WT: wild-type, MT: mutant
Probe sequences for targeting each of the beta-globin gene mutations selected for this study. (Adapted from Ng et al, 2010, copyright Elsevier Inc).
\r\n\r\n
\r\n\r\nDiagnosis and Management of Oral Lesions and Conditions: A Resource Handbook for the Clinician has been produced and distributed through an educational grant from the Colgate-Palmolive Company.\r\n',isbn:null,printIsbn:"978-953-51-1219-8",pdfIsbn:"978-953-51-7193-5",doi:"10.5772/57596",price:119,priceEur:129,priceUsd:155,slug:"diagnosis-and-management-of-oral-lesions-and-conditions-a-resource-handbook-for-the-clinician",numberOfPages:152,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"af0ad767eb677a8e69dac58c0095f72d",bookSignature:"Cesar A. Migliorati and Fotinos S. Panagakos",publishedDate:"February 19th 2014",coverURL:"https://cdn.intechopen.com/books/images_new/3864.jpg",numberOfDownloads:36260,numberOfWosCitations:2,numberOfCrossrefCitations:13,numberOfCrossrefCitationsByBook:null,numberOfDimensionsCitations:39,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:54,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 7th 2014",dateEndSecondStepPublish:"January 28th 2014",dateEndThirdStepPublish:"May 4th 2014",dateEndFourthStepPublish:"August 2nd 2014",dateEndFifthStepPublish:"September 1st 2014",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:'
The detection of biomolecules, be it proteins or nucleic acids such as DNA or RNA, is a critical process in biomedical research and clinical diagnostics. With the former, it helps us to unravel the complexity of our human body, and provides important information down at the cellular and sub-cellular level that allows us to better understand what our bodies are comprised off, how they function, how they respond to disease and aging, or why they fail to respond. This information, when applied to clinical diagnostics, help better manage our health and enhance the quality of life.
\n\t\t\tTo generate any meaningful or conclusive information for clinical diagnostics, it is often needed to detect several targets simultaneously. Therefore technologies for performing biomolecular detection must be able to interrogate several targets at one time i.e. perform multiplexing. These targets can be proteins or nucleic acid targets from different cellular species, such as for infectious disease diagnosis, or from the same species i.e. along the same genome, such as single-nucleotide polymorphisms (SNPs) genotyping for pharmacogenomics. It can also be for identifying aberrant biomolecules from normal ones, such as mutation detection in cancer diagnostics and prognostics. Therefore having a platform capable of performing multiplexed biological detection is an indispensable tool for accurate clinical diagnostics.
\n\t\t\tThrough advancement in molecular biology as well as in areas such as microelectronics, microfabrication, material science, and optics, there have been a proliferation of miniaturized platforms, or biosensors, for performing biological analysis based on a variety of multiplexing technologies. These ranged from those capable of detecting a few targets to those capable of interrogating hundreds or even thousands of targets. Here we attempt to provide a concise overview of such technologies, as well as provide some insight into a simple technology that we developed in-house. Due to the enormous amount of progress in this area, this is by no means a comprehensive overview.
\n\t\tOne of the most widely used technologies for multiplexed detection involves performing the detection within a single homogeneous solution. The best example of this is the multiplexed polymerase chain reaction (PCR). PCR, which is one of the most common techniques used in molecular biology, involves using a pair of primers to amplify a certain fragment of a target DNA or RNA manifold, until there is sufficient amount for detection or further downstream analysis. In multiplex PCR, several pairs of primers are used to simultaneously amplify different fragments. It is relatively easy to perform multiplexing in PCR, because the primers can first be designed to amplify fragments of different sizes, and these fragments can then be detected based on their size differences, either using gel electrophoresis or high-resolution melting on real-time PCR systems. Alternatively, the different fragments can also be targeted by different probes conjugated to fluorescent dyes of a specific color. Upon hybridizing to the targets, the probes emit an optical signal corresponding to their dye, which is detected in a real-time PCR system.
\n\t\t\t\tMultiplex PCR is one of the most common techniques used in clinical diagnostics because the technology has matured significantly since its invention almost three decades ago. This is also rather easy to implement on biosensors, as the process can be carried out in microchambers (Merritt, 2010), or coupled to a capillary electrophoretic module (Thaitrong, 2009). The ability to perform multiplexed detection in PCR results from
\n\t\t\t\tthe unique feature in PCR that allows primers to be designed to amplify fragments of different sizes,
the ability of the gel electrophoresis or real-time PCR system to differentiate the fragments by size as a result of their difference in electrophoretic mobility or melting temperature, and
the ability to differentiate the probes through color-emitting dyes.
Probes used in multiplex PCR are conjugated with fluorescent dyes that emit different wavelengths of light, allowing them to be differentially detected. As a result, there is always a need for powerful optical detection, being capable of exciting and detecting one or multiple wavelengths of light. Due to limitations in the number of different wavelengths of light that can be excited and detected, the number of different multiplexed targets that can be detected in a single reaction is generally not high. One way to overcome this limitation is to combine multiplex PCR with other technologies, such as microarrays.
\n\t\t\tThe development of microarrays is driven by the demand for high throughput multiplexed analysis, such as the mapping of the human genome. This platform enables hundreds of thousands of proteins or DNA probes to be precisely immobilized onto designated locations within a microscopic area of a silicon or glass substrate (Ramsay, 1998; Schena et al, 1995), with the different probes identified through their unique locations. The proteins or oligonuleotides can be immobilized onto the surface using a high precision robotic arrayer or synthesized
Since the inception of the microarrays about two decades ago, there has been a host of companies offering the technology commercially. United States-based Affymetrix is one of the first companies to offer commercial oligonucleotide microarrays, with its GeneChip one of the most widely-used microarrays in a variety of applications, such as in prediction of tumour relapse in hepatocecullar carcinoma patients (Roessler, 2010). Other companies include Agilent, which uses inkjet printing for oligo synthesis on its 2D microarrays (Fig. 1), Applied Microarrays and Roche NimbleGen. CombiMatrix\'s CMOS arrays have addressable electrodes that have been developed for both DNA detection and immunoassays (Gunn, 2010; Cooper, 2010). With the advent of microfabrication technology and with increased competition, the prices of these microarrays have come down significantly over the years, making the technology more accessible to the research and clinical diagnostics community.
\n\t\t\t\tAgilent\'s inkjet printing technology for oligonucleotide synthesis on 2D microarrrays A: the first layer of nucleotides is deposited on the activated microarray surface. B: growth of the oligos is shown after multiple layers of nucleotides have been precisely printed. C: close-up of one oligo as a new base is being added to the chain, which is shown in figure D. (Courtesy of Agilent Technologies. All rights reserved).
Despite its high-throughput potential, the 2-D microarray format is restricted by the diffusion-limited kinetics, and electrostatic repulsion between the solution-phase targets and the densely localized solid-phase probes. Furthermore, the amount of probes that can be immobilized on the planar substrate, and hence the sensitivity and signal-to-noise ratio (SNR), is also somewhat limited. The introduction of 3-D microarrays go some way toward overcoming these limitations. These 3-D microarrays comprised of additional microstructures that are fabricated onto planar substrates to provide a high surface-density platform that increases the immobilization capacity of capture probes, enhances target accessibility and reduces background noise interference in DNA microarrays, leading to improved signal-to-noise ratios, sensitivity and specificity.
\n\t\t\t\tAn example of an early 3-D microarray is the gel-based chip (Kolchinsky & Mirzabekov, 2002). The use of an array of nanoliter-sized polyacrylamide gel pads on a glass slide provides distinct 3D microenvironments for the immobilization of oligonucleotides. Compared to planar glass substrates, the gel-based format can be applied with a higher probe concentration of up to 100 fold, thereby increasing the SNR. The near solution-phase interaction between targets and probes within individual gel pads can also potentially alleviate the problems associated with diffusion-limited kinetics. These gel-based microarrays have been successfully demonstrated for the detection of SNPs associated with β-thalassemia mutations (Drobyshev et al, 1997), and for the identification of polymorphisms in the human mu-opioid receptor gene (LaForge et al, 2000).
\n\t\t\t\tOther 3-D structures fabricated onto planar surfaces include conical dendrons as well as micropillars (Hong et al, 2005). By fabricating conical dendrons, nano-controlled spacings can be created to provide enough room for the target strand to access each probe, thereby creating a reaction format resembling that in a solution (Fig. 2). As a result, the hybridization time can be reduced to significantly to allow effective discrimination of single-nucleotide mismatches (Hong et al, 2005).
\n\t\t\t\tSchematic diagram showing improved DNA hybridization onto a dendron-modified substrate as compared to that of a normal substrate.
\n\t\t\t\t\tRamanamurthy et al (2008) reported the fabrication of ordered, high-aspect ratio nanopillar arrays on the surface of silicon-based chips to enhance signal intensity in DNA microarrays (Fig. 3). These 150-nm diameter nanopillars were found to enhance the hybridization signals by up to 7 times as compared to flat silicon dioxide substrates. In addition, hybridization of synthetic targets to capture probes that contained a single-base variation showed that the perfect matched duplex signals on dual-substrate nanopillars can be up to 23 times higher than the mismatched duplex signals. The Z-Slides microarray from United States-based company Life Bioscience comprises micropillars and nanowells to enhance spot morphology and eliminate cross-talk between probe sites. By detecting only the pillar surfaces which are several hundred microns from the base, background noise is removed from the microarray scan.
\n\t\t\t\tA 3-D microarray which is markedly different from the above-mentioned approaches involves immobilizing oligonucleotide probes onto a single thread instead of a planar substrate (Stimpson et al, 2004). The thread is subsequently wound around a core to form a compact, high-density SNP detection platform. Hybridization can be carried out by immersing the thread-and-core structure into a target solution, and completed within approximately 30 min. This platform has been demonstrated for the analysis of SNPs in CYP2C19, an important cytochrome P450 gene (Tojo et al, 2005).
\n\t\t\t\tSEM images of the nanopillars fabricated on silicon-based biosensors. (a) Single-substrate nanopillars consisting SiO2. (b) Dual-substrate nanopillars consisting SiO2 layer atop the Si pillar. (c) Very high-aspect ratio dual-substrate nanopillars. (d) Dense array of ordered dual-substrate nanopillars. Scale bars are all 500 nm.
One of the best examples of 3-D microarrays, and perhaps also one of the most successful commercially available platforms, is the bead microarray. Unlike 2-D microarrays, the high surface-to-volume ratio of beads allows a larger amount of probes to be immobilized to improve the detection signals and signal-to-noise ratios. The small size of beads can further reduce the reaction volume, and the use of microfluidics in bead arrays can shorten the hybridization time to < 10 min, a 50 to 70-fold reduction as compared to conventional microarrays (Ali et al, 2003). Unlike 2-D or the 3-D microarrays discussed, probes are usually conjugated onto the beads prior to them being immobilized onto the microarrays. The major challenge, therefore, in developing bead arrays is to identify the identities or their corresponding immobilized probes of those randomly assembled beads in multiplexed analyses.
\n\t\t\t\tThe most common strategy is to encode beads with colorimetric signatures using semiconductor nanocrystals, visible dyes or fluorophores, and subsequently decode them through visual or fluorescence detection (Mulvaney et al, 2004). Color-encoded beads are produced by embedding them with semiconductor nanocrystals, visible dyes, or fluorophores and subsequently decoded through visual or fluorescence detection. For example, Li
Quantum dots, which are photostable, have size-tunable emission wavelengths, and can be excited by a single wavelength to emit different colors at one time, are widely used to distinguish beads. Han et al. (2001) incorporated quantum dots at different intensities and colors to yield spectrally distinguishable polymeric beads of up to 10 distinct types (Fig. 4). Using 5-6 colors, each at 6 intensity levels, it is possible to achieve up to 40 000 codes using this approach, although this has yet to be demonstrated. These techniques for color encoding beads are straightforward in that the color-emitting agents are directly impregnated into the beads. However, this also means that the encoder signals cannot be removed, resulting in possible interference between the encoder and reporter signals. To avoid this, the number of reporter dyes available for use would inadvertently be reduced. Also, encoding the beads into unique color codes is challenging as the color-emitting agents must be mixed in precise proportions. The difficulty in distinguishing a large number of color codes further means that only up to 100 color codes have been demonstrated so far, limiting them to low or medium throughout applications (Xu et al, 2003; Li et al, 2004).
\n\t\t\t\ta) A set of 100 distinguishable bead types can be created by mixing precise proportions of two fluorescent dyes, and subsequently detected using a flow cytometer with two laser beams. (Courtesy of Luminex Corporation. All rights reserved). (b) Quantum dot nanocrystals of 10 different emission colors incorporated into the beads to create spectrally distinguishable types. (Adapted by permission from Macmillan Publishers Ltd: Nature Biotechnology, copyright 2001).
Beads within an array can also be individually addressed using barcodes. A graphical barcode can also be written inside fluorescently dyed beads through a technique termed “spatial selective photobleaching of the fluorescence” (Braeckmans, 2001). Using a specially adapted laser scanning confocal microscope, any sort of pattern can be photobleached at any depth inside the fluorescently dyed bead. This technique was used to photobleach a barcode of different band widths onto 45 μm-diameter fluorescent beads. The advantages of this technique are that only a single fluorescent dye is needed in the encoding scheme, and the number of codes achievable is virtually unlimited. However, there is still the problem of interference between the encoder and reporter fluorescence signals, while the effects of photobleaching during the decoding stage might alter or degrade the barcode.
\n\t\t\t\tA widely used bead microarray platform for biological detection and clinical diagnostics is the commercial BeadArray from Illumina, a market leader in high-throughput bead microarrays. It assembles 3-micron silica beads onto a fiber optic of planar silica slides, for a range of DNA and RNA analyses. There is also the Veracode technology, which uses digital holographic barcode to identify the beads (Lin et al, 2009) (Fig. 5). When excited by a laser, each microbead, which has a pillar-like rather than spherical shape, emits an image resembling a barcode. Using this method, it becomes possible to have virtually unlimited number of different bead types. The platform can be applied to both protein-based or DNA-based assays.
\n\t\t\t\tIllumina\'s BeadArray (top panel) and Veracode technology (bottom panel). (Courtesy of Illumina. All rights reserved)
We describe here some of our own work in developing a biosensor that allow different bead types to be incorporated and addressed with minimal efforts for encoding and decoding, simplifying the development and usage of such devices (Ng et al, 2010). To achieve that, different bead types are incorporated and identified based on their spatial addresses (akin to microarrays) without the need for color-coding (Fig. 6). Beads of a certain type are spotted onto a polymeric micro-matrix (or gel pad) fabricated on the surface of the biosensor. The natural immobilization of the beads by the gel pad allows each bead to be anchored within the gel pad on a unique location, acquiring spatial addresses that can be easily recorded via an acquired image. Beads of a second type spotted over the same gel pad take up spatial addresses distinct from those of the first bead type, allowing the two bead types to be easily distinguished. This is repeated for immobilizing and distinguishing further bead types on the gel pad, obviating the need for prior encoding and tedious decoding of beads. The throughput can be increased by further spotting many different bead types onto the hundreds of gel pads on each biosensor. We demonstrate the use of this bead-based biosensor for detection of six common South-east Asian beta-globin gene mutations within 30 min, demonstrating its potential as a simple tool for rapid beta-thalassemia carrier screening.
\n\t\t\tSchematic representation of the spatially addressable bead-based biosensor (Adapted from
The biosensor consisted an array of 19 x 24 polyacrylamide gel pads fabricated on a glass slide (Corning, Corning, NY) pre-treated with Bind Silane (GE Healthcare, Piscataway, NJ). The gel pads had horizontal and vertical pitch of 300 μm, and each gel pad further comprised a 10 x 10 array of micropillars (10x10x10 μm) with horizontal and vertical pitch of 10 μm (Fig. 7). A photopolymerization process described previously was used to create the array of gel pads (Proudnikov et al., 1998), after which the glass slide was treated in 0.1M NaBH4 for 30 min to reduce gel pads auto-fluorescence.
\n\t\t\t\tThe Biochip Arrayer (PerkinElmer, Boston, MA) was used to spot beads onto singular gel pads on the device. Each gel pad was spotted with about 5 nL of a particular bead solution (~ 9000 beads/µL), and then left to dry at room temperature for 2-3 min to allow beads immobilization to the gel. Beads can also be spotted manually using a pipette, although this required a larger amount of bead solution (0.25 μL) per spot and the beads usually covered 2-4 gel pads simultaneously. Positions of each spotted bead type were then recorded via autofluorescence images for determining their spatial addresses. This was repeated until all bead types for detecting a particular target were immobilized on the same gel pad. The device can then be capped with a microfluidic module for sample flow-through, or the buffer can also be applied over the spotted beads without the module. The polydimethylsiloxane (PDMS) module was fabricated using common soft lithographic techniques (Duffy et al., 1998).
\n\t\t\t\tThe bead-based biosensor. (A) The device comprised an array of polyacrylamide gel pads on a glass slide. Each gel pad further comprised an array of micropillars. (B) Image after spotting the first bead type onto the gel pad. The spatial address for each bead is recorded in terms of their x, y coordinates. (C) Image after spotting a second bead type (black arrows) and finally (D) a third bead type (white arrows). (Adapted from
The six common South-east Asian beta-globin gene mutations selected for this study were -28 A→G, -29 A→G, IVSI5 G→C, IVSI1 G→T, Cd26 GAG→AAG, and IVSII654 C→T. For each mutation, allele-specific probes were designed to hybridize with perfect complementary to either the wildtype or mutant variant (Table 1). A biotin moiety was added to the 5’ end of each probe, and conjugation of probes to 9.95 µm streptavidin-modified polystyrene beads was carried out according to previously described protocol (Ng et al., 2008).
\n\t\t\t\tPCR was carried out to amplify two fragments of the beta-globin gene, with the first fragment (319 bp) encompassing the Exon 1 which incudes all the targeted mutations other than IVSII654 C→T, which was contained in the second fragment (128 bp). Primer sequences were: Frag1-F: 5’-Cy3-ACggCTgTCATCACTTAgAC-3’ (Genbank HUMHBB sequence 62010-62029); Frag1-R: 5’-CCCAgTTTCTATTggTCTCC-3’ (HUMHBB sequence 62328-62309); Frag2-F: 5’- Cy3-TgTATCATgCCTCTTTgCACC-3’ (HUMHBB sequence 63227-63247); and Frag2-R: 5’-CAATATgAAACCTCTTACATCAg-3’ (HUMHBB: 63354-63332).
\n\t\t\t\tGenomic DNA (100 ng) was amplified in a total volume of 50 µL containing 0.5 µM each of the two sets of primers, 200 µM of each deoxynucleotide triphosphate, and 1 U of HotStarTaq DNA polymerase in 1× supplied PCR buffer (Qiagen). Amplification was carried out in an iCycler thermal cycler (BioRad) with an initial denaturation at 95 °C for 15 min, followed by 35 cycles at 98 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, and a final extension at 72 °C for 5 min. Products were then re-amplified with only the forward primers to generate ssDNA for allele-specific hybridization.
\n\t\t\t\tProbe name | \n\t\t\t\t\t\t\tMutation targeted | \n\t\t\t\t\t\t\tSequence (5’-3’) | \n\t\t\t\t\t\t
-28,-29_WT | \n\t\t\t\t\t\t\t-28/-29 WT | \n\t\t\t\t\t\t\tCCTgACTTTTATgCCCAg | \n\t\t\t\t\t\t
-28_MT | \n\t\t\t\t\t\t\t-28 MT | \n\t\t\t\t\t\t\tCCTgACTTCTATgCCCAg | \n\t\t\t\t\t\t
-29_MT | \n\t\t\t\t\t\t\t-29 MT | \n\t\t\t\t\t\t\tCCTgACTTTCATgCCCAg | \n\t\t\t\t\t\t
IVSI5,1_WT | \n\t\t\t\t\t\t\tIVSI5/1 WT | \n\t\t\t\t\t\t\tCTTgATACCAACCTgCCC | \n\t\t\t\t\t\t
IVSI5_MT | \n\t\t\t\t\t\t\tIVSI5 MT | \n\t\t\t\t\t\t\tCTTgATAgCAACCTgCCC | \n\t\t\t\t\t\t
IVSI5_WT | \n\t\t\t\t\t\t\tIVSI1 MT | \n\t\t\t\t\t\t\tCTTgATACCAAACTgCCC | \n\t\t\t\t\t\t
Cd26_WT | \n\t\t\t\t\t\t\tCd26 WT | \n\t\t\t\t\t\t\tgggCCTCACCACCAAC | \n\t\t\t\t\t\t
Cd26_MT | \n\t\t\t\t\t\t\tCd26 MT | \n\t\t\t\t\t\t\tgggCCTTACCACCAAC | \n\t\t\t\t\t\t
IVSII654_WT | \n\t\t\t\t\t\t\tIVSII654 WT | \n\t\t\t\t\t\t\tTTgCTATTgCCTTAACCC | \n\t\t\t\t\t\t
IVSII654_MT | \n\t\t\t\t\t\t\tIVSII654 MT | \n\t\t\t\t\t\t\tTTgCTATTACCTTAACCC | \n\t\t\t\t\t\t
WT: wild-type, MT: mutant
Probe sequences for targeting each of the beta-globin gene mutations selected for this study. (Adapted from Ng et al, 2010, copyright Elsevier Inc).
Re-amplified PCR products were purified using the Microcon YM-30 filter device (Millipore) before being diluted to a 10 µL hybridization solution containing 500 mM NaCl and 30% formamide. Hybidization was carried out by pipetting the solution over the spotted beads. After 30 min incubation, the device was rinsed briefly with a solution containing only 500 mM NaCl and 30% formamide, and signal capture was carried out by fluorescence imaging. The imaging system comprised an epifluorescence microscope (BX51, Olympus), 100 W mercury lamp and fluorescence filter set 41007 (Chroma Technology). MetaMorph 5.0 (Molecular Devices) was used to control acquisition of 12-bit monochrome bead images at 2 s exposure from a SPOT-RT Slider cooled-CCD camera (Diagnostic Instruments), and bead signals were quantitated using the modified version of a software developed in-house previously (Ng and Liu, 2005).
\n\t\t\tTo demonstrate detection of the six beta-globin gene mutations, six human samples heterozygous for -28 A→G, -29 A→G, IVSI5 G→C, IVSI1 G→T, Cd26 GAG→AAG, and IVSII654 C→T, and one homozygous for IVSII654 C→T were analyzed using the bead-based biosensor. All samples were genotyped previously by direct sequencing or multiplexed minisequencing (Wang et al., 2003). Wildtype and mutant probes targeting each mutation were conjugated to distinct bead sets, spotted onto a particular gel pad on the device, and distinguished based on their spatial addresses (Fig. 8A). Probes were designed with the targeted mutation as near as possible to its centre region, in order to increase the discrimination between matched and mismatched duplexes. Due to the proximity between the -28 and -29 mutations, as well as between the IVSI1 and IVSI5 mutations, each pair of mutations must be detected simultaneously on a single gel pad by four sets of probes to cover all possible genotypes. However, due to the lack of samples compound heterozygous for -28/-29 and IVSI1/IVSI5, only three sets of probes were required in this study for each pair of mutations.
\n\t\t\t\t\n\t\t\t\t\tFig. 8B shows the signal intensity from the wildtype and mutant probes used to target each mutation. All seven different samples were correctly genotyped using the device. For heterozygous mutations, signal intensities from the wildtype probes did not differ significantly from that of the mutant probes, attaining student t-test p-values > 0.05 for all except IVSII654 which had a slightly lower p-value of about 0.01. In the absence of a mutation, the wildtype probe intensities were significantly higher than that of the mutant probes, with p-values far lower than 0.001. For the homozygous IVSII654 mutation, the mutant probe intensity was significantly higher than the wildtype probe, attaining a p-value < 0.0001. This similarity or significant difference between wildtype and mutant probe intensities allowed correct identification of the heterozygous mutant and homozygous wildtype (or mutant) samples respectively.
\n\t\t\t\tThe spatially addressable bead-based biosensor offers an alternative tool for simple yet efficient and rapid detection of beta-thalassemia mutations. The device is comprised simply of a glass slide fabricated with a thin polyacrylamide matrix on its surface using a photopolymerization process that is faster (~ 45 min) and far less complicated than conventional photolithographic techniques for making silicon chips. The main advantage of the device is its ability to distinguish different bead types without the need for prior time-consuming and laborious techniques such as color-encoding (Braeckmans et al., 2001). This is due to the natural immobilization of the beads to the polyacrylamide gel pads, thus allowing the beads to acquire unique spatial addresses. Detection is achieved by applying the solution of PCR-amplified targets over the region of the spotted beads for passive hybridization to occur, which obviates the need for microfluidic mixing and thus microchannels. This further simplifies the fabrication process, lowers the cost of the device,
\n\t\t\t\tAllele-specific hybridization on the device. (A) Typical example of the beads spotted onto a gel pad. Probe-beads targeting Cd26 wildtype variant were spotted onto a gel pad, followed by those targeting the mutant variant (red arrows). Difference in probe intensities showed sample to be of homozygous Cd26 normal genotype. (B) Signal intensity from the wide-type (■) and mutant (■) probe-bead targeting each of the six mutations selected for this study. (Adapted from
and reduces the sample volume required (< 10 µL). Despite the lack of microfluidic mixing, detection is achieved in 30 min, although this might possibly be even faster, given that we have achieved hybridization on this device within 10 min, albeit with synthetic targets (Ng et al., 2008).
\n\t\t\tThe advent of biosensors has allowed biomedical research and clinical diagnostics to leverage upon the advantages of miniaturization, such as reduced sample volumes, faster reaction times, and the possibility of multiplexed detection. The last point is of particular importance, since the simultaneous detection of multiple targets at once has resulted in significant time savings, particularly for applications requiring high-throughput. Often, multiple targets must be detected in order to draw any meaningful conclusion in clinical diagnosis. So much progress has been made in this field such that it is now possible to utilize high throughput platforms such as microarrays to interrogate thousands of targets at once. The crucial role played by these technologies, such as multiplex PCR and the various forms of 2D, 3D and bead-based microarrays, in the past decades is indisputable, and will continue to be so. However several challenges exist.
\n\t\t\tFirst, it is important to reduce the cost of some of these technologies so as to make it more affordable, particularly for clinical diagnostics. For example, systems for real-time PCR can be quite costly, due in part to the high precision optical detection modules found within. With advances in optics, both light sources (e.g. LEDS) and detectors (e.g. digital cameras) are getting more affordable, which would help to bring down the costs of such systems. Also, part of the costs are attributable to the licensing issues. Manufacturers of real-time PCR systems and reagents have to pay a license fee including royalties to the original patent owners. With time, some of the patent protections will expire soon, so prices should also come down, as in the case of the patent expiry of the Taq polymerase in 2006. The manufacturing costs for microarrays and its bead-based counterpart are also high. Hopefully with advances in manufacturing technologies, the cost can eventually be reduced.
\n\t\t\tSecond, it is important for these technologies to be of sufficient sensitivity and specificity in order to meet the standards required in clinical diagnostics. Real-time PCR has no problems with that, since it is not uncommon for it to achieve a sensitivity and specificity close to 100%. 2-D microarrays, on the other hand, might face more of a challenge. The diffusion-limited kinetics, steric hindrances and high noise contributed by the planar surface might somewhat affect sensitivity and specificity. It is important to ascertain that the microarrays can reproducibly meet the required levels of sensitivity and specificity before its application to clinical diagnostics.
\n\t\t\tThird, the reaction times for some applications can still be rather high, particularly for the microarrays. It is desirable to reduce these times further since clinical diagnostics often require a fast turn around time to minimize patient anxiety and to aid decision making in disease management.
\n\t\t\tFinally, with the advent of modern technologies, some of the multiplexing technologies discussed here might find themselves being slowly displaced. Sequencing is a method used to decipher the order of bases along a DNA. Traditionally slow, it is now possible to perform massively parallel sequencing on high-throughput platforms to speed up its rate. Known as next generation sequencing, thousands of sequences can now be generated at once, using commercial sequencers from companies such as Illumina (Solexa), Roche (454) and Applied Biosystems. Some of these platforms, like the SOLiD system from Applied Biosystems, can generate up to 60 gigabases of DNA sequence per run. With these advances in next generation sequencing comes the race for rapid and low cost full genome sequencing. The Archon X Prize for Genomics was established in October 2006 to award US$10 million to "the first Team that can build a device and use it to sequence 100 human genomes within 10 days or less, with an accuracy of no more than one error in every 100,000 bases sequenced, with sequences accurately covering at least 98% of the genome, and at a recurring cost of no more than $10,000 per genome”. As of January 2011, the prize is yet unclaimed. However, the possibility of being able to sequence an entire human genome accurately, cheaply and rapidly in future might supplant some of today’s multiplexing technologies like the DNA microarray.
\n\t\t\tIn summary, multiplexing capabilities in biosensors have come a long way and will continue to advance rapidly in the next decade, with a large number of companies pouring in large sums of monies into research and development. The ideal platform will be one offering high-throughput, rapid and low cost diagnostics. Whether that can be realised in the near future remains to be seen.
\n\t\tOver the past decades, low-frequency oscillatory modes have been a major concern to power system engineers [1]. These oscillatory modes ranging from 0.1 to 3 Hz tend to be poorly damped especially in moderately to heavily loaded systems that are equipped with high gain, fast-acting automatic voltage regulators (AVRs) [2, 3]. Generally, we distinguish two main oscillation modes: local and inter-area modes. Local modes (0.8–2 Hz) involve local generators oscillating against each other. On the other hand, inter-area modes are caused by groups of generators in one part of the system swinging against other groups in the interconnected power system having frequencies ranging from 0.1 Hz to 0.8 Hz. Compared to local modes, inter-area modes are generally the most critical modes that need to be damped [4, 5]. These modes are found in almost all interconnected power stems. If they are not adequately damped, the oscillations may sustain and grow, and this may lead to system blackout. Power system stabilizers (PSSs) have been proposed to modulate low-frequency oscillations and increase the damping of electromechanical modes [1, 2]. Tuning the PSS parameters is not a trivial task. Power utilities have preferred using conventional PSSs (CPSSs) designed around a nominal operating condition. The design of the CPSS is generally based on conventional control approaches such as root locus, phase compensation, and pole placement techniques [1, 2, 3, 4, 5]. However, since these approaches are not robust, the designed CPSS tends to deviate from optimal operation when the system experiences a range of changes away from the nominal operating conditions. Therefore, new design approaches are required to design a PSS that can operate optimally under a wide range of operating conditions [3, 6]. Evolutionary algorithms (EAs) such as genetic algorithms (GAs) [7, 8, 9, 10, 11, 12], differential evolution (DE) and its variants [13, 14], particle swarm optimization (PSO) [15], population-based incremental learning (PBIL) [16, 17, 18, 19], and breeder genetic algorithms (BGA) [11, 20, 21, 22, 23, 24] are efficient heuristic search methods that are capable of solving complex optimization problems. They do not require the objective function to have properties such as continuity, smoothness, and differentiability. They have many advantages over traditional optimization methods and have attracted considerable attention in recent years. Many of these methods have been applied to power system damping controller design with encouraging results. In particular, GAs have been extensively used to solve global optimization problems in academia and are now being accepted by some industries [9]. DE, PBIL, and BGA are easy to implement yet efficient and robust in solving optimization problems. Therefore, they are considered in this work.
GAs are biologically motivated adaptive systems based on natural selection and genetics. GAs are generally used to solve optimization problems by the exploitation of a random search [7, 8]. Although GAs are seen to be robust and powerful adaptive search mechanisms, they have several drawbacks [9]. One of these drawbacks is related to “genetic drift.” This phenomenon prevents GAs from maintaining diversity in their population. Other issues include the nonexistence of theoretical guidance for selecting optimal GA parameters such as population size, crossover, and mutation rates. Moreover, the natural selection approach used by GAs is not immune from failure [22]. Breeder genetic algorithm (BGA) has been proposed to cope with some of these drawbacks. It applies almost the same ideas as in GA, except that it is based on artificial selection as practiced in animal breeding rather than using natural selection based on Darwinian evolution [23, 24]. Artificial selection (selective breeding) refers to the intentional breeding for certain qualities or a combination of qualities [23]. This is in contrast with the natural selection that is the process whereby organisms survive and produce offspring by naturally adapting to their environment. Generally, individuals in BGA are represented as real numbers instead of binary or integers. The main advantage of using BGA over GA is its simplicity in the selection method and the fewer parameters. The major limitation of this algorithm is that there is a likelihood of premature convergence that could lead BGA to converge to the local optimum rather than the global one. To deal with the problem of premature convergence, an adaptive mutation is used [23, 24]. In this case, the mutation rate is not fixed but varies according to the convergence and performance of the population. This is the type of BGA that will be discussed later in this chapter.
Population-based incremental learning (PBIL) is a combination of GA and competitive learning. It extends the features of the evolutionary genetic algorithm (EGA) through the reexamination of the performance of the GA in terms of competitive learning [16, 17, 18, 19]. It was originally proposed by Baluja [18, 19]. In PBIL, the crossover operator is removed, and the role of the population is redefined. PBIL works on probabilistic vectors (PVs), which control the random bit strings generated by PBIL. The PVs are used to create other vectors through competitive learning. The PV is then updated to increase the likelihood of producing solutions corresponding to the current best individual. It has been shown that PBIL is simpler than GA and in many cases performs better than GA and has less overhead [11, 16, 17, 18, 19].
Differential evolution (DE) is a powerful stochastic optimizer whose search mechanism involves a differential mutation technique [12, 13, 25]. The algorithm is both simple and robust, with several variants exhibiting different tradeoffs between convergence speed and robustness. Most often DE outperforms its counterparts in efficiency and robustness [12, 13, 14, 25].
This chapter discusses the optimal design of power system stabilizers (PSSs) using four evolutionary algorithm (EAs) techniques, namely, genetic algorithms (GAs), breeder genetic algorithm (BGA) with adaptive mutation, population-based incremental learning (PBIL), and differential evolution (DE). For comparison purposes, the conventional PSS (CPSS) is also included in this work. The performance and effectiveness of the PSSs in damping the electromechanical modes are investigated using both frequency-domain analysis and time-domain simulations. Simulation results show that all the EA-based PSSs (GA-PSS, BGA-PSS, PBIL-PSS, and DE-PSS) perform better than the CPSS for all the operating conditions considered. Frequency domain simulation suggests that DE-PSS, PBIL-PSS, and BGA-PSS have similar performances in terms of the damping ratios that they provided. Time-domain simulations however suggest that overall, DE-PSS performs slightly better than PBIL-PSS and BGA-PSS in terms of undershoot and subsequent swings, albeit with a slightly large 1st swing overshoot. GA-PSS is shown to give the worst performance amount to the EAs. The chapter is organized as follows: Sections 2–4 present the overview of BGA, PBIL, and DE, respectively; Section 5 discusses the system model; Section 6 is concerned with the objective function; Section 7 presents the design of the PSSs; Section 8 discusses the simulation results; and the conclusions are presented in Section 9.
As discussed previously, breeder genetic algorithm (BGA) is similar to genetic algorithms (GAs), with the exception that it uses artificial selection and has fewer genetic parameters. Also, BGA uses real-valued representation as opposed to GAs that mainly use binary and sometimes floating or integer representation. BGA is a versatile and effective function optimizer. It has the advantage of being simpler than GA. To deal with the issue of premature convergence that is common with BGA, a modified version of BGA called adaptive mutation BGA is used in this work [11, 20, 23]. In the truncation selection method that has been adopted, the
Recombination is similar to a crossover in GAs. The adaptive mutation BGA proposed in this work allows various possible recombination methods to be used, each of them searching the space with a particular bias. Because we do not have prior knowledge as to which bias is likely to suit the optimization task, it is better to include several recombination methods and allow selection to do the elimination. Two recombination methods were used in this work: volume and line recombination [11].
In volume recombination, a random vector
Where
The child can be said to be located at a point inside the hyper box defined by the parents as shown in Figure 1.
Volume recombination.
In line recombination, a single uniformly random number
Where
As mentioned before, one of the main concerns in GA has been the issue of premature convergence. This issue is also encountered in the classical BGA. This problem can be reduced in BGA by using an adaptive mutation [11, 21, 23]. The diversity in the population is preserved by adding small, normally distributed zero-mean random numbers to each child before inserting it into the population. The random numbers have a certain standard deviation R [18]. The value of R should be selected carefully because it is critical in determining the convergence of the optimization. If the value of R is too small, the solution might result in premature convergence, while a high value of R might be detrimental to the optimal convergence of the algorithm [11, 23]. The adaptive mutation method proposed here allows us to determine the appropriate value of R. To achieve this, the population is divided into two halves, P1 and P2. P1 is assigned a mutation rate of double R (2R), while P2 is assigned a mutation rate of half R (R/2). The mutation rate R is adjusted depending on the performance of each half of the population (P1 or P2). If P1 gives better and fitter individuals, the mutation rate is increased by a certain percentage (10% in this case); similarly, if P2 produces better and fitter individuals, then the mutation rate gets reduced by a similar percentage. The pseudo code for BGA with adaptive learning can be found in [11, 23].
Population-based incremental learning (PBIL) is a combination of competitive learning derived from artificial neural networks and genetic algorithms [18, 19]. There is no crossover operator in PBIL, instead, the probability vector is updated using a solution with the highest fitness values [18]. The values of the probability vector are initially set to 0.5 to ensure that the probability of generating 0 or 1 is equal. As the search progresses, these values are moved away from 0.5, toward either 0.0 or 1.0.
Learning in PBIL is based on using the current probability distribution to create N individuals. The probability vector is updated using the best individual so far, thereby increasing the probability of producing solutions similar to the current best solutions. Learning rate is required to update the probability vector. The selection of the learning rate value should be made with care as it determines how fast or slow the prototype vector is shifted toward the best individuals. A larger rate speeds up convergence, but it reduces the function space to be searched, while a smaller rate will slow down the convergence, even though it increases the exploration of a bigger search space, thereby increasing the likelihood of better optimal solutions. The (positive) update rule of the probability vector is given as:
where PV is the probability vector, LR ∈ [0 1] is the learning rate, B is the best solution, and
Like in GA, the mutation is used in PBIL to maintain diversity in the population. Mutation in PBIL can be performed in two ways: either on the sample solutions generated or on the PV. In this study, the mutation is performed on the PV; a forgetting factor is used to relax the probability vector toward a neutral value of 0.5 [11, 16, 17] as shown in the equation below.
where
Differential evolution (DE) can be defined as a parallel direct search method that uses a population of points to search for a global minimum or maximum of a function over a wide search space [13]. It is a simple and efficient adaptive scheme for global optimization over continuous space. DE is designed to efficiently solve non-differentiable and nonlinear functions and yet retains its simplicity and good convergence to a global optimum [12]. Similar to most EAs, DE explores the search space by maintaining a population of candidate solutions and by using Darwinian evolution theory to direct its search toward prospective areas. The candidates with better fitness values survive and enter the next generation [12, 13, 14, 25]. The process continues until the termination criterion is satisfied. It should be mentioned that DE has proved to be one of the best among EAs. It was able to secure competitive rankings in CEC competitions [25]. One of the main advantages of DE over GA is the mutation scheme and the selection process. Unlike GAs where the best solutions are selected for the next generation, in DE, all solutions have an equal chance of being selected as parents independently of their fitness values.
In the context of DE, “mutation” is defined as a process of taking a small random sample of vectors from the current population and combining them algebraically to form a new vector, which is referred to as a
where
The above process is repeated
DE/best/1: This strategy resembles DE/rand/1, except that all mutants use the best vector in the current generation as the base vector:
where
This strategy has faster convergence than DE/rand/1, but often fails to reach the global optimum [12].
DE/best/2: This strategy uses two mutation differences to create a mutant vector:
where
DE/local-to-best/2: This strategy resembles DE/best/2 in that two mutation differences are used, but the base vector is randomly sampled and the “best” vector is used in one of the scaled differences:
This approach has similar convergence properties to DE/best/2 [13].
DE/rand/2: This strategy samples 5 random vectors in the current generation to form two random differences that are scaled and added to the base vector:
where,
DE/rand/2 has been used in this work due to our objective to appropriately tune the PSS with optimal time constants values for a robust performance.
In DE, “crossover” refers to the process of creating a new vector (called the
where
This process consists of choosing the individuals that will enter the next generation. DE employs a “one-to-one survivor selection,” which consists of comparing each trial vector to its corresponding target vector. Mathematically, the vector
This process ensures that the best vector at each index is retained. Furthermore, this also guarantees that the very best-so-far solution is kept. Once the selection is performed for all target vectors in the current generation
The power system considered in this paper is the two-area four-machine power system as shown in Figure 2 [1]. Each machine is represented by the detailed six-order differential equations. The machines are equipped with simple exciter systems of first-order differential equations as given in the
Two-area system model.
where,
Several operating conditions have been considered during the design stage of the controller. However, only three operating conditions are listed in Table 1 for simplicity. Case 1 is the nominal operating condition. At the nominal operating condition, approximately 146 MW is transferred from area 1 to area 2 via the two tie lines, with each line carrying half of the total power. Under these conditions, the load on bus 4 was 1137 MW, while the load on bus 14 was 1367 MW. Case 2 is the moderate load condition, where about 409 MW of real power is transferred from area 1 to area 2. For this case, the load on bus 4 was 967 MW, while the load on bus 14 was 1767 MW. case 3 is the heavy load condition (worst case scenario) where approximately 512 MW of power is transferred from area 1 to area 2. For this case, the load on bus 4 was 876 MW, while the load on bus 14 was 1876 MW. It should be mentioned that the system exhibits inter-area oscillatory modes due to the flow of power between the two areas that causes the two areas to oscillate against each other. In addition, two local area modes were also observed, one in each area. However, in this chapter, we will concentrate only on the inter-area modes since they are the most critical and difficult to control. Table 2 shows the open-loop eigenvalues of the inter-area modes. It can be seen that without PSSs, the inter-area modes were stable but poorly damped for case 1, with a damping ratio of 0.011. However, the system became unstable for case 2 and the instability became more pronounced for case 3 with damping ratios of −0.0057 and − 0.0130, respectively. This suggests that with the increase in active power transfer between the two areas, the oscillations have now increased making the system unstable. The frequency of oscillations of the inter-area modes ranges from 0.588 Hz to 0.634 Hz.
Case | Active power transfer from area 1 to area 2 [MW] | Number of tie-line between areas 1 and 2 | Load’s active power at bus 4 [MW] | Load’s active power at bus 14 [MW] |
---|---|---|---|---|
1 | 146 | 2 | 1137 | 1367 |
2 | 409 | 2 | 967 | 1767 |
3 | 512 | 2 | 876 | 1876 |
Selected operating conditions.
Case | Inter-area mode | Damping ratio (%) | Frequency of oscillations (Hz) |
---|---|---|---|
1 | −0.044 ± j3.98 | 1.10 | 0.634 |
2 | 0.022 ± j3.78 | −0.57 | 0.602 |
3 | 0.048 ± j3.69 | −1.30 | 0.588 |
Open-loop eigenvalues of the inter-area modes for selected operating conditions.
Therefore, a supplementary controller known as a power system stabilizer (PSS) will be required to damp the system’s oscillations. The block diagram of the PSS is shown in Figure A.1 in the
The objective is to optimize the parameters of the PSSs simultaneously such that the controllers can stabilize the system over a wide range of operating conditions. The parameters that were to be optimized are K (gain of the PSS) as well as the lead-lag time constants T1, T2, T3, and T4. The objective function used was to maximize the lowest damped ratio over a wide range of operating conditions. This objective function was used for GA, BGA, PBIL, and DE. The objective function is given as:
where
where
In total 10 PSSs parameters were optimized (i.e., 5 parameters for each area) for generators 1–4. The parameters that were optimized are K, T1, T2, T3, and T4. The washout time constant (Tw) was set at 10 seconds and was not optimized since Tw is not critical to the design. The following parameter domain constraints were considered when designing the PSSs.
where
For comparison purposes, a CPSS was also designed using the phase compensation technique. Details can be found in [1, 2].
The parameters used in the optimization for GAs, BGA, PBIL, and DE are shown in Table 3.
Parameters | GA | BGA | PBIL | DE |
---|---|---|---|---|
Population | 100 | 100 | 100 | 50 |
Generation | 120 | 120 | 500 | 180 |
Selection | Normal geometric | Truncation selection | — | Greedy |
Crossover/Recombination | Arithmetic | Line and volume | — | Binomial (CR: 0.95) |
Mutation | Nonuniform | Adaptive random (initial | Forgetting Factor (FF:0.005) | DE/rand/2 (F: 0.95) |
Learning rate (LR) | — | — | 0.1 | — |
Parameters used in GA, BGA, PBIL, and DE.
An observation of the parameters given below in Table 3 shows that PBIL uses few parameters. There is no crossover or selection in PBIL compared to BGA, GA, and DE. In addition, 500 generations were used in the PBIL optimization to allow for adequate learning to take place within the optimization. This is because PBIL that works by learning from the previous best and trying to find the very best individual takes time to explore the search space. Another difference is the way in which the initial population is generated. In GA, BGA, and DE, the initial population is selected randomly, while in PBIL the role of the population is redefined using probability vectors (PV). It should be mentioned that a population size of 50 was also tested in PBIL and it was found that it yielded similar results to the population size of 100. However, in this work a population of 100 was used.
Figures 3–6 show the fitness value (minimum damping ratio) of the system when GA, BGA, PBIL, and DE are used in the optimization. The final value obtained from the GA optimization is 0.1867 as compared to 0.205, 0.2095, and 0.227 for BGA, PBIL, and DE, respectively. As discussed previously, GA and BGA were run for 120 generations, DE for 180 generations, while the PBIL was run for 500 generations. Since a smaller population was used for DE, it was decided to increase its generations. The reason for using 500 generations in PBIL is that it starts to settle only around 300 generations and therefore there is a need for a longer simulation period.
Fitness value curve from the GA optimization.
Fitness value curve from the BGA optimization.
Fitness value curve from the PBIL optimization.
Fitness value curve from DE optimization.
Table 4 shows the inter-area modes for the system with the PSSs. It can be seen that with the PSSs, the inter-area modes are very well damped as compared to the open-loop system in Table 2. CPSS performs adequately for the nominal operating condition. The damping ratios provided by the CPSS under the three cases 1, 2, and 3, are 0.1666, 0.1442, and 0.1339, respectively. BGA-PSS provides a damping ratio of 0.2321, 0.2393, and 0.2412 for cases 1, 2, and 3, respectively. On the other hand, the PBIL-PSS and DE-PSS provide a damping ratio of 0.2341 and 0.2377, respectively, for case 1; 0.2387 and 0.2321, respectively, for case 2; 0.2385 and 0.23, respectively, for case 3.
Case | CPSS | GA-PSS | BGA-PSS | PBIL-PSS | DE-PSS |
---|---|---|---|---|---|
1 | −0.62 ± j3.67 (0.1666) | −0.80 ± j3.86 (0.2029) | −0.89 ± j3.73 (0.2321) | −0.91 ± j3.78 (0.2341) | −0.94 ± j3.84 (0.2377) |
2 | −0.50 ± j3.43 (0.1442) | −0.75 ± j3.65 (0.2013) | −0. 86 ± j3.49 (0.2393) | −0.87 ± j3.54 (0.2387) | −0.89 ± j3.73 (0.2321) |
3 | −0.45 ± j3.33 (0.1339) | −0.72 ± j3.54 (0.1993) | −0. 84 ± j3.38 (0.2412) | −0.84 ± j3.42 (0.2385) | −0.87 ± j3.68 (0.2300) |
Inter-area modes and the respective damping ratios in brackets.
It is observed that PBIL-PSS, DE-PSS, and BGA-PSS provide similar damping ratios to the system for operating condition considered. In case 1, DE provides the best damping ratio, whereas BGA provides the best damping ratios for cases 2 and 3. Among the evolutionary algorithm-based PSSs, GA-PSS provides the lowest damping ratios of 0.2029, 0.2013, and 0.1993 for cases 1, 2, and 3, respectively.
Figure 7 shows the spread of the eigenvalues for the system equipped with the different PSSs. CPSS is the lowest compared to the damping provided by all the other EA-based PSSs. It is observed that among the EA-based PSSs, GA-PSS provides the least damping. The damping provided by the PBIL-PSS, BGA-PSS, and DE-PSS is very similar and higher than that provided by GA-PSS.
Spread of the eigenvalues for the different PSSs-nominal condition.
To investigate the performance of the PSSs under small disturbance, a small disturbance of 5% step response is applied to the reference voltage of generator 2 in area 1. The responses of the active power output of generators 2 and 3 are are shown in Figures 8–13 for cases 1, 2, and 3, respectively. It can be seen that the system is well-damped across all three operating conditions when it is equipped with DE-PSS, BGA-PSS, GA-PSS, and PBIL-PSS. The CPSS is seen to give the worst performance.
Response of G2 under the 5% step change in Vref of G2 – Case 1.
Response of G3 under the 5% step change in Vref of G2 – Case 1.
Response of G2 under the 5% step change in Vref of G2 – Case 2.
Response of G3 under the 5% step change in Vref of G2 – Case 2.
Response of G2 under the 5% step change in Vref of G2 – Case 3.
Response of G3 under the 5% step change in Vref of G2 – Case 3.
Figures 8 and 9 show the active power output responses of generators 2 and 3, respectively, for case 1. The system equipped with GA-PSS, BGA-PSS, DE-PSS, and PBIL-PSS has a similar settling time of approximately 4 sec., whereas the system equipped with CPSS has a longer settling time of around 6 sec. DE-PSS is seen to give the best performance in terms of undershoot and the amplitude of subsequent swings, albeit with a relatively large 1st swing overshoot as seen in Figure 8. It is observed that DE-PSS gives a large 1st swing overshoot in Figure 8. The relatively large 1st swing overshoot can be attributed to the high gain of the controller. Note that DE-PSS’s gain has almost reached the allowable maximum value [20]. The performance of BGA-PSS is comparable to that of PBIL-PSS. Compared with other EA-based PSS, GA-PSS gives the worst performance. However, it performed better than the CPSS. In Figure 9, BGA-PSS is seen to give a slightly high 1st swing overshoot but the subsequent swings are well-damped. Overall, CPSS is seen to give the worst performance.
Figures 10 and 11 show the active power responses of generators 2 and 3, respectively, for case 2. It can be seen that the CPSS has a longer settling time of around 7 sec. Compared to a settling time of around 4 sec. for the EA-based PSSs. This suggests that the oscillations have increased in case 2 compared to case 1. The EA-based PSSs are able to damp the oscillations adequately when compared to the CPSS. In terms of undershoot and subsequent swings, DE-PSS is seen to give the best responses albeit with a relatively large 1st swing overshoot as seen in Figure 10. The performances of BGA-PSS and PBIL-PSS are similar. Overall, CPSS gives the worst performance followed by GA-PSS.
Figures 12 and 13 show the active power responses of generators 2 and 3, respectively, for case 3. It can be seen that the system response is similar to case 2 except that the oscillations have now increased as can be seen in the system’s responses. The system equipped with the CPSS settled around 10 sec. (see Figure 13). It can be seen that the performance of the CPSS has now deteriorated significantly. On the other hand, the performances of GA-PSS, BGA-PSS, PBIL-PSS, and DE-PSS have deteriorated only slightly. This means that the EA-based PSSs are more robust. In terms of settling time, the EA-based PSSs have similar settling times of approximately 6.5 sec., which is comparable to case 2. Although DE-PSS has a larger 1st swing overshoot as seen in Figure 12, it gave the best responses in terms of undershoot and subsequent swing amplitudes, followed by BGA-PSS and PBIL-PSS. The performance of GA-PSS although better than that of CPSS is not as good as the other EA-based PSS.
A large disturbance was considered by applying a three-phase fault to the system at bus 3. The fault was cleared by removing one of the transmission lines between bus 3 and bus 13. The fault was applied for 0.1 seconds. After the fault was cleared, the faulted line was removed and the system settled to a different operating condition with only one tie line transmitting power from area 1 to area 2. This means the system is weaker after the fault was cleared compared to its state before the fault. Figures 14 and 15 show the electric power output for generator 3 for case 1 and case 2, respectively. The responses for case 3 are not shown because the system was unable to survive this large disturbance after the fault was removed. It can be seen from Figure 14 (case 1) that the output power of generator 3 has a high overshoot in the first swing after the fault was cleared but settled down quickly after a few seconds, with all the PSSs providing adequate damping to stabilize the system. However, when the power that was transferred from area 1 to area 2 increased,the CPSS was unable to maintain the stability of the system as seen in Figure 15 (case 2). On the other hand, all the EA-based PSSs were able to stabilize the system, which suggests that they are more robust than the CPSS.
Electric power output of generator 3 following a three-phase fault on bus 3 for case 1.
Electric power output of generator 3 following a three-phase fault on bus 3 for case 2.
PSS block diagram.
An optimal PSS design for small signal stability improvement of a multi-machine power system using four evolutionary algorithms (GA, BGA, PBIL, and DE) has been presented. Frequency-domain and time-domain simulations have been presented to show the effectiveness of the EA-based PSSs in damping low-frequency oscillations. It is shown that in the frequency domain, the performances of BGA-PSS, PBIL-PSS, and DE-PSS are comparable and better than that of the GA-PSS for all cases investigated. However, time-domain simulations show that DE-PSS performs better than BGA-PSS and PBIL-PSS in terms of undershoot and subsequent swings albeit with a relatively large 1st swing overshoot. This overshoot could be attributed to the high gain of the controller. One way to deal with this overshoot is to reduce the gain of the controller; however, this could also affect the damping. GA-PSS is shown to give the worst performance among the EA-based PSSs, but it performed better than the CPSS. In designing the PBIL-PSS, more generations were required compared to GA-PSS, BGA-PSS, and DE-PSS. Since PBIL works by learning from the previous best individual, it takes time for the algorithm to explore the search space. Compared to the EA-based PSS, the CPSS that was designed using the conventional method has been shown to perform poorly and is not robust. Further research will be done in the direction of improving the EAs algorithms by self-adapting the genetic parameters and using multi-objective functions in the optimization.
This research was funded in part by the National Research Foundation (NRF) of South Africa, Grant UID 118550.
where
where
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The chapter will analyze the current development and present the main shifts due to it.",book:{id:"7830",slug:"consumer-behavior-and-marketing",title:"Consumer Behavior and Marketing",fullTitle:"Consumer Behavior and Marketing"},signatures:"Urška Starc Peceny, Jurij Urbančič, Simon Mokorel, Vesna Kuralt and Tomi Ilijaš",authors:[{id:"217400",title:"Dr.",name:"Urška",middleName:null,surname:"Starc Peceny",slug:"urska-starc-peceny",fullName:"Urška Starc Peceny"},{id:"286485",title:"Dr.",name:"Vesna",middleName:null,surname:"Kuralt",slug:"vesna-kuralt",fullName:"Vesna Kuralt"},{id:"286486",title:"Dr.",name:"Jurij",middleName:null,surname:"Urbančič",slug:"jurij-urbancic",fullName:"Jurij Urbančič"},{id:"286632",title:"Dr.",name:"Simon",middleName:null,surname:"Mokorel",slug:"simon-mokorel",fullName:"Simon Mokorel"},{id:"288765",title:"M.Sc.",name:"Tomi",middleName:null,surname:"Ilijas",slug:"tomi-ilijas",fullName:"Tomi Ilijas"}]},{id:"59751",doi:"10.5772/intechopen.74527",title:"Theory of New Product Development and Its Applications",slug:"theory-of-new-product-development-and-its-applications",totalDownloads:5525,totalCrossrefCites:6,totalDimensionsCites:9,abstract:"A product which can be a physical object or a service should be functional and emotional to satisfy the customer’s need, and to offer value, be delivered as the way customer demanded. 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These theories can be classified as (i) product-service systems, (ii) the Kano model, (iii) conjoint analysis, (iv) the product value matrix and (v) quality function deployment.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Esen Gurbuz",authors:[{id:"227728",title:"Dr.",name:"Esen",middleName:null,surname:"Gurbuz",slug:"esen-gurbuz",fullName:"Esen Gurbuz"}]},{id:"69005",doi:"10.5772/intechopen.89282",title:"Reputation Management",slug:"reputation-management",totalDownloads:589,totalCrossrefCites:4,totalDimensionsCites:9,abstract:"The problem of building a reputation in the traditional brick-and-mortar world has been known for centuries; we know how to build a good reputation, or more precisely how to help in building a good reputation. Even if we are a target of various half-truths and slanders, we are aware that if they are only spoken words, their durability over time is quickly fleeting. However, written text is different from spoken words; its life durability over time is much longer. In our chapter, we bring the overview of what happens if we must suddenly face the problem of building and maintaining a good reputation in the virtual world of the Internet. Thus, the objective of this chapter is to summarize and present the state of the art in the field of reputation; it consists of the definition of basic terminology and then offers the well-arranged theoretical determination of the problem of reputation in both the traditional brick-and-mortar and virtual world.",book:{id:"9044",slug:"promotion-and-marketing-communications",title:"Promotion and Marketing Communications",fullTitle:"Promotion and Marketing Communications"},signatures:"František Pollák, Peter Dorčák and Peter Markovič",authors:null},{id:"56485",doi:"10.5772/intechopen.70161",title:"Citrus Value Chain(s): A Survey of Pakistan Citrus Industry",slug:"citrus-value-chain-s-a-survey-of-pakistan-citrus-industry",totalDownloads:2227,totalCrossrefCites:6,totalDimensionsCites:8,abstract:"Pakistan is producing more than 30 types of different fruits of which citrus fruit is leading among all fruit and constitutes about 30% of total fruit production in the country. Above 90% of citrus fruits are produced in Punjab province and distributed through different value chains in domestic as well as in international markets. A large part of citrus fruit produced in Pakistan is mostly consumed locally without much value addition; however, 10–12% of total production is exported after value addition. The value chains are very diverse, and a number of different players actively participate in these chains, which ultimately decide the destination of citrus fruit in these supply chain(s). Knowing all these facts, the main aim of this research is to identify different value chains of citrus fruit (Kinnow) in Pakistan and also to identify and discuss the role and function of different value chain players in the citrus industry in Pakistan. A survey involving of different players of Pakistan’s citrus industry was conducted in 2013–2014 to better understand the citrus value chain(s). Using a convenience sampling technique, a total of 245 respondents were interviewed during a period of 4–5 months from three leading citrus-producing districts. It was found that citrus value chains can be classified into two major types: unprocessed citrus value chain and processed citrus value chains. It was also found that in the past, a large number of citrus growers were involved in preharvest contracting for their orchards and only a small number of citrus growers sold their orchards directly into local and foreign markets. The proportion has been gradually changed now and growers are becoming progressive and more market oriented.",book:{id:"5762",slug:"agricultural-value-chain",title:"Agricultural Value Chain",fullTitle:"Agricultural Value Chain"},signatures:"Muhammad Imran Siddique and Elena Garnevska",authors:[{id:"181547",title:"Dr.",name:"Elena",middleName:null,surname:"Garnevska",slug:"elena-garnevska",fullName:"Elena Garnevska"},{id:"196724",title:"Dr.",name:"Muhammad Imran",middleName:null,surname:"Siddique",slug:"muhammad-imran-siddique",fullName:"Muhammad Imran Siddique"}]},{id:"64050",doi:"10.5772/intechopen.81281",title:"Recycling of Polymeric Composite Materials",slug:"recycling-of-polymeric-composite-materials",totalDownloads:1650,totalCrossrefCites:6,totalDimensionsCites:8,abstract:"This chapter treats studies about the methods and technologies used to recycle the polymeric composite materials and develop new recipes using waste of polymer composite materials resulted from recycling. Composite materials obtained from recycling are presented, with a complete recovery of waste glass fibers. Also, the mechanical properties for new structures of polymeric composite materials, containing additional materials were presented. These were obtained from the recycling of composite waste. A morphology analysis of fracture area of composites samples was done. At present, the polymeric composite materials present a great scientific and technical interest, which justify both the development of research in this field, and the expansion of production of such materials. The author treats aspects regarding a current problem due to the large number of polymeric composite materials waste, and reduced of environmental impact. This field is representing one of the top viable research directions.",book:{id:"7489",slug:"product-lifecycle-management-terminology-and-applications",title:"Product Lifecycle Management",fullTitle:"Product Lifecycle Management - Terminology and Applications"},signatures:"Emilia Sabău",authors:[{id:"248080",title:"Dr.Ing.",name:"Emilia",middleName:null,surname:"Sabau",slug:"emilia-sabau",fullName:"Emilia Sabau"}]}],mostDownloadedChaptersLast30Days:[{id:"59152",title:"Marketing Strategies for the Social Good",slug:"marketing-strategies-for-the-social-good",totalDownloads:1669,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Social network sites (SNS) have proven to be a good environment to promote and sell goods and services, but marketing is more than creating commercial strategies. Social marketing strategies can also be used to promote behavioral change and help individuals transform their lives, achieve well-being, and adopt prosocial behaviors. In this chapter, we seek to analyze with a netnographic study, how SNS are being employed by nonprofits and nongovernment organizations (NGOs) to enable citizens and consumers to participate in different programs and activities that promote social transformation and well-being. A particular interest is to identify how organizations are using behavioral economic tactics to nudge individuals and motivate them to engage in prosocial actions. By providing an understanding on how SNS can provide an adequate environment for the design of social marketing strategies, we believe our work has practical implications both for academicians and marketers who want to contribute in the transformation of consumer behavior and the achievement of well-being and social change.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Alicia De La Pena",authors:[{id:"196878",title:"Dr.",name:"Alicia",middleName:null,surname:"De La Pena",slug:"alicia-de-la-pena",fullName:"Alicia De La Pena"}]},{id:"66643",title:"Social Media, Consumer Behavior, and Service Marketing",slug:"social-media-consumer-behavior-and-service-marketing",totalDownloads:3585,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"This study examined the impact of social media platforms and brand awareness in relation to the consumer decision-making and buying behavior patterns influenced by social media. It also depicts how companies can effectively make use of social media platforms as marketing strategy tools in business performances. Social media platforms seem to be increasingly and effectively bringing brand awareness and influence consumers’ purchase decision-making and later on realize repeat purchases that bring about customer loyalty. Social media also has some influence to both the consumer and the marketers and is becoming the most welcomed online selling point by the millennial. Marketers/producers have noticed the rise in social media consumers; however, most of the business entities have not yet utilized social media to its fullest in their marketing activities and business strategies and performances. The study highlights the benefits of using social media platforms and brand awareness strategies that can be utilized through the online social media systems and gives a contemporary research gap, in how frequent businesses are engaging with social media.",book:{id:"7830",slug:"consumer-behavior-and-marketing",title:"Consumer Behavior and Marketing",fullTitle:"Consumer Behavior and Marketing"},signatures:"Abigail Chivandi, Michael Olorunjuwon Samuel and Mammo Muchie",authors:[{id:"267975",title:"Dr.",name:"Abigail",middleName:null,surname:"Chivandi",slug:"abigail-chivandi",fullName:"Abigail Chivandi"},{id:"275118",title:"Prof.",name:"Michael Olorunjuwon",middleName:null,surname:"Samuel",slug:"michael-olorunjuwon-samuel",fullName:"Michael Olorunjuwon Samuel"},{id:"275121",title:"Prof.",name:"Mammo",middleName:null,surname:"Muchie",slug:"mammo-muchie",fullName:"Mammo Muchie"}]},{id:"59751",title:"Theory of New Product Development and Its Applications",slug:"theory-of-new-product-development-and-its-applications",totalDownloads:5518,totalCrossrefCites:6,totalDimensionsCites:9,abstract:"A product which can be a physical object or a service should be functional and emotional to satisfy the customer’s need, and to offer value, be delivered as the way customer demanded. Also, it has to include other specific elements like providing customer services. New product is the result of a creative and unique idea that is able to make consumers satisfied. In the process of new product development, it should not be thought that the change will only be on product physically but also on every aspect of the product. The difference between ideas increases production of different goods. The different kind of goods can positively affect the customers’ opinion about a business. When a new business starts to produce a product which satisfies customer’s need, then the demand of competitor’s product which was already in the market may be decreased. Establishment of new product development (NPD) departments and their direct influence in the production process is crucial for businesses. They can determine demand and needs of consumers by applying different theories. These theories can be classified as (i) product-service systems, (ii) the Kano model, (iii) conjoint analysis, (iv) the product value matrix and (v) quality function deployment.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Esen Gurbuz",authors:[{id:"227728",title:"Dr.",name:"Esen",middleName:null,surname:"Gurbuz",slug:"esen-gurbuz",fullName:"Esen Gurbuz"}]},{id:"63867",title:"Product Development and Management Strategies",slug:"product-development-and-management-strategies",totalDownloads:3007,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The chapter seeks to discuss and describe the concept of product from the marketing perspective, how companies come about new product, product development options, and the various strategies available to a company to manage new and existing products. It is a conceptual paper which reviews relevant literatures from various sources. In essence, the proposed chapter will be divided into three main sections. Section 1 will introduce the concept of product, its meaning, and core components from the marketing point of view. It will also contain discussion on the various connotations of the term product, what constitute a new product, and new product adoption. Section 2 will dwell on the options available to a company on how to come up with new products including mergers, acquisition, and licensing, franchising, and proactive new product development. It will also explain the two product development strategies that companies adopt in the market place. The last section will discuss the product management strategies available to a firm either from the product life cycle way or the individual product management strategies or both. At the end, conclusions and the general context of the paper are drawn.",book:{id:"7489",slug:"product-lifecycle-management-terminology-and-applications",title:"Product Lifecycle Management",fullTitle:"Product Lifecycle Management - Terminology and Applications"},signatures:"Musa Gambo Kasuwar Kuka",authors:[{id:"248588",title:"Ph.D.",name:"Musa",middleName:null,surname:"Gambo K.K.",slug:"musa-gambo-k.k.",fullName:"Musa Gambo K.K."}]},{id:"64024",title:"Product Design Process and Methods",slug:"product-design-process-and-methods",totalDownloads:2846,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Suitable design procedures and methods will lead to twice the result with half the work. Hence, good products need a good beginning in the design process. The design procedure is the basis for guiding the steps of design process, while the design method is the guarantee for effectively developing the design process and improving its quality. A clear and reasonable process can lead to a simple and smooth way in design, while the proper use of creating techniques can let the designer find a better way to solve the problems in a wider range, so as to develop and design a good product.",book:{id:"7489",slug:"product-lifecycle-management-terminology-and-applications",title:"Product Lifecycle Management",fullTitle:"Product Lifecycle Management - Terminology and Applications"},signatures:"Jinxia Cheng",authors:[{id:"251440",title:"Mrs.",name:"Jinxia",middleName:null,surname:"Cheng",slug:"jinxia-cheng",fullName:"Jinxia Cheng"}]}],onlineFirstChaptersFilter:{topicId:"74",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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261",scope:"Modern physiology requires a comprehensive understanding of the integration of tissues and organs throughout the mammalian body, including the cooperation between structure and function at the cellular and molecular levels governed by gene and protein expression. While a daunting task, learning is facilitated by identifying common and effective signaling pathways mediated by a variety of factors employed by nature to preserve and sustain homeostatic life. \r\nAs a leading example, the cellular interaction between intracellular concentration of Ca+2 increases, and changes in plasma membrane potential is integral for coordinating blood flow, governing the exocytosis of neurotransmitters, and modulating gene expression and cell effector secretory functions. Furthermore, in this manner, understanding the systemic interaction between the cardiovascular and nervous systems has become more important than ever as human populations' life prolongation, aging and mechanisms of cellular oxidative signaling are utilised for sustaining life. \r\nAltogether, physiological research enables our identification of distinct and precise points of transition from health to the development of multimorbidity throughout the inevitable aging disorders (e.g., diabetes, hypertension, chronic kidney disease, heart failure, peptic ulcer, inflammatory bowel disease, age-related macular degeneration, cancer). With consideration of all organ systems (e.g., brain, heart, lung, gut, skeletal and smooth muscle, liver, pancreas, kidney, eye) and the interactions thereof, this Physiology Series will address the goals of resolving (1) Aging physiology and chronic disease progression (2) Examination of key cellular pathways as they relate to calcium, oxidative stress, and electrical signaling, and (3) how changes in plasma membrane produced by lipid peroxidation products can affect aging physiology, covering new research in the area of cell, human, plant and animal physiology.",coverUrl:"https://cdn.intechopen.com/series/covers/10.jpg",latestPublicationDate:"July 20th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:14,editor:{id:"35854",title:"Prof.",name:"Tomasz",middleName:null,surname:"Brzozowski",slug:"tomasz-brzozowski",fullName:"Tomasz Brzozowski",profilePictureURL:"https://mts.intechopen.com/storage/users/35854/images/system/35854.jpg",biography:"Prof. Dr. Thomas Brzozowski works as a professor of Human Physiology and is currently Chairman at the Department of Physiology and is V-Dean of the Medical Faculty at Jagiellonian University Medical College, Cracow, Poland. His primary area of interest is physiology and pathophysiology of the gastrointestinal (GI) tract, with the major focus on the mechanism of GI mucosal defense, protection, and ulcer healing. He was a postdoctoral NIH fellow at the University of California and the Gastroenterology VA Medical Center, Irvine, Long Beach, CA, USA, and at the Gastroenterology Clinics Erlangen-Nuremberg and Munster in Germany. He has published 290 original articles in some of the most prestigious scientific journals and seven book chapters on the pathophysiology of the GI tract, gastroprotection, ulcer healing, drug therapy of peptic ulcers, hormonal regulation of the gut, and inflammatory bowel disease.",institutionString:null,institution:{name:"Jagiellonian University",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"10",title:"Animal Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",isOpenForSubmission:!0,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"11",title:"Cell Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/11.jpg",isOpenForSubmission:!0,editor:{id:"133493",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",profilePictureURL:"https://mts.intechopen.com/storage/users/133493/images/3091_n.jpg",biography:"Prof. Dr. Angel Catalá \r\nShort Biography Angel Catalá was born in Rodeo (San Juan, Argentina). He studied \r\nchemistry at the Universidad Nacional de La Plata, Argentina, where received aPh.D. degree in chemistry (Biological Branch) in 1965. From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). W inner of the Bimbo PanAmerican Nutrition, Food Science and Technology Award 2006 and 2012, South AmericaHuman Nutrition, Professional Category. 2006 award in pharmacology, Bernardo\r\nHoussay, in recognition of his meritorious works of research. Angel Catalá belongto the Editorial Board of Journal of lipids, International Review of Biophysical ChemistryFrontiers in Membrane Physiology and Biophysics, World Journal oExperimental Medicine and Biochemistry Research International, W orld Journal oBiological Chemistry, Oxidative Medicine and Cellular Longevity, Diabetes and thePancreas, International Journal of Chronic Diseases & Therapy, International Journal oNutrition, Co-Editor of The Open Biology Journal.",institutionString:null,institution:{name:"National University of La Plata",institutionURL:null,country:{name:"Argentina"}}},editorTwo:null,editorThree:null},{id:"12",title:"Human Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/12.jpg",isOpenForSubmission:!0,editor:{id:"195829",title:"Prof.",name:"Kunihiro",middleName:null,surname:"Sakuma",slug:"kunihiro-sakuma",fullName:"Kunihiro Sakuma",profilePictureURL:"https://mts.intechopen.com/storage/users/195829/images/system/195829.jpg",biography:"Professor Kunihiro Sakuma, Ph.D., currently works in the Institute for Liberal Arts at the Tokyo Institute of Technology. He is a physiologist working in the field of skeletal muscle. He was awarded his sports science diploma in 1995 by the University of Tsukuba and began his scientific work at the Department of Physiology, Aichi Human Service Center, focusing on the molecular mechanism of congenital muscular dystrophy and normal muscle regeneration. His interest later turned to the molecular mechanism and attenuating strategy of sarcopenia (age-related muscle atrophy). His opinion is to attenuate sarcopenia by improving autophagic defects using nutrient- and pharmaceutical-based treatments.",institutionString:null,institution:{name:"Tokyo Institute of Technology",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"331519",title:"Dr.",name:"Kotomi",middleName:null,surname:"Sakai",slug:"kotomi-sakai",fullName:"Kotomi Sakai",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000031QtFXQA0/Profile_Picture_1637053227318",biography:"Senior researcher Kotomi Sakai, Ph.D., MPH, works at the Research Organization of Science and Technology in Ritsumeikan University. She is a researcher in the geriatric rehabilitation and public health field. She received Ph.D. from Nihon University and MPH from St.Luke’s International University. Her main research interest is sarcopenia in older adults, especially its association with nutritional status. Additionally, to understand how to maintain and improve physical function in older adults, to conduct studies about the mechanism of sarcopenia and determine when possible interventions are needed.",institutionString:null,institution:{name:"Ritsumeikan University",institutionURL:null,country:{name:"Japan"}}},editorThree:null},{id:"13",title:"Plant Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/13.jpg",isOpenForSubmission:!0,editor:{id:"332229",title:"Prof.",name:"Jen-Tsung",middleName:null,surname:"Chen",slug:"jen-tsung-chen",fullName:"Jen-Tsung Chen",profilePictureURL:"https://mts.intechopen.com/storage/users/332229/images/system/332229.png",biography:"Dr. Jen-Tsung Chen is currently a professor at the National University of Kaohsiung, Taiwan. He teaches cell biology, genomics, proteomics, medicinal plant biotechnology, and plant tissue culture. Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. He has published more than ninety scientific papers and serves as an editorial board member for Plant Methods, Biomolecules, and International Journal of Molecular Sciences.",institutionString:"National University of Kaohsiung",institution:{name:"National University of Kaohsiung",institutionURL:null,country:{name:"Taiwan"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:16,paginationItems:[{id:"82135",title:"Carotenoids in Cassava (Manihot esculenta Crantz)",doi:"10.5772/intechopen.105210",signatures:"Lovina I. Udoh, Josephine U. Agogbua, Eberechi R. Keyagha and Itorobong I. 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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:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{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:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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