\r\n\tCell viability is defined as the number of healthy cells in a sample and proliferation of cells is a vital indicator for understanding the mechanisms inaction of certain genes, proteins, and pathways involved in cell survival or death after exposure to toxic agents. The methods used to determine viability are also common for the detection of cell proliferation. A cell viability assay is performed based on the ratio of live and dead cells. This assay is based on an analysis of cell viability in cell culture for evaluating in vitro drug effects in cell-mediated cytotoxicity assays for monitoring cell proliferation. Various methods are involved in performing a cell viability assay, including the dilution method, surface viable count, roll tube technique, nalidixic acid method, fluorogenic dye assay, and the Trypan Blue Cell Viability Assay. The cell viability assays can determine the effect of drug candidates on cells and be used to optimize the cell culture conditions. The parameters that define cell viability can be as diverse as the redox potential of the cell population, the integrity of cell membranes, or the activity of cellular enzymes. \r\n\tCytotoxicity is the degree to which a substance can cause damage to a cell. Cytotoxicity assays measure the ability of cytotoxic compounds to cause cell damage or cell death. Cytotoxicity assays are widely used in fundamental research and drug discovery to screen libraries for toxic compounds. The cell cytotoxicity and proliferation assays are mainly used for drug screening to detect whether the test molecules have effects on cell proliferation or display direct cytotoxic effects. In a cell-based assay, it is important to know how many viable cells are remaining at the end of the experiment. There are a variety of assay methods based on various cell functions such as enzyme activity, cell membrane permeability, cell adherence, ATP production, co-enzyme production, and nucleotide uptake activity. These methods could be classified in to different categories: (I) dye exclusion methods such as trypan blue dye exclusion assay, (II) methods based on metabolic activity, (III) ATP assay, (IV) sulforhodamine B assay, (V) protease viability marker assay, (VI) clonogenic cell survival assay, (VII) DNA synthesis cell proliferation assays and (V) Raman micro-spectroscopy. \r\n\tMedical devices have been widely used in various clinical disciplines and these devices have direct contact with the tissues and cells of the body, they should have good physical and chemical properties as well as good biocompatibility. Biocompatibility testing assesses the compatibility of medical devices with a biological system. It studies the interaction between the device and the various types of living tissues and cells exposed to the device when it comes into contact with patients.
\r\n
\r\n\t \r\n\tThe book will cover original studies, reviews, all aspects of Cell Viability and Cytotoxicity assays, methods, Biocompatibility of studies of biomedical devices, and related topics.
",isbn:"978-1-80356-246-9",printIsbn:"978-1-80356-245-2",pdfIsbn:"978-1-80356-247-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"ad664980a1e5007239b6de58fcf0bd9a",bookSignature:"Prof. Sukumaran Anil",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11678.jpg",keywords:"Cytotoxicity, Cytotoxicity Testing, Biocompatibility, ATP Assay, MTT Assay, Cell Viability, DNA Synthesis Cell Proliferation Assays, Raman Micro-Spectroscopy, Trypan Blue Dye Exclusion Assay, Medical Devices, Drugs, Safety Testing",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 10th 2022",dateEndSecondStepPublish:"March 10th 2022",dateEndThirdStepPublish:"May 9th 2022",dateEndFourthStepPublish:"July 28th 2022",dateEndFifthStepPublish:"September 26th 2022",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Prof. Anil Sukumaran is currently Senior Consultant and Professor of Periodontics and Implant Dentistry, Hamad Medical Corporation/Qatar University, Doha, Qatar. 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1. Introduction
The introduction of transcatheter aortic valve implantation (TAVI) has changed the management of patients affected by severe aortic stenosis.
The calculated incidence rate of severe AS is 4.4‰/year in patients ≥65 years [1]. The prevalence of the disease was estimated of about 13% in general population aged ≥75 years [2].
Approximately 40% of symptomatic severe AS patients do not undergo SAVR [1].
Nowadays, TAVI procedure represents the treatment of choice in patients with severe AS inoperable or at high risk. The improvement in early and midterm results has extended the indication for TAVI also to elderly patients at intermediate and low risk for surgery. This is the result of a continuous improvement in patients’ selection and procedural assessment which is supported by the progress in imaging technology which is central for better diagnostic planning and more precise technical procedure.
2. Selection of patients
Actually, the main surgical risk score used in clinical practice and in randomized trial is the STS score: a STS score > 8% identifies patient at high risk; a STS score between 4 and 8% corresponds to an intermediate-risk profile; a STS lower than 4% encloses patients at low surgical risk [3].
The last 2017 ESC/EACTS guidelines for the management of valvular disease recommend that in patients who are at increased surgical risk, the decision between SAVR and TAVI should be made by the Heart Team, according to the individual patient characteristics, with TAVI being favored in elderly patients suitable for transfemoral access (class of recommendation Ib) [4].
AHA/ACC guidelines, also, underline the importance of a multidisciplinary Heart Team in the evaluation of a patient affected by severe AS [5].
However, it seems to be clear that the only evaluation based on surgical preoperative risk score (STS or Euroscore II) is not enough. In fact, specific risk factors for TAVI, such as frailty, chest radiation, porcelain aorta, and vessel tortuosity are not included [6]. Thus, it is difficult to create an appropriate risk score for TAVI due to heterogeneity of patient characteristics.
In conclusion, the role of the Heart Team to include a cardiac surgeon, a cardiologist with interest in heart valve disease and an interventional cardiologist, other specialist such as anesthesiologists and geriatricians, and, if necessary, other clinicians for a complete patient assessment is fundamental [6].
3. Imaging assessment
The use of multidetector computed tomography (MDCT) is a daily practice in the preoperative planning of TAVI. MDCT improves the precision in prosthesis sizing and reduce peripheral vascular complications [3].
MDCT provides information about aortic annulus, LVOT calcifications, valve morphology, coronary ostial height, sinus of Valsalva, sino-tubular junction, ascending aorta, and optimal projection curve.
Moreover, through MDCT it is possible to have an analysis of coronary arteries status: if it detects significant coronary stenosis, a coronary angiography should be performed.
MDCT has a key role in vascular access assessment: by routine use of CT scan in preprocedural planning, the rate of vascular complication has dropped to 4–5%.
3.1 Aortic annulus
The aortic annulus is defined as the luminal contour within a virtual plane aligned with the most basal attachment points of the three aortic valve cusps [7]. Usually, the annulus can be measured into the systole, corresponding to 30–40% of the R-R interval: in this way the under-sizing of the prostheses can be avoided [3].
The measurement of the aortic annulus can be obtained manually or semiautomatically. It is clear that the support of software facilitates the work, but it requires a supervision by an expert operator.
In the manual procedure, once the central axis is aligned with the long axis of the aortic root, turning counterclockwise, the lower insertions of the single cusps are identified (Figure 1).
Figure 1.
Aortic annulus (virtual basal ring) in sagittal view (A), axial (B), and coronal (C).
Once aortic annulus is obtained, the measurements of minimal and maximal diameter, perimeter, and area can be calculated (Figure 2).
Figure 2.
(A) aortic annulus. (B) Left main height. (C) Right coronary height.
Next, we can proceed to measure coronaries’ height, inter-commissural distances, sino-tubular junction, and ascending aorta diameters (Figure 2).
It is very important to identify LVOT calcifications, their distribution, and their protrusions. The amount of calcium in LVOT is correlated with AV blocks and annular rupture, representing one of the factors keeping in mind the choice of the type of prostheses (balloon-expandable or self-expanding).
3.2 Bicuspid aortic valve
The incidence of bicuspid aortic valve (BAV) is up to 2–3% in the general population, but probably echocardiography underestimates the real percentage [8]. In the USA, Transcatheter Valve Therapy registry, 2% of the patients had BAVs [9].
According to the number of cusps, the presence of raphes, and the orientation, bicuspid aortic valve was classified into three types (0, 1, 2) and in subcategories [10]. A novel non-numerical classification seems to be useful in TAVI procedure. This classification identifies three BAV morphologies: tri-commissural (one commissure completely fused between two cusps), bi-commissural raphe type (two cusps fused by a fibrous or calcified ridge of various height), and bi-commissural non-raphe type (two cusps completely fused; there are only two commissures and no raphe or third commissure) [11].
For a long time, BAV was considered a risk condition in TAVI procedures. The association between BAV and some risk factors for TAVI is clear: calcified raphe, asymmetrical annulus, the risk of annulus rupture, coronary obstruction, and paravalvular leaks (PVL). Moreover, it is relevant to underline that BAV is associated more frequently with aortopathy (ascending aorta aneurism and coarctation). Thus, it is important to investigate these conditions prior to TAVI, because they should be a contraindication to implantation [6].
In addition, the height and location of coronary ostia in BAV could be very different than in tricuspid valves, and a specific assessment is fundamental to avoid coronary occlusion. About that, in those cases in which there is an uncertain sizing, a pre-dilatation angiography with one or more undersized balloon could be useful.
In BAV, the implantation is associated with lower success rate and higher rate of PVL, but similar outcomes, especially with new generations of prostheses [9, 12]. No significant differences were observed comparing the results of balloon-expandable to that of self-expanding valves [12].
3.3 Coronary disease
Observational data suggest that coronary artery disease (CAD) co-exists in up to 60% of patients candidate for TAVI [13]. Recent multicenter trials also report these percentages [14, 15].
Currently, the decision to perform PCI is discussed in Heart Team, considering the location and the complexity of PCI, as the latest ESC guidelines suggest.
Actually, severe lesions (>70%) involving proximal segments of coronary arteries should be treated prior to TAVI [4].
After all, it should be kept in mind that the use of FFR and iFR is theoretically influenced by the presence of a low aortic pressure due to severe aortic stenosis.
When a revascularization is required in TAVI candidates, it can be performed before TAVI, in a single procedure or after TAVI. Recent evidences showed no clinical advantages in terms of cardiovascular mortality, stroke, myocardial infarction, and acute kidney injury, when PCI is performed before or concomitant to TAVI procedure [16].
On the other hand, performing PCI after TAVI can be challenging because of the presence of valve struts and the relative difficulty to access coronary ostia. For this, the decision should be individualized on each case.
3.4 Vascular access
The choice of the most appropriate vascular access represents a key role in TAVI procedure. It is fundamental to evaluate the thoracoabdominal aorta, carotid arteries, and iliofemoral vessels.
Different vascular sites can be chosen: transfemoral (TF), transapical (TA), transaortic (TAo), trans-subclavian (TS), trans-carotid (Tc), and others [3, 17].
Different studies and registries have shown that a non-transfemoral TAVI accesses may be necessary up to 10–15% of patients. This is due, mainly, to obstructive peripheral vascular disease, severe vessel tortuosity, aortic aneurysm or atheroma, and previous peripheral grafts [18].
3.4.1 Transfemoral TAVI
The transfemoral access represents the preferred route in TAVI patients: because of its minimal invasiveness and large feasibility, TF access is recommended as first choice [18]. Analysis of iliofemoral vessel size, calcification, and tortuosity is necessary to establish if TF access can be used or not [19].
Multidetector CT provides all these aspects with a greater predictive value for vascular complications than invasive angiography [20].
The combination of a very small iliofemoral artery diameter and the presence of moderate or severe calcifications is associated with a high rate of vascular complications.
The minimum iliofemoral vessel diameter required is 5 mm for current self-expanding valves and 5.5 mm for balloon-expandable prostheses (ideally 6.5 mm for a 18F delivery system) [18].
Iliofemoral tortuosity does not predict vascular complications, but it can determine vascular elongation. In the case of severe tortuosity, the use of a stiff wire can be helpful, but particular attention should be paid to the distance between the access site and the aortic annulus [21].
For TF procedures, both percutaneous and cutdown access can be used: when the site of access is adequately large and without significant calcifications, percutaneous access is the first choice. In this case, the access is usually closed with various percutaneous devices available in commerce [11]. In the percutaneous approach, the anatomy of femoral artery should be always accurately studied, paying attention to the level of femoral bifurcation [19].
When the risk of percutaneous access is too high, it is preferable to opt for a cutdown approach, avoiding vascular complications in the site of puncture.
Several retrospective studies and one randomized trial have analyzed the differences in outcomes between a percutaneous approach and a surgical cutdown access. None of the available studies could demonstrate a superiority of a percutaneous approach, and the benefit of this choice remains controversial [22, 23, 24].
A step-by-step approach is mandatory in order to minimize the risk of complications.
General anesthesia is optional for transfemoral TAVI, but less used rather than the initial TAVI era.
The first step is to identify the femoral puncture site, ideally 1 cm above the femoral bifurcation. Usually, on the basis of CT scan, the best femoral access (right or left) is chosen. Two sheaths (6 Fr) are placed in the contralateral femoral artery and vein, so that a pigtail arterial catheter and a right ventricular pacemaker lead are placed [25].
To proceed more safely, in the case of percutaneous access, the puncture could be done under ultrasound view or fluoroscopy (preferably after a selective angiography from the contralateral site). A standard 0.035 inch wire is retrogradely introduced in the iliac-femoral axis, and a 8 Fr vascular introducer sheath is placed in order to pre-dilate the artery before the placement of a preclosure suture devices. At this point, a 8 Fr sheath is reintroduced over a standard 0.035 guidewire, an Amplatz catheter is placed in descending aorta, and the previous guidewire is changed with a 260 cm stiff wire.
The native aortic valve is crossed by a straight tip guidewire with the support of an Amplatz catheter (AL1–AL2). Once the aortic valve is crossed, the catheter is replaced by a stiff wire with a pigtail curve, paying attention to the risk of ventricular perforation [26].
At this point, a balloon aortic valvuloplasty (BAV) could be performed. BAV is particularly useful in some settings: in very severe and calcified aortic valves, in the case of borderline transcatheter aortic valve sizes, in bicuspid aortic valves, and in the presence of very low coronary ostia to observe the movement of the aortic valvular leaflets.
Then, the transcatheter aortic bioprosthesis is advanced up to the aortic valve, being careful during aortic arch crossing.
The balloon-expandable prostheses need rapid ventricular pacing (180–200 bpm) during the deployment while for self-expanding valves are not mandatory.
It is important to find an optimal implantation plane. Usually, it is calculated before the procedure on the basis of CT scan and verified by angiography, in order to have a single plane with all three cusps aligned.
Once the valve has been implanted, it is necessary to evaluate, by angiography and echocardiography, the position of prosthesis (not too high or too low), the possible presence of perivalvular regurgitation, and the trans-prosthesis gradients.
To have an easier vascular access closure, in some centers, a crossover with a secure wire from the contralateral femoral artery is performed: in this way, a rapid balloon occlusion and/or a delivery of a covered stent could be performed. A final angiography should be performed to check the result.
3.4.2 Transapical TAVI
The transapical (TA) approach, first described by Ye and colleagues in 2006 [27], is historically considered the alternative to transfemoral access. Usually, it is performed when the peripheral axis is impractical. The presence of large apical thrombus represents the major contraindication to this procedure.
The apex can be localized by fluoroscopy, TTE, or TEE. The more appropriate site of puncture is on the LV apex, which is, usually, more anterior and proximal than anatomic cardiac apex [3].
For ventricular pacing, both intracavitary (via femoral vein) and epicardial ventricular leads could be positioned.
The apex is exposed via an anterolateral left anterior mini-thoracotomy in the fifth or sixth intercostal space, under general anesthesia. After placing two purse-string sutures at the puncture site, ventricular puncture is done, and a standard 0.035 inch guidewire is advanced across the degenerated aortic valve, paying attention to avoid mitral valve apparatus. After, with a support of a Judkins catheter, a long stiff wire (260 cm) is placed into the abdominal aorta. The delivery sheath is introduced into the ventricular cavity, and the balloon-expandable valve is opened during rapid ventricular pacing. The final result is checked by TOE and aortic ascending angiography.
The balloon-expandable valves (Edwards Sapien) are the most used in transapical access; anyway, self-expanding bioprostheses (Symetics or Jena-Valve) could be implanted having CE mark. In the latter case, there is no need for rapid ventricular pacing.
Possible complications of the TA approach are bleeding at the site of the apical puncture, myocardial tears, apical scarring, aneurysm formation, or chronic pain [28].
3.4.3 Alternative routes
For transaortic access, an upper partial sternotomy or a right mini-thoracotomy at the second or the third intercostal space can be performed. Felt-reinforced purse-string suture is placed in the ascending aorta 4–5 cm above the aortic valve. Once the aortic valve is crossed retrogradely with a guidewire, the procedure is similar to transapical approach [3]. There are few contraindications to transaortic access: chest deformities, porcelain aorta, very short ascending aorta, and previous CABG with patent grafts [18].
The left subclavian artery can be used as an alternative access; the proximal third of the axillary artery represents the ideal site. In the evaluation of the axillary access, the same principles for the TF approach should be kept in mind. Particular attention should be paid at the level of the emergency of left subclavian artery, because of high rate of localization of atherosclerotic calcific plaques. Most of the times, the surgical approach is preferred in case of choice of the trans-axillary access [18]. Most of the time, in the trans-axillary access, self-expanding prostheses are preferred over balloon-expandable.
Rarely, if all types of approaches are not adequate, the trans-carotid access could be a valid alternative.
4. Valve choice
Currently, the types of valves most implanted are balloon-expandable and self-expanding valves.
Basically, the choice of valve depends on anatomical considerations and available valve sizes. Attention should be paid to annulus dimension and its geometry, bicuspid/tricuspid valve, amount and distribution of calcifications in LVOT and proximal aorta, sino-tubular and ascending aorta dimensions, and coronary height [11].
Usually, in the case of extensive calcifications with an increased risk of rupture, in extreme oval-shape annulus or in the presence of iliofemoral diameter between 5.0 and 5.5 mm, a self-expanding valve is preferred [29, 30, 31].
Instead, when we are in the presence of dilated ascending aorta (> 43 mm), very angulated aorta (particularly for transfemoral approach), or in the presence of an elevated preoperative risk of postprocedural pacemaker implant, the choice can fall on the balloon-expandable valves.
Anyway, most of the time the choice depends on operator experience and confidence [3].
5. Conclusions
The use of TAVI in patients affected by aortic stenosis is spreading more and more, treating intermediate- and low-risk patients.
The selection of patients seems to be a crucial point, pointing out the role of the Heart Team.
MDCT is the technique of choice in preoperative planning: it provides information regarding aortic valve and vascular access. Particular attention should be paid on bicuspid valve morphology.
The choice of valve is, basically, based on confidence and experience of the operator and the center.
\n',keywords:"transcatheter aortic valve implantation, aortic stenosis, patient selection, imaging, access site",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/70579.pdf",chapterXML:"https://mts.intechopen.com/source/xml/70579.xml",downloadPdfUrl:"/chapter/pdf-download/70579",previewPdfUrl:"/chapter/pdf-preview/70579",totalDownloads:604,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:43,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"February 10th 2019",dateReviewed:"November 19th 2019",datePrePublished:"December 23rd 2019",datePublished:"May 20th 2020",dateFinished:"December 23rd 2019",readingETA:"0",abstract:"The introduction of transcatheter aortic valve implantation (TAVI) have changed the management of patients affected by severe aortic stenosis. Nowadays, TAVI procedure represents the treatment of choice in patients with severe AS inoperable or at high risk. MDCT is the technique of choice in preoperative planning: it provides information regarding aortic valve and vascular access. Particular attention should be paid on bicuspid valve morphology. The choice of valve is, basically, based on confidence and experience of the operator and center.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/70579",risUrl:"/chapter/ris/70579",book:{id:"8819",slug:"cardiac-surgery-procedures"},signatures:"Antonio Giovanni Cammardella, Federico Ranocchi, Antonio Lio, Amedeo Pergolini, Francesco Nicolò, Marina Comisso, Vitaliano Buffa and Francesco Musumeci",authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Selection of patients",level:"1"},{id:"sec_3",title:"3. Imaging assessment",level:"1"},{id:"sec_3_2",title:"3.1 Aortic annulus",level:"2"},{id:"sec_4_2",title:"3.2 Bicuspid aortic valve",level:"2"},{id:"sec_5_2",title:"3.3 Coronary disease",level:"2"},{id:"sec_6_2",title:"3.4 Vascular access",level:"2"},{id:"sec_6_3",title:"3.4.1 Transfemoral TAVI",level:"3"},{id:"sec_7_3",title:"3.4.2 Transapical TAVI",level:"3"},{id:"sec_8_3",title:"3.4.3 Alternative routes",level:"3"},{id:"sec_11",title:"4. Valve choice",level:"1"},{id:"sec_12",title:"5. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Durko AP, Osnabrugge RL, Van Mieghem NM, Milojevic M, Mylotte D, Nkomo VT, et al. Annual number of candidates for transcatheter aortic valve implantation per country: Current estimates and future projections. European Heart Journal. 2018;39(28):2635-2642'},{id:"B2",body:'De Sciscio P, Brubert J, De Sciscio M, Serrani M, Stasiak J, Moggridge GD. Quantifying the shift toward transcatheter aortic valve replacement in low-risk patients: A meta-analysis. Circulation. Cardiovascular Quality and Outcomes. 2017;10(6):e003287'},{id:"B3",body:'Otto CM, Kumbhani DJ, Alexander KP, Calhoon JH, Desai MY, Kaul S, et al. ACC expert consensus decision pathway for transcatheter aortic valve replacement in the management of adults with aortic stenosis: A report of the American College of Cardiology task force on clinical expert consensus documents. The Journal of the American College of Cardiology. 2017;69(10):1313-1346'},{id:"B4",body:'Baumgartner H, Falk V, Bax JJ, De Bonis M, Hamm C, Holm PJ, et al. ESC/EACTS guidelines for the management of valvular heart disease. The European Heart Journal. 2017;38(36):2739-2791'},{id:"B5",body:'Nishimura RA, Otto CM, Bonow RO, Carabello BA, Erwin JP 3rd, Fleisher LA, et al. 2017 AHA/ACC focused update of the 2014 AHA/ACC guideline for the management of patients with valvular heart disease: A report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Circulation. 2017;135(25):e1159-e1e95'},{id:"B6",body:'Bax JJ, Delgado V, Bapat V, Baumgartner H, Collet JP, Erbel R, et al. Open issues in transcatheter aortic valve implantation. Part 1: Patient selection and treatment strategy for transcatheter aortic valve implantation. European Heart Journal. 2014;35(38):2627-2638'},{id:"B7",body:'Blanke P et al. Computed tomography imaging in the context of transcatheter aortic valve implantation (TAVI)/Transcatheter aortic valve replacement (TAVR): An expert consensus document of the Society of Cardiovascular Computed Tomography. 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A bicuspid aortic valve imaging classification for the TAVR era. JACC: Cardiovascular Imaging. 2016;9(10):1145-1158'},{id:"B12",body:'Yoon S-H, Bleiziffer S, De Backer O, et al. Outcomes in transcatheter aortic valve replacement for bicuspid versus tricuspid aortic valve stenosis. Journal of the American College of Cardiology. 2017;69(21):2579-2589'},{id:"B13",body:'Abdel-Wahab M, Zahn R, Horack M, Gerckens U, Schuler G, Sievert H, et al. Transcatheter aortic valve implantation in patients with and without concomitant coronary artery disease: Comparison of characteristics and early outcome in the German multicenter TAVI registry. Clinical Research in Cardiology. 2012;101(12):973-981'},{id:"B14",body:'Leon MB, Smith CR, Mack MJ, Makkar RR, Svensson LG, Kodali SK, et al. Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. The New England Journal of Medicine. 2016;374(17):1609-1620'},{id:"B15",body:'Reardon MJ, Van Mieghem NM, Popma JJ, Kleiman NS, Sondergaard L, Mumtaz M, et al. Surgical or transcatheter aortic-valve replacement in intermediate-risk patients. The New England Journal of Medicine. 2017;376(14):1321-1331'},{id:"B16",body:'Kotronias RA, Kwok CS, George S, Capodanno D, Ludman PF, Townend JN, et al. Transcatheter aortic valve implantation with or without percutaneous coronary artery revascularization strategy: A systematic review and meta-analysis. Journal of the American Heart Association. 2017;6(6)'},{id:"B17",body:'Bax JJ, Delgado V, Bapat V, Baumgartner H, Collet JP, Erbel R, et al. Open issues in transcatheter aortic valve implantation. Part 2: Procedural issues and outcomes after transcatheter aortic valve implantation. European Heart Journal. 2014;35(38):2639-2654'},{id:"B18",body:'Biasco L, Ferrari E, Pedrazzini G, Faletra F, Moccetti T, Petracca F, et al. Access sites for TAVI: Patient selection criteria, technical aspects, and outcomes. Frontiers in Cardiovascular Medicine. 2018;5:88'},{id:"B19",body:'Blanke P, Weir-McCall JR, Achenbach S, Delgado V, Hausleiter J, Jilaihawi H, et al. Computed tomography imaging in the context of transcatheter aortic valve implantation (TAVI)/transcatheter aortic valve replacement (TAVR): An expert consensus document of the society of cardiovascular computed tomography. JACC Cardiovasc Imaging. 2019;12(1):1-24'},{id:"B20",body:'Okuyama K, Jilaihawi H, Kashif M, Takahashi N, Chakravarty T, Pokhrel H, et al. Transfemoral access assessment for transcatheter aortic valve replacement: Evidence-based application of computed tomography over invasive angiography. Circulation. Cardiovascular Imaging. 2015;8(1)'},{id:"B21",body:'Toggweiler S, Gurvitch R, Leipsic J, Wood DA, Willson AB, Binder RK, et al. Percutaneous aortic valve replacement: Vascular outcomes with a fully percutaneous procedure. Journal of the American College of Cardiology. 2012;59(2):113-118'},{id:"B22",body:'Genereux P, Webb JG, Svensson LG, Kodali SK, Satler LF, Fearon WF, et al. Vascular complications after transcatheter aortic valve replacement: Insights from the PARTNER (placement of AoRTic TraNscathetER valve) trial. Journal of the American College of Cardiology. 2012;60(12):1043-1052'},{id:"B23",body:'Kadakia MB, Herrmann HC, Desai ND, Fox Z, Ogbara J, Anwaruddin S, et al. Factors associated with vascular complications in patients undergoing balloon-expandable transfemoral transcatheter aortic valve replacement via open versus percutaneous approaches. Circulation. Cardiovascular Interventions. 2014;7(4):570-576'},{id:"B24",body:'Bernardi FL, Gomes WF, de Brito FS Jr, Mangione JA, Sarmento-Leite R, Siqueira D, et al. Surgical cutdown versus percutaneous access in transfemoral transcatheter aortic valve implantation: Insights from the Brazilian TAVI registry. Catheterization and Cardiovascular Interventions. 2015;86(3):501-505'},{id:"B25",body:'Cribier A, Litzler PY, Eltchaninoff H, Godin M, Tron C, Bauer F, et al. Technique of transcatheter aortic valve implantation with the Edwards-Sapien heart valve using the transfemoral approach. Herz. 2009;34(5):347-356'},{id:"B26",body:'Stortecky S, Buellesfeld L, Wenaweser P, Windecker S. Transcatheter aortic valve implantation: The procedure. Heart. 2012;98(Suppl 4):iv44-iv51'},{id:"B27",body:'Ye J, Cheung A, Lichtenstein SV, Carere RG, Thompson CR, Pasupati S, et al. Transapical aortic valve implantation in humans. The Journal of Thoracic and Cardiovascular Surgery. 2006;131(5):1194-1196'},{id:"B28",body:'Pascual I, Carro A, Avanzas P, Hernandez-Vaquero D, Diaz R, Rozado J, et al. Vascular approaches for transcatheter aortic valve implantation. Journal of Thoracic Disease. 2017;9(Suppl 6):S478-SS87'},{id:"B29",body:'Koehler T, Buege M, Schleiting H, Seyfarth M, Tiroch K, Vorpahl M. Changes of the eSheath outer dimensions used for transfemoral transcatheter aortic valve replacement. BioMed Research International. 2015;2015:572681'},{id:"B30",body:'Kasel AM, Cassese S, Bleiziffer S, Amaki M, Hahn RT, Kastrati A, et al. Standardized imaging for aortic annular sizing: Implications for transcatheter valve selection. JACC: Cardiovascular Imaging. 2013;6(2):249-262'},{id:"B31",body:'Hansson NC, Norgaard BL, Barbanti M, Nielsen NE, Yang TH, Tamburino C, et al. The impact of calcium volume and distribution in aortic root injury related to balloon-expandable transcatheter aortic valve replacement. Journal of Cardiovascular Computed Tomography. 2015;9(5):382-392'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Antonio Giovanni Cammardella",address:"agcammardella@gmail.com",affiliation:'
Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, Italy
Department of Cardiac Surgery and Heart Transplantation, San Camillo Forlanini Hospital, Italy
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1. Introduction
The increase in the cost of energy and their availability, as well as the growing impact of energy-consuming technological systems on the environment, require the introduction of systematic measures aimed at improving the energy efficiency of production. At the same time, it is predicted [1] that energy consumption will increase in the future. This will especially affect electricity. Worldwide, about 50% of total electricity consumption is carried out in industry by conversion using electric motor systems [2]. The highly automated industry of developed countries converts almost 60-75% of all electricity distributed in four types of electric motor systems into mechanical movement of about 40%, of which compressors 25%, pumps 20% and fans 15%. This study shows that the control system, based on the analysis of technological parameters and intensity of electricity consumption from electric motor systems, can be widely used in industry [3].
The gradual transition from traditional to renewable energy sources requires an improvement in the balance of supply and demand, the creation of active energy consumers capable of controlling their own energy efficiency and maintaining their own energy sources. Eliminating hidden energy losses in both energy consumption and generation gives businesses additional benefits.
Improving the energy efficiency of consumers in industry was mainly achieved through the introduction of new technologies, new more economical equipment, construction using new energy-saving materials, heat recovery, and so on. To a lesser extent, organizational approaches to energy consumption management were introduced, i.e. planning and production management taking into account the economically feasible use of energy resources. This is due to the fact that organizational approaches are closely integrated into production planning and control systems, and require additional investment costs for the development of information technology aimed at energy saving. Obtaining information on energy consumption and linking it to operational information to get an idea of the behavior of equipment and technological systems has proven to be technically difficult. However, the potential for energy savings from organizational approaches is quite significant [4]. Energy management combines the overall management of the enterprise with the needs of improving the energy efficiency of technological systems.
This work is aimed at the study of operational monitoring and analysis of energy consumption as a sequence of cases of stationary and quasi-stationary energy consumption. With regard to energy saving, real-time information can raise awareness of employees of the presence of hidden energy losses and influence their actions. Operational monitoring of deviations of energy consumption from the accepted values of efficient energy consumption can be an indicator of equipment performance and a tool to support maintenance measures. Specific equipment failures can be predicted by identifying certain patterns of energy consumption. Responding quickly to such events can prevent catastrophic equipment failures. Thus, energy monitoring becomes a driver of equipment reliability, technological process stability and product quality.
2. Purpose of work
Most energy consumers convert the received energy and raw materials into a useful product. Technological equipment, production products and performers are involved in this transformation process. Each of these links can affect energy consumption and energy losses. The specific energy consumption of the technological system E is characterized by the vector of influential technological parameters X = {x1, x2, …, xk} and a set of random uncontrolled factors Z that cause hidden energy losses ΔE.
E=fXZ=φX+ΔEE1
Hidden energy losses from the consumer can be caused by violations of the technical condition of the equipment, deterioration of the properties of raw materials, erroneous actions of personnel, and other factors that are usually not subject to automated control. The problem is to provide real-time staff with information on the presence and extent of hidden energy losses in each technological system of the enterprise, based solely on influential technological parameters and specific energy consumption. The purpose of this work is to develop methods for estimating latent energy losses based on the analysis of cases of quasi-stationary energy consumption.
3. Known methods of monitoring energy losses
Energy efficiency of the consumer is determined by the ratio of the amount of energy consumed to the amount of product received, or services provided when working in a certain mode. In the absence of energy losses, this figure will take the minimum values in all modes of operation. When energy losses occur, it will increase. At the same time, the performance of the technological system depends on the parameters that determine its mode of operation, and accordingly on the intensity of energy consumption.
The dependence of energy consumption on the set of technological parameters X in the absence of random uncontrolled factors Z will be called the function of efficient energy consumption, and the value of this function at certain technological parameters X - efficient energy consumption Eef. That is, energy consumption without hidden energy losses:
Eef=φX=fXZ=0E2
Energy efficiency monitoring and, accordingly, the estimation of latent energy losses are based on the comparison of the current energy consumption E with the efficient energy consumption Eef obtained from Eq. (2).
ΔЕ=E–EefE3
The function of efficient energy consumption, and consequently the value of efficient energy consumption, at certain technological parameters, is usually determined in several ways. There is a method when efficient energy consumption is obtained by calculating the known empirical or analytical dependences of electrical engineering, heat engineering, mechanics, hydraulics [5]. This method gives an approximate estimate of the reference energy in the real technological process. Another way to obtain an estimate of efficient energy consumption is based on testing tests of equipment and determining the normative energy consumption when operating equipment under specified conditions [6]. Such estimates of regulatory energy consumption do not always correspond to effective standards in real production [7]. There is also a method when energy efficiency is considered to be achieved a certain period of time ago, when the technological parameters were similar. In most cases, energy management uses a linear regression model of the dependence of “standard” energy consumption on controlled technological parameters according to the “Monitoring and Targeting” method [8] for comparison with current energy consumption.
4. Assessment of hidden energy losses
The paper proposes to use the methods of precedent analysis [9], based on cases of efficient energy consumption, to evaluate and monitor the standards of efficient energy consumption. To do this, each energy consumer in the production process must be equipped with a system of sensors of influential technological parameters associated with a programmable controller of the monitoring system, whose task is to recognize the precedents of quasi-stationary energy consumption.
The precedent of quasi-stationary energy consumption CaseE will be the case in which all normalized influential technological parameters X = {X1, X2, …, Xn} for a certain period of time remain within predetermined allowable values ± Δxn = σ. The precedent has the following structure:
CaseE=MX1,…,MXn;DX1,…,DXn;rX1,…,rXn;Е,τ,SE4
where: M (X1), …, M (Xn) - mathematical expectations of the factors of influence X1, …, Xn;
D (X1), …, D (Xn) - statistical variance of influencing factors X1, …, Xn;
r (X1), …, r (Xn) - autocorrelation coefficients of influencing factors X1, …, Xn;
n - is the number of interdependent influential technological parameters;
E - specific energy consumption for the period of quasi-stationary state;
τ - duration of steady state;
S - probable diagnosis of technical condition.
The precedents obtained in this way form in the n-dimensional space of influential technological parameters {X1, X2, …, Xn} a cloud of precedents of quasi-stationary states of the technological system with different estimates of energy consumption. In this cloud, precedents with minimal energy consumption form the surface of cases of efficient energy consumption.
Eef=φX1X2…Xn=fXZ=0E5
The estimation of latent energy losses in an arbitrary i-th case (Ei) is based on the determination of a local standard of efficient energy consumption for the i-th precedent. To do this, from the base of precedents of efficient consumption is selected n precedents closest to the current precedent and on them, by the method of least squares, calculate the regression coefficients b0, b1, b2, …, bn – the function of efficient energy consumption for the current precedent. The value of effective consumption for the current i-th precedent is calculated by the formula:
Eefi=b0+b1MX1i+b2MX2i+…+bnMXniE6
After that, the difference between the obtained value of Eefi and the current specific energy consumption Ei is calculated: ΔEi = Eefi - Ei. Depending on the obtained value of ΔEi it is possible to draw a conclusion about the energy efficiency of the equipment that is subject to monitoring. If ΔЕі≈ 0, the equipment is considered to be operating efficiently, if ΔЕі > 0, the equipment is operating with reduced energy consumption and energy savings are equal to ΔЕі, if ΔЕі < 0, the equipment is operating with energy losses up to ΔЕі.
5. Information support for monitoring hidden energy losses
The flow of derived data from the sensors enters the monitoring system in the form of a time series. The difficulty lies in the synchronization and subsequent search for the relationship between the vector of technological parameters and the intensity of energy costs. This complexity can be overcome by segmenting the time series and allocating stationary areas from its composition, which will be considered as precedents for energy consumption. An overview of possible methods of time series segmentation is given in [10].
Time series T = {(X1, E1); (X2, E2); …, (Xm, Em)}; 1 ≤ m ≤ M is a finite set of M n-dimensional structured sequences of regime parameters X = [x1, x2,…, xn] and the corresponding energy consumption intensity E, marked by timestamps t1,…, tM. Segmentation of the time series T divides it into a sequence of independent series T = {S1, S2, …, Si} that do not overlap. The segment S of the time series T is a finite set of structured elements (Xm, Em) marked with timestamps ta, …, tn, …, tb: Si = {(Xа Eа); …; (Xb, Eb)}.
The purpose of the procedure of segmentation of the time series of mode parameters is to divide the data flow into separate disparate areas with similar characteristics and to allocate among them areas with signs of stationarity [11]. To formalize the relationship between the elements of the series, a special function of the price of entry into the segment Cost{S} is introduced, which determines the relationship between the elements of the series. Usually the function of distance between elements of a series or groups of elements is used for segmentation [12, 13]. In this paper, for time series segmentation, it is proposed to use the function of the distance d between the elements X1 and Xi of the time series in the n-dimensional Euclidean space of mode parameters X:
CostS=dX1Xi=x11−x1i2+x21−x2i2+⋯+xk1−xki2E7
The condition of inclusion of the next, i-th, group of the received mode parameters Xi, Ei to the next segment S, which is filled with data:
CostS=dX1Xi≤σE8
where: σ is a threshold value that is determined empirically.
The pseudocode of the algorithm segmentation of the time series of determining mode parameters is as follows:
open a time series segment;
get and save in the open segment the first set of mode parameters X1, E1;
obtain the next set of mode parameters Xi, Ei and calculate the entry price of this set in the open segment according to formula (7).
as long as the segment entry price for the new sets of mode parameters is less than the threshold value σ, according to formula (8), they join the open segment. Otherwise, the open segment is closed and passed for further processing, a new segment is opened and it is the first to enter a set of mode parameters for which the entry price was higher than the threshold value. And then the cycle repeats.
The algorithm in the pace of the technological process processes the derivative flow of mode parameters, determines the segments with signs of stationarity and transmits them for further processing to calculate the hidden energy losses. The procedure of segmentation of the flow of mode parameters of the technological system can be implemented in a separate programmable controller located in the area of energy metering.
6. Formation of a base of precedents
During the assessment of latent or abnormal energy losses in the composition of the upper-level computing device, a base of precedents of quasi-stationary consumption is formed. The initial filling of the precedent database is performed with the participation of an expert. Let Case be a set of precedents. It is believed that all precedents of energy consumption are located in an unlimited space of mathematical expectations of regime factors. In this space the function of distance between precedents is set:
dCasei Casej=∑MXni−MXnj2E9
Considerations based on the analysis of precedents are to search among all recorded precedents of the nearest neighbors to the current one and to build a linear approximation of the function of efficient energy consumption in the nearest neighbors. The search for the nearest neighbors is carried out among a large number of precedents. To simplify this procedure, it is proposed to involve a clustering mechanism. Clustering of precedents is the process of combining precedents into groups characterized by similar features. Unlike the usual classification, where the number of groups of objects is fixed and predetermined by a set of precedents, here neither groups nor their number are predetermined and formed in the process of the system, based on a certain degree of proximity of precedents [14, 15].
The formed clusters represent separate disjoint areas of function (2). It can be assumed that the precedents assigned to the same cluster belong to one area of the energy efficiency function. These clusters, in turn, are proposed to be used as a basis for determining the proximity of precedents. Suppose that we gradually obtain a sequence of precedents of static energy consumption. It is necessary to assign each of them to one of the disjoint subsets at the rate of obtaining precedents so that each cluster consists of precedents, which according to the metric d(Casei Casej) were in the area of existence of the cluster. In this case, each precedent is assigned the identifier of the cluster n∈ N, to which it belongs. We assume that the space of mathematical expectations of influencing factors has zero precedent Case0 with zero values of influencing factors. The distance from the zero precedent to any other precedent is determined by the dependence:
dCase0Casei=MiX12+MiX22+⋯+MiXn2E10
Each of the clusters is characterized by an initial radius Ri and a depth of existence ΔR. The initial radius of the cluster is the distance between the zero precedent of Case0 and the nearest cluster boundary (Figure 1) Ri = d (Case0, Casei). The area of existence of the cluster is a set of Casei precedents (marked with asterisks in the figure) that satisfy the condition Rі ≤ d (Case0, Casei,) < Rі + ΔR.
Figure 1.
Cluster diagram.
The clustering algorithm is a function a: Casei → n which matches the cluster identifier n ∈ N to any precedent, and the set N is unknown in advance. As the identifier n of the cluster, it is advisable to use its radius, which is calculated by the formula:
nі=Rі=dCase0CasejΔRE11
In the future, we look for the nearest neighbors only among the precedents of the current cluster. New precedents are placed in the base of effective precedents only in the absence of energy losses (see Figure 2).
Figure 2.
Cyclic algorithm for forming a base of precedents and determining the amount of power of efficient energy consumption.
Numerical modeling of the method of precedent estimation of energy losses was performed on the data of chemical production given in [16]. The volumes of produced ammonia and consumed natural gas are accepted as factors influencing electricity consumption. The actual consumed electricity calculated according to the regression and precedent model is compared. The simulation results are shown in Table 1.
Produced ammonia, tone
Used natural gas, 1000 m3/hour
Actually used electricity, 1000 kWh
Used electricity according to the regression model, 1000 kWh
Used electricity according to the precedent model, 1000 kWh
39,699
45,211
31,988
31,988
31,959
39,292
45,182
32,005
32,066
31,608
39,644
45,357
31,932
31,966
32,055
39,929
45,122
31,923
32,038
31,934
39,684
45,481
32,105
32,191
32,003
39,967
45,782
32,056
32,164
32,499
39,422
45,761
32,063
32,251
32,123
43,174
45,602
32,098
32,05
31,873
42,055
45,608
31,971
33,247
32,178
40,449
45,023
31,953
32,79
31,842
41,385
44,973
31,86
32,27
31,922
46,907
53,037
35,576
35,576
35,551
46,923
52,979
35,532
35,699
35,565
46,109
52,78
35,521
35,67
35,594
46,772
52,693
35,491
35,395
35,508
46,583
52,644
35,501
35,598
35,446
46,821
52,893
35,538
35,581
35,565
46,865
52,657
35,504
35,616
35,469
Table 1.
Comparison of regression and precedent approaches.
When building a precedent model, 18 precedents were considered. For each of the precedents, 4 nearest neighbors were selected, on which, by the method of least squares, the linear function of efficient energy consumption was built and the efficient energy consumption was calculated.
7. Conclusion
This chapter proposes a method for detecting and estimating latent and abnormal energy losses in production technology systems. The methodology is based on a retrospective analysis of cases of quasi-stationary energy consumption.
\n',keywords:"Energy losses, energy efficiency, energy saving, precedent analysis, energy consumers",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76470.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76470.xml",downloadPdfUrl:"/chapter/pdf-download/76470",previewPdfUrl:"/chapter/pdf-preview/76470",totalDownloads:159,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 24th 2021",dateReviewed:"March 30th 2021",datePrePublished:"April 26th 2021",datePublished:"January 26th 2022",dateFinished:"April 26th 2021",readingETA:"0",abstract:"Most industrial or municipal energy consumers involve the conversion of electricity, either into useful products or into other types of energy. For example, lighting systems, heating systems, air or water supply systems. And in all such systems there are energy losses, which can be divided into open, or technological and hidden, or abnormal. Open losses are inherent in the technological process itself and depend on the principle of energy conversion, flow conditions, the type of equipment received, and so on. Hidden losses in the technological system occur accidentally due to the appearance of defects in the equipment, erroneous actions of personnel, changes in uncontrolled external conditions. The paper considers a method of detecting and estimating hidden energy losses, based on the analysis of energy consumption precedents and building a decision support system aimed at eliminating such energy losses. Models of energy consumption precedents are formed on the basis of controlled technological parameters and their statistical estimates. In the future, local standards of efficient energy consumption are formed from individual precedents. The advantage of this method of estimating latent energy losses is the adaptation of standards of efficient energy consumption to the conditions of the consumer.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76470",risUrl:"/chapter/ris/76470",signatures:"Borys Pleskach",book:{id:"10377",type:"book",title:"Electric Power Conversion and Micro-Grids",subtitle:null,fullTitle:"Electric Power Conversion and Micro-Grids",slug:"electric-power-conversion-and-micro-grids",publishedDate:"January 26th 2022",bookSignature:"Majid Nayeripour and Mahdi Mansouri",coverURL:"https://cdn.intechopen.com/books/images_new/10377.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-389-2",printIsbn:"978-1-83969-388-5",pdfIsbn:"978-1-83969-390-8",isAvailableForWebshopOrdering:!0,editors:[{id:"66929",title:"Prof.",name:"Majid",middleName:null,surname:"Nayeripour",slug:"majid-nayeripour",fullName:"Majid Nayeripour"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"345292",title:"Ph.D.",name:"Borys",middleName:null,surname:"Pleskach",fullName:"Borys Pleskach",slug:"borys-pleskach",email:"bn.pleskach@gmail.com",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. Purpose of work",level:"1"},{id:"sec_3",title:"3. Known methods of monitoring energy losses",level:"1"},{id:"sec_4",title:"4. Assessment of hidden energy losses",level:"1"},{id:"sec_5",title:"5. Information support for monitoring hidden energy losses",level:"1"},{id:"sec_6",title:"6. Formation of a base of precedents",level:"1"},{id:"sec_7",title:"7. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'World Energy Outlook 2019, International Energy Agency, Paris. Available from: http://https://www.iea.org/reports/world-energy-outlook-2019'},{id:"B2",body:'Waide P., Brunner C.U. Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems: International Energy Agency, 2011'},{id:"B3",body:'Soner Emec, Jörg Krüger, Günther Seliger. Online fault-monitoring in machine tools based on energy consumption analysis and non-invasive data acquisition for improved resource-efficiency. In: 13th Global Conference on Sustainable Manufacturing, Procedia CIRP 40, 2016, p. 236 – 243'},{id:"B4",body:'Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., Ernst, F.O. Integrating energy efficiency performance in production management - gap analysis between industrial needs and scientific literature: Journal of Cleaner Production, 2011, 19, 667–679, https://doi.org/10.1016/j.jclepro.2010.11.011'},{id:"B5",body:'ShaohuaHuFeiLiu YanHeTongHu, An on-line approach for energy efficiency monitoring of machine tools: Journal of Cleaner Production, Volume 27, May 2012, pp. 133-140'},{id:"B6",body:'Berezhny C.V., O.Ye. Melnyk OE, Methods for determining the specific norms of electricity consumption: Engineering in agricultural production, industrial engineering, automation, 2012, issue 25, part II p. 145-155'},{id:"B7",body:'Nakhodov VF, Borychenko OV, Ivanko DO Control of energy efficiency in the energy management system: Bulletin of KNUTD, 2013, №6, p. 67 - 76'},{id:"B8",body:'Efficiency Direct, Energy Monitoring and Targeting [Online]. Available from: https: //efficiency-direct.co.uk/services/energy-monitoring-and-targeting/'},{id:"B9",body:'Osipov G.S., Methods of artificial intelligence: M.: FIZMATLIT, 2011. 296 p'},{id:"B10",body:'Eamonn Keogh, Selina Chu, David Hart, Michael Pazzani, Segmenting Time Series: A Survey and Novel Approach, 2003 Available from: https://www.cs.rutgers.edu/∼pazzani/Publications/survey.pdf'},{id:"B11",body:'Shumway R.H., Stoffer D.S. Time Series Analysis and Its Applications: With R Examples, 3rd Edition. — Springer, 2011. — 609 p'},{id:"B12",body:'Vasko K, Toivonen H, Estimating the number of segments in time series data using permutation tests. In: Proceedings of the IEEE International Conference on Data Mining, 2002, pp. 466–473'},{id:"B13",body:'Himberg J, Korpiaho K, Mannila H, Tikanmaki J, Toivonen H. Time-series segmentation for context recognition in mobile devices. In: Proceedings of the IEEE International Conference on Data Mining (ICDM01), 2001, San Jose, California pp. 466–473'},{id:"B14",body:'Kohonen T. Self-Organizing Maps: Springer, 1995. –501 p'},{id:"B15",body:'Zmitrovich A.I., Intelligent information systems: Mn.: NTOOO "TetraSystems", 1997, 368 p'},{id:"B16",body:'І.В. Stetsenko, Ya. S. Bederak, Construction of multifactor mathematical models of energy consumption in chemical production: Energy saving. Energy. Energy audit, 2013, № 7, С. 41 - 48'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Borys Pleskach",address:"bn.pleskach@gmail.com",affiliation:'
Department of Energy Saving, Pukhov Institute for Modelling in Energy Engineering, Kyiv, Ukraine
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Studies focusing on glucose transport and utilization, lipid storage and consumption, handling of energy rich phosphates, and measurements of newly emerging noninvasive biomarkers, i.e., acetylcarnitine and carnosine are summarized. Muscle metabolism connections to exercise physiology and the development as well as possible treatment of metabolic diseases, such as obesity and diabetes are also discussed. Taken together, multinuclear in vivo MRS on humans helped to uncover defects in skeletal muscle metabolic pathways in insulin-resistant conditions; and to discover links between defects in mitochondrial activity/capacity and lipid metabolism, as well as defects in whole-body and/or muscle glucose metabolism. There is also to mention that several of the MR-derived readouts are affected by both training status and metabolic disease in a specific way, and thus could serve as potential markers of training status and metabolic flexibility.",signatures:"Ladislav Valkovič, Radka Klepochová and Martin Krššák",authors:[{id:"239658",title:"Associate Prof.",name:"Martin",surname:"Krssak",fullName:"Martin Krssak",slug:"martin-krssak",email:"martin.krssak@meduniwien.ac.at"},{id:"239659",title:"Dr.",name:"Ladislav",surname:"Valkovic",fullName:"Ladislav Valkovic",slug:"ladislav-valkovic",email:"ladislav.valkovic@cardiov.ox.ac.uk"},{id:"239660",title:"Dr.",name:"Radka",surname:"Klepochova",fullName:"Radka Klepochova",slug:"radka-klepochova",email:"raduss.tuskova@gmail.com"}],book:{id:"6609",title:"Muscle Cell and Tissue",slug:"muscle-cell-and-tissue-current-status-of-research-field",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"147665",title:"Dr.",name:"Jonathan",surname:"Paul",slug:"jonathan-paul",fullName:"Jonathan Paul",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"236519",title:"Dr.",name:"Arlek",surname:"Gonzalez-Jamett",slug:"arlek-gonzalez-jamett",fullName:"Arlek Gonzalez-Jamett",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"236631",title:"Dr.",name:"Marina",surname:"Bugrova",slug:"marina-bugrova",fullName:"Marina Bugrova",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"236882",title:"Dr.",name:"Koyal",surname:"Garg",slug:"koyal-garg",fullName:"Koyal Garg",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"244355",title:"Dr.",name:"Marina",surname:"Ilicic",slug:"marina-ilicic",fullName:"Marina Ilicic",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"245774",title:"MSc.",name:"Andrew",surname:"Dunn",slug:"andrew-dunn",fullName:"Andrew Dunn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"245775",title:"BSc.",name:"Madison",surname:"Marcinczyk",slug:"madison-marcinczyk",fullName:"Madison Marcinczyk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"245776",title:"Mr.",name:"Muhamed",surname:"Talovic",slug:"muhamed-talovic",fullName:"Muhamed Talovic",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"246979",title:"Dr.",name:"Jorge",surname:"Bevilacqua",slug:"jorge-bevilacqua",fullName:"Jorge Bevilacqua",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"246980",title:"Dr.",name:"Ana Maria",surname:"Cardenas",slug:"ana-maria-cardenas",fullName:"Ana Maria Cardenas",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"prior-publication-policy",title:"Prior Publication Policy",intro:"
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A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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The significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
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Other than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
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Some basic information about the editorial treatment of different varieties of prior publication is laid out below:
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\n\n
Given that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\n\n
All submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
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Newspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
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Submitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
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As with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
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\n\n
White papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
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Although such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
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\n\n
4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
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We feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
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Nevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
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In cases where there is any overlap between the Author´s submitted manuscript and related internet postings, we will generally not consider it to be an instance of self-plagiarism. This also holds true for any co-Author as well.
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He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"86",type:"subseries",title:"Business and Management",keywords:"Demographic shifts, Innovation, Technology, Next-gen leaders, Worldwide environmental issues and clean technology, Uncertainty and political risks, Radical adjacency, Emergence of new business ecosystem type, Emergence of different leader and leader values types, Universal connector, Elastic enterprise, Business platform, Supply chain complexity",scope:"
\r\n\tThe Business and Management series topic focuses on the most pressing issues confronting organizations today and in the future. Businesses are trying to figure out how to lead in a time of global uncertainty. In emerging markets, issues such as ill-defined or unstable policies, as well as corrupt practices, can be hugely problematic. Changes in governments can result in new policy, regulations, and interest rates, all of which can be detrimental to foreign businesses and investments. A growing trend towards economic nationalism also makes the current global political landscape potentially hostile towards international businesses.
\r\n
\r\n\tThe demographic shifts are creating interesting challenges. People are living longer, resulting to an aging demographic. We have a large population of older workers and retirees who are living longer lives, combined with a declining birthrate in most parts of the world. Businesses of all types are looking at how technology is affecting their operations. Several questions arise, such as: How is technology changing what we do? How is it transforming us internally, how is it influencing our clients and our business strategy? It is about leveraging technology to improve efficiency, connect with customers more effectively, and drive innovation. The majority of innovative companies are technology-driven businesses. Realizing digital transformation is today’s top issue and will remain so for the next five years. Improving organizational agility, expanding portfolios of products and services, creating, and maintaining a culture of innovation, and developing next -generation leaders were also identified as top challenges in terms of both current and future issues.
\r\n
\r\n\tThe most sustained profitable growth occurs when a company expands its core business into an adjacent space. This has significant implications for management because innovation in business ecosystems differs from traditional, vertically integrated firms. Every organization in the ecosystem must be aware of the bigger picture. Innovation in ecosystems necessitates collaborative action to invent and appraise, efficient, cross-organizational knowledge flows, modular architectures, and good stewardship of legacy systems. It is built on multiple, interconnected platforms. Environmental factors have already had a significant impact in the West and will continue to have an impact globally. Businesses must take into account the environmental impact of their daily operations. The advantage of this market is that it is expected to grow more rapidly than the overall economy. Another significant challenge is preparing the next generation of leaders to elevate this to the number one priority within the next five years. There can be no culture of innovation unless there is diverse leadership or development of the next generation of leaders; and these diverse, next-generation leaders are the ones who will truly understand the digital strategies that will drive digital transformation.
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