of acidophiles is the lack of genetic tools for in-depth analysis. Therefore, the development of genetic tools and biochemical methods in acidophile would facilitate elucidating the molecular mechanisms of acidophile adapting to extremely acidic environments, such as vector development remain largely unexplored. In addition, as most acidophiles are difficult to isolate and culture, our ability to understand acid resistance of acidophile is limited. The emerging omics technologies would be a crucial step to explore the spatiotemporal transformation patterns of acidophilic microbial communities, microbial eco-physiology and evolution in the future.
Acknowledgments
We are grateful to Pro. Jianqiang Lin from Shandong University for providing the opportunity to review the acid-resistant mechanisms of acidophiles and Pro. Linxu Chen (Shandong University) for suggestions and comments on the outline and manuscript. We also thank Wenwen Xiang (Xiamen University) and Yujie Liu (Shandong University) for English language editing.
Conflict of interest
We declare no conflicts of interest.
\n',keywords:"acidophiles, acid-resistance, pH homeostasis, adaptation, evolution",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/75730.pdf",chapterXML:"https://mts.intechopen.com/source/xml/75730.xml",downloadPdfUrl:"/chapter/pdf-download/75730",previewPdfUrl:"/chapter/pdf-preview/75730",totalDownloads:328,totalViews:0,totalCrossrefCites:2,totalDimensionsCites:2,totalAltmetricsMentions:0,introChapter:null,impactScore:1,impactScorePercentile:67,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"January 25th 2021",dateReviewed:"February 14th 2021",datePrePublished:"May 23rd 2021",datePublished:"November 24th 2021",dateFinished:"March 15th 2021",readingETA:"0",abstract:"Acid resistance of acidophiles is the result of long-term co-evolution and natural selection of acidophiles and their natural habitats, and formed a relatively optimal acid-resistance network in acidophiles. The acid tolerance network of acidophiles could be classified into active and passive mechanisms. The active mechanisms mainly include the proton efflux and consumption systems, generation of reversed transmembrane electrical potential, and adjustment of cell membrane composition; the passive mechanisms mainly include the DNA and protein repair systems, chemotaxis and cell motility, and quorum sensing system. The maintenance of pH homeostasis is a cell-wide physiological process that adopt differently adjustment strategies, deployment modules, and integration network depending on the cell’s own potential and its habitat environments. However, acidophiles exhibit obvious strategies and modules similarities on acid resistance because of the long-term evolution. Therefore, a comprehensive understanding of acid tolerance network of acidophiles would be helpful for the intelligent manufacturing and industrial application of acidophiles.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/75730",risUrl:"/chapter/ris/75730",book:{id:"9767",slug:"acidophiles-fundamentals-and-applications"},signatures:"Xianke Chen",authors:[{id:"339185",title:"Ph.D. Student",name:"xianke",middleName:null,surname:"chen",fullName:"xianke chen",slug:"xianke-chen",email:"chenxianke20@mails.ucas.ac.cn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Acid-resistant mechanisms of acidophiles",level:"1"},{id:"sec_2_2",title:"2.1 Active support of acidophiles pH homeostasis",level:"2"},{id:"sec_4",title:"3. Passive strategies for acidophiles living",level:"1"},{id:"sec_5",title:"4. Evolution of low pH fitness of acidophiles",level:"1"},{id:"sec_6",title:"5. Conclusions",level:"1"},{id:"sec_7",title:"Acknowledgments",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Johnson DB, Aguilera A. Extremophiles and Acidic Environments. In: Schmidt TM, editor. Encyclopedia of Microbiology (Fourth Edition). Oxford: Academic Press; 2019. p. 206-27. DOI:10.1016/B978-0-12-809633-8.90687-3'},{id:"B2",body:'Meyer-Dombard DA, Shock E, Amend J. Archaeal and bacterial communities in geochemically diverse hot springs of Yellowstone National Park, USA. Geobiology. 2005;3:211-27. DOI:10.1111/j.1472-4669.2005.00052.x'},{id:"B3",body:'Mirete S, Morgante V, González-Pastor JE. Acidophiles: Diversity and Mechanisms of Adaptation to Acidic Environments. In: Stan-Lotter H, Fendrihan S, editors. Adaption of Microbial Life to Environmental Extremes: Novel Research Results and Application. 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1. Introduction
The science of decision support is foundational for every type of policy, and this work offer a proposal to analyze its role in energy policy.
An example of application of a particular machine learning (ML) technique to an energy policy problem is presented. It is important to understand the role of ML in energy and environmental analysis, for two solid reasons.
The first concerns the need to process large volumes of data and to elaborate and model complex relationships, typical of the energy analysis and of the environmental analysis. In this context, the use of AI (Artificial Intelligence) and machine learning is almost mandatory.
The second concerns the need to a concerted effort to identify how these tools may best be applied to tackle major problems of recent years, like climate change [1]: about this, CO2 emissions is key variable that we must control to achieve the global objective of mitigating damage for humanity.
This work has a specific goal. Using known tools from the scientific literature on energy generation costs, we intend to show how the use of a machine learning technique (the support vector machines, SVM) can produce a more accurate modeling of these costs.
The link with CO2 emissions is provided by the possibility of using the cost model in a cost-effectiveness analysis (C-E A), in which the cost is represented by the Levelised Cost of Energy (LCOE) and the effectiveness is represented by the CO2 emissions of the technologies considered per unit of energy produced.
The CO2 estimation is then obtained by selecting the best generation options according to the C-E A results.
The meaning of this work is the following.
Imagine that you are an energy analyst, in the public or private sector, and you need to use only one or just few variable/s (such as a forecast on the cost of natural gas), to estimate the costs of an electricity generation technology.
This task can be accomplished using a cost model of electricity generation in which a single piece of information can vary, leaving everything else unchanged (or imposing a certain trend on it).
The metric used is the indicator LCOE (Levelised Cost of Energy) provided by IEA (International Energy Agency), using 2020 data.
Once you have obtained a certain level of accuracy in estimate of energy cost, it is possible to move into a context of cost-effectiveness analysis, in which the best energy option in terms of Incremental Cost-Effectiveness Ratio (ICER) was selected to produce energy and, finally, provide a certain level of CO2 emissions for the time horizon in which such a technology is still the “best option”.
In other words, the estimate of energy cost and the cost-effectiveness analysis, allow us to trace the scenarios for electricity generation mix and, finally, calculate a quantitative forecast of the CO2 emitted.
The proposed work just intends to show the application of one of the existing machine learning techniques to the estimation of the LCOE, starting from some explanatory variables.
A linear model (LM) and an SVM are compared in the prediction of the LCOE value for a combined cycle gas plant (CCGT) with a focus on the fuel cost, Operation and Maintenance (O&M) cost and CO2 price using IEA data for Italy in 2020.
The work carried out intends to highlight the possibilities of applying machine learning techniques not only in the purely engineering aspects of energy systems, but also in the statistical-economic ones at a higher level of abstraction.
Some words about why to focus on power generation systems.
As countries work towards a low carbon world, it is crucial that policymakers, modelers, and experts have at their disposal reliable information on the cost of generation.
IEA [2] reports that the levelised costs of electricity generation of low-carbon generation technologies are more and more low the costs of conventional fossil fuel generation. Renewable energy costs continue their descent in recent years and their costs are now competitive with dispatchable fossil fuel-based electricity generation for many countries.
2. Methodology
This section presents the main tools used in this work: the LCOE methodology provided by IEA and the SVM, the used machine learning technique. Just before SVM presentations a very brief remind about ML and its use in energy systems and CO2 emissions estimates will be provided.
2.1 Levelised cost of energy
The Levelised Cost of Energy (LCOE) is the selected tool to measure the cost of an energy unit produced by the considered technologies. LCOE is a methodology described in the joint report by the International Energy Agency and the OECD (Organization for Economic Co-operation and Development) Nuclear Energy Agency (NEA) (now at the ninth edition in a series of studies on electricity generating costs) [1]. This report includes cost data on power generation from natural gas, coal, nuclear, and a broad range of renewable technologies.
The metric for plant-level cost chosen is the well-known levelised cost of electricity (LCOE) (IEA are now considering system effects and system costs with the help of the broader value-adjusted LCOE, or Levelised Cost of Value-Adjusted LCOE, VALCOE metric, here not considered).
The LCOE is widely considered as the principal tool for comparing the plant-level unit costs of different base load technologies over their operating lifetimes since indicates the economic costs of a technology family, not the financial costs of a certain projects in a certain market. Due to the equality between discounted average costs and the stable remuneration over lifetime electricity production LCOE recall the costs of electricity production in regulated electricity markets with stable tariffs than to the variable prices in deregulated markets.
Despite many limitations, LCOE has maintained its utility and appeal since it is a uniquely straightforward, transparent, comparable, and well understood metrics remaining a widely used tool for modeling, policy making and public debate.
The calculation of the LCOE is based on the equivalence of the present value of the sum of discounted revenues and the present value of the sum of discounted costs. Another way on the left-hand side one finds the discounted sum of benefits and on the right-hand side the discounted sum of costs:
PMWh The constant lifetime remuneration to the supplier for electricity;
MWh The amount of electricity produced annually in MWh;
1+r−t The real discount rate corresponding to the cost of capital;
Capitalt Total capital construction costs in year t;
O&Mt Operation and maintenance costs in year t;
Fuelt Fuel costs in year t;
Carbont Carbon costs in year t;
Dt Decommissioning and waste management costs in year t
PMWh is equal to levelised cost of electricity (LCOE).
Eq. (1) is the formula used here to calculate average lifetime levelized costs based on the costs for investment, operation and maintenance, fuel, carbon emissions and decommissioning and dismantling provided by OECD countries and selected non-member countries.
2.2 Machine learning
Machine learning (ML) is the field of artificial intelligence (AI) that provide methods to learn from data over time creating algorithms not being programmed to do so.
The literature about ML is relatively recent but is so vast that only some hint to review works can be made here, as an access point to this world1.
Machine learning approaches are normally categorized as in the follows.
Supervised machine learning, that trains itself on a labeled data set; unsupervised machine learning that uses unlabeled data with algorithms to extract the features required to label, sort, and classify the data in real-time, without human intervention; semi-supervised learning (SsL) namely a medium between supervised and unsupervised learning: SsL uses a smaller labeled data set during training and make classification and feature extraction from a larger, unlabeled data set; reinforcement machine learning is like supervised learning, but do not requires sample data for training (since using “trial and error” mode).
About the machine learning algorithms for use with labeled data the regression algorithms (as linear and logistic regression); decision trees (based on a set of decision rules to perform classification); instance-based algorithms: it uses classification to estimate how likely a data point is to be a member of one group, or another based on its proximity to other data points.
Methods based for use with on unlabeled data are: clustering algorithms: (like K-means, TwoStep, and Kohonen clustering); association algorithms: (that find patterns in data by identifying ‘if-then’ relationships namely association rules); neural networks: (that create a layered network of calculations featuring an input layer, when data in; one or more hidden layer, where calculations are performed; and an output layer. Where each conclusion is assigned a probability); deep neural network that uses multiple hidden layers, each of which successively refines the results of the previous layer. Deep learning models are typically unsupervised or semi-supervised. Certain types of deep learning models—including convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.
In this work, the machine learning approach used is the SVM one.
SVMs2 are machine learning algorithms built on statistical learning theory for structural risk minimization. In pattern recognition, classification, and analysis of regression, SVMs outperform other methodologies. The significant range of SVM applications in the field of load forecasting is due to its ability to generalize (also, local minima lead to no problems in SVM).
SVM was chosen, in this work, for the sake of simplicity, since the performed Support Vector Regression (SVR) [5], extremely easy to understand in comparing a traditional statistical tool with a competing machine learning based one.
Often, the available applications of SVM in the energy sector are oriented on the engineering side3 while in this work the approach is oriented in support decisions for energy policy field.
Using one of the possibilities offered by SVMs, namely the SVR, the follows show how it is possible to obtain more accurate forecasts of costs per unit of energy produced, using LCOE as a metric.
The best available accuracy is then used in a context of cost-effectiveness analysis.
In the following, a method to select among competing options (options that can be differ even for slight changes in some significant LCOE parameters), the one characterized by the best Incremental Cost-Effectiveness Ratio (ICER) is presented.
The possibility of making this choice during the lifetime of the plant leads to the possibility of identifying the best technology available, year by year, to get the corresponding profile of the associated CO2 emissions.
2.2.1 Machine learning for energy systems and CO2 emission estimation
The growing utilization of data collectors in energy systems has resulted in a massive amount of data accumulated (an increasing mass of mart sensors are now extensively used in energy production and energy consumption) leading to a continuous production of big data and, consequently, to a massive number of opportunities and challenges in decision support science.
Today, ML models in energy systems are essential for predictive modeling of production, consumption, and demand analysis due to their accuracy, efficacy, and speed or to provide an understanding on energy system functionality in the context of complex human interactions.
[7] propose a comprehensive review of essential ML to present the state of the art of ML models in energy systems and discuss their likely future trends.
Machine learning was used for estimate CO2 emission from energy systems in several context, using different approach. It is possible to recall, among an increasing number of works in recent years:
[8] about flexibility of the electricity demand, a machine learning algorithm developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions in Danish bidding zone DK2;
[9] an investigation on the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries;
[10] on the linkage between energy resources and economic development the focus of that work is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources with a higher predictive accuracy;
[11] about proposing a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs using a random forest machine learning method);
[12] on the relationship among iron and steel industries, air pollution and economic growth in China (using a Long Short Term Memory, LSTM, approach);
[13] on the forecasting of energy consumption related carbon emissions for the Beijing-Tianjin-Hebei region.
[14] on the uses of gray relational analysis to identify the factors that have a strong correlation with carbon emissions for China to reduce carbon emissions by studying prediction of carbon emissions (using LSTM).
[15] on the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities for the Peruvian Amazon (using deep learning models).
[16] on the use of a random forest machine learning regression workflow to map country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into, to create a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries as a transformative tool to quantify the climate change mitigation services that forests provide.
[17] To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, through a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040.
2.3 Our methodology
The present work reports an experiment performed using a simple LCOE model, built according to basic methodology proposed by IEA. The performed experiment is simple and straightforward. Two energy scenarios were produced, one based on a certain hypothesis of change in the fuel cost, the other based on a hypothesis of change in fuel cost, O&M cost, and CO2 price, for the CCGT type plant, over a period of 30 years.
In each scenario, a certain LCOE profile is obtained for the time horizon considered. A simple regression analysis is then performed on this variable, using as explanatory variables, first the cost of fuel, and then the operating costs.
The analysis is carried out both using a LM and the SVM, with further manual tuning of the last to improve its performance. The manual tuning for SVR was used for the sake of simplicity since the main goal of the study is to suggest the application of this ML technique to gain forecasting accuracy to use in the following phase, the cost-effectiveness analysis.4
To evaluate the accuracy of the forecast, the Root Mean Square Error (RMSE), the Mean Average Error (MAE) and the Mean Average Percentage Error (MAPE) were used.5
This simple test was performed to show the accuracy of the fuel cost and O&M cost as a predictor of CCGT LCOE.
Once established the best technique, the data from the two scenarios in a third scenario are modified, under certain hypothesis explained in the follows, to made a C-E A between a technology represented by IEA data and another of the same type with little changes in O&M costs. Using ICER as a winning criterion, it is possible to select the best energy generation option and, finally, to trace the corresponding CO2 emission estimate trend over the plant’s lifetime.
First, a LCOE model based on IEA Eq. (1), with the following level of detail, was built.
The basic relationships of the model are:
PF=Power∗8760∗AVLF∗AAF100∗1−AuxPE2
ws=1−wdE3
ks=krft+EMRP∗BE4
i=wd∗kd+ws∗ksE5
d=i/1+iE6
dfi=∑j1/1+ijE7
icfinal=icfinal+icCnsT∗dfiE8
df=∑j1/1+djE9
icfinal=icfinal+icCnsT∗dfi1E10
dfi=∑j1/1+ijE11
Pro=Pro+PF∗dfE12
OM=FOM+VOM∗PF∗dfE13
Fue=CFue∗PF∗dfE14
CO2=PCO2∗PF∗dfE15
Cost=∑jOM+Fue+CO2E16
Decom=n∗Decom∗ProE17
LCOE=Power∗icfinal∗1000+Cost+Decom/ProE18
Where:
CC Cost of Capital (USD/MWh)
Power net capacity (MWe)
AVLFmin AVerage Load Factor min value (%)
AVLFmax AVerage Load Factor max value (%)
AAF Average Availability Factor (%)
AuxP Auxiliary Power (%)
Lifetime Time horizon of plant (years).
wdmin min weight of cost of debt on total cost (%)
wdmax max weight of cost of debt on total cost (%)
kdmin min value of debt rate (%)
kdmax max value of debt rate (%)
tmin min value of taxation (%)
tmax max value of taxation (%)
krftmin min value of free risk rate (%)
krftmax max value of free risk rate (%)
EMRPmin min value of Expected Market Risk Premium (%)
EMRPmax max value of Expected Market Risk Premium (%)
Bmin min value of Beta (%)
Bmax max value of Beta (%)
CnsTmin min value of Construction Time (years)
CnsTmax max value of Construction Time (years)
FOMmin Fixed Operation and Maintenance Costs min (USD*MWh)
FOMmax Fixed Operation and Maintenance Costs max (USD*MWh)
VOMmin Variable Operation and Maintenance Costs min (USD*MWh)
VOMmax Variable Operation and Maintenance Costs max (USD*MWh)
Cfuemin min value of Costs of Fuel (USD*MWh)
Cfuemax max value of Costs of Fuel (USD*MWh)
Effmin min value of Efficiency (%)
Effmax max value of Efficiency (%)
PCO2min min value of CO2 price (USD*MWh)
PCO2max max value of CO2 price (USD*MWh)
Decommin min value of Decommissioning (USD*MWh)
Decommax max value of Decommissioning (USD*MWh)
All other parameters are settled using the IEA values.
We have set two type of scenario, basing on the following assumptions about certain variables of the model. The basic hypothesis is a constant decreasing of 2% for every variable changed, except every 6 years (a totally arbitrary choice), simulating an increasing amplification of this cycle (every 6 years, the percentage variation of the cost respect to the previous value is double than it and then is multiplied for the number of the occurring, so the first time at year 6, this value is roughly 4, namely 2% multiplied by 2 and then multiplied per variation 1).
Table 1 describes the hypothesis used in this first step of the analysis.
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 1
Linear decreasing of 2% per year except every 6 years
constant
constant
Scenario 2
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Table 1.
Scenarios used for the regression of LCOE on fuel cost and O&M cost
3. Results
Figure 1 shows the results obtained by performing a SVR about the data from IEA [1] for the first scenario considered (Figure 2).
Figure 1.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
Figure 2.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
1,30E-14
8,39E-15
8,39E-17
SVM
5,25E-01
4,01E-01
4,01E-03
Tuned SVM
1,74E-03
1,54E-03
1,54E-05
with a clear improvement of performance of the SVM after tuning. The linear model since the strong relationships between the fuel cost and the LCOE is clearly preferable respect to the SVM (Figures 1 and 2).
Figure 3.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
Figure 4.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
3.87E+00
2.70E+00
2.70E-02
SVM
2.77E+00
1.59E+00
1.59E-02
Tuned SVM
2.61E+00
1.45E+00
1.45E-02
Recalling that in the second case the O&M cost was used as a predictor, we can more appreciate the gain in terms of RMSE obtained by using the SVM.
The increasing accuracy of the SVR respect to the LM, can be used to perform a CO2 emission estimation in a cost-effectiveness analysis.
Let us look at a simple and plain experiment based on IEA data [2] for Italy, 2020 in the following scenario:
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 3
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
In scenario 3 we made a simulation basing on the hypothesis of a sudden shock for the three variables above reported in the 15th year, immediately followed by a linear decrease of them until end of the lifetime, starting from IEA 2020 data as a baseline value.
For scenario 3 the errors in predicting LCOE using O&M Cost over the considered time horizon are:
RMSE
MAE
MAPE
Linear Model
4.25878
3.49147
0.03491
SVM
2.70117
1.52912
0.01529
Tuned SVM
2.58541
1.52378
0.01524
In Cost-Effectiveness Analysis it is possible to calculate the Incremental Cost-Effectiveness Ratio (ICER), used as a measure of cost the LCOE and used as a measure of effectiveness through the quantity of CO2 emitted. The ICER can be used as a selection criterion between different options then, the winning options will be producing a certain level of emissions.
Now, let us imagine comparing two types of plants of the same technological family, in this case the CCGT. In this hypothetical exercise, the second type of plant is characterized by higher operating costs (+5% of the IEA base value).
In addition to this, let us imagine that the second type of plant has an average load factor of 94%.
Now, let us repeat the simulation performed for scenario 3 for the first type of CCGT plant (the real one), but only from the 20th year.
The meaning of this operation is as follows:
to use systems with different characteristics (in this case we have changed the O&M costs and the load factor of a single technology family);
to calculate the ICER corresponding to each plant in a defined time interval (in this case, from when the LCOE starts to vary);
to calculate the degree of uncertainty on the value of the ICER thanks to the MAPE of the SVR, defining the variation range for the ICER6;
to select the technology that has the lowest ICER and then we calculate the corresponding emissions over the time horizon considered;
finally, to calculate the emissions profile corresponding to the winning technology, year by year.
CO2 emissions from different kind of CCGT plants in scenario 3 (sources: IEA, 2020 + imaginary data).
Figure 5 illustrates what happens using the ICER criterion as a selector of the winning generation option. For the first 20 years, the first type of installation is selected, and the corresponding emissions are those of the blue line. From 20 years of age onwards, using the ICER as a criterion means choosing the second type of plant and the curve that shows the new profile of the emissions is the orange one.
4. Conclusions
ML can help in providing accurate forecasts of CO2 emissions from power generation, especially when we face simultaneous variation of major driver (like fuel cost, operating cost of the plant and so on); only a little piece of the possible comparisons between traditional techniques and a particular ML method was shown, focusing on the better performance of the ML one (SVM) respect to the traditional one (the LM).
In our case, the performed step was:
improving LCOE forecasting performance,
comparing multiple competing options by use of the ICER in Cost-Effectiveness Analysis;
consider the uncertainty about ICER using the MAPE (in this case, but is just an option) calculated by SVM;
choosing the best technology and calculating the CO2 emissions for it;
defining the trend of the CO2 emissions in the lifetime of the plant by step 4.
Recalling that a basic LCOE model can be brought to a great level of granularity, it is easy to imagine how this type of analysis could gain in depth and significance if the required data are available. Indeed, also in case of missing data, significant simulation can be provided by using each available piece of information on energy costs.
The experiment performed was conducted at the highest level of simplicity to better focus on the reasons that suggest ML integration not only about the engineering features of electricity generation field but also in support decision tools about energy policy.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"CO2 emissions, energy systems, machine learning, support vector machines, cost-effectiveness analysis, forecasting",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76238.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76238.xml",downloadPdfUrl:"/chapter/pdf-download/76238",previewPdfUrl:"/chapter/pdf-preview/76238",totalDownloads:150,totalViews:0,totalCrossrefCites:1,dateSubmitted:"November 12th 2020",dateReviewed:"March 26th 2021",datePrePublished:"April 12th 2021",datePublished:null,dateFinished:"April 12th 2021",readingETA:"0",abstract:"In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76238",risUrl:"/chapter/ris/76238",signatures:"Marco Rao",book:{id:"10627",type:"book",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Rehab O. 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Conclusions",level:"1"},{id:"sec_12",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Rolnick, D. and Donti, P. and H. Kaack, L.H., and Kochanski, K. and Lacoste, A. and Sankaran, K. and Ross, A. and Milojevic-Dupont, N., and Jaques, N., and Waldman-Brown, A., and Luccioni, A., and Maharaj, T., and Sherwin, E., and Mukkavilli, S.K., and Konrad P. K, and Carla Gomes, K., and Ng, A., and Hassabis, D., and C. Platt, J.C., and Creutzig, F., and Chayes, J. and Bengio, Y., Tackling Climate Change with Machine Learning, 2019'},{id:"B2",body:'IEA, Projected Cost of Generating Electricity 2020, IEA, 2020'},{id:"B3",body:'Saravanan and Sujatha, P., “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 945–949, doi: 10.1109/ICCONS.2018.8663155'},{id:"B4",body:'Wang, L. Support vector machines: theory and applications, Springer-Verlag, Berlin, 2005'},{id:"B5",body:'Awad M., Khanna R. (2015) Support Vector Regression. In: Efficient Learning Machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_4'},{id:"B6",body:'Zendehboudi, A., Baseer, M.A., Saidur, R. Application of support vector machine models for forecasting solar and wind energy resources: A review, Journal of Cleaner Production, Volume 199, 2018, Pages 272-285, DOI: 10.1016/j.jclepro.2018.07.164'},{id:"B7",body:'Salimi, M., Mosavi, A., Faizollahzadeh, A.S., Amidpour, M., Rabczuk, T., and Shamshirband, S., State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies 2019, 12, 1301; doi:10.3390/en12071301'},{id:"B8",body:'Leerbeck, K., Bacher, P., Grønborg Junker, R., Goranović, G., Corradi, O., Ebrahimy, R., Tveit, A., Madsen, H., Short-term forecasting of CO2 emission intensity in power grids by machine learning, Applied Energy, Volume 277, 2020, DOI:10.1016/j.apenergy.2020.115527'},{id:"B9",body:'Magazzino, C., Mele, M., Schneider, N. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions, Renewable Energy, Volume 167, 2021, Pages 99–115 DOI: 10.1016/j.renene.2020.11.050'},{id:"B10",body:'Cogoljević, D., Alizamir, M., Piljan, I., Piljan, T., Prljić, K., Zimonjić, S. A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, Volume 495, 2018, Pages 211-214, DOI: 10.1016/j.physa.2017.12.082'},{id:"B11",body:'Wu, Y., Sharifi, A., Yang, P., Borjigin, H., Murakami, D., Yamagata, Y. Mapping building carbon emissions within local climate zones in Shanghai, Energy Procedia, Volume 152, 2018, Pages 815-822, DOI: 10.1016/j.egypro.2018.09.195'},{id:"B12",body:'Mele, M., Magazzino, C. A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China, Journal of Cleaner Production, Volume 277, 2020, 123293, DOI: 10.1016/j.jclepro.2020.123293'},{id:"B13",body:'Li, M.; Wang, W.; De, G.; Ji, X.; Tan, Z. Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm. Energies 2018, 11, 2475. https://doi.org/10.3390/en11092475'},{id:"B14",body:'Huang, Y., Shen, L., Liu, H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China, Journal of Cleaner Production, Volume 209, 2019, Pages 415-423, DOI: 10.1016/j.jclepro.2018.10.128'},{id:"B15",body:'Csillik, O. and Asner, G.P. 2020 Environ. Res. Lett. 15 014006'},{id:"B16",body:'Csillik, O., Kumar, P., Mascaro, J. et al. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Sci Rep 9, 17831 (2019). https://doi.org/10.1038/s41598-019-54386-6'},{id:"B17",body:'Niu, D. Wang, K., Wu, J., Sun, L., Yi Liang, Y., Xu, X., Yang, X. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network, Journal of Cleaner Production, Volume 243, 2020, 118558, DOI: 10.1016/j.jclepro.2019.118558'},{id:"B18",body:'Korovkinas, K., Danènas, P., Garsva, G., Support vector machine parameter tuning based on particle swarm optimization metaheuristic, Nonlinear Analysis: Modelling and Control, Vol. 25, No. 2, 266–281, DOI=10.15388/namc.2020.25.16517'},{id:"B19",body:'Botchkarev, A. A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms, Interdisciplinary Journal of Information, Knowledge, and Management, Volume 14, pp. 045–076, 2019, DOI=10.28945/4184'}],footnotes:[{id:"fn1",explanation:"Here we just remind a recent review of the state of art in machine learning techniques [3]."},{id:"fn2",explanation:"For a good introduction to this topic see [4]."},{id:"fn3",explanation:"See, for example [6]."},{id:"fn4",explanation:"Indeed, manual tuning is often considered as one of the most significant choice [18]."},{id:"fn5",explanation:"See [19] for a complete discussion about the used metrics."},{id:"fn6",explanation:"Namely, ICER max/min = ICER +/− ICER*MAPE."}],contributors:[{corresp:"yes",contributorFullName:"Marco Rao",address:"marco.rao@enea.it",affiliation:'
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. 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Social marketing strategies can also be used to promote behavioral change and help individuals transform their lives, achieve well-being, and adopt prosocial behaviors. In this chapter, we seek to analyze with a netnographic study, how SNS are being employed by nonprofits and nongovernment organizations (NGOs) to enable citizens and consumers to participate in different programs and activities that promote social transformation and well-being. A particular interest is to identify how organizations are using behavioral economic tactics to nudge individuals and motivate them to engage in prosocial actions. 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Community service is among the three pillars of the university’s business along with teaching and research tasks. Employing a qualitative case study design, this research inspects the practices of community services against the ascribed principles and identifes the pitfalls of community service in Debre Markos University. Both primary and secondary data were collected. Primary data were collected through key informants interviews, semistructured interviews, and non-participant observation. Thirteen participants, five through key informant interview and eight through a semistructured interview were addressed. Participants were purposively selected from both the university and the nearby community. Lecturers, vice-presidents, and directors have participated in the interview. Articles, books, different reports, newspapers, and magazines were reviewed and used as sources of secondary data. Thematic data analysis technique was employed to analyze the primary data, and document analysis was used to analyze the data gained from secondary sources. The results show that, though community service is rendered since 2006 at Debre Markos University, there are still limitations in adhering to the principles of community service. These include shortage of budget, low level of University-Industry Linkage (UIL), less commitment of the staff, and the low level of monitoring and evaluation.",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Adane Mengist"},{id:"82845",title:"Revisiting Crisis Governance: Toward Collaborative Crisis Management",slug:"revisiting-crisis-governance-toward-collaborative-crisis-management",totalDownloads:3,totalDimensionsCites:0,doi:"10.5772/intechopen.106129",abstract:"This chapter attends to three main modes of crisis governance: centralization, decentralization, and collaborative crisis management (CCM). While the first two modes focus almost exclusively on government actors, CCM goes beyond them by involving private sectors and civil society. CCM is a more robust form of crisis governance since it combines knowledge and resources from multiple actors, which is a key to managing the more complex nature of modern crises. This chapter uses the case of Indonesia in dealing with the COVID-19 pandemic to show the dynamics of crisis governance. Indonesia moved from a centralized mode of crisis governance toward a more decentralized one. Simultaneously, there were several collaborative initiatives involving multiple stakeholders to deal with the crisis, such as in the case of SONJO. The case illustrates that while CCM provides a more effective response, it has some limitations as it has a smaller scale, may create internal conflict, lacks sustainability, and has a nonbinding character. The experience of Indonesia lends the lesson that for CCM to be robust crisis governance, and there needs to be a clear arrangement to boost its scale, manage internal conflict, improve sustainability, and induce a more permanent and binding framework.",book:{id:"11439",title:"Crisis Management - Principles, Roles and Application",coverURL:"https://cdn.intechopen.com/books/images_new/11439.jpg"},signatures:"Gabriel Lele"},{id:"82858",title:"Corporate Social Responsibility a Case of the Provision of Recreational Facilities",slug:"corporate-social-responsibility-a-case-of-the-provision-of-recreational-facilities",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.105608",abstract:"Corporate social responsibility (CSR) connotes Government agencies and private enterprises services for effective change and in this regards the recreational provision. The inadequate provision of the recreational services thwarted recreation, resulting to unsuitable funding of recreational facilities and unsuccessful synergy between government and the private enterprises embarking on CSR. This paper examines the roles of government and the private enterprises in the services of CSR with the view to enhance their performances in the provision of recreational facilities. The paper applied the qualitative method using atlas ti.8 for the data analysis. The findings reveal inadequate facilities provision for recreation resulting from lack of funding, lacklustre attitude and poor synergy of the stakeholders. The paper recommends that government should be positive in implementing policies that promote recreational activities and improving the efforts of the private enterprises for CSR. With the effectiveness and efficiency of the provision of recreation facilities, CSR will be acknowledged as a case of Greater Jos. Plateau State, Nigeria.",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Peter Musa Wash, Shida Irwana Omar, Badaruddin Mohamed and Mohd Ismail Isa"},{id:"82786",title:"Discussion of Purchasing Virtual Digital Nature and Tourism",slug:"discussion-of-purchasing-virtual-digital-nature-and-tourism",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.105869",abstract:"This chapter discusses the potential and prospects of consumers purchasing virtual digital nature and smart tourism. During the lockdown period, people experienced a trend toward increased subjective well-being as a result of their familiarity with the digital nature. In order to academically validate these experiences, this study examines how interaction with nature in the digital environment stimulates new consumer behavior in post-pandemic life. The study will apply structural equation modeling (SEM) to 300 data collected through a questionnaire to develop the discussion, with a particular focus on the mediating effects of digital forest bathing. The results show that digital forest bath ing has a mediating effect in stimulating people’s environmentally oriented behavior, and that the more active they are in digital space and interact with others, the more consumers enjoy interacting with nature in cyberspace and, in turn, the more willing they are to commune with digital nature through smart tourism. This can be expected to provide an effective reference for marketing strategies that contribute to the promotion of smart tourism in the age of symbiosis with COVID.",book:{id:"11581",title:"A New Era of Consumer Behavior - Beyond the Pandemic",coverURL:"https://cdn.intechopen.com/books/images_new/11581.jpg"},signatures:"Hiroko Oe and Yasuyuki Yamaoka"},{id:"82777",title:"Sustainability and Social Investment: Community Microhydropower Systems in the Dominican Republic",slug:"sustainability-and-social-investment-community-microhydropower-systems-in-the-dominican-republic",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105995",abstract:"Sustainability remains an underestimated concept when assessing the impact of philanthropic and social investments in communities due to the difficult task of conciliating human development, economy, and environmental protection. Currently, financial cost-effectiveness is one of the main criteria for decision-making. However, under a social investing and climate justice framework, monetary valuation of impacts is never enough to assess the complexity of livelihoods. A multi-stakeholder approach, based on common objectives and synergy among entities, is key for sustainability and social investments. Public institutions, private sector, international cooperation, and local civil society organizations work together in the development of initiatives that promote integral development. In the Dominican Republic and Haiti, community microhydropower systems have proved to be an effective model of social investment, climate justice, and sustainability. The response to a social need, such as access to electricity, has turned into a means for promoting a different approach, based on community empowerment. This article contains the experience of the successes and challenges of more than 50 community microhydropower systems, managed by local groups, which are working and demonstrating the meaning of sustainability and the positive nonmonetary impacts of social investing, opening future opportunities to expand the present 5% of private investment.",book:{id:"11476",title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg"},signatures:"Michela Izzo, Alberto Sánchez and Rafael Fonseca"},{id:"81403",title:"Do the Collaboration Dimensions Pay in Manufacturing Reverse Supply Chain? An Empirical Approach",slug:"do-the-collaboration-dimensions-pay-in-manufacturing-reverse-supply-chain-an-empirical-approach",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.103068",abstract:"The purpose of this paper is to examine empirically the enablers and practices of collaboration in relation to reverse supply chain. The research method used in this research was a quantitative method using a survey approach to empirically test if the following collaboration enables and practices are applicable. The statistical approach was AMOS 26. The findings revealed that, the relationship building and management for implementing collaboration was ranked highest, resource investment and development in reverse supply chain was ranked the next. Furthermore, quick response on returned goods and information sharing with suppliers on the returned products were highest ranked. The research was limited because the study was based in the Gauteng region, which means that a generalised statement cannot be made of the finding, as well there is a need for the study to be industry specific such as electronics, online retailers. The practical implications of the findings are that the enablers and practices are needed for reverse supply practices to achieve its aims. There is lack of research in the reverse collaboration space, this has paper has fulfilled the following gap.",book:{id:"11253",title:"Sustainable Rural Development",coverURL:"https://cdn.intechopen.com/books/images_new/11253.jpg"},signatures:"Ifije Ohiomah, Clinton Aigbavboa and Nita Sukdeo"}],onlineFirstChaptersTotal:72},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:107,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:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,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:11,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:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:20,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. Vouros",slug:"deep-multiagent-reinforcement-learning-methods-addressing-the-scalability-challenge",totalDownloads:19,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Multi-Agent Technologies and Machine Learning",coverURL:"https://cdn.intechopen.com/books/images_new/11445.jpg",subseries:{id:"27",title:"Multi-Agent Systems"}}},{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:14,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. 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He is on the editorial board of several international peer-reviewed journals and has published many papers. Additionally, he has participated in many international and national congresses, seminars, and workshops with oral and poster presentations. He is an active member of many local and international organizations.",institutionString:"İskenderun Technical University",institution:{name:"İskenderun Technical University",country:{name:"Turkey"}}},{id:"61139",title:"Dr.",name:"Sergey",middleName:null,surname:"Tkachev",slug:"sergey-tkachev",fullName:"Sergey Tkachev",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/61139/images/system/61139.png",biography:"Dr. Sergey Tkachev is a senior research scientist at the Institute of Fundamental Medicine and Biology, Kazan Federal University, Russia, and at the Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia. He received his Ph.D. in Molecular Biology with his thesis “Genetic variability of the tick-borne encephalitis virus in natural foci of Novosibirsk city and its suburbs.” His primary field is molecular virology with research emphasis on vector-borne viruses, especially tick-borne encephalitis virus, Kemerovo virus and Omsk hemorrhagic fever virus, rabies virus, molecular genetics, biology, and epidemiology of virus pathogens.",institutionString:"Russian Academy of Sciences",institution:{name:"Russian Academy of Sciences",country:{name:"Russia"}}},{id:"310962",title:"Dr.",name:"Amlan",middleName:"Kumar",surname:"Patra",slug:"amlan-patra",fullName:"Amlan Patra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/310962/images/system/310962.jpg",biography:"Amlan K. Patra, FRSB, obtained a Ph.D. in Animal Nutrition from Indian Veterinary Research Institute, India, in 2002. He is currently an associate professor at West Bengal University of Animal and Fishery Sciences. He has more than twenty years of research and teaching experience. He held previous positions at the American Institute for Goat Research, The Ohio State University, Columbus, USA, and Free University of Berlin, Germany. His research focuses on animal nutrition, particularly ruminants and poultry nutrition, gastrointestinal electrophysiology, meta-analysis and modeling in nutrition, and livestock–environment interaction. He has authored around 175 articles in journals, book chapters, and proceedings. Dr. Patra serves on the editorial boards of several reputed journals.",institutionString:null,institution:{name:"West Bengal University of Animal and Fishery Sciences",country:{name:"India"}}},{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.png",biography:"László Babinszky is Professor Emeritus, Department of Animal Nutrition Physiology, University of Debrecen, Hungary. He has also worked in the Department of Animal Nutrition, University of Wageningen, Netherlands; the Institute for Livestock Feeding and Nutrition (IVVO), Lelystad, Netherlands; the Agricultural University of Vienna (BOKU); the Institute for Animal Breeding and Nutrition, Austria; and the Oscar Kellner Research Institute for Animal Nutrition, Rostock, Germany. In 1992, Dr. Babinszky obtained a Ph.D. in Animal Nutrition from the University of Wageningen. His main research areas are swine and poultry nutrition. He has authored more than 300 publications (papers, book chapters) and edited four books and fourteen international conference proceedings.",institutionString:"University of Debrecen",institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"201830",title:"Dr.",name:"Fernando",middleName:"Sanchez",surname:"Davila",slug:"fernando-davila",fullName:"Fernando Davila",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201830/images/5017_n.jpg",biography:"I am a professor at UANL since 1988. My research lines are the development of reproductive techniques in small ruminants. We also conducted research on sexual and social behavior in males.\nI am Mexican and study my professional career as an engineer in agriculture and animal science at UANL. Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Prof.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,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",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón Poggi",slug:"juan-carlos-gardon-poggi",fullName:"Juan Carlos Gardón Poggi",position:null,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",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,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",country:{name:"United Kingdom"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",biography:"Samir El-Gendy is a Professor of anatomy and embryology at the faculty of veterinary medicine, Alexandria University, Egypt. Samir obtained his PhD in veterinary science in 2007 from the faculty of veterinary medicine, Alexandria University and has been a professor since 2017. Samir is an author on 24 articles at Scopus and 12 articles within local journals and 2 books/book chapters. His research focuses on applied anatomy, imaging techniques and computed tomography. Samir worked as a member of different local projects on E-learning and he is a board member of the African Association of Veterinary Anatomists and of anatomy societies and as an associated author at local and international journals. Orcid: https://orcid.org/0000-0002-6180-389X",institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"246149",title:"Dr.",name:"Valentina",middleName:null,surname:"Kubale",slug:"valentina-kubale",fullName:"Valentina Kubale",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246149/images/system/246149.jpg",biography:"Valentina Kubale is Associate Professor of Veterinary Medicine at the Veterinary Faculty, University of Ljubljana, Slovenia. Since graduating from the Veterinary faculty she obtained her PhD in 2007, performed collaboration with the Department of Pharmacology, University of Copenhagen, Denmark. She continued as a post-doctoral fellow at the University of Copenhagen with a Lundbeck foundation fellowship. She is the editor of three books and author/coauthor of 23 articles in peer-reviewed scientific journals, 16 book chapters, and 68 communications at scientific congresses. Since 2008 she has been the Editor Assistant for the Slovenian Veterinary Research journal. She is a member of Slovenian Biochemical Society, The Endocrine Society, European Association of Veterinary Anatomists and Society for Laboratory Animals, where she is board member.",institutionString:"University of Ljubljana",institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",biography:"Dr. Fonseca-Alves earned his DVM from Federal University of Goias – UFG in 2008. He completed an internship in small animal internal medicine at UPIS university in 2011, earned his MSc in 2013 and PhD in 2015 both in Veterinary Medicine at Sao Paulo State University – UNESP. Dr. Fonseca-Alves currently serves as an Assistant Professor at Paulista University – UNIP teaching small animal internal medicine.",institutionString:null,institution:{name:"Universidade Paulista",country:{name:"Brazil"}}},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",biography:"María de la Luz García Pardo is an agricultural engineer from Universitat Politècnica de València, Spain. She has a Ph.D. in Animal Genetics. Currently, she is a lecturer at the Agrofood Technology Department of Miguel Hernández University, Spain. Her research is focused on genetics and reproduction in rabbits. The major goal of her research is the genetics of litter size through novel methods such as selection by the environmental sensibility of litter size, with forays into the field of animal welfare by analysing the impact on the susceptibility to diseases and stress of the does. Details of her publications can be found at https://orcid.org/0000-0001-9504-8290.",institutionString:null,institution:{name:"Miguel Hernandez University",country:{name:"Spain"}}},{id:"350704",title:"M.Sc.",name:"Camila",middleName:"Silva Costa",surname:"Ferreira",slug:"camila-ferreira",fullName:"Camila Ferreira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/350704/images/17280_n.jpg",biography:"Graduated in Veterinary Medicine at the Fluminense Federal University, specialist in Equine Reproduction at the Brazilian Veterinary Institute (IBVET) and Master in Clinical Veterinary Medicine and Animal Reproduction at the Fluminense Federal University. She has experience in analyzing zootechnical indices in dairy cattle and organizing events related to Veterinary Medicine through extension grants. I have experience in the field of diagnostic imaging and animal reproduction in veterinary medicine through monitoring and scientific initiation scholarships. I worked at the Equus Central Reproduction Equine located in Santo Antônio de Jesus – BA in the 2016/2017 breeding season. I am currently a doctoral student with a scholarship from CAPES of the Postgraduate Program in Veterinary Medicine (Pathology and Clinical Sciences) at the Federal Rural University of Rio de Janeiro (UFRRJ) with a research project with an emphasis on equine endometritis.",institutionString:null,institution:null},{id:"41319",title:"Prof.",name:"Lung-Kwang",middleName:null,surname:"Pan",slug:"lung-kwang-pan",fullName:"Lung-Kwang Pan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41319/images/84_n.jpg",biography:null,institutionString:null,institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain.Dr. Satué is accredited as a Private University Doctor Professor, Doctor Assistant, and Contracted Doctor by AVAP (Agència Valenciana d'Avaluació i Prospectiva) and currently, as a full professor by ANECA (since January 2022). To date, Katy has taught 22 years in the Department of Animal Medicine and Surgery at the CEU-Cardenal Herrera University in undergraduate courses in Veterinary Medicine (General Pathology, integrated into the Applied Basis of Veterinary Medicine module of the 2nd year, Clinical Equine I of 3rd year, and Equine Clinic II of 4th year). Dr. Satué research activity is in the field of Endocrinology, Hematology, Biochemistry, and Immunology in the Spanish Purebred mare. She has directed 5 Doctoral Theses and 5 Diplomas of Advanced Studies, and participated in 11 research projects as a collaborating researcher. She has written 2 books and 14 book chapters in international publishers related to the area, and 68 scientific publications in international journals. Dr. Satué has attended 63 congresses, participating with 132 communications in international congresses and 19 in national congresses related to the area. Dr. Satué is a scientific reviewer for various prestigious international journals such as Animals, American Journal of Obstetrics and Gynecology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology, among others. Since 2014 she has been responsible for the Clinical Analysis Laboratory of the CEU-Cardenal Herrera University Veterinary Clinical Hospital.",institutionString:null,institution:null},{id:"201721",title:"Dr.",name:"Beatrice",middleName:null,surname:"Funiciello",slug:"beatrice-funiciello",fullName:"Beatrice Funiciello",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201721/images/11089_n.jpg",biography:"Graduated from the University of Milan in 2011, my post-graduate education included CertAVP modules mainly on equines (dermatology and internal medicine) and a few on small animal (dermatology and anaesthesia) at the University of Liverpool. After a general CertAVP (2015) I gained the designated Certificate in Veterinary Dermatology (2017) after taking the synoptic examination and then applied for the RCVS ADvanced Practitioner status. After that, I completed the Postgraduate Diploma in Veterinary Professional Studies at the University of Liverpool (2018). My main area of work is cross-species veterinary dermatology.",institutionString:null,institution:null},{id:"291226",title:"Dr.",name:"Monica",middleName:null,surname:"Cassel",slug:"monica-cassel",fullName:"Monica Cassel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/291226/images/8232_n.jpg",biography:'Degree in Biological Sciences at the Federal University of Mato Grosso with scholarship for Scientific Initiation by FAPEMAT (2008/1) and CNPq (2008/2-2009/2): Project \\"Histological evidence of reproductive activity in lizards of the Manso region, Chapada dos Guimarães, Mato Grosso, Brazil\\". Master\\\'s degree in Ecology and Biodiversity Conservation at Federal University of Mato Grosso with a scholarship by CAPES/REUNI program: Project \\"Reproductive biology of Melanorivulus punctatus\\". PhD\\\'s degree in Science (Cell and Tissue Biology Area) \n at University of Sao Paulo with scholarship granted by FAPESP; Project \\"Development of morphofunctional changes in ovary of Astyanax altiparanae Garutti & Britski, 2000 (Teleostei, Characidae)\\". She has experience in Reproduction of vertebrates and Morphology, with emphasis in Cellular Biology and Histology. She is currently a teacher in the medium / technical level courses at IFMT-Alta Floresta, as well as in the Bachelor\\\'s degree in Animal Science and in the Bachelor\\\'s degree in Business.',institutionString:null,institution:null},{id:"442807",title:"Dr.",name:"Busani",middleName:null,surname:"Moyo",slug:"busani-moyo",fullName:"Busani Moyo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Gwanda State University",country:{name:"Zimbabwe"}}},{id:"439435",title:"Dr.",name:"Feda S.",middleName:null,surname:"Aljaser",slug:"feda-s.-aljaser",fullName:"Feda S. Aljaser",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"423023",title:"Dr.",name:"Yosra",middleName:null,surname:"Soltan",slug:"yosra-soltan",fullName:"Yosra Soltan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Alexandria University",country:{name:"Egypt"}}},{id:"349788",title:"Dr.",name:"Florencia Nery",middleName:null,surname:"Sompie",slug:"florencia-nery-sompie",fullName:"Florencia Nery Sompie",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sam Ratulangi University",country:{name:"Indonesia"}}},{id:"428600",title:"MSc.",name:"Adriana",middleName:null,surname:"García-Alarcón",slug:"adriana-garcia-alarcon",fullName:"Adriana García-Alarcón",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"428599",title:"MSc.",name:"Gabino",middleName:null,surname:"De La Rosa-Cruz",slug:"gabino-de-la-rosa-cruz",fullName:"Gabino De La Rosa-Cruz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"428601",title:"MSc.",name:"Juan Carlos",middleName:null,surname:"Campuzano-Caballero",slug:"juan-carlos-campuzano-caballero",fullName:"Juan Carlos Campuzano-Caballero",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}}]}},subseries:{item:{id:"3",type:"subseries",title:"Bacterial Infectious Diseases",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools",scope:"
\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
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