Electrode specification.
\r\n\tThis book discusses the anatomy and pathophysiological characteristics of the biliary tract, the latest progress in the treatment of different diseases of the biliary tract, and the management of complications. We hope that this book will provide clinicians with evidence for clinical decision-making and provide scientists with a comprehensive overview of current developments in this vital area.
",isbn:"978-1-80356-699-3",printIsbn:"978-1-80356-698-6",pdfIsbn:"978-1-80356-700-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"75ea7752fb410b6d399b549bb66e9b58",bookSignature:"Prof. Qiang Yan and Dr. Huaping Shen",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11852.jpg",keywords:"Physiological Anatomy, Pathophysiology, Choledochal Cyst, Bile Duct Stone, Inflammation, Obstructive Jaundice, Gallbladder Carcinoma, Cholangiocarcinoma, Diagnosis, Therapeutics, Surgical Treatment, Management of Complications",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 23rd 2022",dateEndSecondStepPublish:"April 20th 2022",dateEndThirdStepPublish:"June 19th 2022",dateEndFourthStepPublish:"September 7th 2022",dateEndFifthStepPublish:"November 6th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Medical doctor, adjunct professor, and FACS, Chairman of Department of General Surgery at Zhejiang University Huzhou Hospital and Director of Department of Hepatopancreatic and Biliary Surgery and Department of Surgery Teaching and Research.",coeditorOneBiosketch:"Associate chief of the Department of Hepatopancreatic & Biliary (HPB) Surgery, Zhejiang University Huzhou Hospital who has been engaged in hepatobiliary and pancreatic surgery for 9 years, and is an expert in diagnosis and treatments of diseases in hepatobiliary and pancreatic fields.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"247970",title:"Prof.",name:"Qiang",middleName:null,surname:"Yan",slug:"qiang-yan",fullName:"Qiang Yan",profilePictureURL:"https://mts.intechopen.com/storage/users/247970/images/system/247970.png",biography:"Qiang Yan, MD, is a master’s supervisor, adjunct professor, and Fellow of the American College of Surgeons (FACS). He is the chairman of the Department of General Surgery and the director of the Department of Hepatopancreatic and Biliary Surgery and Department of Surgery Teaching and Research, Zhejiang University Huzhou Hospital.\nDr. Yan is a member of the Committees of Biliary Surgeons, Department of Surgeons and Hepatobiliary Minimal of Non-invasive Surgery, Chinese Medical Doctor Association, and many other academic associations. He is also a special member of the editorial committees of the Chinese Journal of General Surgery, Liver Cancer, and Chinese Journal of Clinicians. He is a participant in the High-Level Talents of Zhejiang Medicine and Zhejiang Province 151 Talents. Dr. Yan completed advanced studies in hepatobiliary pancreatic surgery at Stanford University Medical Center, California, and University Hospital Regensburg, Germany. He has more than thirty high-quality papers to his credit.",institutionString:"Huzhou Central Hospital",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Zhejiang University",institutionURL:null,country:{name:"China"}}}],coeditorOne:{id:"344080",title:"Dr.",name:"Huaping",middleName:null,surname:"Shen",slug:"huaping-shen",fullName:"Huaping Shen",profilePictureURL:"https://mts.intechopen.com/storage/users/344080/images/system/344080.png",biography:"Huaping Shen is an associate chief of the Department of Hepatopancreatic and Biliary (HPB) Surgery, Zhejiang University Huzhou Hospital. He has been engaged in hepatobiliary and pancreatic surgery for 10 years and is an expert in diagnosis and treatment of hepatobiliary and pancreatic diseases. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"10318",title:"Recent Advances in Pancreatitis",subtitle:null,isOpenForSubmission:!1,hash:"4660cc6cfdbc562d1f25e6e05b6b77f1",slug:"recent-advances-in-pancreatitis",bookSignature:"Qiang Yan",coverURL:"https://cdn.intechopen.com/books/images_new/10318.jpg",editedByType:"Edited by",editors:[{id:"247970",title:"Prof.",name:"Qiang",surname:"Yan",slug:"qiang-yan",fullName:"Qiang Yan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7869",title:"Advanced Endoscopy",subtitle:null,isOpenForSubmission:!1,hash:"92f6ce51b737e9086a6059ab7470eee9",slug:"advanced-endoscopy",bookSignature:"Qiang Yan and Xu Sun",coverURL:"https://cdn.intechopen.com/books/images_new/7869.jpg",editedByType:"Edited by",editors:[{id:"247970",title:"Prof.",name:"Qiang",surname:"Yan",slug:"qiang-yan",fullName:"Qiang Yan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"41629",title:"Preparation, Physicochemical Properties and Battery Applications of a Novel Poly(Ionic Liquid)",doi:"10.5772/51162",slug:"preparation-physicochemical-properties-and-battery-applications-of-a-novel-poly-ionic-liquid-",body:'Ionic liquids (ILs) are generally defined as a salt with a melting point lower than 100 °C whose properties include non-volatility, non-flammability, and a relatively high ionic conductivity [1]. Recently, therefore, interest has increased in the possible use of this type of liquid as an electrolyte in energy storage devices, for example, a lithium rechargeable battery [2–9] and an electric double layer capacitor [10–14].
At the same time, to further the development of large, thin, prismatic electrochemical devices for which there is a high market demand, solid electrolytes are generally preferred over liquid electrolytes from the view point of ease of production and long device lifetime. In the last decade, many researchers and battery companies have been developing a “solid state polymer electrolyte” or a “gel polymer battery” with a film-like shape. More recently, growing attention has been paid to poly(ionic liquids) as a class of polymeric materials that are highly non-flammable. Examples of new polyelectrolytes poly(ILs) have been produced from polymerizable ionic liquid monomers by several polymerization processes. Research into the application of poly(ILs) for polymer electrolytes were under intense study by Ono et al around at 2005 [15-17]. The ensuing intensive studies on poly(ILs) in the last five years significantly expanded the research scope of this new type of polymer, and some valuable review [18-19] and feature articles have been published [20].
Amid such developments, we also developed a polymerizable ionic liquid,
On the other hand, the use of ILs as the solvent in free radical polymerization media instead of a conventional organic solvent markedly affects the rate and degree of polymerization. It has been reported that poly(methylmethacrylate) (PMMA) reactions are much more rapid in an ionic liquid than in a nonpolar solvents, and that a PMMA prepared in an ionic liquid has a molecular weight approximately five times higher than in benzene and toluene [21]. The ionic liquid monomer, DEMM-TFSI, also gives the ultra high molecular weight poly(DEMM-TFSI) by bulk polymerization with a conventional 2-2\'-azoisobutyronitrile (AIBN) initiator [22]. However, control of the molecular weight was difficult due to the strong enhancement of the propagation rate coefficients (
For electrochemical device applications, a good method to produce a flexible polymer electrolyte membrane with high conductivity and non-flammability is to use poly(ILs) as a host polymer for the gelation of ILs [22, 25]. Poly(ILs) have a higher affinity for ionic liquids than that of conventional polymers such as PMMA, poly(ethylene oxide) (PEO) and poly(vinylidenefluoride) (PVdF). In fact, the ionic liquid gel composite material including poly(ILs) showed higher ionic conductivity than that of the materials including conventional polymer such as a PMMA. A high degree of compatibility with the ionic liquid results in a high ionic conductivity and good physical properties even when only a small amount of polymer material is added. This allows the IL to be completely encapsulated, thereby avoiding liquid leakage accidents. Work in this area will be also described in this chapter.
We developed a new type of polymerizable IL,
Molecular structure of DEMM-TFSI
The ionic conductivity is 0.6 mScm-1 at 25 °C measured by a conductivity meter (HM-30R, DKK-TOA Corporation).
Fig.2 depicts the differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA) curves of the DEMM-TFSI. This polymerizable IL did not exhibit a clear melting or freezing temperature in a typical DSC measurement; however, the glass transition temperature
(a) Differential scanning calorimetry (DSC) and (b) thermal gravimetric analysis (TGA) results for the ionic liquid monomer, DEMM-TFSI at a heating rate of 5 °C min−1.
Cyclic voltammogram of an ionic liquid monomer, an organic electrolyte at 25 °C. Scan rate: 10mVs−1; platinum working and counter electrodes; Ag/Ag+ reference electrode. The potential value (V) was referenced to the ferrocene (Fc)/ferrocenium (Fc+) redox couple in each salt. (a) 0.1 mol kg−1 of DEMM-TFSI in propylene carbonate (PC) solution, (b) 0.1 mol kg−1 of LiTFSI in PC solution. The concentration is defined in terms of molality = (mol solute/kg solvent). The dashed line arrow indicates the potential of the Li/Li+ couple.
Fig. 3 shows both the limiting reduction potentials (
The poly(IL), poly(DEMM-TFSI), was synthesized by the bulk polymerization method. First, the monomer was dissolved in acetonitrile, and the solution was treated with activated carbon; the resultant acetonitrile solution was evaporated and the purified monomer was dried in vacuum at 25 °C. The ionic liquid monomer and 2,2\'-Azoisobutyronitrile (AIBN), at a ratio of 1.0 mol% to the amount of methacryl groups present in the monomer, were mixed until they became homogeneous. The mixture was degassed in vacuum at 50 °C, and kept standing at 70 °C for 15 h. After polymerization, the product polymer was dissolved in acetonitrile and precipitated into ethanol and water, before a final drying in vacuum at 70 °C. The preparation scheme is shown in Fig. 4.
Synthesis of ionic liquid monomer (DEMM-TFSI) and poly(DEMM-TFSI).
It has been reported that poly(methyl methacrylate) (PMMA) reactions are much more rapid in an ionic liquid than in a nonpolar solvent such as benzene, and that a PMMA prepared in an ionic liquid has a molecular weight approximately five times higher than in benzene [21]. The ionic liquid monomer DEMM-TFSI also gives the ultra high molecular weight poly(DEMM-TFSI) by bulk polymerization with an AIBN initiator. Fig. 5 illustrates the GPC trace of the poly(DEMM-TFSI) product polymerized using a mol ratio of [monomer]/[AIBN] = 1:0.01. The resulting polymer was a rubbery solid, with a weight average molecular weight (
GPC trace of poly(DEMM-TFSI).
It is well known when the radical polymerizations are conducted in an ionic liquid, a significant incerase of the
Fig. 6 illustrates the relationships between the number average molecular weights and the concentration of the charge transfer reagent, 3-mercapto-1-hexanol in bulk radical polymerization media using AIBN as a initiator at 50 °C. We were able to synthesize poly (DEMM-TFSI) over
The relationship between the number average molecular weight of polymerized ionic liquid, DEMM-TFSI and the concentration of the charge transfer reagent 3-mercapto-1-hexanol, using 2,2’-azobis(isobutyronitrile) (AIBN) as initiator at 50 °C. Molar ratio: [DEMM-TFSI] / [AIBN] = 1 / 0.015. Number-average molecular weights were as determined by GPC.
Using the controlled/living radical polymerization techniques that have shown remarkable progress recently is perhaps the best choice for preparation of the poly(ILs) with controlled molecular weight and a narrow PDI. Two such possibilities are atom transfer radical polymerization (ATRP) and reversible addition-fragmentation chain transfer (RAFT) polymerization [27]. We have already achieved exceptionally dense grafting of well-defined pol(DEMM-TFSI) on a solid surface, known as a concentrated polymer brush prepared by surface initiated ATRP. Such a polymer brush surface showed unique properties, including high modulus, super lubrication, and unique size exclusion that were quite different and even unpredictable from those of the previously studied semidilute polymer brushes [24]. The polymerization of DEMM-TFSI was well controlled and exhibited living characteristics when a Cu(I)Cl and Cu(II)Cl2 mixture and 2,2’-bipyridine complex was used as the catalyst system and ethyl-2-bromoisobutyrate as initiator. However, if such synthesized poly(ILs) are used for the electrochemical devices, some residue of the metallic catalyst may ruin the reliability of the devices. An apparent disadvantage of applying ATRP for the synthesis of poly(ILs) is the unavoidable complexation of polymers with the catalytic copper ions. However, RAFT polymerization is free of this problem, as no metal source is involved.
Compared to ATRP, there are not many examples of research into RAFT polymerization. Because papers concerning the RAFT polymerization of polymerizable quaternary ammonium type ionic liquids are very few in number, we investigated the kinetics of the RAFT polymerization of DEMM-TFSI. Figure 7(a) shows the variation in ln([M]0 / [M]t) versus polymerization time for the polymerization of DEMM-TFSI in acetonitrile at 50 °C with AIBN in the presence of 2-cyano-2-propyl benzodithioate (CPBT). The reaction was conducted at a ratio of [DEMM-TFSI]0 / [CPBT]0= 200 / 1, the concentration of DEMM-TFSI and AIBN being 75 wt.% and 0.5 wt.% in acetonitrile, respectively. Almost full conversion was reached after 5 hours and an almost linear first-order kinetic plot is seen until almost 100% conversion. Nevertheless, a linear increase in the number-average molecular weight determined by GPC spectroscopy with conversion is observed, indicating a constant number of propagating chains throughout the polymerization (Figure 7(b)). The PDI value is consistently small (
(a) Plot of ln([M]0/[M]t) versus time and (b) evolution of number average molecular weight (
In Fig. 8 are shown the
Glass transition temperatures, measured calorimetrically during heating at 10 deg. / min, versus number average molecular weight.
The glass transition temperature is related to the start of the segmental motion of polymers. In general, polymers with more free volume have a lower
A poly-cation type material such as poly(DEMM-TFSI) has bulky tetra-alkyl ammonium functional groups in each unit and has a relatively low
It was thought that a excellent solid polymer electrolyte with high physical strength and high ionic conductivity could be developed by the polymerization of ionic liquid monomers, so much attention has been directed toward developing poly(ILs) in order to avoid some disadvantages of liquid electrolytes, such as leakage and flammability, in energy device applications, including the lithium ion rechargeable battery, the electric double layer capacitor and dye-sensitized solar cell. However, the ionic conductivity of the polymerized ionic liquids was generally considerably lowered due to both the considerable elevation of the glass transition temperature and a reduced number of mobile ions, as one of ions is fixed to the polymer chain and so cannot move.
Fig. 9 shows the temperature dependence of the ionic conductivity for a series of poly(DEMM-TFSI)s with different
Temperature dependence of the ionic conductivity for poly(DEMM-TFSI)s with different
Hayamizu et al. have succeeded in measuring the self diffusion coefficients (
Fig. 10 represents the NMR echo signal attenuation of a poly(DEMM-TFSI) solution in acetonitrile at 25 °C probed by 1H and 19F nuclear signals attributed to the methyl proton on the ammonium cation and acetonitrile and the TFSI anion. The attenuation due to free diffusion in the Stejskal and Tanner sequence using half-sine-shaped gradient pulses is given by
where
(a) PGSE attenuation plots for the single N-CH3 of [Poly(DEMM-TFSI)] and the CH3 signal of acetonitrile probed by the 1H nucleus and the TFSI anion probed by 19F at 25 °C obtained by varying
Some investigators have attempted to develop a non-flammable polymer electrolyte system; we have developed a polymer-gel electrolyte system consisting of a lithium salt in an ionic liquid and poly(ILs), which we have called a
Poly (DEMM-TFSI) has the capability to dissolve a lithium salt independent of the presence of a liquid electrolyte. In a polarizing microscope analysis of Poly(DEMM-TFSI) which contained 1M concentration of dissolved lithium salt LiClO4, we did not observe the birefringence indicating the existence of crystals. Thus, there must have been a complete dissociation of the lithium salt in the polymer matrix as the polymer itself has a non-crystalline nature. DSC measurements indicate that prepared poly(DEMM-TFSI) has a glass transition temperature (
Moreover, poly(DEMM-TFSI) dissolves in a variety of quaternary ammonium ionic liquids to make a gel. For example, the ionic liquid,
The ionic liquid/poly(ILs) composite from which the solvent is removed by vacuum evaporation at a relatively high temperature has no components that leak out of the electrode. However, the high-polarity
Molecular structure of ionic liquid, DEME-TFSI
Fig. 12 shows both the limiting reduction (
We selected high power active electrode materials, and combined them with a
Cyclic voltammogram of poly(DEMM-TFSI) in PC at 25 °C. Scan rate: 10mVs−1; platinum working and counter electrodes; Ag/Ag+ reference electrode. The potential value (V) was referenced to the ferrocene (Fc)/ferrocenium (Fc+) redox couple in each salt. Polymer concentration, 0.002 mol kg−1 poly(DEMMTFSI) in PC solution. The concentration is defined in terms of molality = (mol solute/kg solvent). The dashed line arrow indicates the potential of the Li/Li+ couple.
Code | \n\t\t\tActive material | \n\t\t\tPolarity | \n\t\t\tCharge capacity (mAh cm-2)\n\t\t\t | \n\t\t\tArea density (mg cm-2)\n\t\t\t | \n\t\t\tActive layer thickness (μm) \n\t\t\t | \n\t\t\tCurrent collector/(μm) | \n\t\t
AKO-6 CKT-22 | \n\t\t\tLi4Ti5O12 LiMn2O4 | \n\t\t\tNegative Positive | \n\t\t\t0.42 0.49 | \n\t\t\t3.00 6.60 | \n\t\t\t25 36-37 | \n\t\t\tCopper/13 Aluminium/20 | \n\t\t
AKT-2 CKT-9 | \n\t\t\tHard carbon LiMn2O4\n\t\t\t | \n\t\t\tNegative Positive | \n\t\t\t1.29 1.075 | \n\t\t\t2.96 12.51 | \n\t\t\t33 65 | \n\t\t\tCopper/13 Aluminium/20 | \n\t\t
Electrode specification.
In Fig. 13, we show the first and second charge–discharge potential curves at 40 °C of the demonstration cells, consisting of hard carbon/
First and second charge/discharge curves of lithium polymer cell at 0.05C current at 40 °C. Positive electrode = LiMn2O4; negative electrode = hard carbon; electrolyte = 1.30 mol kg−1 of LiTFSI in a DEME-TFSI and poly(DEMM-TFSI) composite. The concentration is defined in terms of molality = (mol solute/kg polymer-ionic liquid composite). The polymer concentration for the composite electrolyte was 5.4 wt%.
The rate capability of this cell at 40 °C appears in Fig. 14. As the discharge current increased, the discharge capacity of this cell decreased significantly faster than that of a cell using conventional materials. The capacity at 1C discharge was approximately 63% of that at 0.1C discharge. Most likely, the greater decrease at large discharge currents in the capacity of the cell using the
We then prepared another type of lithium polymer cell, comprising Li4Ti5O12 /
negative electrode,
positive electrode,
overall reaction,
The theoretical capacity of Li4Ti5O12 was expected to be approximately 175 mAh g-1. In our electrode, the discharge capacity was 171 mAh g-1. The performances of the prepared cell are shown in Figs. 16-18.
Discharge curves of the lithium polymer cell at various current densities at 40 °C. The positive electrode = LiMn2O4; negative electrode = hard carbon; electrolyte = 1.30 mol kg−1 of LiTFSI in DEME-TFSI and poly(DEMM-TFSI) composite. The concentration is defined in terms of molality = (mol solute/kg polymer-ionic liquid composite). The polymer concentration for the composite electrolyte was 5.4 wt%.
Cycle life of lithium polymer cell including hard carbon as negative active material. The charge–discharge process was performed at 0.1C at 40 °C. The cut-off voltages were 4.2V and 2.5V for nine cycles, and 4.0V and 2.5V from 10th to 24th cycle.
First and second charge/discharge curves of lithium polymer cell at 0.05 C current at 40 °C. The positive electrode = LiMn2O4; negative electrode = Li4Ti5O12; electrolyte = 1.30 mol kg−1 of LiTFSI in DEME-TFSI and poly(DEMM-TFSI) composite. The concentration is defined in terms of molality = (mol solute/kg polymer-ionic liquid composite). The polymer concentration for the composite electrolyte was 5.4 wt%.
Discharge curves of lithium polymer cell at various current densities at 40 °C. The positive electrode = LiMn2O4; negative electrode = Li4Ti5O12; electrolyte = 1.30 mol kg−1 of LiTFSI in DEME-TFSI and poly(DEMM-TFSI) composite. The concentration is defined in terms of molality = (mol solute/kg polymer-ionic liquid composite). The polymer concentration for the composite electrolyte was 5.4 wt%.
The discharge specific capacities and Columbic efficiency of the cell at the 1st. was 11.8 mAh, and 82 %; at the second cycle, they were 11.4 mAh, and 92 %. After the 5th. cycle, the Columbic efficiency of this cell remained at approximately 97-98 %.
The battery in Fig. 17 that combined the Li4Ti5O12 anode with the LiMn2O4 cathode showed an excellent rate discharge character as for a lithium polymer battery. Evidently, this battery retains 83 % or more of the capacity maintenance rate at a 3C high power discharge. Thus, it was possible to create a new, leak-free battery with a vapor-free, practical discharge performance, and a prismatic cell design by selecting an electrode material with a high speed charge/discharge reaction when combined with an
Cycle life of lithium polymer cell including Li4Ti5O12 as negative active material. The charge- discharge process was performed at 0.1C at 40 °C. The cut off voltages were 3.0V and 1.5V. Three cells were tested.
As in the case of the hard carbon electrode, we expected that electrochemical degradation of the DEME-TFSI or poly(DEMM-TFSI) probably occurred. In our previous study, we suggested that some kinds of organic solvents, such as VC and ethylene carbonate, are effective as
On the other hand, however, we should point out that the use of Li4Ti5O12 as the negative electrode for the lithium-ion
This chapter has reported the synthesis and physicochemcal properties of a quaternary ammonium polymerizable ionic liquid and of the corresponding polymer. In conclusion we find;
The polymerizable ionic liquid, DEMM-TFSI having a methacryl functional group as part of the ionic liquid’s cation species has a remarkably low glass transition temperature (
The DEMM-TFSI monomer has a high reactivity for radical polymerization. We could prepare poly(DEMM-TFSI) with an ultra high molecular weight, with an
We succeeded in achieving molecular weight control by the radical polymerization of the ionic liquid monomer via three methods; atom transfer radical polymerization (ATRP), chain transfer radical polymerization (CTRP) and reversible addition-fragmentation chain transfer polymerization (RAFT). The control of the molecular weight occurred extremely easily in CTRP because of the addition of 3-mercapto-1-hexanol as a chain transfer reagent. In RAFT polymerization, molecular weight controlled poly(IL) with a low polydispersity index can be prepared.
A poly-cation type material such as poly(DEMM-TFSI) has bulky tetra-alkyl ammonium functional groups in each unit and has a relatively low
The bulk poly(DEMM-TFSI) has a relatively low ionic conductivity that is not practical for use in electrochemical devices at room temperature. However, the temperature dependence of the ionic conductivity showed an interesting property, unlike that of a non-ionic polymer electrolyte, such as polyethylene oxide. Ionic conduction also appeared below the glass transition temperature in the polymer matrix. The anion seemed to show ionic conduction, independent of the segmental motion of the polymer chain. A new ionic conduction mechanism in the poly-cation matrix might be discovered by a further research.
We measured the self-diffusion coefficient value
We obtained a polymer electrolyte by combining a small amount of the poly(ILs) and a binary Li-IL. The system of a lithium salt dissolved in an ionic liquid polymer and ionic liquid composite (
The authors thank Ms. Shoko Marukane and Mrs. Saika Honma at Tsuruoka National College of Technology for various assistance in the lithium polymer cell preparation and measurements.
Nuclear reactors rely on instrumentation and control (I&C) systems to maintain critical primary and secondary processes within desired parameters to ensure safe and efficient operation. Safety-related I&C systems are specifically designed to protect against critical failures that can lead to high consequence events. Designers rely on traditional safety-analyses, such as failure modes and effects analysis and probabilistic risk assessments (PRA), to inform them of specific protections needed in the design of these systems to maintain safe operation and the health and safety of the public.
I&C systems maintain real-time response, high availability, predictability, reliability, and distributed intelligence via a set of interconnected assets and subsystems that perform three main operations: acquisition, control, and supervision. Reactors have historically used analog I&C systems. As modernization occurs in the existing reactor fleet and as new advanced reactors are designed and commissioned, analog systems are replaced with digital I&C (DI&C) systems due to their many advantages, including reliability, efficiency, additional functionality, and data analytics. While DI&C provides enhanced operational capabilities, new risks associated with adverse impacts from cyber incidents are introduced. Whereas nuclear safety is the primary focus of reactor design, cyber risk must now also be considered in any digital-based reactor design. Cyber risk not only includes digital failures and unintentional cyber incidents, but the possibility that an adversary may try to purposefully disrupt, deter, deny, degrade, or compromise digital systems in such a manner as to place a reactor outside its intended design.
Since the complete set of failure modes for DI&C may never be fully known, and since DI&C can never be completely secured, a robust process is required to address and reduce cyber risk throughout the entire systems engineering lifecycle. Specifically, engineering and design personnel must be fully cognizant of the cyber risks and understand how to protect against intentional and unintentional cyber incidents. Cyber-Informed Engineering (CIE) is an approach in which cyber risks are considered at the earliest design stages and are continually reanalyzed throughout the entire lifecycle. Regardless of reactor design, cyber risk must be eliminated or reduced as much as possible to sustain a safe and secure nuclear industry.
The remainder of this chapter is organized as follows: Section 2 provides a background on nuclear reactor I&C systems, both analog and digital, as well as considerations for use of DI&C in new advanced reactor designs and applications. Section 3 steps through aspects of cyber risk analysis and cyber risk management for nuclear reactors. Section 4 provides an overview of CIE along with detailed descriptions of each CIE principle prior to concluding the chapter in Section 5.
Chemical, manufacturing, and nuclear processes rely on instrumentation, such as pressure, temperature, and flow sensors, to measure and monitor process parameters. These industrial processes are then maintained by control systems that operate physical equipment, such as valves, pumps, and heaters, to keep the process parameters within predefined limits. Nuclear reactors vary by type (e.g., pressurized water reactor, pool-type reactor, liquid metal cooled reactor, molten salt reactor, gas cooled reactor) and purpose (e.g., power reactor, research reactor, nuclear propulsion). The remainder of this section first describes the fundamentals of generic nuclear reactor I&C prior to discussing the transition to digital technology, including its benefits and challenges. The section concludes with an overview of future DI&C applications, including those in new and advanced reactors, as well as integrated systems and decision support systems.
Nuclear reactors initiate and control nuclear fission or fusion reactions. These processes must be monitored and closely controlled to ensure reliable and efficient operation while maintaining the health and safety of the public. The number and type of parameters monitored in a reactor will vary depending on the reactor type and purpose, but both nuclear and non-nuclear instrumentation will likely be used. Nuclear instrumentation includes detectors to monitor neutron and gamma flux for routine reactor monitoring and control as well as reactor safety. Neutron detectors, such as proportional counters and ion chambers, are commonly used to provide source range, intermediate range, and power range monitoring, while gamma detectors are used for post-accident monitoring. These detectors may be out-of-core or in-core, or a combination thereof, depending on the reactor type. Other compact in-core detectors, such as small fission chambers or self-powered neutron detectors, are also commonly used for continuous real-time monitoring of reactor core conditions, including reactor power distributions.
Non-nuclear instrumentation includes sensors used to monitor process parameters, such as temperature, pressure, differential pressure, level, and flow. Additionally, non-nuclear instrumentation may be used to monitor other parameters, including control rod position, area radiation, fuel-pin fission gas pressure, vibrations, acoustics, fuel or vessel strain, process fluid chemistry, moisture and gas analysis, and leaks.
Local instrumentation data is transmitted from the sensors to control board indicators, data recorders, applications, and control systems via analog or digital circuits, often through multiplexers or combinatorial logic circuits. Applications are commonly used to auctioneer (e.g., signal selection), aggregate, and/or perform calculations on the data to provide real-time reactor and plant status indications to operators.
While operators will also perform manual actions on a reactor, such as starting and stopping pumps or opening and closing valves, I&C systems are commonly used to automatically control reactor operations and maintain reactor safety. Control systems can be simple, like a single programmable logic controller, or complex, like a reactor control system. Control systems can combine numerous sensors, transmitters, controllers, and actuators to change the physical state of process equipment, such as a valves, pumps, or motors, by using signal feedback loops to monitor and maintain desired conditions. In a nuclear power plant (NPP), non-safety control systems may include feedwater control (or fluid control), turbine control, and reactor control.
Most nuclear reactors will have at least two types of control systems—reactor control systems and reactor safety systems. Depending on a reactor’s purpose, there may also be other control systems, such as plant control systems in an NPP or experiment/sample control systems in a research and test reactor. Reactor control systems are used to control the nuclear fission or fusion reaction within specified acceptable fuel design limits by adjusting physical components according to the reactor design. For example, a reactor control system may raise or lower control rods in a light water reactor (LWR), turn control drums in a heat pipe reactor, or start or stop feedwater flow in a research reactor.
In an LWR, reactor control systems are used to maintain desired thermal megawatts by balancing primary and secondary systems. For example, as shown in Figure 1, an integrated control system may automatically maneuver reactor, feedwater, and turbine systems to match megawatts generated to megawatts demanded by adjusting control rod positions, valve positions, and pump speeds.
A notional automatic integrated reactor control system for an LWR.
In comparison to reactor control systems, reactor safety systems are used to shut down and maintain safe shutdown of a reactor in the event a reactor safety limit is met or exceeded to assure reasonable protection against uncontrolled release of radioactivity. For example, reactor safety systems in an LWR include Reactor Protection Systems (RPS), Engineered Safety Feature Actuation Systems (ESFAS), and diverse actuation or diverse trip systems. Reactor safety systems often use either two-out-of-four or two-out-of-three logic. For instance, an RPS may have four redundant instrumentation channels that monitor key parameters, such as reactor power, reactor coolant temperature, reactor coolant pressure, reactor coolant flow, reactor building pressure, reactor pump status, and steam generator level. If any design limits are exceeded on two separate channels, an automatic trip signal is sent to the control rod system to shut down the reactor. A notional representation of an RPS is shown in Figure 2.
Signals for a notional RPS.
In an LWR, an ESFAS is designed to provide emergency core cooling for the reactor and to reduce the potential for offsite release of radiation. Comparable to an RPS, ESFAS uses multiple channels of equipment in two-out-of-three logic (or similar) to monitor signals such as reactor coolant pressure and containment pressure. Based upon the specific coincident actuation signals received, ESFAS will start the required safety system, such as emergency core cooling systems, emergency feedwater, containment isolation and ventilation, containment spray, or emergency diesel generators.
Although the first closed-loop industrial computer control system was installed by Texaco Company at its Port Arthur refinery in 1959 [1], I&C in nuclear reactors largely remained analog until about 30 years ago when digital transmitters, indicators, controllers and data recorders began replacing analog sensors, indicators, actuators, and pen-based chart recorders. And, while non-safety digital control systems (e.g., feedwater control systems, turbine control systems and reactor control systems) are now commonly installed in nuclear reactors, safety-related digital control systems (e.g., RPS, ESFAS) are much less common, especially in the United States. The United States has been slow to adopt digital technology because of previously unanalyzed risks associated with new and unknown attributes, including common cause failures and cyber risks. International adoption of digital technology in nuclear reactors, including safety-related control systems, has been more aggressive than in the United States. Of course, new advanced reactors are being primarily designed with DI&C.
As described by the Nuclear Energy Institute (NEI), an I&C device in the U.S. power reactor industry is typically considered ‘digital’ if it contains any combination of hardware, firmware, and/or software that can execute internally stored programs and algorithms without operator action [2]. Hardware includes microelectronics, such as digital or mixed signal integrated circuits, as well as larger assemblies, such as microprocessors, memory chips, and logic chips. Hardware may also include other peripherals, such as expansion drives or communication controllers. Software includes operating systems, platforms, and applications used for process control, human machine interfaces, and other specific programs used for device or system operation. Firmware is software stored in non-volatile memory devices that provides low-level control specific to the hardware. Firmware executes higher-level operations and controls basic functionality of the device, including communication, program execution, and device initialization.
Field sensors and controllers may be standalone, small local systems, or larger distributed control systems. Devices may be connected by physical cables or wireless technology (e.g., WiFi, cellular, satellite, Bluetooth, radio frequency identification). There is also a range of communication protocols used in DI&C depending on the design and manufacturer.
The systems, structure, and components (SSCs) used in U.S. NPP safety-related protection systems are categorized as Institute of Electrical and Electronics Engineers (IEEE) class 1E technologies as defined by IEEE 308-1971 (and later) [3]. They must be designed to conform with General Design Criteria (GDC) in 10 CFR 50 Appendix A [4], IEEE 279-1971 [5], IEEE 308-1971 [3], and IEEE Std 603-1991 [6], as applicable based on construction permit dates. Guidance in Regulatory Guide 1.152 [7] and IEEE 7-4.3.2-2003 [8] may also be used to comply with Nuclear Regulatory Commission (NRC) regulations. Internationally, applications or components that perform IEC category A safety-related functions may fall under IEC 61513 [9], International Atomic Energy Agency (IAEA) SSR-2/1 [10], and IAEA SSG-39 [11] requirements.
These general design criteria include conformance requirements for independence and single-failure criterion such as defense-in-depth, diversity (i.e., different technology), redundancy (i.e., secondary equipment that duplicates the essential function), physical separation, and electrical isolation. The purpose of single-failure criterion is to ensure no single failure of a component interferes with the safety function and proper operation of the safety system [6]. Generally, it is impossible to prove that digital systems are error free. And, while common-cause failures can occur with analog equipment, it is more likely that software errors will result in common-cause failures, such as identical software-based logic errors that could cause simultaneous functional failure of all four RPS divisions. Thus, since unanticipated common-cause failures are more likely in digital systems than analog systems, there is increased burden to prove to the regulator that the design adequately meets the general design criteria outlined in the applicable requirements.
The systems engineering lifecycle for analog modifications, such as changing mechanical relay logic, can take significant time to design, procure, reconfigure, and test hard-wired devices installed inside control cabinets. These changes can require many hours for maintenance personnel to rewire, physically rearrange components, and/or add new cabinets, terminal blocks, power supplies, and wiring. Labor resources are also required for post installation quality checks.
Contrary to analog I&C, a significant benefit of DI&C is the ability to quickly reprogram the functionality of a device or system with minimal physical hardware changes. These modifications are performed via microprocessors, expansive memory storage, and standardized communications that allow for remote connectivity. Moreover, the utilization of reusable software and common microprocessors lowers overall product costs. Moreover, the global supply chain has promoted further innovation, improved efficiencies, better product availability, and reduced costs.
An additional benefit of DI&C is the capability to incorporate numerous functions within one device. This capability reduces overall size of the I&C systems (e.g., fewer racks and cabinets) and relieves potential space constraints within facilities. Furthermore, the ability to choose from a wide array of functions in one device not only reduces the cost, but also allows for unique control algorithms not necessarily available in the past. Whereas analog I&C was limited to using a single proprietary signal conveying only one piece of information (e.g., the process value), adding a digital signal overtop an analog signal allowed for increased device diagnostics and calibration capabilities without any additional hardware changes and helped pave the way for logical extension of DI&C in nuclear facilities.
Other applications enabled by DI&C include enhanced online monitoring for condition-based maintenance systems. These systems improve visibility into equipment conditions to improve maintenance activities and potentially reduce or eliminate required preventive maintenance. Additionally, training departments are now able to simulate plant operations with fine detail that was difficult to achieve before.
On the other hand, digital technology introduces new challenges. As existing nuclear reactors are modernized, plant personnel throughout the organization must be trained on their design, installation, operation, and maintenance. This skillset is often very different than what is required for analog I&C and can take many years to acquire. Moreover, not only is there an increase in common-cause failures and potentially unknown failure modes with DI&C, but there is also additional risk associated with malicious and unintentional cyber threats not typically seen with analog I&C. These DI&C cyber risks are further described in Section 3.
While existing reactors primarily designed and built with analog technology are transitioning to DI&C, new generation III+, small modular reactor (SMR), microreactor, and advanced reactor designs will likely apply digital technology from project inception to take advantage of increased flexibility, better performance, and improved reliability. It is anticipated that these designs will also include hybrid approaches, similar to existing reactors, incorporating both analog and DI&C components and systems for reactor control and reactor safety. However, since most of the new reactor designs will likely incorporate passive safety features, they may have fewer (or no) safety-related control systems compared to current LWRs.
Nuclear reactors are primarily designed with safety as the underlying principle. Ensuring safety of reactor personnel and maintaining the health and safety of the public is more important than secondary objectives, such as producing electricity or medical isotopes. Thus, any new reactor technology that challenges the nuclear safety paradigm is met with strong caution. However, as new advanced reactors are designed with DI&C, significant effort and analysis will be undertaken to ensure cyber risks are fully understood such that the designs will fully withstand regulatory and public scrutiny and not interfere with reactor safety. Nevertheless, the inclusion of passive safety features that reduce the footprint of digital safety systems not only reduces the number of high-consequence design basis accidents (DBAs), it also reduces overall cyber risk.
Sites built with multiple reactor modules (e.g., SMRs) may have additional I&C systems to enable integrated and coordinated operation across multiple units. Furthermore, proposed advantages of SMRs and microreactors include the capability for remote and autonomous (or nearly autonomous) operation, including anticipatory control strategies to maintain operational limits for both planned and unplanned internal or external disturbances which increase overall operational flexibility. The passive safety systems in advanced reactors may enable fewer operators and more automation, however, these new modes of operation and previously unanalyzed consequences require careful evaluation by designers and regulators to ensure minimization of cyber risks. Mobile reactor designs must also anticipate and address additional requirements for safe and secure transportation.
Similar concerns exist for remote operations, which is under consideration for advanced reactors in isolated environments or reactors connected to microgrids using autonomous distributed energy control schemes. Remote operations imply some finite distance between reactor and operator utilizing digital communications for both monitoring and control. Not only does the external pathway potentially enable an exploitable pathway for adversaries, it also potentially presents unanticipated cyber risks from communication failures.
Whereas remote and autonomous reactor operation may have a long timescale for development, regulatory acceptance, and construction, integrated energy systems may be available on a shorter timescale. As shown in Figure 3, integrated energy systems use the thermal heat from reactors for other purposes, such as hydrogen generation, district heating, water purification, and chemical manufacturing. They may also have direct electrical connections to integrated systems. The interconnections between a reactor and these secondary processes will likely contain additional sensors, controllers, and actuators in order to balance the electrical and heat demands of the plant with the demands from the integrated energy systems.
Conceptual integrated energy system including generation sources and applications.
Digital twins are virtual replications of a physical system that can be used to provide various capabilities and decision-support at a nuclear facility. The degree of representation by a digital twin depends upon the computing power and the ability to accurately model both reactor physics and data-driven processes. Proposed applications include the use of digital twins for running artificial intelligence or machine learning (AI/ML) applications for hybrid control schemes, such as flexible operation for electric grid load-following, anticipatory control, or autonomous control; the use of AI/ML on digital twins for equipment condition monitoring, diagnostics, prediction, and prognostics; and the use of digital twins for designing engineering modification prior to building the actual physical system.
Using digital twins for reactor and/or system design may enable vulnerability discovery, such as potential for equipment failure, process anomalies, human error, or cyber compromise. Understanding system operation as well as potential vulnerabilities and consequences prior to construction is not only a benefit to designing better and safer reactors but also, if used with CIE principles as described in Section 4, a reactor with reduced cyber risk.
Applications of digital twins will likely continue to expand. The capabilities of digital twins, AI/ML, and other monitoring and control systems will be enabled with the increased use of wireless technologies (e.g., Wi-Fi, radio frequency identification, Bluetooth, Zigbee, cellular) in addition to traditional wired networks. Moreover, the use Internet of Things (IoT) or Industrial Internet of Things (IIoT) will continue to expand within nuclear facilities enabling improved efficiencies, reduced maintenance, and real-time insights for decision-making. Whereas the difference between operational technology (OT) and information communications technology (ICT) is that OT uses digital devices to control physical processes, such as nuclear reactors, IIoT uses a wide range of lower cost sensors that are traditionally connected via wireless networks to increase the number datapoints available for machine-to-machine communication and enhanced monitoring using data analytics, big data, and AI/ML.
Risk is classically defined by Kaplan and Garrick as the possibility of loss or injury, including the degree of probability of such a loss [12]. Traditional safety PRA in the nuclear industry uses a logical framework combining fault tree analysis and event tree analysis to identify the likelihood and consequence of severe accidents which could lead to radiation release impacting the health and safety of the public. Nuclear safety PRAs typically use data on functional failures (i.e., manufacturer failure analyses, historical plant and industry failure data) along with known events (i.e., historical data on prior nuclear-significant events).
Unfortunately, the PRA approach is insufficient for cyber risk analysis as the complete set of failure modes for digital assets and systems may be unknown as they can fail in unexpected ways. Additionally, deliberate actions, such as intentional, intelligent, and adaptive actions by an adversary are challenging, if not impossible, to effectively model. Furthermore, threats and vulnerabilities are constantly evolving, a reality which does not lend itself to PRA. Therefore, rather than follow the Kaplan and Garrick risk triplet of ‘scenario, likelihood, consequence’ [12], cyber risk is better identified by evaluating threats, vulnerabilities, and consequences [13].
It is important to note that cyber risk includes all risk from both intentional and unintentional actions. Holistic cyber risk includes human performance errors and equipment failures as well as adversarial events. Adversarial events include malicious actions, including those by an unwitting insider, intended to cause damage or disruption to reactor and facility operations. Adding to the concern, intelligent threat actors can potentially adversely impact nuclear DI&C by remotely exploiting vulnerabilities, a threat that does not exist with analog I&C.
A nuclear reactor has a licensing basis that identifies high-consequence DBAs that can potentially lead to radiological release. This licensing basis includes those safety-related SSCs that must remain functional during a DBA to protect the health and safety of the public. While safety-related impacts are the primary concern, consequences from a cyber incident at a nuclear reactor could potentially range from intangible impacts (e.g., reputation damage, industry perception) to financial impacts (e.g., lost generation, equipment damage, repair costs) to adverse public health and safety impacts due to radiological release or theft of special nuclear material (SNM). Examples of low to high consequence impacts from a cyber incident are illustrated in Figure 4. Table 1 expands on several of these consequences to provide causal examples of functional failures from hypothetical cyber incidents.
Potential consequences from a cyber incident at a reactor.
Potential consequence | Functional failure | Initiating cyber incident |
---|---|---|
Radiological release | Failure of a safety system to actuate when needed | Digital RPS does not trip the reactor on low feedwater flow |
Lost generation | Inadvertent actuation of a safety system leads to extended plant shutdown | Digital high-pressure coolant injection actuation with no loss of coolant |
Lost generation | Inappropriate operator action | Operator does not recognize that a digital indicator on the main control board is incorrect |
Equipment damage | Pump suction valve closed | Digital valve controller closed valve with pump running |
Potential consequences from a cyber incident at a reactor along with hypothesized functional failure and initiating cyber incident.
Cyber-induced consequences at a nuclear reactor can be minimized by maintaining availability, integrity, and confidentiality of DI&C components and systems. Nuclear reactors may be designed to run continuously (e.g., NPP) or intermittently (e.g., research and test reactor). In either case, data and communication flow must remain available to ensure safe and reliable operation of the reactor. Delay, disruption, or prevention of data or communication within an OT system can result in unintended control actions, such as inadvertent component actuation or reactor trip. As listed in Table 2, cyber incidents that impact availability can be malicious and intentional, such as from a denial of service attack [14], or non-malicious and unintentional, such as excessive network traffic from failing equipment [15].
Security objective | Malicious incident | Non-malicious incident |
---|---|---|
Availability | Denial of service attack [14] | Failing equipment leading to excessive network traffic [15] |
Integrity | Man-in-the middle attack [16] | Software update resetting plant data [17] |
Confidentiality | Reconnaissance attack leads to data exfiltration [18] | Sensitive data posted on external site |
Examples of malicious and non-malicious cyber incidents by security objective.
The integrity of DI&C information, data, and system parameters must also be maintained. Control systems require accurate, truthful, and complete information for safe and reliable operation. For instance, unintended modification of data, logic, or commands by man-in-the-middle attacks can cause equipment failure [16] or poorly executed software updates can reset plant data and cause actuation of a safety system [17]. Operators also rely on truthful and accurate data for decision making; inaccurate data on indicators or human-machine interfaces could cause operators to make improper decisions or perform incorrect actions. Operationally, it is often more dangerous to have a reactor in an unknown state instead of safely shut down. Consider an unexpected cyber incident that is visible to the operator—the operator can detect and respond to the incident, thereby minimizing further impacts. On the other hand, cyber incidents that are invisible to the operator can potentially result in persistent and higher consequence adverse impacts as operators are unaware of true reactor status.
While not as important in OT systems, confidentiality is also a cybersecurity objective. Loss of confidentiality, such as unauthorized exfiltration of sensitive information [18] or inadvertent posting of sensitive data in the public domain, can enable development of further attacks or cause other business-related concerns. Gaining sensitive nuclear information can provide adversaries roadmaps, schedules, vendors, plant layouts, and a host of other sensitive information shortening the attack timeline and delivering potential pathways to be considered towards ransomware, blackmail, or general political unrest.
Cyber threat vectors into a nuclear reactor include wired and wireless networks or connections, portable media and maintenance devices (e.g., USB drives, maintenance laptops), insiders, and the supply chain. Furthermore, cyber threats can be classified as non-malicious or malicious. Non-malicious actions are often caused by employees or other facility personnel who perform actions not intending to cause harm. These actions are often human performance errors in which a worker mistakenly performs an adverse action, such as misconfiguring a device, selecting the wrong option, or disclosing sensitive information.
Malicious threats against nuclear reactors are initiated by adversaries with the intent to cause harm. Adversaries include recreational hackers, malicious and unwitting insiders, criminals, terrorist organizations, and nation states. Sophisticated attacks against nuclear reactors will likely be launched by organizations that have greater resources (e.g., skilled personnel, funding, time) and sufficient motivation (e.g., economic gain, military advantage, societal instability). Additionally, cyber-attacks may be one-dimensional or multi-dimensional, hybrid, coordinated attacks combining multiple threat vectors in both physical and cyber domains. For instance, adversaries may use cyber means to gain access to enable physical destruction or theft of SNM or use physical means to gain access to computer systems to enable unauthorized theft of sensitive information or sabotage.
In the United States, power reactors licensed by the NRC must provide high assurance that critical digital assets (CDAs) are protected against cyber-attacks, up to and including the design basis threat (DBT) [19]. CDAs are defined as digital assets associated with safety-related, important-to-safety, security, or emergency preparedness functions as well as support systems and equipment which, if compromised, would adversely impact these functions. A DBT describes adversarial attributes and characteristics, including level of training, weapons, and tactics, that must be defended against to safeguard the reactor against radiological sabotage and prevent theft or diversion of SNM. Generally, a beyond-DBT, a threat from an adversary who has capabilities beyond what is defined by the DBT, is considered nation-state activity which falls under responsibility of the state (e.g., federal government) for prevention, detection, and response.
Vulnerabilities are known or unknown weaknesses. Vulnerabilities in hardware, firmware, and/or software can leave digital assets susceptible to accidental failure or unintentional human error. Additionally, vulnerabilities may be exploitable, enabling adversaries to extract information or insert compromises allowing unauthorized access to perform malicious activities. Vulnerabilities can allow adversaries to penetrate and move throughout systems without the user’s knowledge to compromise the availability, integrity, and confidentiality of complex control systems.
Most digital devices can be reprogrammed or modified to perform unintended or undesired functions. Any vulnerability that allows an unauthorized reprogramming or modification of a critical digital asset can result in adverse function of the DI&C systems. As most design approaches wait until system implementation to evaluate vulnerabilities, vulnerability response and mitigation often relies on bolted on security controls. However, if engineers who design and maintain complex control systems are trained to identify, understand, and mitigate these vulnerabilities throughout the lifecycle, including during design stages, vulnerabilities can be addressed early and often, thereby leading to lower overall cyber risk.
From a maintenance perspective, manufacturers often identify vulnerabilities and send information notices to asset owners along with mitigation measures, if applicable. Numerous vulnerability tracking databases and notification services also exist which serve to improve awareness and facilitate mitigation or protection [20, 21, 22, 23]. Engineers and stakeholders should maintain awareness of these external vulnerability notifications or sites for their digital assets throughout the entire lifecycle so that they can be addressed immediately.
Of course, cyber risk cannot be calculated by simply multiplying numerically derived values of threats, vulnerabilities, and consequences together. For instance, low-threat, high-consequence cyber incidents will likely have a much different risk significance at a nuclear reactor than a high-threat, low-consequence incident. While many techniques have been proposed for incorporating the results of consequence, threat, and vulnerability analyses into a final cyber risk analysis [13], determining, evaluating, and prioritizing cyber risk is highly dependent on the reactor design, regulatory requirements, and organization’s risk tolerance.
Cyber risk management is the continual process of analyzing cyber risk, evaluating and prioritizing the identified risk against organizational and regulatory requirements, and then applying risk treatments. In the United States, current nuclear power reactors typically follow guidance in NRC Regulatory Guide 5.71 [24] or the NEI cybersecurity series (NEI 10-04 [2], NEI 08-09 [25], and NEI 13-10 [26]) to identify CDAs and risk treatments. Corresponding cyber security guidelines for the international nuclear community are provided in IAEA Nuclear Security Series (NSS) No. 13 [27], NSS 17-T (Rev. 1) [28], NSS 42-G [29], NSS 33-T [30], and IEC 62645 [31]. For risk management activities, IAEA NSS 17-T (Rev. 1) refers readers to ISO/IEC 27005. Additionally, IEC 62443-3-2 provides an international security risk assessment standard for I&C systems [32]. Cybersecurity regulation and guidance for advanced reactors is still in development.
Regardless of the equation or formula used, cyber risk is managed by analyzing the potential worst-case consequences and then using risk treatments (e.g., avoidance or elimination, mitigation, transference, or acceptance) to lower the risk to a level acceptable to the organization. Unlike analog I&C, where failure analysis was the primary focus of PRA, the use of DI&C has resulted in the capability for hardware, firmware, and software to be altered in a manner not intended by the original design. Since both malicious and unintentional actions can potentially adversely impact operational functions, continually evaluating cyber threats, vulnerabilities, and consequences in a cyber risk management program is necessary to maintain awareness into the constantly evolving risk environment. The goal of this consequence-driven analysis is to prioritize risk treatments for those DI&C components needed to ensure critical reactor functions are maintained.
Consequence-driven, Cyber-Informed Engineering (CCE) is a formal cyber risk management approach that focuses on reducing the impact from high consequence events (HCE) for an overall business entity [33]. As shown in Figure 5, CCE is a four-step process. In phase 1, HCEs are identified and prioritized using a severity score calculated based upon consequence criteria weights and criteria severity. For the identified HCE(s), a system of systems analysis identifies the most critical functions in phase 2 and potential cyber-attack scenarios on those functions are then identified in phase 3. In phase 4, appropriate protection and mitigation strategies are developed.
The four-phase CCE process [
Additionally, cyber risk management must not only be considered for nuclear reactor SSCs, but also any digital technology used in their design, operation, and maintenance. For instance, AI/ML and digital twin applications are susceptible to both adversarial and unintentional cyber risk. These technologies are often considered ‘black box’ techniques in which the end-user is unaware of how the insights are determined. Even if more ‘gray box’ techniques are used, trust in AI/ML and digital twin models must be established to gain acceptance and approval by operators and regulators. Similarly, adversaries can gain access to these tools and cause data and/or model corruption to adversely affect model operation.
Digital technology will be increasingly used in both existing and future nuclear reactors. While DI&C enables improved operations and new capabilities, the cyber risks must not only be understood, but risk treatments and protections must be put in place to lower this risk from malicious and unintentional actions. Whereas significant strides have occurred with securing ICT systems, these ICT-based solutions are not always effective for OT systems which are often designed to perform a limited set of functions and therefore have limited processing, memory, storage, retrieval, and proprietary communication protocols. Additionally, cyber risk mitigations have historically been applied after DI&C systems are installed, which limits the range of risk treatments available. On the other hand, applying the concepts of CIE throughout the entire systems engineering lifecycle can reduce overall cyber risk.
Engineers, operators, maintenance personnel, and other technical staff who support the systems engineering process are critical to the design, implementation, and secure operation of complex control systems. Nevertheless, this staff often lacks the necessary knowledge, skills, and abilities to effectively address and mitigate cyber risk. Given the critical functions of DI&C in nuclear reactors, this gap must be filled. For this reason, the Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response is developing a national strategy for CIE to fundamentally change the culture of the engineering discipline to consider cybersecurity as a fundamental design principle.
Figure 6 illustrates the typical stages in the systems engineering lifecycle. While this model is intended to be used iteratively and potentially out-of-order throughout the lifecycle as design modifications occur, the left side of the V-model indicates a top-down approach moving from system to subsystem to component levels and the right side indicates a bottom-up approach through implementation, integration, and testing. This model is useful for both new builds (e.g., new reactor designs) or existing builds (e.g., engineering modifications).
Systems engineering V-model [
CIE is a multidisciplinary approach that advocates the use of CIE principles in each of the systems engineering lifecycle stages to ensure that cyber considerations are included in every aspect of design, testing, implementation, operation, maintenance, and disposal or decommissioning [36]. CIE is fundamentally a cyber risk management tool that complements existing OT cybersecurity risk standards and guidelines by incorporating engineering solutions along with ICT and OT cyber solutions to minimize risks from malicious and unintentional cyber incidents. Considering cyber risk and cyber risk treatments early and often throughout the lifecycle provides simpler, more secure solutions at lower cost, precluding the need to use ineffective, bolt-on solutions during later lifecycle stages.
As shown in Figure 7, the primary CIE principle that encompasses the entire CIE methodology is cyber risk analysis. The remaining CIE principles are divided into two categories: design principles and organizational principles. The CIE design principles are fundamental engineering design practices and techniques that build cybersecurity and cyber-resilience into DI&C early in the systems engineering lifecycle and then continue to ensure cyber-awareness is maintained throughout the remaining stages. This secure-by-design approach is more effective and less expensive than bolting on security controls after installation as the design can be influenced by factors that improve the ease, simplicity, and effectiveness of cyber considerations without impacting the performance of the intended system function.
CIE principles adapted from [
Cyber risk is also reduced by instilling cyber-awareness at organizational- or facility-level functions. CIE organizational principles are fundamental cyber practices that enable holistic integration of cyber considerations into other programs within the facility, such as asset inventory, supply chain, response planning, and training.
While cyber risk and cyber risk analysis were discussed in Section 3, it is important to remember that consequence-driven risk analysis is necessary to prioritize design requirements and risk treatments of those digital SSCs required for ensuring reactor safety and the health and safety of the public. Like the CCE methodology, since resources are often limited, organizations should first ensure that the most stringent protections are around those critical functions that, if compromised or lost, could lead to unacceptable radiological consequences, sabotage, or theft of SNM.
Risk management is the process of identifying, evaluating, and responding to risk. Traditional risk treatments for responding to risk include risk avoidance or elimination, risk transference, risk mitigation, and risk acceptance. As shown in Figure 8, engineering risk treatments for cyber risk are similar, where risk can be designed out, shifted to another organization, mitigated with security controls or countermeasures, or accepted by making a conscious decision to tolerate the risk without implementing changes.
Engineering cyber risk treatments [
Security controls, as identified by National Institute of Standards and Technology (NIST) SP 800-82 [37], NRC Regulatory Guide (RG) 5.71 [24], or NEI 08-09 [25] are typically considered administrative, physical, or technical. As indicated in Figure 8, these controls mitigate cyber risk that cannot be eliminated. Unfortunately, engineering risk treatments, including security controls, are typically not considered until after installation. However, waiting until after installation is often too late to provide adequate protection. On the other hand, implementing engineering risk treatments during design stages can actually eliminate specifically identified risks by designing it out altogether or more efficiently and effectively reduce risk by incorporating security controls into the design.
The goal of the secure architecture CIE principle is to establish network and system architectures that segregate and limit data flows to trusted devices and connections within and between subsystems, systems, and systems of systems. Properly designed architectures reduce cyber risk by isolating critical functions, minimizing the cyber-attack surface, and lowering the probability of unauthorized access or compromise of critical SSCs.
To ensure defense in depth, the design should consider use of isolated (e.g., air-gapped) or segregated network levels and zones, boundary devices, data flow rules, and unidirectional, deterministic communication, such as data diodes. In the United States, NRC Regulatory Guide 5.71 recommends power reactors to implement a defensive architecture with only one-way data flow from safety and security network levels outward to the plant network [24]. Internationally, as illustrated in Figure 9, the IAEA recommends implementing security levels with common requirements and zones separated by decoupling devices, such as data diodes and other boundary devices, such as gateways, routers, or firewalls, to minimize communications to untrusted devices [38]. Engineers should consider these secure architecture approaches during design stages to limit overall risks from compromised pathways or devices.
Example implementation of a secure architecture [
A cyber incident can only adversely impact DI&C functions if a vulnerability is exploited by a threat (intentional or unintentional). Vulnerabilities decrease as the complexity of DI&C decreases. Thus, the goal of the design simplification principle is to reduce the complexity of the system, component, and architecture while maintaining the intended function. Design simplification minimizes vulnerabilities and reduces overall cyber risk.
Design simplification is considered in conjunction with the secure architecture, resilient design, and engineering risk treatment principles. Complex or overbuilt designs result in a digital footprint larger than necessary. As the number of digital assets increases in a system, the number of digital failure possibilities and exploit locations also increases. Additionally, it is possible for adversaries to repurpose unused or latent functions and features on SSCs to behave in unanticipated ways.
On the other hand, simplifying the design, such as by using simpler digital devices or hardening the system by eliminating, limiting, or disabling unnecessary functions or capabilities, minimizes the overall cyber-attack surface and reduces vulnerabilities. The intent of design simplification is to simplify the engineering design itself, not sacrifice security requirements for the sake of simplicity. Nevertheless, in cases where extreme safeguards are required, analog I&C may be implemented instead of DI&C to protect against cyber incidents.
Resilience is a system’s capability to withstand internal and external disruptions, including equipment failure, grid disturbances, or cyber incidents. A control system is resilient if it continues to carry out its mission by providing its required functionality despite disturbances that may cause disruptions or degradation. In nuclear reactors, general design criteria of separation, redundancy, diversity, and defense in depth are used for designing safety-related systems. Separation and independence are achieved by physical separation and electrical isolation. Redundancy is achieved by using more than one component to perform the same function. Diversity is achieved by using different technology within the system and with the redundant components.
Current DI&C systems operate in an untrusted environment, which presumes that users, devices, and systems cannot be trusted (e.g., users can be unauthorized, devices can be infected with malware). Additionally, it is impossible to design DI&C systems to withstand every malicious or unintentional cyber incident. Thus, resilient design is required to ensure continued safe and secure operation of the reactor and facility not only during an incident, but afterward as well.
While safety-related DI&C systems in nuclear reactors should be designed using the general design criteria, consideration should be given to designing similar features into non-safety DI&C systems to address this zero-trust paradigm, depending on the cyber risk prioritization. The objective of resilient DI&C design is to ensure continued operation of critical functions when possible, or graceful degradation when not possible, in the event of an SSC failure or cyber incident. Failure of one function, device, or system should not result in failure of another function. System design and control logic should attempt to eliminate the possibility of such cascading failures.
Additionally, resilient design may also include contingency planning and situational awareness. Contingency planning provides alternative methods for continued operation of critical functions. Using techniques, such as network and system monitoring, to provide situational awareness enables rapid decision making that may be needed for continued operation during a cyber incident. Moreover, operators have been trained to trust their instruments and indicators. This training model may need to be revisited due to the new zero-trust environment.
Finally, it should be noted that while resilient design may seem contrary to design simplification, the intent is to ensure that critical functions remain operational during a cyber incident. If additional devices are required to adequately assure resilience, there may be a tradeoff between resiliency and simplicity.
Security countermeasures and protections can be applied passively or actively. Passive defenses include those defensive architecture techniques described in Section 4.4.2. These passive defenses establish barriers using defense-in-depth techniques to deter and protect against a malicious adversary. This technique, however, is static and reactionary. It is also at a disadvantage for defending against dynamic and adaptive adversary capabilities.
Instead of reliance on passive capabilities, engineers need to build in active defenses to preemptively prevent, detect, and respond to cyber incidents. This paradigm shift is needed to proactively identify malicious and inadvertent cyber incidents to quickly stop the incident and remove the threat before degradation or unrecoverable damage occurs. Active defenses include security information event monitoring and other real-time anomaly detection and response tools that may not yet be developed or deployed. The objective is to enhance resilience capabilities by improving operational situational awareness via dynamic and testable strategies. Ideally, active defense tools can identify cyber anomalies in all five threat vectors (e.g., wired networks, wireless networks, portable media and maintenance devices, insiders, and supply chain).
The CIE organizational principles listed in Figure 7 are those fundamental cybersecurity practices that enable holistic integration of cybersecurity into other programs within the facility or organization. Technical and administrative interdependencies are necessary for safe and secure reactor operation. From a technical perspective, this principle ensures that cybersecurity is considered within all the interconnections between systems and systems of systems, including extended data pathways. Additionally, 10CFR73.54 not only requires adequate protection of safety-related and important-to-safety SSCs but also those support systems relied upon to ensure safe operation of those functions. Support systems may include power, communications, water, or HVAC. Even though there is the potential for adverse safety or security consequences if a cyber incident impacts a support system, these interdependencies are often overlooked.
From an administrative perspective, the interdependency principle promotes a multidisciplinary approach to ensure all project personnel are involved. For instance, when designing or modifying a reactor safety system to perform specific functions, a design engineer relies on safety engineers to provide expertise on safety-related functions, quality engineers to verify correct design implementations, maintenance personnel to provide perspectives on accessibility and maintainability, operators to provide operational feedback under various conditions, and competent authorities to provide safety and security requirements.
With the shift towards DI&C, cyber engineers or specialists should also be included throughout the systems engineering lifecycle to provide valuable insight into cyber risk and risk (and cost) minimization strategies, such as cyber risk treatments, policies, and procedures. Additionally, it is paramount to ensure other disciplines, such as engineering, safety, risk, design, maintenance, operations, human factors, and ICT, are knowledgeable about these system interdependencies and the potential consequences of a cyber incident on a facility function, digital asset, system, or system of systems. While the nature of the multidisciplinary engagements may differ with each stage, similar to safety, the intent is to ensure cyber engineering remains a core domain throughout the entire lifecycle.
Although new installations or modifications to existing facilities will include equipment database inventories of SSCs, this list often is out-of-date, incomplete, and without enough information to support cyber requirements and incident response decisions. Thus, the digital asset inventory CIE principle is intended to ensure that an accurate as-built digital asset inventory is maintained throughout the systems engineering lifecycle, including initial design, maintenance, configuration changes, and upgrades or modifications.
It is impossible to provide adequate protection against cyber incidents if there are unknown digital assets installed in a facility. Therefore, it is necessary to establish complete, accurate, and detailed asset inventories for the entire digital bill of materials (DBOM), including make, model, and version information for hardware, firmware, and software. For instance, if a vendor or intelligence agency provides vulnerability and threat information for a specific digital asset, a facility can easily use their inventory to determine if they have that asset installed. Accurate digital asset inventories improve the overall vulnerability management process. Without the inventory, it is very difficult to track whether newly identified cyber risks are applicable to the facility.
In addition to the DBOM, configuration information, backup requirements, and restoration information should be maintained for each digital SSC. Since cyber compromises do occur within the supply chain and early lifecycle stages, this complete design record should be maintained under secured configuration control such that all modifications or updates are captured. When used in conjunction with the incident response planning principle, this detailed information can be used to restore or rebuild a system after a cyber incident.
The use of third-party digital hardware, firmware, and software has increased tremendously in the past several decades. The cost-benefit of purchasing general purpose multifunctional digital devices has become a mainstay for many custom in-house and engineered solutions. However, since vendors, integrators, and service providers are profit driven, they will likely not invest in additional cyber security designs and controls for their products and services unless required by procurement specifications.
Since the supply chain is one of five threat vectors into a nuclear facility, it is imperative to develop supply chain controls that incorporate techniques into the procurement and acquisition process to prevent malicious or inadvertent compromise of hardware, firmware, software, and system information, where system information is defined as the “complete record of information regarding a digital system or component, including system level and component level information and/or data such as requirements specifications, design documentation, fabrication, assembly or manufacturing details; validation and verification documentation; operation and maintenance manuals; credential, authentication, or cryptographic information; and product lifecycle plans” [39].
The primary objectives of cyber supply chain risk management include the ability to maintain authenticity, integrity, confidentiality, and exclusivity throughout the system engineering lifecycle [39]. Authenticity assures the components are genuine; integrity assures the components are trustworthy and uncompromised; confidentiality assures there is no unauthorized loss of data or secrets; and exclusivity assures there are limited touchpoints to reduce the number of attack points [40].
A simplified, notional DI&C supply chain cyber-attack surface is illustrated in Figure 10. It is important to understand this attack surface so appropriate risk treatments can be implemented to reduce cybersecurity risk throughout the lifecycle. Logically, the parallel use of the design simplification CIE principle reduces this supply chain cyber-attack surface by reducing the number of stakeholders and touchpoints. Ensuring cyber supply chain provenance and trustworthiness is easier with a smaller supply chain cyber-attack surface.
A notional DI&C supply chain cyber-attack surface illustrating the complexity of the supply chain lifecycle overlaid with potential supply chain attacks at key stakeholder locations and touchpoints [
Procurement contracts should include cybersecurity requirements, such as those provided by the Department of Homeland Security [41], the Energy Sector Control Systems Working Group [42], or Electric Power Research Institute [43]. This procurement language should include all aspects of a product or service including the ability to review the supply chain stakeholder’s cybersecurity program, including any assessments or cybersecurity testing. Without inclusion of cybersecurity requirements into procurement contracts, the likelihood of insecure or compromised products and services increases.
It is important to recognize that supply chain cybersecurity is necessary during early lifecycle stages even when only system information is available. Reconnaissance is a primary method used by an adversary to acquire preliminary information about an organization, operations, and system designs. Theft of confidential or proprietary system information may result in loss of intellectual property, counterfeiting, and enable development of future sophisticated cyber-attacks. In addition, compromise or falsification of system information could lead to developers inadvertently including malicious codes, falsified data, latent vulnerabilities, or backdoors into the system or component during supply chain activities.
Unfortunately, protection of sensitive information is historically inadequate—sensitive information can often be found on social media, corporate websites, conferences, business and employment-oriented online services, vendor advertising, and other third-party entities that store nuclear-related information, such as nuclear regulators. Of course, poor cybersecurity hygiene can occur at every stakeholder in the supply chain, including hardware manufacturers, programmers, and integrators, as well as the reactor designer and operator. Since engineering records, asset inventories, master drawings, procedures, specifications, analysis, and other sensitive system information is much more accessible today, responsibility for protecting system information lies not only with the entire nuclear organization but all supply chain stakeholders.
Incident response planning, in conjunction with contingency planning in resilient design and an accurate and complete digital asset inventory, ensures that procedures, current backups, and accurate configurations are available to respond to and recover from deliberate or inadvertent cyber incidents. Cyber incidents can occur at any stage in the lifecycle. For example, theft of system information or IP can occur during design, introduction of malware by a subcontractor can occur during testing, and downloading of corrupted firmware can occur as part of maintenance. Incident response planning should occur in each stage of the systems engineering lifecycle to safeguard the stakeholder, system information, and DBOM against a cyber incident. IAEA TDL006 [44] and NIST 800-61 [45] provide incident response guidelines.
An organization’s culture is demonstrated every day through the actions of its employees. Nuclear facilities are guided by a nuclear safety and security culture which emphasizes protection of public health and safety over other competing goals, such as electricity generation. Personnel are instilled with the understanding that they can and should speak up when there are safety or security concerns. Since cybersecurity is part of the overarching nuclear security policy to guard against theft and sabotage, developing and maintaining a cybersecurity culture and training program is just as important.
The human-in-the-loop is essential for maintaining a robust security posture. As digital technology is prevalent in both OT and ICT systems, every person is responsible for cybersecurity, not just ICT or engineering staff. Similar to the nuclear safety culture, an organization-wide cybersecurity culture and training program will equip all personnel with the knowledge, skills, and abilities to recognize, prevent, and respond to cyber incidents. The goal of CIE is to develop cyber-informed engineers and personnel as opposed to cybersecurity specialists. Development of cyber-awareness and cross-functional cyber capabilities will provide personnel with information on the importance of their role in an organization’s overall security plan. Simply recognizing and reporting phishing emails or suspicious activity can prevent an adversary’s entry into an organization. Without this knowledge of how cyber incidents can occur and what unauthorized interactions can look like, compromises can remain persistent and undetected, thereby leading to greater consequences for the organization or nuclear reactor.
Applying the CIE approach throughout the entire systems engineering lifecycle, from design and testing to maintenance and decommissioning, provides enhanced capabilities for cyber protection, detection, and response. Figure 11 is a notional diagram summarizing potential usage of CIE principles throughout the lifecycle. The primary objective of CIE is to ensure engineers and stakeholders consider CIE principles during each activity within every stage of the lifecycle. Continual cyber risk analysis ensures that new or updated consequences, threats, and vulnerabilities are quickly identified. CIE design principles ensure that approaches to address and reduce the identified cyber risk are considered to the greatest extent possible. And, finally, CIE organizational principles provide long-term cyber risk reduction benefits by holistically integrating cyber considerations throughout the facility and organization.
Notional usage of CIE principles throughout the systems engineering lifecycle.
Since nuclear engineering projects differ in scope, it is impractical to define a standard level of effort for all CIE principles across each stage. For instance, the design and construction of an advanced reactor will likely have a very long timeline and involve multiple organizations, while a simple modification at a research reactor may occur relatively quickly and include only a small group of people. As an applied integrated energy system example, the CIE approach was used during the high-level design of a hydrogen generation project in which heat and electricity were provided by an interconnected NPP [35]. The use of a multi-disciplinary team to address system of system interdependencies through a structured risk analysis process resulted in new insights into the potential for both adversarial and unintentional cyber risks. As a result, the system was immediately redesigned to eliminate specific identified risk as well as to incorporate more simplified and resilient design features [35].
With the continued modernization of the existing nuclear fleet and future advanced reactor designs and applications, the use of DI&C in nuclear reactors will continue to grow. Additionally, once DI&C is installed or new reactors are commissioned, maintenance and updates will occur throughout a reactor’s lifetime. The fundamental CIE objective to consider cyber requirements from the onset of conceptual design provides expanded opportunities for recognizing cyber risks, thereby enabling cyber risk reduction through redesign prior to initiation of any procurement or construction activities. While CIE can positively impact design modifications in existing reactors, it may have even greater potential in improving the security posture of new reactors. Convening multidisciplinary teams will enable novel cyber solutions that otherwise would not be possible, thus minimizing cybersecurity-related costs and expensive rework later in the lifecycle. Addressing cyber concerns after installation with bolt-on solutions is arguably less effective and less efficient, especially given the fact that some SSCs may not tolerate or allow the use of security controls.
CIE is a multidisciplinary approach incorporating design and organizational principles to protect digital technology from cyber risk. The continued adoption of CIE in nuclear organizations as well as the development of curriculum in academic engineering and industry education programs furthers the goal of globally reducing nuclear cyber risk.
The authors wish to acknowledge the contributions of Dr. Katya Le Blanc and Timothy R. McJunkin, who provided critical reviews and suggestions.
The authors declare no conflict of interest.
This work was supported by the U.S. DOE Office of Nuclear Energy Cybersecurity Crosscutting Technology Development program under the DOE Idaho Operations Office, Contract DE-AC07-05ID14517.
AI | artificial intelligence |
CDA | critical digital asset |
CCE | consequence-driven, cyber-informed engineering |
CIE | cyber-informed engineering |
DBA | design basis accident |
DBOM | digital bill of material |
DBT | design basis threat |
DI&C | digital instrumentation and control |
DOE | Department of Energy |
ESFAS | engineered safety feature actuation system |
GDC | general design criteria |
HCE | high consequence event |
I&C | instrumentation and control |
IAEA | International Atomic Energy Agency |
ICT | information and communications technology |
IEEE | Institute of Electrical and Electronics Engineers |
IIoT | industrial internet of things |
IoT | internet of things |
LWR | light water reactor |
ML | machine learning |
NEI | Nuclear Energy Institute |
NIST | National Institute of Standards and Technology |
NPP | nuclear power plant |
NRC | Nuclear Regulatory Commission |
NSS | nuclear security series |
OT | operational technology |
PRA | probabilistic risk analysis |
RPS | reactor protection system |
SMR | small modular reactor |
SNM | special nuclear material |
SSC | systems, structures, and components |
USB | universal serial bus |
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Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognised opportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.",book:{id:"6653",slug:"ehealth-making-health-care-smarter",title:"eHealth",fullTitle:"eHealth - Making Health Care Smarter"},signatures:"Sandeep Reddy",authors:[{id:"230704",title:"Associate Prof.",name:"Sandeep",middleName:null,surname:"Reddy",slug:"sandeep-reddy",fullName:"Sandeep Reddy"}]},{id:"56615",doi:"10.5772/intechopen.69792",title:"Computer Aided Diagnosis - Medical Image Analysis Techniques",slug:"computer-aided-diagnosis-medical-image-analysis-techniques",totalDownloads:3099,totalCrossrefCites:12,totalDimensionsCites:20,abstract:"Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.",book:{id:"5926",slug:"breast-imaging",title:"Breast Imaging",fullTitle:"Breast Imaging"},signatures:"Bhagirathi Halalli and Aziz Makandar",authors:[{id:"202101",title:"Mrs.",name:"Bhagirathi",middleName:null,surname:"Halalli",slug:"bhagirathi-halalli",fullName:"Bhagirathi Halalli"},{id:"202105",title:"Prof.",name:"Aziz",middleName:null,surname:"Makandar",slug:"aziz-makandar",fullName:"Aziz Makandar"}]},{id:"56617",doi:"10.5772/intechopen.69794",title:"Breast Ultrasound Tomography",slug:"breast-ultrasound-tomography",totalDownloads:1474,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"Both mammography and standard ultrasound (US) rely upon subjective criteria within the breast imaging reporting and data system (BI-RADS) to provide more uniform interpretation outcomes, as well as differentiation and risk stratification of associated abnormalities. In addition, the technical performance and professional interpretation of both tests suffer from machine and operator dependence. We have been developing a new technique for breast imaging that is based on ultrasound tomography which quantifies tissue characteristics while also producing 3-D images of breast anatomy. Results are presented from clinical studies that utilize this method. In the first phase of the study, ultrasound tomography (UST) images were compared to multi-modal imaging to determine the appearance of lesions and breast parenchyma. In the second phase, correlative comparisons with MR breast imaging were used to establish basic operational capabilities of the UST system. The third phase of the study focused on lesion characterization. Region of interest (ROI) analysis was used to characterize masses. Our study demonstrated a high degree of correlation of breast tissue structures relative to fat subtracted contrast-enhanced MRI and the ability to scan ~90% of the volume of the breast at a resolution of 0.7 mm in the coronal plane.",book:{id:"5926",slug:"breast-imaging",title:"Breast Imaging",fullTitle:"Breast Imaging"},signatures:"Nebojsa Duric and Peter Littrup",authors:[{id:"202080",title:"Dr.",name:"Nebojsa",middleName:null,surname:"Duric",slug:"nebojsa-duric",fullName:"Nebojsa Duric"},{id:"202081",title:"Dr.",name:"Peter",middleName:null,surname:"Littrup",slug:"peter-littrup",fullName:"Peter Littrup"}]},{id:"72859",doi:"10.5772/intechopen.93228",title:"Machine Learning in Wearable Biomedical Systems",slug:"machine-learning-in-wearable-biomedical-systems",totalDownloads:715,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.",book:{id:"9158",slug:"sports-science-and-human-health-different-approaches",title:"Sports Science and Human Health",fullTitle:"Sports Science and Human Health - Different Approaches"},signatures:"Muhammad E.H. Chowdhury, Amith Khandakar, Yazan Qiblawey, Mamun Bin Ibne Reaz, Mohammad Tariqul Islam and Farid Touati",authors:[{id:"129681",title:"Dr.",name:"Mamun Bin Ibne",middleName:null,surname:"Reaz",slug:"mamun-bin-ibne-reaz",fullName:"Mamun Bin Ibne Reaz"},{id:"244639",title:"Dr.",name:"Muhammad E.H.",middleName:null,surname:"Chowdhury",slug:"muhammad-e.h.-chowdhury",fullName:"Muhammad E.H. Chowdhury"},{id:"245398",title:"MSc.",name:"Amith M. A.",middleName:null,surname:"Khandakar",slug:"amith-m.-a.-khandakar",fullName:"Amith M. A. Khandakar"},{id:"279345",title:"Prof.",name:"Mohammad Tariqul",middleName:null,surname:"Islam",slug:"mohammad-tariqul-islam",fullName:"Mohammad Tariqul Islam"},{id:"312319",title:"Prof.",name:"Farid",middleName:null,surname:"Touati",slug:"farid-touati",fullName:"Farid Touati"},{id:"321692",title:"M.Sc.",name:"Yazan",middleName:null,surname:"Qiblawey",slug:"yazan-qiblawey",fullName:"Yazan Qiblawey"}]},{id:"51702",doi:"10.5772/64620",title:"Empowering Diabetes Patient with Mobile Health Technologies",slug:"empowering-diabetes-patient-with-mobile-health-technologies",totalDownloads:2499,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"Chronic diseases, especially diabetes mellitus, are huge public health burden. Therefore, new health care models for sharing the responsibility for care among health care providers and patients themselves are needed. The concept of empowerment promotes patient’s active involvement and control over their own health. It can be achieved through education, self-management, and shared decision making. All these aspects can be covered by mobile health technologies, the so-called mHealth. This term comprises mobile phones, patient monitoring devices, tablets, personal digital assistants, other wireless devices, and numerous apps. Many challenges of diabetics can be addressed by mHealth, including glycemic control, nutrition control, physical activity, high blood pressure, medication adherence, obesity, education, diabetic retinopathy screening, diabetic foot screening, and psychosocial care. However, mHealth plays only minor role in diabetes management, despite numerous apps on the market. Namely, these apps have many shortcomings and the majority of them does not include important functions. Moreover, these apps lack the perceived additional benefit by the user and the ease of use, important factors for acceptance of mHealth. Studies of diabetes apps regarding usability and accessibility have shown moderate results. Beside improvements of apps usability, the future of diabetes mHealth lies probably in personalized education and self-management with the help of decision support systems. At the same time, work on artificial pancreas is in progress and smartphone could be used as user interface.",book:{id:"5172",slug:"mobile-health-technologies-theories-and-applications",title:"Mobile Health Technologies",fullTitle:"Mobile Health Technologies - Theories and Applications"},signatures:"Matjaž Krošel, Lana Švegl, Luka Vidmar and Dejan Dinevski",authors:[{id:"15129",title:"Prof.",name:"Dejan",middleName:null,surname:"Dinevski",slug:"dejan-dinevski",fullName:"Dejan Dinevski"},{id:"186406",title:"M.D.",name:"Matjaž",middleName:null,surname:"Krošel",slug:"matjaz-krosel",fullName:"Matjaž Krošel"},{id:"186407",title:"Mrs.",name:"Lana",middleName:null,surname:"Švegl",slug:"lana-svegl",fullName:"Lana Švegl"},{id:"186408",title:"Dr.",name:"Luka",middleName:null,surname:"Vidmar",slug:"luka-vidmar",fullName:"Luka Vidmar"}]}],mostDownloadedChaptersLast30Days:[{id:"63955",title:"Creative Haptic Interface Design for the Aging Population",slug:"creative-haptic-interface-design-for-the-aging-population",totalDownloads:1161,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Audiovisual human-computer-interfaces still make up the majority of content to the public; however, haptic interfaces offer unique advantage over the dominant information infrastructure, particularly for users with a disability or diminishing cognitive and physical skills like the elderly. The tactile sense allows users to integrate new, unobstructive channels for digital information into their sensorium, one that is less likely to be overwhelmed compared to vision and audition. Haptics research focus on the development of hardware, improving resolution, modality, and fidelity of the actuators. Despite the technological limitations, haptic interfaces are shown to reinforce physical skill acquisition, therapy, and communication. This chapter will present key characteristics intuitive tactile interfaces should capture for elderly end-users; sample projects will showcase unique applications and designs that identify the limitations of the UI.",book:{id:"6714",slug:"assistive-technologies-in-smart-cities",title:"Assistive Technologies in Smart Cities",fullTitle:"Assistive Technologies in Smart Cities"},signatures:"Eric Heng Gu",authors:[{id:"237761",title:"M.Sc.",name:"Eric Heng",middleName:null,surname:"Gu",slug:"eric-heng-gu",fullName:"Eric Heng Gu"}]},{id:"56615",title:"Computer Aided Diagnosis - Medical Image Analysis Techniques",slug:"computer-aided-diagnosis-medical-image-analysis-techniques",totalDownloads:3097,totalCrossrefCites:12,totalDimensionsCites:20,abstract:"Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.",book:{id:"5926",slug:"breast-imaging",title:"Breast Imaging",fullTitle:"Breast Imaging"},signatures:"Bhagirathi Halalli and Aziz Makandar",authors:[{id:"202101",title:"Mrs.",name:"Bhagirathi",middleName:null,surname:"Halalli",slug:"bhagirathi-halalli",fullName:"Bhagirathi Halalli"},{id:"202105",title:"Prof.",name:"Aziz",middleName:null,surname:"Makandar",slug:"aziz-makandar",fullName:"Aziz Makandar"}]},{id:"60562",title:"Use of Artificial Intelligence in Healthcare Delivery",slug:"use-of-artificial-intelligence-in-healthcare-delivery",totalDownloads:2886,totalCrossrefCites:23,totalDimensionsCites:27,abstract:"In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognised opportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.",book:{id:"6653",slug:"ehealth-making-health-care-smarter",title:"eHealth",fullTitle:"eHealth - Making Health Care Smarter"},signatures:"Sandeep Reddy",authors:[{id:"230704",title:"Associate Prof.",name:"Sandeep",middleName:null,surname:"Reddy",slug:"sandeep-reddy",fullName:"Sandeep Reddy"}]},{id:"60985",title:"Terminology Services: Standard Terminologies to Control Medical Vocabulary. “Words are Not What they Say but What they Mean”",slug:"terminology-services-standard-terminologies-to-control-medical-vocabulary-words-are-not-what-they-sa",totalDownloads:1342,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"Data entry is an obstacle for the usability of electronic health records (EHR) applications and the acceptance of physicians, who prefer to document using “free text”. Natural language is huge and very rich in details but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, etc. In order to address this point, numerous terminological systems for the systematic recording of clinical data have been developed. These systems interrelate concepts of a particular domain and provide reference to related terms and possible definitions and codes. The purpose of terminology services consists of representing facts that happen in the real world through database management. This process is named Semantic Interoperability. It implies that different systems understand the information they are processing through the use of codes of clinical terminologies. Standard terminologies allow controlling medical vocabulary. But how do we do this? What do we need? Terminology services are a fundamental piece for health data management in health environment.",book:{id:"6653",slug:"ehealth-making-health-care-smarter",title:"eHealth",fullTitle:"eHealth - Making Health Care Smarter"},signatures:"Daniel Luna, Carlos Otero, María L. Gambarte and Julia Frangella",authors:null},{id:"66085",title:"Connected Insurance Reshaping the Health Insurance Industry",slug:"connected-insurance-reshaping-the-health-insurance-industry",totalDownloads:1189,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The role of today’s insurer is changing toward a more preventive and digital or connected approach. In this context, connected health insurance has the potential to contribute toward the health and the general well-being of the population. New technologies like e-health and wearables employed by the insurance industry might even help deal with major issues related to the rising number of people, of chronic disease patients, and of elders while keeping them healthier and at the same time protected by insurance. The aim of this chapter is to briefly illustrate the concept of “connected insurance” with specific focus on “connected health” and “wearables” and to present two case studies: Discovery’s Vitality program which aims to create healthier lifestyles for its customers through the use of wearables and rewards and ICS Maugeri’s MOSAIC project based on AI and predictive models aimed at helping with the management of treatment and quality of life in type 2 diabetes patients.",book:{id:"7952",slug:"smart-healthcare",title:"Smart Healthcare",fullTitle:"Smart Healthcare"},signatures:"Andrea Silvello and Alessandro Procaccini",authors:[{id:"288367",title:"Mr.",name:"Andrea",middleName:null,surname:"Silvello",slug:"andrea-silvello",fullName:"Andrea Silvello"},{id:"294610",title:"MSc.",name:"Alessandro",middleName:null,surname:"Procaccini",slug:"alessandro-procaccini",fullName:"Alessandro Procaccini"}]}],onlineFirstChaptersFilter:{topicId:"1015",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:17,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. 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:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"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. 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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. 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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"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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