\r\n\tThe present book intends to provide to the reader a comprehensive overview of the state of art in empathy studies, embracing the different theoretical points of view and illustrating the advanced research such as the application of new technologies to promote perspective-taking. The critical aspects and the future directions of the study on empathy will also be presented.
",isbn:"978-1-80356-612-2",printIsbn:"978-1-80356-611-5",pdfIsbn:"978-1-80356-613-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"4c1042dfe15aa9cea6019524c4cbff38",bookSignature:"Ph.D. Sara Ventura",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11443.jpg",keywords:"Theoretical Model, Skill, Perspective Taking, Training Programs, Practical Implications, Advanced Research, Future Directions, Virtual Reality, Augmented Reality, New Trends, Assistive Technology",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 1st 2022",dateEndSecondStepPublish:"June 8th 2022",dateEndThirdStepPublish:"August 7th 2022",dateEndFourthStepPublish:"October 26th 2022",dateEndFifthStepPublish:"December 25th 2022",remainingDaysToSecondStep:"22 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Passionate researcher in the application of new technologies to psychological treatments, neuro-rehabilitation, human behavior, and the evolution of the human-computer interaction. In 2017 Dr. Ventura won a competitive grant (Santiago Grisolia) at the University of Valencia at LABPSITEC group, where she was awarded her Ph.D. degree, supervised by Prof. Rosa Baños at the University of Valencia, and co-directed by Prof. Giuseppe Riva of the Catholic University of Milan.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"227763",title:"Ph.D.",name:"Sara",middleName:null,surname:"Ventura",slug:"sara-ventura",fullName:"Sara Ventura",profilePictureURL:"https://mts.intechopen.com/storage/users/227763/images/system/227763.jpg",biography:"Sara Ventura gained a B.Sc in Psychology at the University of Padua (Italy) in 2013 and an M.Sc. in Ergonomic Psychology at the Catholic University of Milan (Italy) in 2015. In 2016, she carried out a postgraduate training at Universidad Nacional Autónoma de Mexico (Mexico) at the Ciberpsychology lab, working on a rehabilitation protocol for people with acquired brain injury through Virtual Reality. In 2020, Sara gained the Ph.D. in Clinical Psychology at University of Valencia (Spain) working with the LabPsitec group and focusing her research on the study of embodiment and empathy with the support of Virtual Reality. Actually, she is working both with Alma Mater Studiorum – University of Bologna (Italy), and the University of Valencia (Spain) on the fields of embodiment, stroke rehabilitation, empathy and patient care. Her research interests mainly focus on the adoption of new technologies, particularly Virtual/Augmented Reality and Artificial Intelligence for the psycho-social wellbeing with clinical and non-clinical populations, the study of human-computer interaction, and the user experience. She is the author of several scientific papers and various presentations at national and international conferences.",institutionString:"University of Valencia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Valencia",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"21",title:"Psychology",slug:"psychology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"455410",firstName:"Dajana",lastName:"Jusic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/455410/images/20500_n.jpeg",email:"dajana.j@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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1. Introduction
Biological locomotion is defined as the movement of animals from one place to another place with the use of its whole body or appendages. The modes of locomotion existing in nature are classified as self-propelled, e.g., swimming (fish, shark, whale, seal, and dolphin), running (cheetah, wolf, and zebra), flying (swallow, hawk, owl, crow, and bat), jumping, slither (snakes and other reptiles), hopping (kangaroo, rabbit, and grasshopper) and gliding (flying squirrel) are mentioned in Table 1. This is termed as active locomotion, where it uses its own body muscles or legs for locomotion. Passive locomotion is defined as animal species that depend on the environment for transportation, e.g., beetles and spiders. The entire biological locomotion is classified into four types—aquatic locomotion (underwater or surface swimming organisms), terrestrial locomotion (moving on the ground or any other surface, tree-dwelling), fossorial locomotion (underground) and aerial locomotion (birds flying in the air). These animals use water and air as its medium for its locomotion. Flying fish and diving birds use the air-water medium for its locomotion. The classification of biological locomotion in nature is shown in Figure 1. The use of fossorial and terrestrial-inspired biorobots is currently used in the exploration of Martian atmospheres, robots in various defense sectors, etc. The aquatic locomotion is currently used in designed and development of autonomous surface ships and underwater vehicles for deep-sea exploration. The aerial locomotion is used for developing flapping micro aerial vehicles and reduction of noise vibrations from wind turbine applications.
Figure 1.
Locomotion in nature. (a) Fossorial locomotion (underground) (e.g., Cape ground squirrel and mole salamanders). (b) Terrestrial locomotion—walking, hopping, running, crawling (e.g., cheetah, kangaroo, zebra, and snails). (c) Aquatic locomotion—lift based, undulation (body-caudal fin and median-paired fin modes), jet mode propulsion (e.g., turtles, dolphins, rays, and jellyfish). (d) Aerial locomotion—lift-based propulsion (e.g., bats, parrots, and eagles).
1.1 Lift-based propulsion
In lift-based propulsion, flapping foils or wings are used to create lift and horizontal thrust force by moving through water. It creates a pressure difference between the top and bottom surfaces of the flapping wing. The sea turtles and penguins use their paired wings for creating lift and thrust force. Mainly, lift-based propulsors are vortex enhanced propulsors. They use this vortex enhancing mechanism of converting von Karman vortex street to reverse von Karman vortex street. All the lift-based animals produce drag at low Strouhal numbers. These animals sense the opposing fluid force on the body and increase the frequency and amplitude of fins, thereby increasing the Strouhal number that is in the range of 0.2–0.5. The von Karman vortex street and reverse von Karman vortex street are shown in Figure 2.
Figure 2.
(a) von Karman vortex street behind a cylinder and (b) reverse von Karman vortex street during fish swimming.
1.2 Drag-based propulsion
The drag-based animals such as ducks, Nile crocodiles, frogs, and other diving animals uses cyclic motion where they push water back in power stroke and return their limbs forward, which is defined as recovery stroke (Figure 3). In power stroke, the animals move their body forward by using its power stroke. In return stroke, the animals move their limbs to start position by pushing water forward minimally, which results in moving the body of the animal slightly backward. During the return stroke, the swimming movement of the animal is opposed and the opposing force is known as drag. This type of propulsion for conventional vehicles results in low efficiencies.
Figure 3.
Drag-based propulsion (e.g., ducks and Nile crocodile).
1.3 Undulatory propulsors
In undulation mode, aquatic animals swim through the water by the creation of undulations or flag movements with their bodies or using fins. The undulation mode is classified as a body-caudal fin (BCF) propulsion and median-paired fin (MPF) propulsion. The BCF mode is again subdivided into (a) anguilliform, (b) carcharhiniform (c) sub-carangiform (d) carangiform, (e) thunniform (f) esociform fish and (g) ostraciiform modes. The anguilliform and ostraciiform modes are slow swimmers and carangiform, thunniforms are relatively fast swimmers. In the anguilliform mode, slender body fish such as sea snakes, eels generate propulsive wave starting from head portion of the body to the tail portion. The sub-carangiform and carangiform group increase its wave amplitude from the middle portion of the body to rear part of the fish body. The thunniform mode of fish is high-speed long-distance swimmers. The ostraciiform group of fish is box-shaped bodies with caudal fin. Only the caudal fin itself oscillates to generate thrust. Similarly, MPF mode is classified as (a) tetraodoniform, (b) balistiform, (c) diodontiform, (d) rajiform, (e) amiiform, (f) gymnotiform and (g) labriform. In MPF mode, most of the fish use a pair of pectoral fins, anal fins or a combination of both for generating a propulsive force. The detailed classification is given in Table 2.
Active locomotion
Passive locomotion
Fossorial locomotion
Terrestrial locomotion
Aquatic locomotion
Aerial and arboreal locomotion
Earthworms, golden mole, marsupial mole, ground squirrels.
Walking and running: ostrich, emu, humans. Hopping or jumping: kangaroo, rabbit, hare, jerboa. Crawling or slithering: snakes, sand snakes.
Hydraulic propulsion: jelly fish. Waterjet propulsion: ocean squids. Fish: sharks, eels, tunas. Dual medium animals: fish: eels, flying fish. Other marine mammals: whales and birds: ducks, swans, ostriches.
In Jet propulsion mode, aquatic animals fill their cavity by compressing the fluid and eject the fluid out to propel themselves in the opposite direction of the ejected fluid. In jet propulsion mode, the propulsion mechanisms are different from one animal to another animal. In jellyfish, it draws water from the rear and ejects it through the rear part. In scallops, fluid enters from its front portion and ejects out at its rear portion of the body.
1.5 Literature review: state of the art
Experimental and numerical methods are the oldest and most reliable sources of information on any physical studies and some do apply for the present studies on marine vehicle propulsion. The experimental studies are carried out on flapping foil propulsor by considering the effects of variations on the Strouhal number and maximum angle of attack on the thrust force which results in increasing hydrodynamic efficiency of flapping foil [1]. The pectoral fin model of bluegill sunfish is studied during its maneuvering to avoid the obstacles. This study involves biological studies of pectoral fins of sunfish, developing kinematic models of the motion of pectoral fins, 3D computational studies of pectoral fins flow characteristics and forces, and implementation of simplified models of pectoral fin kinematics and mechanical properties in a physical model [2]. The mechanical design of fish fin rays that allows active muscular control of curvature and flexibility of the fish body in propulsion are studied [3]. Experimental studies are performed on purely-pitching foil with a rigid and flexible flap of suitable stiffness. The rigid pitching foil produces a weak and meandering jet (and thrust), whereas the flexible foil produces a strong jet of the wake. Deformations of the flexible flap suppress meandering by increasing the initial gap between two successive vortices and by imposing a convective motion on the shed vortices even in the absence of free-stream velocity [4]. Four foils made of adhering layers of plastic sheets to produce discrete regions of high and low flexible stiffness of biologically-relevant magnitudes. Using a mechanical flapping foil controller, the forces and torques in three directions (x,y,z) and the swimming performance under both heaving motion and constant 0° angles of attack case studies are carried out experimentally [5]. The 2D flapping foils in a tandem configuration in forwarding propulsion at Re = 5000 using experimental and numerical techniques are studied. Both tandem foils are subject to symmetrical translational and rotational motions with a Strouhal number of 0.32. The observations show that the foil-wake interaction is favorable to thrust generation, which requires the tandem wing has to cross the shear layer shed from the front wing and results in an increase in angle of attack [6]. Three-dimensional numerical simulations are carried out on tuna fish to study the flow characteristics and vorticity pattern around the fish when swimming in a straight line. The body undulation occurs at the rear half of the fish, and the caudal fin of the tuna fish oscillates with combined sway and yaw motions [7]. The underwater vehicle (UV) which has both flapping-foil and gliding function are studied. Numerical simulations are carried out in the fluent code using dynamic meshing on a single degree of freedom flapping-foil movement, which demonstrated the mechanism of flapping foil. The turbulence effects were taken into account, and the turbulent stress was evaluated by means of the realizable k-epsilon model [8]. The detailed hydrodynamic analysis of the thrust generated by an oscillating hydrofoil is carried out using numerical and experimental studies. The hydrofoil is mounted at the bow of a platform supply vessel as a means of auxiliary propulsion and, is vertically oscillating due to the ship’s heave and pitch motions in head-waves [9]. On scrutinizing the works presented above it is clearly evident that the experimental approaches have been quite successful over years in predicting the performance of biomimetic propulsion systems for ships and AUV’s. A high degree of robustness and reliability has also been observed in methods based on systems and parametric identification in the design and development of bioinspired systems for marine vehicles. Other theoretical methods can only give rough estimates of the thrust generated by flapping foils. Numerical methods Reynolds-averaged Navier-Stokes equations (RANSE)-based computational fluid dynamics (CFD) techniques have been successfully employed in simulating two-dimensional, flapping foils. The method is still in its developing stage, where a confidence level in its application to three-dimensional flapping foils problems is yet to be achieved. Many authors are currently concentrating on this research area of numerical study of 3D flapping foils for marine vehicles.
As far as the use of bioinspired propulsion systems for ocean-going vessels is concerned, the literature to date has been able to give only laboratory demonstrative models. The application of oscillating fins as propellers to conventional marine vehicles is being explored and this has given the impetus in this chapter to concentrate on bioinspired propulsion for marine vehicles. This necessitates the need for detailed experimental and numerical study on three-dimensional fish-like propulsion system used in surface and sub-surface marine vehicles. These studies are envisaged to understand the fluid particle level movements around different fish fins during a live fish swimming, mimicking these fins and its motion using the physical model in a laboratory, attach fins to surface ship and underwater vehicle models to determine the self-propulsion parameters and resulting thrust, torque and propulsive efficiency. The entire study has been divided into four sections. Section 1 gives an introduction to the study, presents the review of relevant literature and the motivation for this study. Section 2 shows the various mathematical models used for the determining hydrodynamic forces of bioinspired propulsors. Section 3 introduces examples of bioinspired propulsion systems. Section 4 summarizes the work and the conclusions from the above studies are presented.
2. Empirical methods to determine hydrodynamic forces and propulsive efficiencies of bioinspired propulsors
2.1 Estimation of propulsive forces using numerical techniques
The non-dimensional flow variables are used for the analysis of the results obtained from the numerical simulations of lift-based propulsion systems. The forces acting on flapping foil (Figure 4a and b) subjected to heaving and pitching motions are resolved in x and y-direction as FX(t) and FY(t), respectively, and the moment about the pivot point is denoted as M(t). The force coefficients are given as
Figure 4.
Foil motion with respect to angle of attack (α), sway and yaw modes.
CX=F¯X12ρU2cSE1
CY=FY12ρU2cSE2
CM=M12ρU2c2SE3
where U and c are the free stream velocity and hydrofoil chord length, ρ is the density of fluid respectively, and S is the span of the hydrofoil and is assumed to be unity. The thrust force created by flapping hydrofoil is FX(t). The average thrust over n complete flapping cycles over period T is given as
F¯X=1nT∫0nTFXdtE4
The power required for the flapping foil, defined as the amount of energy imparted to the hydrofoil to overcome the fluid force, is given as
Pt=−Fytdydt−MtdψdtE5
The sign on both the terms in Eq. (5) is negative as vertical force and moment are the reaction forces created by the fluid as the hydrofoil moves through it.
The average power over n complete cycles of flapping, over a period T, is given as
P¯=1nT∫0nTPdtE6
P′=P¯12ρU3cSE7
The propulsive efficiency η is defined as the ratio of the output power (F¯X)U to the input power (P¯). From Eqs. (1) and (7), η is given by
η=F¯XUP¯=CXP′E8
2.2 Estimation of thrust and torque of pectoral fins using propeller theories
Flapping foil is composed of a series of blade elements each of which produces hydrodynamic forces such as lift, drag, thrust and torque due to the motion of the fluid. The axial component is the element thrust & the tangential component is the element torque. Integration over the radius of all sections gives total thrust & torque of the flapping foil. In this method, the pectoral flapping wing is divided into a number of independent sections along its span. At each section, a force balance is applied involving lift, drag, thrust, and torque produced by the section shown in Figure 5. At the same time, a balance of axial and angular momentum is also applied. This approach is applied to the flapping pectoral fins and the resulting thrust (Th) and torque (Tq) are obtained as,
Figure 5.
Flapping foil blade section.
Th=∫RoRtCLsinγ−CDcosγ×0.5×ρ×V2×CrdrE9
Tq=∫RoRtCLcosγ+CDsinγ×0.5×ρ×V2×CrrdrE10
Here CL and CD are lift coefficient and drag coefficient with a given angle of attack.
(α) and V is the velocity of flow and is given by,
Vrt=U2+rωt2E11
The inflow angle γ is calculated as,
γrt=tan−1rωtUE12
As the flapping motion is subjected to sinusoidal motion, the flapping angle θ(t) and the angular velocity ω(t) are given by the following expressions
θt=θmaxsin2πTtE13
ωt=2πTθmaxcos2πTE14
2.3 Estimation of torque of caudal fin
The surface ship model is fitted with caudal fin oscillation mechanism where DC servo motors are used as the prime mover. The power demand for fin oscillation depends on the fin size, its oscillation frequency, and amplitude. The stroke of the caudal fin in the x-y plane is yG (Figure 6) is given by,
Figure 6.
Caudal fin motion mode.
yG=rsinψE15
where ψ is the yaw angle. The fin is oscillating with a frequency f (Hz) and hence the instantaneous position of the fin CG is given by
yG=rsinψsin2πftE16
The velocity, vG (m/s) is given by,
vG=dyGdt=2πfrsinψcos2πftE17
when caudal fin moves to initial centerline of the fish body, cos2πft become one and hence the maximum velocity is given by,
vGmax=2πfrsinψE18
The maximum drag force on the caudal fin is,
Dmax=CD12ρAfvGmax2E19
where CD is the drag force coefficient and Af is the fin profile area.
The maximum torque developed by the caudal fin oscillation is,
Qmax=Dmaxr=2π2CDρr3Aff2sin2ψE20
2.4 Estimation of propulsive forces from PIV experiments
In flow visualization experiments, the quantitative measures of hydrodynamic forces acting on the fish body and impulse can be determined. There are two methods for estimating the forces in a fluid, one based on the fluid momentum which is used for unsteady flows and the other one is based on fluid vorticity which is used for steady flows.
In both the cases, one first estimates impulse I, the integral of force F applied to the fluid over time, the mean force Fmean applied to the fluid multiplied by the total time T over which it was applied:
I=∫0TFdtE21
I=FmeanTE22
To obtain the force, differentiating I by T. Therefore,
F=∂I∂tE23
2.4.1 Vortex analysis
The circulation is defined as the total vorticity within an area circumscribed by a closed loop. For a steadily moving vortex ring, the procedure to estimate the force generated is quite straightforward. Any sufficiently large loop that passes through the center of vortex ring has a constant circulation that is proportional to the force required to generate the ring:
Fmean=πρΓD24E24
where ρ is the density of the fluid, Γ is the circulation, D is the distance between the two vortex centers in the plane.
Mathematically, circulationΓcan be written in two equivalent ways:
Γ=∫ΣωdΣE25
Γ=∮cudlE26
where Σ is a surface in the fluid, C is the closed contour around the edge of the surface, and l is the tangent vector along the vorticity contour.
3. Examples of aquatic propulsion systems
3.1 Experimental and numerical study of a surface ship fitted with tandem flapping foils
In the present study, experimental and numerical studies are carried out to ascertain the thrust producing mechanism of flapping foils [10]. The propulsive performance of flapping foils fitted to surface ships in tandem mode at its midship position is studied. The current work attempts to investigate the thrust generation capability and efficiency of the rigid and flexible hydrofoil in open water condition and the results of which are compared with those obtained from ship model tests in self-propulsion mode. The foil flexibility in computational fluid dynamics (CFD) environment is introduced using an appropriate user-defined function. The lift-based penguin type propulsion system installed in a ship model, used in the study, is shown in Figure 7. A pair of flexible flapping foils is attached to the ship model bottom. The amplitude and frequency of these flapping foil oscillations are varied using the electro-mechanical systems. In the experimental study, ship model resistance tests, bollard pull tests, and self-propulsion tests are carried out in the towing tank and the results are recorded, presented and discussed.
The ship model fitted with fins and other fixtures were run in the towing tank to experimentally determine the model resistance at different speeds. The resistance force of a ship at a constant speed is the force required to tow the ship in calm water at that speed. The model is accelerated to the desired speed and the speed is kept constant. The average values of the measurements for the period of constant speed are recorded. The ship model was built in fiberglass to the scale 1:15. The towing tank dimensions are 82 m long × 3.2 m wide × 2.5 m (water depth). The model was ballasted to the loaded condition with an even keel. Model towing tests were conducted in the speed range covering the design speed. The resistance test results are shown in Figure 8. The effective power of the model with fins is estimated at 33.15 W at a speed of 1.2 m/s. The ship hull model (below bottom) is attached with chain sprockets, chain drives, bearing blocks, two circular shafts of 25 mm diameter to mount the flexible flapping foils stiffened with steel plates. This additional fitting attached to the ship hull caused an increase in resistance.
Figure 8.
Resistance tests of the ship model with and without the foils fitted.
3.1.2 Bollard pull tests
The ship model is tied to a stationary post and the flaps oscillate at different frequencies and amplitudes corresponding to different Strouhal numbers. The thrust developed by the flapping foils is measured in each case using a load cell. The maximum thrust was found to be 21.3 N at f = 3 Hz and oscillation amplitude of 300 mm.
3.1.3 Self-propulsion tests
In self-propulsion tests, the ship model is fitted with an electric motor and load cell to measure the thrust force generated by the flapping foils. The ship speed is matched with towing carriage speed to determine the speed achieved by the ship at different frequencies and amplitudes and the forces are measured using the load cell and data acquisition system. The maximum thrust force of 13.18 N at St = 0.53 is measured and shown in Figure 9. The thrust force generated by the foils in bollard pull condition is higher when compared to the self-propulsion tests as it appears in the vessels fitted with the conventional screw propellers.
Figure 9.
Thrust developed in self-propulsion mode V≠0.
3.1.4 Numerical study of flexible and rigid flapping foil
The standard National Advisory Committee for Aeronautics airfoil section (NACA0012) with a chord length c = 0.1 m is used for the hydrofoil modeling. NACA0012 is symmetrical foil, so flap angle of attack α, is symmetrical about 0°. The NACA0012 section is mainly used for plunging and combined pitching and plunging motions in numerical simulations for fully laminar and fully turbulent flow regimes.
3.1.5 Results and discussions
The wake characteristics of rigid and flexible flapping foils are studied at different Strouhal numbers (St = 0.1–0.4) with different maximum angles of attack such as 10°, 15°, and 20°. As the flapping frequency and amplitudes are increasing (St = 0.1–0.4), the fluid flow separates from the trailing edges forms a jet of alternating vortices which results in thrust production. This formation of vortices is termed as reverse von Karman Vortices [10]. As the angle of attack increases from 10° to 20°, the vortices becomes stronger at St = 0.1 and the resulting thrust force increases. The absolute vorticity contours for flexible flapping hydrofoil at α0 = 20°at different Strouhal numbers are shown in Figure 10. The variation of force and moment coefficients for flexible flapping foil is shown in Figure 11. The average thrust coefficient (CX) and efficiency (η) of both rigid and flexible foils are shown in Figures 12 and 13 for different angles of attack (10°, 15°, 20°). From the experimental studies in self-propulsion mode, the thrust (CX) = 2.75 is measured at St = 0.53. In numerical studies, the maximum thrust coefficient (CX) = 2.38 is obtained for α0 = 20° at St = 0.55.
Figure 10.
Absolute vorticity contours of flexible flapping hydrofoil at α0 = 20° for (a) St = 0.1, (b) St = 0.2, (c) St = 0.3 and (d) St = 0.4.
Figure 11.
Variation of (a) CX, (b) CY for St = 0.1–0.4 at α0 = 20° for flexible foil.
Figure 12.
Thrust coefficient at α0 = 10°, 15° and 20° for tandem flapping foil.
Figure 13.
Propulsive efficiency at α0 = 10°, 15° and 20° for single foil.
3.2 Obtaining 3D kinematics of fish locomotion using digital X-ray fluoroscopy
Carangiform fish play a major role in propulsion and maneuvering performance. In this section, skeletal view of the carangiform fish spine and caudal fin kinematics during forward swimming are presented using digital X-ray fluoroscopy technique. The aim of this study is to obtain the 3D kinematics of the fish spine and caudal fin motions. The X-ray recordings were possible through collaboration between the Department of Ocean Engineering, Indian Institute of Technology (IIT) Madras, India, and Sri Ramachandra Medical College (SRMC), Chennai, India. Biplanar high-speed cineradiographic fluoroscopy with an image resolution of 1536 × 1024 was taken from top view. The fish swims freely in a glass tank during the experiments. The experimental set up is shown in Figure 14. The spinal chord and caudal fin motions were recorded during fluoroscopy tests. These motions are tracked manually for all the videos in which fish swims straight and achieves steady state locomotion. The fluoroscopy images of the fish spine and caudal fins are shown in Figures 15 and 16. As shown in Figure 16, the number of joints in the fish spinal chord and representation of pectoral fins, the caudal fin is marked. By using custom software, OpenCV we can track these points in the images in order to get the 3D kinematics of fish swimming which can be taken as a future study in this area.
Figure 14.
Digital fluoroscopy recording of fish swimming.
Figure 15.
Fluoroscopy image of fish swimming—skeletal view.
Figure 16.
Manual tracking of fish spinal chord joint and caudal fin.
3.3 Experimental study of fish locomotion using 2D particle image velocimetry
Two-dimensional flow visualization experiments are carried out to visualize the flow pattern around the caudal, pectoral, anal and dorsal fins of a freely swimming fish particle image velocimetry (PIV) system [11]. The freshwater black shark fish with a body length of 26 cm is used for the present experimental study. The fish is placed inside a glass tank of size L × B × D = 75 × 29 × 37 cm and it is allowed to swim freely in the tank shown in Figure 17. The PIV system used in this experiment consists of (i) a double-pulsed Nd-YAG (neodymium-doped yttrium aluminum garnet) laser with 200 mJ/pulse energy at 532 nm wavelength, (ii) a charge-coupled device (CCD) camera with a 2048 × 2048 pixels and an image capturing speed of 14 frames per second (fps), (iii) a set of laser and a camera controllers and, (iv) a data acquisition system. The laser sheet is aligned with the longitudinal vertical XZ and transverse YZ planes. The camera is positioned in front of the test section perpendicular to the laser sheet.
Figure 17.
Freely swimming freshwater shark fish.
The PIV technique involves the introduction of tiny particles called ‘seeder particles’ into the fluid path. Hollow glass spheres with a mean diameter of 10 μm are used as the tracer particles. The PIV setup used in the present experiment is shown in Figure 18. The flows around the fins of freely swimming fish are analyzed and the velocity vector fields are presented here. In this analysis, we are presenting a raw CCD (charged coupled device) image and the processed image at different time intervals. The white boundary line represents the body contour of fish. The primary vortex regions are marked as V1, V2 in images. Figure 19 shows the CCD image and velocity vector field around adipose and anal fins for Δt = 900 ms. The adipose and anal fins generated vortices, that passes downstream, interacts with caudal fin vortices which results in increasing the propulsive performance. Figure 20 shows CCD image and velocity vector field around caudal fin stroke in YZ plane for Δt = 900 ms while the fin is at the center plane. A jet of high-velocity flow is observed at the top of the caudal fin.
Figure 18.
PIV system arrangement.
Figure 19.
CCD image and velocity vector field around adipose (upper one) and anal (below one) fins for Δt = 900 ms.
Figure 20.
Consecutive CCD image and velocity vector field around caudal fins stroke at center in YZ plane for Δt = 900 ms.
3.4 Experimental study of a remotely operated surface ship fitted with caudal fin
The thunniform mode caudal fin propulsion system is designed and developed for a surface ship model. The caudal fin acts as a main propulsor is operated at different frequencies and amplitudes to experimentally find its propulsive performance [12]. The length of the ship model is 1.4 and 0.35 m wide. The caudal fin is actuated by single DC servo motor powered by the lithium-polyimide(lipo) battery of 25.9 V. The crank slider mechanism is used for converting the rotary motion of the motors into the fin oscillation mechanism. By coordinating the movements of the tail fin, the ship model can simulate the movements of fish in the water. Figures 21 and 22 shows the surface ship model and caudal fin propulsion mechanism used here. The control system consists of a wireless communication unit, microprocessor, strain gauge sensors for force measurement, data acquisition system. The block diagram and tail fin motor connections are shown in Figure 23.
Figure 21.
Remotely operated surface ship model.
Figure 22.
Caudal fin propulsion mechanism.
Figure 23.
Block diagram and caudal fin mechanism connections.
3.4.1 Results and discussions
The remotely operated surface ship model in self-propulsion mode is shown in Figure 24. In self-propulsion tests, the caudal fin amplitude is kept constant at 0.15 m and the frequencies are varied from 0.5 to 1.75 Hz. The caudal fin of ship model is tested at different frequencies and constant amplitude and the thrust forces are measured using strain gauges mounted on the caudal fin shaft. The data acquisition system (consists of oscilloscope and amplifier) shown in Figure 25. The forward velocity (V) of remotely operated surface ship model, fitted with caudal fin, increases until the caudal fin frequency reached about 1.5 Hz and above that the speed of the ship decreases and shown in Figure 26. The maximum speed achieved by the vehicle is 0.4 m/s. In self-propulsion tests, a maximum thrust of 8.6 N was recorded at St = 0.4 shown in Figure 27 where the thrust force is plotted against the frequency of flap. In bollard pull, the maximum thrust is found to be 14.8 N at f = 1 Hz.
Figure 24.
Remotely operated surface ship model in the self-propelling mode.
Figure 25.
Data acquisition system.
Figure 26.
Ship model velocity at different oscillation frequencies of the flap.
Figure 27.
Thrust force developed by the oscillating foil in different ship model operating conditions.
3.5 Experimental study of the remotely operated underwater vehicle with bioinspired caudal fin
The bioinspired robotic fish is shown in Figure 28 where the body is an axis-symmetric one with a long cylindrical body having a hemispherical nose and conical aft portion, and with a pair of pectoral fins at the forward area and tail (caudal) fin at the extreme aft. The principal dimensions of the body are 1.2 m long and 0.16 m diameter. The biomimetic caudal fin fitted at the end of the tail portion of the body acts as the main propulsor for generating thrust. The robotic fish comprises of three individual modules, pectoral fin mechanism, a caudal fin module, and a control system and its battery modules. The hull is made of fiber reinforced plastic (FRP) material of 6 mm thick. The vehicle is propelled by a bioinspired caudal fin in wireless mode. The CFD estimated drag of the underwater vehicle with fins is 48 N and that for the bare hull is 33 N at a speed of 2.5 m/s. As expected, high-pressure regions are seen at the leading edges of pectoral and caudal fins and low-pressure regions are created at the trailing edges of the fins as shown in Figure 29. The high-pressure regions are also present in the forward nose of the hull.
Figure 28.
Robotic fish with pectoral and caudal fins.
Figure 29.
Flow around the vehicle pectoral and caudal fins (V = 1 m/s).
3.5.1 Results and discussions
Experiments are conducted to predict the swimming and propulsive behavior of the bioinspired robotic fish in different operating conditions. The tests are conducted in the towing tank and the vehicle swimming velocity and fin developed force is measured. The towing tank dimensions are 82 m long × 3.2 m wide × 3 m depth. The water depth used for all experiments is 2.6 m. The underwater vehicle is propelled by the thrust produced due to the oscillation of caudal fin fitted at the stern part of the vehicle. The swimming velocities at different oscillation frequencies are shown in Figure 30. The results show that the fin oscillation generates thrust forces for a range of frequency. At free surface condition, the velocity of 0.53 m/s is achieved at a frequency of 1.5 Hz with an amplitude of 180 mm. In submerged condition, the vehicle achieved a speed of 0.29 m/s. The body attains higher velocity at the surface condition, having less wetted surface area and thus reduced body resistance. In the case of submerged condition, the body resistance is more and hence moved with lesser speed. The thrust force generated by the caudal fin of robotic fish is measured by using strain gauge sensors mounted on the caudal fin shaft. Figure 31 shows the thrust force developed by the oscillating fin at a different frequency. It is observed that the thrust force increases up to f = 1.0 Hz and then decreases with increase in frequency. The maximum thrust force of about 34.06 N is measured at f = 1.0 Hz in surface condition. A maximum thrust force of 18.6 N is observed in submerged condition at f = 0.75 Hz. In submerged condition, caudal fin deformed more due to higher loading and flexibility of fin resulting in reduced performance. As in the surface condition, the thrust produced by the oscillating fin reduced after f = 1.0 Hz. Higher frequency of oscillation results in more flow separation and results in thrust reduction.
Figure 30.
Vehicle velocity at different frequencies of the fin at surface piercing and submerged conditions.
Figure 31.
Thrust force developed by the foil at different frequencies at the surface and submerged conditions.
4. Summary and conclusions
The design and development of biomimetic robots based on nature locomotion involves multidisciplinary studies such as the biological behavior of fish, flow visualization around it and its fins during swimming, and numerical and experimental determination of thrust and drag forces. Experimental and numerical studies are carried out to determine the propulsive performance of bioinspired aquatic vehicle fitted with tandem flapping foils. The work has taken shape with the selection of a ship model fitted with a pair of flexible flapping foil at its bottom resembling penguin flippers. The propulsion mechanism developed in Central Workshop (CWS) at IIT Madras is installed at the ship model bottom and then the fins oscillate at different amplitudes and frequencies. Rigid and flexible flapping foils are used in the studies to determine the hydrodynamic forces generated by the flapping foils. In understanding the limitations of potential theory in determining different hydrodynamic parameters, RANSE-based CFD method is used extensively for the numerical studies. A general purpose fluid flow solver FLUENT is used by exploring and exploiting its features to induce dynamic simulations to the fins so as to duplicate the oscillating fin system in the numerical environment. Experimental studies on the ship model, fitted with these penguin type flipper at its bottom, are carried out in the towing tank facility. The thrust generation capability and efficiency of flapping hydrofoils at different parameters (α0, St, ψ) are studied experimentally and numerically.
In order to understand the natural characteristics of fish swimming, PIV measurements are carried out on a live shark fish to understand and analyze the hydrodynamic behavior of its propulsion using the caudal fin. Sharks exhibit high-performance aquatic locomotion through oscillation of its forked caudal fin. The velocity vector plots show that caudal fins produce reverse von Karman vortex street resulting in an afterward jet formation that gives the propulsive force. This study helped in understanding the flow pattern around the fish and its fins to take the information further to engineering applications. These type of propulsors with improved propulsive characteristics, maneuverability, and creation of less turbulence are inspiring qualities for its adoption in the propulsion of autonomous marine vehicles.
Inspired by the natural aquatic swimming fish, bioinspired remotely operated surface ship and underwater vehicle fitted with caudal fin is studied in detail. The performance of a remotely operated surface ship model propelled by tuna-mimicking caudal fins is evaluated. Design of caudal fin and its control system are carried out. Using the experimental set-up, swimming velocities and thrust forces of a surface ship fitted with the caudal fin in the axial direction are measured at different oscillation frequencies and amplitude of the fin. This information inspired and helped in thinking about the development of a combination of oscillating foil system constituted by the caudal and a pair of pectoral fins to propel underwater vehicles. Numerical studies are used to estimate the drag forces on the body with the bare hull and also the hull fitted with pectoral and caudal fins. The vehicle fitted with caudal fin is studied experimentally at different frequencies and constant amplitude. The thrust force is measured using the strain gauges mounted on the caudal fin connecting rod. The propulsive forces are measured at different oscillation frequencies for the case when the body is surface and below the surface. The vehicle has the ability for better performance, by incorporating pectoral fins also, which will generate additional thrust and also help in the better maneuvering of the vehicle.
Acknowledgments
The authors would like to thank Department of Ocean Engineering, IIT Madras, India, for providing financial and technical support for doing this project.
Conflict of interest
The authors declare that they have no competing interests.
\n',keywords:"bioinspired propulsors, digital fluoroscopy and particle image velocimetry (PIV), fish with caudal fin and pectoral fins, ships and underwater vehicles, propulsive efficiency",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68530.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68530.xml",downloadPdfUrl:"/chapter/pdf-download/68530",previewPdfUrl:"/chapter/pdf-preview/68530",totalDownloads:997,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:2,totalAltmetricsMentions:1,impactScore:1,impactScorePercentile:58,impactScoreQuartile:3,hasAltmetrics:1,dateSubmitted:"June 23rd 2018",dateReviewed:"December 1st 2018",datePrePublished:null,datePublished:"October 7th 2019",dateFinished:"August 8th 2019",readingETA:"0",abstract:"Regulations and performance requirements related to technology development on all modes of transport vehicles for reduced pollution and environmental impact have become more stringent. Greening of transport system has been recognized as an important factor concerning global warming and climate change. Thus environment-friendly technical solutions offering a reduction of noxious exhaust gases are in demand. Aquatic animals have good swimming and maneuvering capabilities and these observations have motivated research on fish-like propulsion for marine vehicles. The fish fin movements, used by fish for their locomotion and positioning, are being replicated by researchers as flapping foils to mimic the biological system. Studies show that flapping foil propulsion systems are generally more efficient than a conventional screw propeller, which suffers efficiency losses due to wake. The flapping foil propulsors usually do not cavitate and have less wake velocity variation. These aspects result in the reduction of noise and vibration. The present study will cover an overview of aquatic propulsion systems, numerical simulations of flapping foils and ship model self-propulsion experiments performed using flapping foil system, particle image velocimetry (PIV), and digital fluoroscopy studies conducted on fish locomotion. Studies performed on underwater and surface vehicles fitted with flapping fins will also be presented.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68530",risUrl:"/chapter/ris/68530",book:{id:"7198",slug:"propulsion-systems"},signatures:"Naga Praveen Babu Mannam and P. Krishnankutty",authors:[{id:"264509",title:"Dr.",name:"Naga Praveen Babu",middleName:null,surname:"Mannam",fullName:"Naga Praveen Babu Mannam",slug:"naga-praveen-babu-mannam",email:"mpraveenmn@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"264512",title:"Prof.",name:"P.",middleName:null,surname:"Krishnankutty",fullName:"P. Krishnankutty",slug:"p.-krishnankutty",email:"pkrishnankutty@iitm.ac.in",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Lift-based propulsion",level:"2"},{id:"sec_2_2",title:"1.2 Drag-based propulsion",level:"2"},{id:"sec_3_2",title:"1.3 Undulatory propulsors",level:"2"},{id:"sec_4_2",title:"1.4 Jet propulsors",level:"2"},{id:"sec_5_2",title:"1.5 Literature review: state of the art",level:"2"},{id:"sec_7",title:"2. Empirical methods to determine hydrodynamic forces and propulsive efficiencies of bioinspired propulsors",level:"1"},{id:"sec_7_2",title:"2.1 Estimation of propulsive forces using numerical techniques",level:"2"},{id:"sec_8_2",title:"2.2 Estimation of thrust and torque of pectoral fins using propeller theories",level:"2"},{id:"sec_9_2",title:"2.3 Estimation of torque of caudal fin",level:"2"},{id:"sec_10_2",title:"2.4 Estimation of propulsive forces from PIV experiments",level:"2"},{id:"sec_10_3",title:"2.4.1 Vortex analysis",level:"3"},{id:"sec_13",title:"3. Examples of aquatic propulsion systems",level:"1"},{id:"sec_13_2",title:"3.1 Experimental and numerical study of a surface ship fitted with tandem flapping foils",level:"2"},{id:"sec_13_3",title:"3.1.1 Flapping foil propulsion ship: resistance tests",level:"3"},{id:"sec_14_3",title:"3.1.2 Bollard pull tests",level:"3"},{id:"sec_15_3",title:"3.1.3 Self-propulsion tests",level:"3"},{id:"sec_16_3",title:"3.1.4 Numerical study of flexible and rigid flapping foil",level:"3"},{id:"sec_17_3",title:"3.1.5 Results and discussions",level:"3"},{id:"sec_19_2",title:"3.2 Obtaining 3D kinematics of fish locomotion using digital X-ray fluoroscopy",level:"2"},{id:"sec_20_2",title:"3.3 Experimental study of fish locomotion using 2D particle image velocimetry",level:"2"},{id:"sec_21_2",title:"3.4 Experimental study of a remotely operated surface ship fitted with caudal fin",level:"2"},{id:"sec_21_3",title:"3.4.1 Results and discussions",level:"3"},{id:"sec_23_2",title:"3.5 Experimental study of the remotely operated underwater vehicle with bioinspired caudal fin",level:"2"},{id:"sec_23_3",title:"3.5.1 Results and discussions",level:"3"},{id:"sec_26",title:"4. Summary and conclusions",level:"1"},{id:"sec_27",title:"Acknowledgments",level:"1"},{id:"sec_30",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Schouveiler L, Hover FS, Triantafyllou MS. Performance of flapping foil propulsion. Journal of Fluids and Structures. 2005;20(7):949-959'},{id:"B2",body:'Gottlieb JR, Tangorra JL, Esposito CJ, Lauder GV. A biologically derived pectoral fin for yaw turn maneuvers. Applied Bionics and Biomechanics. 2010;7(1):41-55'},{id:"B3",body:'Lauder GV, Madden PGA, Tangorra JL, Anderson E, Baker TV. Bioinspiration from fish for smart material design and function. Smart Materials and Structures. 2011;20(9):094014'},{id:"B4",body:'Shinde SY, Arakeri JH. Flexibility in flapping foil suppresses meandering of the induced jet in absence of free stream. Journal of Fluid Mechanics. 2014;757:231-250'},{id:"B5",body:'Lucas KN, Thornycroft PJ, Gemmell BJ, Colin SP, Costello JH, Lauder GV. Effects of non-uniform stiffness on the swimming performance of a passively-flexing, fish-like foil model. Bioinspiration & Biomimetics. 2015;10(5):056019'},{id:"B6",body:'Lua KB, Lu H, Zhang XH, Lim TT, Yeo KS. Aerodynamics of two-dimensional flapping wings in a tandem configuration. Physics of Fluids. 2016;28(12):121901'},{id:"B7",body:'Yang L, Su YM. CFD simulation of flow features and vorticity structures in tuna-like swimming. China Ocean Engineering. 2011;25(1):73-82'},{id:"B8",body:'Hao D, Bao-Wei S, Yong-hui C. Design and prototype development of a 1-DOF flapping-foil and gliding UUV. Journal of Marine Science: Research and Development. 2012;S10:001. DOI: 10.4172/2155-9910.S10-001'},{id:"B9",body:'Ahmed T, Welaya Y, Abdulmotaleb S. Numerical modeling of the hydrodynamic performance of hydrofoils for auxiliary propulsion of ships in regular head-waves. In: Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering OMAE 2017; 25-30 June 2017; Trondheim, Norway; 2017'},{id:"B10",body:'Mannam NP, Krishnankutty P, Vijayakumaran H, Sunny RC. Experimental and numerical study of penguin mode flapping foil propulsion system for ships. Journal of Bionic Engineering. 2017;14(4):770-780'},{id:"B11",body:'Babu MNP, Mallikarjuna JM, Krishnankutty P. Hydrodynamic study of freely swimming shark fish propulsion for marine vehicles using 2D particle image velocimetry. Robotics and Biomimetics. 2016;3(1):3'},{id:"B12",body:'Mannam NP, Krishnankutty P. Hydrodynamic study of flapping foil propulsion system fitted to the surface and underwater vehicles. Ships and Offshore Structures. 2018:1-9'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Naga Praveen Babu Mannam",address:"mpraveenmn@gmail.com",affiliation:'
Indian Institute of Technology Madras, Chennai, India
Indian Institute of Technology Madras, Chennai, India
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1. Introduction
Over the last two decades, many nature-inspired metaheuristic optimisation algorithms (MOAs) have been proposed to solve many abstract and real-world problems including combinatorial, binary and real engineering problems [1]. MOAs are generally iterative processes developed imitating natural phenomena and animal behaviours to obtain optimal solutions for given problems [2]. Although these algorithms provide satisfactory solutions to various optimisation problems, they do not always guarantee an optimal way of solving them in different scales [3, 4]. In this respect, MOAs has recently received increasing interest from scholars in related fields (e.g. engineering, business, logistics, etc.) as a way to design algorithms that specify a solution to general problems using a certain pattern of behaviour inspired by a given society. An example in this respect is the study of a colony of insects or the behaviour of other animals’ societies [5]. Swarm intelligence algorithms tend to simulate social behaviours of the imitated societies, where some renown swarm intelligence algorithms are particle swarm optimisation (PSO) inspired of bird flocks and fish schooling [6, 7], artificial bee colony (ABC) simulates collective food searching behaviour of honey bees [8, 9], cuckoo search algorithm is based on the brood parasitism of some cuckoo species [10, 11], group search optimiser (GSO) is based on animal searching behaviour [12, 13], and firefly algorithm (FA) is based on the flashing light patterns of tropic fireflies [14, 15].
Crow Search Algorithm (CSA) is another swarm intelligence metaheuristic framework inspired of feeding behaviours of crow flocks [1]. Similar to many other MOAs, CSA needs a balance in between exploration and exploitation activities throughout the search process in order to approximate global optimal solutions [16]. In this respect, exploitation refers to the use of gained experience in deepening the search within the locality of the current solution while exploration implies diversification, e.g. jumping to another region of the search space, in order to reach the optimal solution. The majority of the real-world problems are multi-model problems, which possess numerous local optimum solutions hard to avoid, the exploitative search has higher probability to get stuck into a local optimum. In contrast, exploration helps enrich the search with higher diversity to let the search move to other feasible regions within the search space, which may end up with a better payoff due to that a new region may lead to global best. In addition, a well-balanced exploration would let the algorithm to avoid sub-optimal solutions. It is known that a search strategy with balance in between exploration and exploitation facilitates finding sub-optimal solutions close to the global optimal [17] in shorter time, while CSA is known with its higher exploitation capability as designed [18].
In order to balance exploration and exploitation capabilities for MOAs, a number of approaches have been implemented; among these, three categories can be highlighted; (i) dynamic parameterisation on the fly [19], (ii) implementing different perturbation search strategies [20] and (iii) using multiple strategies in the search [21] instead of single one. For the latest approach, a strategy pool consisting of different strategies is constructed. The algorithm chooses the appropriate strategy according to search process circumstances and dynamics. In this article, an implementation of CSA approach with multiple strategies is introduced to solve functional optimisation problems using a pool of six different search strategies. The main contribution of the study is the use of multi-strategy search policy with CSA and suggesting a selection scheme in order to respond best to the search dynamics for solving functional optimisation problems.
2. Related works
Studies related to this work can be viewed into two categories: modified version of CSA and adaptation approaches. Zamani et al. [22] proposed new version of CSA, called Conscious Neighbourhood-based CSA (CCSA), with three new search strategies to improve local and global search capabilities of CSA. Sonuc [23] proposed BinCSA, a pure binary version of CSA, in which a logic gate was used as a search strategy. Majhi et al. [24] developed a new version of CSA, called OBL-CSA-MO, in which they tried to escape from local optima by using opposition learning strategy and mutation operator.
CSA has two control parameters: namely awareness probability (AP) and flight length (FL). AP is randomly generated solution probability and controls intensification and diversification balance. FL controls searching effect of algorithm. Rizk-Allah et al. [25] proposed a new version of CSA integrated with chaos theory for solving fractional optimisation problems. Coelho et al. [26] developed a modified CSA in which Gaussian distribution function is used in order to control two algorithmic parameters. Necira et al. [27] proposed an enhanced version of CSA, called dynamic crow search algorithm (DCSA), in which there are two modifications on CSA. Awareness probability is linearly adjusted throughout iterations, and FL is expressed as pareto probability density function. There are several studies aiming to improve the efficiency and robustness of CSA; for more details and further modifications of CSA, the reader is referred to the surveys [28, 29]. To the best knowledge of the authors, CSA has not been studied with multiple search strategies to avoid local optima problem, and this article presents the first ever study in this respect.
3. Materials and methods
3.1 Crow search algorithm
Crows are known to be among the smartest bird species based on their natural talents [30]; they can remember the location where they or other bird species store food [31], often times even called ‘thieves’. With such a sound memory skill, they can even recall faces and warn the flock using complex communication channels in dangerous situations [32]. CSA is developed as a metaheuristic optimisation algorithm inspired by such smart behaviours [1] where each crow represents a valid solution within the search space. The crow’s memory represents the best solutions for the given problem set. Let N be the number of crows in the flock and the search space be D dimensional. Then the position of the ith crow at jth time should be as xi,j=xdi,j, where d∈D, i∈N and j∈T.
The position of the information held by each crow in the memory should be mi,j, where m information stored in memory holds the best position information available up to that iteration, and T is the maximum iteration number. There are two options once the crows move towards the food positions in mind: (i) moving towards position m blind of being followed by another crow (k); (ii) moving to any random position aware of being followed by another curious crow. The choice between these two options is modelled as follows:
where ri and rj are two uniformly distributed random variables within the range of 01, FL is the flight distance, and AP is the probability of awareness, k is a randomly specified integer parameter that takes value from 1 to N. If the obtained position information of the solution—new solution—is better than the one in the memory, then, the position information is updated. The exploration and exploitation capabilities are managed with FL and AP in this approach, where FL is used to obtain better solutions, while AP is used to discover new solutions. If a high value is set for the AP, it will then come into play at the local minimum in the exploration phase of the algorithm. If a low value is set for the AP, late convergence will occur in the exploitation phase. Therefore, the AP value needs to be adjusted well [1].
The general overview of the CSA is given in Algorithm 1 in a pseudo code. Firstly, the algorithmic parameters are determined based on the position of each crow randomly generated in a d-dimensional search space. Once the first population is adopted as the initial best set of solution in the flock memory. Then the main loop of the algorithm starts working. Each crow in the flock makes decision for whom to follow within the flock. Next, the probability of AP is calculated randomly, as a threshold value, to identify if the crow is to move towards the food or to reach a new random position. If the new position information is a valid/feasible one and better than the solution in the memory, the crow updates its own memory. The method continues until the specified criteria are obtained.
Search strategies are used to help algorithms to switch from one solution to another within the neighbourhood. However, use of single search strategy with any metaheuristic algorithm ends up with serious limitations with respect to the balance in between the exploration ant exploitation capabilities. In order to deliver search with a balance, we decided to use a pool of search strategies composed of six different search strategies. The five different approaches picked up from to the literature inspired of the update rules used in differential evolution (DE) and artificial bee colony (ABC) algorithms, where some modifications applied [5, 33].
Eq. (2) is the original update rule used by CSA to generate candidate solution, while Eq. (3) is extended with global best guided search strategy (GCSA) [34] in order to improve exploitation capability. Eq. (4) is a global best-guided search strategy embedded with flock memory (BCSA). Eq. (5) is added to the strategy pool to enhance exploration ability with which it brings two different randomly chosen crows’ position in the scene (CBCSA). Eq. (6) is developed inspiring of DE/RAND/1 to offer higher exploration capability (RCSA). Eq. (7) is also an implementation of DE/RAND/1 rule inserting memory solutions in use (RMCSA). In the all strategies, k is a randomly selected neighbour solution from the flock.
xi,j+1=xi,j+ri×FL×mi,j−xi,jE2
xi,j+1=xi,j+ri×FL×mk,j−xi,j+gbest−xi,jE3
xi,j+1=gbest+ri×FL×mk1,j−mk2,jE4
xi,j+1=xi,j+ri×mi,j−xi,j+xk1,j−xk2,jE5
xi,j+1=xk1,j+ri×xk2,j−xk3,jE6
xi,j+1=mk1,j+ri×mk2,j−mk3,jE7
where i=1,..,N and j=1,..,T are indexes for the number of crows and iterations, respectively, while k1,k2,k3 are three random integers required to hold k1≠k2≠k3.
3.3 CSA with multi-strategy search (CSA-MSS)
As explained in the previous section, search-based on single strategy imposes limitations which enforce local optima. This renowned problem is tackled with various ways in the literature. Memetic algorithms [35], modified swarm intelligence algorithms [36], hybrid solutions [37] are the studies seeking for resolutions for this issue. Recently, adaptive operator selection schemes [8, 38] look promising to overcome this issue in an easier way. The proposed approach in this study is based on a multi-strategy search approach, CSA-MSS, which implies adaptive use of multiple search strategies in order to overcome the limitations of single strategy search. It consists of two main components: credit assignment and strategy selection. In order to evaluate the performance of the applied strategy, reward value, ri,j, is calculated as follows:
ri,j=fxgbestfx−fx′E8
where fx is the objective value of the current solution, and fx′ is the objective value of the candidate solution which is generated by search strategy i. To balance reward value in the course of iterations, improvement on objective function is normalised over global best solution value. In Eq. (9), credit values are assigned to each strategy.
ci,j+1=1−αci,j+αri,jsi,j+1E9
where α is the learning rate, ci,j and si,j are credit value and the number of successful improvements so far for strategy i at iteration j, respectively. The average reward is calculated for iteration j.
Adaptive pursuit approach is used as the strategy selection scheme for selecting the search strategy to operate due to its proven good performance in literature [8]. In this selection scheme, each strategy has a probability value which is calculated with Eq. (10).
where β is the learning rate in 01, pmin is the minimum selection probability, and ij∗ is the best strategy for iteration i. In this approach, the best strategy wins the maximum selection probability. The general overview of the CSA-MSS is given in Algorithm 2. The proposed approach starts with selecting a strategy to operate. If there is any non-rewarded strategy within the pool, a random selection is conducted to let these strategies have a chance to be opted. The selected strategy is operated on the current solution to generate a candidate solution—a neighbouring solution. A reward value is calculated for the opted strategy and recorded if it is successful in the running iteration. The credit values are updated from the reward values obtained/recorded at the end of each iteration. Both average and maximum reward values are calculated through a sliding window over the last W iterations.
In order to compare and analyse the performance of the approaches under-consideration alongside CSA, a number of well-known numeric benchmark functions have been used, which are collected from the literature [39, 40] as in Table 1. The characteristics of each are depicted in the second column with UN, MN, SP and NP standing for unimodality, multimodality, separable and non-separable, respectively. If the functions have only one local optima as the global optima, then it is called as unimodal function [41]. These types of functions can be used to demonstrate exploitation ability of the algorithms. If the functions have more than one local optima, then it is called a multimodal function, where exploration is also needed significantly to travel among the regions, i.e. modals, that have their local optima. If the relationship between any two inputs of a function is loose, i.e. can be easily operated standalone, then it is considered separable, non-separable otherwise [42].
This section presents the experimental study for testing the performance of proposed algorithm in comparison to a number of state-of-the-art algorithms. To fairly compare the performance of proposed approach, each algorithm was executed with 30 different runs; each seeded randomly. The algorithmic parameters of proposed approach and other variants, AP, FL, N are set to 0.1,2,30, respectively. The maximum function evaluation is set to 1.5E+5. Firstly, proposed approach is compared with the original CSA in Table 2. After that, CSA-MSS is compared with all strategies in Tables 3-6. The parameters of strategy selection scheme, i.e. Adaptive Pursuit, are set to α=0.1, beta=0.1, the reward value is calculated with extreme approach, i.e. maximum, over the last W=5 iterations. All parametric settings are delivered using parameter tuning experiments.
30D
60D
F. No
CSA
CSA-MSS
CSA
CSA-MSS
Mean
Std
Mean
Std
Sign
Mean
Std
Mean
Std
Sign
F1
4.77E-05
2.81E-05
1.79E-74
6.79E-74
+
7.87E+04
1.99E+04
7.63E-17
3.10E-16
+
F2
3.21E+00
5.95E+00
3.18E−66
1.70E-65
+
2.71E+09
4.69E+08
1.27E-12
5.75E-12
+
F3
6.60E-06
4.00E-06
1.78E-69
9.59E-69
+
2.50E+04
5.06E+03
9.11E-18
4.79E-17
+
F4
6.71E-14
1.21E-13
1.62E-78
8.73E-78
+
5.81E+40
1.37E+41
3.72E−31
2.00E-30
+
F5
1.32E-03
2.78E-04
6.17E-34
2.98E-33
+
3.17E+18
8.63E+18
1.74E-08
9.13E-08
+
F6
5.72E-02
1.95E-02
4.37E-01
1.89E-01
+
8.35E+00
2.73E-01
2.94E+00
4.17E-01
+
F7
0.00E+00
0.00E+00
3.30E+00
2.15E+00
+
7.75E+04
2.16E+04
3.15E+01
1.94E+01
+
F8
5.18E-14
8.21E-14
7.10E−62
3.82E-61
+
2.39E+02
7.05E+01
2.70E-31
9.43E-31
+
F9
1.91E-02
5.43E-03
9.85E-03
4.71E-03
+
2.16E+02
7.95E+01
5.40E-02
1.41E-02
+
F10
2.74E+01
9.95E+00
8.48E+00
6.43E+00
+
2.97E+06
8.81E+05
9.13E+01
3.25E+01
+
F11
3.73E+01
1.37E+01
5.01E+01
1.76E+01
+
7.86E+02
3.09E+01
1.16E+02
1.62E+01
+
F12
5.47E+01
2.29E+01
5.37E+01
1.67E+01
−
7.49E+02
2.83E+01
1.18E+02
2.70E+01
+
F13
6.30E-03
7.08E-03
3.93E-02
4.93E-02
+
6.57E+02
2.23E+02
8.54E-02
1.16E-01
+
F14
8.37E+03
3.37E+02
4.38E+03
7.61E+02
+
1.92E+04
3.24E+02
1.00E+04
1.07E+03
+
F15
1.40E-03
3.93E-04
3.24E+00
1.28E+00
+
1.98E+01
9.61E-01
6.89E+00
1.48E+00
+
F16
3.78E-07
2.89E-07
2.08E-01
4.50E-01
−
4.63E+08
1.41E+08
3.76E-01
6.87E-01
+
F17
4.60E-06
3.78E-06
4.84E-03
2.61E-02
+
8.62E+08
3.08E+08
3.33E-02
1.62E-01
+
F18
1.19E-03
1.03E-03
2.17E-09
1.17E-08
+
1.08E+02
8.18E+00
3.14E-05
1.69E-04
+
F19
3.19E-06
2.84E-06
4.47E-01
1.26E+00
−
1.16E+03
3.39E+02
7.76E-01
1.84E+00
+
F20
7.41E-02
1.82E-02
7.75E+00
2.28E+00
+
8.90E+01
5.61E+00
2.81E+01
3.89E+00
+
F21
5.73E-02
2.02E-02
8.01E-02
1.45E-02
+
3.97E-01
8.65E-03
1.66E-01
2.17E-02
+
F22
-6.89E+01
2.17E+00
-6.88E+01
2.88E+00
−
-3.65E+01
1.79E+00
-6.72E+01
1.55E+00
+
F23
−1.56E+01
3.43E+00
−2.58E+01
1.20E+00
+
−1.47E+01
8.09E-01
−5.04E+01
2.53E+00
+
Table 2.
The comparison of CSA and CSA-MSS on benchmark functions.
F No
CSA
GCSA
BCSA
CBCSA
RCSA
RMCSA
CSA-MSS
Mean
STD
Mean
STD
Mean
STD
Mean
STD
Mean
STD
Mean
STD
Mean
STD
F1
4.77E-05
2.81E-05
1.75E-19
1.45E-19
3.63E+04
1.22E+04
5.90E+03
5.21E+02
3.58E+04
5.30E+03
1.55E-39
6.04E-39
1.79E-74
6.79E-74
F2
3.21E+00
5.95E+00
4.48E-09
1.25E-08
6.03E+08
1.45E+08
7.49E+07
1.23E+07
5.83E+08
9.55E+07
1.36E-34
5.01E-34
3.18E−66
1.70E−65
F3
6.60E-06
4.00E-06
4.06E-20
3.41E-20
5.02E+03
1.18E+03
7.96E+02
1.06E+02
5.23E+03
7.18E+02
5.20E-39
2.21E-38
1.78E-69
9.59E-69
F4
6.71E-14
1.21E-13
4.82E-17
5.72E-17
1.32E+17
2.45E+17
1.80E+08
1.78E+08
5.81E+16
1.13E+17
8.72E+01
4.25E+02
1.62E-78
8.73E-78
F5
1.32E-03
2.78E-04
4.68E-11
2.34E-11
1.51E+06
2.29E+06
3.75E+01
3.23E+00
9.98E+05
1.83E+06
8.04E-27
3.13E-26
6.17E-34
2.98E-33
F6
5.72E-02
1.95E-02
2.10E-02
2.29E-02
7.11E+00
2.23E-01
3.23E+00
1.92E-01
7.06E+00
1.88E-01
2.30E+00
4.71E-01
4.37E-01
1.89E-01
F7
0.00E+00
0.00E+00
0.00E+00
0.00E+00
4.03E+04
4.31E+03
5.79E+03
7.46E+02
3.58E+04
4.35E+03
1.34E+01
3.25E+01
3.30E+00
2.15E+00
F8
5.18E-14
8.21E-14
7.12E-39
1.65E-38
4.39E+01
1.09E+01
1.24E+00
2.28E-01
3.48E+01
1.02E+01
4.80E-38
2.55E-37
7.10E-62
3.82E-61
F9
1.91E-02
5.43E-03
1.63E-02
5.13E-03
4.69E+01
1.18E+01
1.56E+00
2.75E-01
3.55E+01
7.44E+00
1.85E-02
1.88E-02
9.85E-03
4.71E-03
F10
2.74E+01
9.95E+00
2.85E+01
2.17E+01
1.05E+06
4.52E+05
3.30E+04
7.35E+03
9.65E+05
2.20E+05
2.65E+01
2.01E+01
8.48E+00
6.43E+00
F11
3.73E+01
1.37E+01
4.75E+01
1.08E+01
3.41E+02
1.14E+01
2.22E+02
1.19E+01
3.29E+02
1.64E+01
3.58E+01
7.61E+00
5.01E+01
1.76E+01
F12
5.47E+01
2.29E+01
2.97E+01
8.55E+00
3.04E+02
1.50E+01
1.89E+02
9.60E+00
2.92E+02
2.00E+01
5.19E+01
1.41E+01
5.37E+01
1.67E+01
F13
6.30E-03
7.08E-03
1.73E-02
3.45E-02
3.62E+02
3.72E+01
5.42E+01
7.96E+00
3.29E+02
3.57E+01
1.33E-02
1.70E-02
3.93E-02
4.93E-02
F14
8.37E+03
3.37E+02
5.51E+03
6.15E+02
8.40E+03
2.89E+02
8.31E+03
2.03E+02
8.39E+03
2.91E+02
4.51E+03
1.52E+03
4.38E+03
7.61E+02
F15
1.40E-03
3.93E-04
3.87E-02
2.08E-01
1.97E+01
5.59E-01
1.36E+01
4.54E-01
1.95E+01
2.02E-01
9.61E-01
7.36E-01
3.24E+00
1.28E+00
F16
3.78E-07
2.89E-07
2.81E-01
1.33E+00
1.82E+08
3.58E+07
1.81E+05
8.24E+04
1.26E+08
3.23E+07
3.90E-01
8.09E-01
2.08E-01
4.50E-01
F17
4.60E-06
3.78E-06
3.74E-19
5.71E-19
3.60E+08
1.13E+08
2.90E+06
1.02E+06
2.88E+08
8.23E+07
2.90E-01
9.00E-01
4.84E-03
2.61E-02
F18
1.19E-03
1.03E-03
2.49E-04
7.46E-04
4.53E+01
2.61E+00
2.32E+01
1.19E+00
4.33E+01
2.31E+00
1.67E-15
1.36E-15
2.17E-09
1.17E-08
F19
3.19E-06
2.84E-06
2.65E-13
3.97E-13
5.58E+02
7.14E+01
8.59E+01
1.02E+01
5.08E+02
6.01E+01
9.84E-02
1.63E-01
4.47E-01
1.26E+00
F20
7.41E-02
1.82E-02
1.14E+01
3.19E+00
4.16E+01
4.07E+00
2.72E+01
7.16E-01
4.12E+01
1.63E+00
8.72E-01
7.99E-01
7.75E+00
2.28E+00
F21
5.73E-02
2.02E-02
1.14E-01
2.52E-02
2.76E-01
3.35E-03
1.10E-01
1.99E-02
2.63E-01
1.88E-02
6.54E-02
2.82E-02
8.01E-02
1.45E-02
F22
-6.89E+01
2.17E+00
-6.76E+01
2.75E+00
−4.54E+01
4.79E+00
−5.29E+01
1.49E+00
−4.46E+01
1.62E+00
−7.12E+01
2.17E+00
−6.88E+01
2.88E+00
F23
−1.56E+01
3.43E+00
−2.56E+01
1.24E+00
−9.43E+00
6.27E-01
−1.16E+01
4.97E-01
−9.32E+00
3.83E-01
−2.61E+01
1.08E+00
−2.58E+01
1.20E+00
Table 3.
The comparison of the search strategies on 30D benchmark functions.
F No
CSA
GCSA
BCSA
CBCSA
RCSA
RMCSA
CSA-MSS
Rank
Sign
Rank
Sign
Rank
Sign
Rank
Sign
Rank
Sign
Rank
Sign
Rank
F1
4
+
3
+
7
−
5
−
6
−
2
+
1
F2
4
−
3
+
7
−
5
−
6
−
2
+
1
F3
4
+
3
+
6
−
5
−
7
−
2
+
1
F4
3
+
2
+
7
−
5
−
6
−
4
−
1
F5
4
+
3
+
7
−
5
−
6
−
2
+
1
F6
2
+
1
+
7
−
5
−
6
−
4
−
3
F7
1
+
1
+
7
−
5
−
6
−
4
−
3
F8
4
+
2
+
7
−
5
−
6
−
3
+
1
F9
4
+
2
+
7
−
5
−
6
−
3
+
1
F10
3
−
4
−
7
−
5
−
6
−
2
−
1
F11
2
−
3
−
7
−
5
−
6
−
1
−
4
F12
4
−
1
−
7
−
5
−
6
−
2
−
3
F13
1
+
3
+
7
−
5
−
6
−
2
+
4
F14
5
−
3
−
7
−
4
−
6
−
2
−
1
F15
1
+
2
−
7
−
5
−
6
−
3
−
4
F16
1
+
3
−
7
−
5
−
6
−
4
−
2
F17
2
+
1
+
7
−
5
−
6
−
4
−
3
F18
4
+
3
+
7
−
5
−
6
−
1
+
2
F19
2
+
1
+
7
−
5
−
6
−
3
−
4
F20
1
+
4
−
7
−
5
−
6
−
2
−
3
F21
1
+
5
+
7
+
4
+
6
+
2
+
3
F22
2
−
4
−
6
−
5
−
7
−
1
−
3
F23
4
−
3
−
6
−
5
−
7
−
1
−
2
Mean:
2.73913
2.608696
6.869565
4.913043
6.130435
2.434783
2.26087
Table 4.
The statistical details of comparison of the search strategies on 30D benchmark functions.
F No
CSA
GCSA
BCSA
CBCSA
RCSA
RMCSA
CSA-MSS
Mean
STD
Mean
STD
Mean
STD
Mean
STD
Mean
STD
Mean
STD
Mean
STD
F1
7.87E+04
1.99E+04
8.51E-13
4.83E-13
1.13E+05
4.89E+03
2.90E+04
2.32E+03
1.14E+05
5.66E+03
5.27E-09
2.05E-08
7.63E-17
3.10E-16
F2
2.71E+09
4.69E+08
2.66E+00
3.27E+00
2.88E+09
4.26E+08
6.08E+08
1.02E+08
2.83E+09
3.31E+08
2.83E+08
7.80E+08
1.27E-12
5.75E-12
F3
2.50E+04
5.06E+03
6.64E-13
3.17E-13
3.20E+04
1.42E+03
8.04E+03
5.22E+02
3.19E+04
2.11E+03
1.08E-08
4.21E-08
9.11E-18
4.79E-17
F4
5.81E+40
1.37E+41
3.26E-14
2.84E-14
2.11E+41
9.55E+41
6.83E+27
1.42E+28
7.94E+40
1.43E+41
2.38E+40
6.95E+40
3.72E−31
2.00E-30
F5
3.17E+18
8.63E+18
2.37E-07
1.04E-07
6.61E+19
1.69E+20
4.04E+02
4.16E+02
5.83E+19
1.16E+20
1.62E+17
7.84E+17
1.74E-08
9.13E-08
F6
8.35E+00
2.73E-01
1.49E+00
2.84E-01
8.41E+00
2.04E-01
5.37E+00
1.71E-01
8.42E+00
2.01E-01
4.96E+00
1.73E+00
2.94E+00
4.17E-01
F7
7.75E+04
2.16E+04
2.00E-01
5.42E-01
1.15E+05
5.48E+03
3.03E+04
1.29E+03
1.15E+05
4.54E+03
1.88E+02
1.64E+02
3.15E+01
1.94E+01
F8
2.39E+02
7.05E+01
1.24E-26
9.93E-27
3.46E+02
3.04E+01
3.03E+01
3.88E+00
3.41E+02
2.53E+01
8.70E-12
4.44E-11
2.70E-31
9.43E-31
F9
2.16E+02
7.95E+01
7.06E-02
1.91E-02
3.53E+02
3.04E+01
2.86E+01
4.47E+00
3.45E+02
3.60E+01
1.75E-01
5.41E-02
5.40E-02
1.41E-02
F10
2.97E+06
8.81E+05
8.99E+01
4.53E+01
4.76E+06
5.80E+05
4.18E+05
5.10E+04
4.74E+06
4.95E+05
1.52E+02
4.80E+01
9.13E+01
3.25E+01
F11
7.86E+02
3.09E+01
1.14E+02
2.44E+01
8.17E+02
1.68E+01
5.72E+02
1.96E+01
8.07E+02
2.57E+01
1.05E+02
6.56E+01
1.16E+02
1.62E+01
F12
7.49E+02
2.83E+01
1.04E+02
2.23E+01
7.54E+02
2.62E+01
5.20E+02
2.25E+01
7.55E+02
3.01E+01
3.09E+02
2.95E+02
1.18E+02
2.70E+01
F13
6.57E+02
2.23E+02
6.16E-03
7.24E-03
1.03E+03
5.50E+01
2.69E+02
1.64E+01
1.04E+03
4.44E+01
1.72E-02
3.56E-02
8.54E-02
1.16E-01
F14
1.92E+04
3.24E+02
1.43E+04
1.87E+03
1.91E+04
5.35E+02
1.92E+04
4.43E+02
1.92E+04
4.70E+02
1.93E+04
4.05E+02
1.00E+04
1.07E+03
F15
1.98E+01
9.61E-01
7.41E+00
2.29E+00
2.04E+01
6.71E-02
1.69E+01
2.91E-01
2.04E+01
9.49E-02
4.52E+00
1.44E+00
6.89E+00
1.48E+00
F16
4.63E+08
1.41E+08
6.56E-01
7.70E-01
7.97E+08
8.99E+07
2.08E+07
6.58E+06
7.88E+08
1.03E+08
2.00E+04
1.03E+05
3.76E-01
6.87E-01
F17
8.62E+08
3.08E+08
7.58E+00
1.25E+01
1.59E+09
1.37E+08
8.28E+07
1.47E+07
1.58E+09
1.87E+08
5.30E+03
2.59E+04
3.33E-02
1.62E-01
F18
1.08E+02
8.18E+00
1.09E-02
1.20E-02
1.11E+02
4.63E+00
6.81E+01
2.56E+00
1.13E+02
4.00E+00
4.14E+01
5.45E+01
3.14E-05
1.69E-04
F19
1.16E+03
3.39E+02
3.66E-03
1.97E-02
1.67E+03
8.70E+01
4.58E+02
2.73E+01
1.68E+03
7.68E+01
4.64E-01
1.22E+00
7.76E-01
1.84E+00
F20
8.90E+01
5.61E+00
3.77E+01
4.39E+00
9.61E+01
1.37E+00
6.81E+01
1.64E+00
9.56E+01
1.64E+00
1.02E+01
3.15E+00
2.81E+01
3.89E+00
F21
3.97E-01
8.65E-03
2.25E-01
2.60E-02
4.07E-01
7.33E-03
2.57E-01
1.87E-02
4.04E-01
7.64E-03
1.71E-01
2.17E-02
1.66E-01
2.17E-02
F22
-3.65E+01
1.79E+00
−6.65E+01
1.54E+00
−3.57E+01
1.35E+00
−4.34E+01
1.07E+00
−3.60E+01
1.66E+00
−6.49E+01
1.08E+01
−6.72E+01
1.55E+00
F23
−1.47E+01
8.09E-01
−4.73E+01
1.75E+00
−1.49E+01
7.20E-01
−1.80E+01
7.08E-01
−1.46E+01
5.62E-01
−1.48E+01
7.80E-01
−5.04E+01
2.53E+00
Table 5.
The comparison of the search stragies on 60D benchmark functions.
F No
CSA
GCSA
BCSA
CBCSA
RCSA
RMCSA
CSA-MSS
Rank
Sign
Rank
Sign
Rank
Sign
Rank
Sign
Rank
Sign
Rank
Sign
Rank
F1
5
+
2
+
6
+
4
+
7
+
3
+
1
F2
5
+
2
+
7
+
4
+
6
+
3
+
1
F3
5
+
2
+
7
+
4
+
6
+
3
+
1
F4
5
+
2
+
7
+
3
+
6
+
4
+
1
F5
5
+
2
+
7
+
3
+
6
+
4
+
1
F6
5
+
1
+
6
+
4
+
7
+
3
+
2
F7
5
+
1
+
7
+
4
+
6
+
3
+
2
F8
5
+
2
+
7
+
4
+
6
+
3
+
1
F9
5
+
2
+
7
+
4
+
6
+
3
+
1
F10
5
+
1
+
7
+
4
+
6
+
3
+
2
F11
5
+
2
+
7
+
4
+
6
+
1
+
3
F12
5
+
1
+
6
+
4
+
7
+
3
+
2
F13
5
+
1
+
6
+
4
+
7
+
2
+
3
F14
6
+
2
+
3
+
4
+
5
+
7
+
1
F15
5
+
3
+
7
+
4
+
6
+
1
+
2
F16
5
+
2
+
7
+
4
+
6
+
3
+
1
F17
5
+
2
+
7
+
4
+
6
+
3
+
1
F18
5
+
2
+
6
+
4
+
7
+
3
+
1
F19
5
+
1
+
6
+
4
+
7
+
2
+
3
F20
5
+
3
+
7
+
4
+
6
+
1
+
2
F21
5
+
3
+
7
+
4
+
6
+
2
+
1
F22
5
+
2
+
7
+
4
+
6
+
3
+
1
F23
6
+
2
+
4
+
3
+
7
+
5
+
1
Mean:
5.086957
1.869565
6.434783
3.869565
6.26087
2.956522
1.521739
Table 6.
The statistical details of comparison of the search stragies on 60D benchmark functions.
Table 2 shows the results of CSA and CSA-MSS algorithms on 30- and 60-dimensional benchmark functions. For the 30D problems, CSA looks slightly competing with the CSA-MSS. The column with Sign headings represents Wilcoxon signed sum rank test result. If the sign is +, it means that the result is statistically sound, otherwise not sound, where the results of four out of 23 functions do not look statistically sound. In eight out of 19 functions, CSA produces better solutions while, in 11 out 19 functions, CSA-MSS performs better. When we look at the characteristics of the functions best obtained with CSA, it is clearly shown that most of them are multi-modal, because CSA has good performance on these types of problems. On the other side, CSA-MSS has improved the success over the CSA on uni-modal functions. For the 60D functions, CSA-MSS outperforms CSA in both mean and standard deviation statistics of the best solutions. And the success of CSA-MSS is statistically sound for all benchmark functions.
The comparative experimental results with 30 D benchmark functions for different search strategies are presented in Tables 3 and 4. CSA-MSS has produced the minimum mean values for F1, F2, F3, F4, F5, F8, F9, F10, F14 functions, while CSA achieves slightly better results for F7, F13, F15, F16, F20, F21 functions. For F6, F7, F12, F17, F19 functions, GCSA produced better solutions than the others. RMCSA found the minimum mean values for F11, F18, F22, F23 functions, while CBCSA and RCSA have lower performance than the others presumably due to their poorer exploitation capabilities. The tables for both dimensions are combined to conduct statistical confidence test of the success, which demonstrated that the success of CSA-MSS over the others is found statistically sound with an exemption that the success of RMCSA for the functions F11 and F18 is not statistically different from CSA-MSS. It is noted that the mean rank values by CSA-MSS remain slightly better than the other methods.
The comparison of the experimental results for 60 D benchmark functions with different search strategies are provided in Tables 5 and 6. CSA-MSS has the minimum mean values except for F6, F7, F10, F11, F12, F13, F15, F19, F20 functions. Nevertheless, for all functions considered in the test, CSA-MSS produced better solutions than CSA. It is also observed that GCSA produced better solutions than CSA-MSS for F6, F7, F10, F12, F13, F19 functions, and RMCSA produced better solutions than CSA-MSS for F11, F15, F20 functions. CSA turns to poorer performance when the problem dimension increases. The statistical soundness of the success of CSA-MSS is approved except for F21, F22 functions. For the comparison of mean rank values, CSA-MSS outperforms the other methods.
Figure 1 depicts the convergence comparison of the methods over the iterations. As seen, CSA-MSS has higher convergence speed from the others for F1,F2,F3 and F4 functions. For the 60 D problems, Figure 2 shows the convergence history for the all methods.
Figure 1.
The converge graphs of compared methods on 30D benchmark functions. (a) F1 Function. (b) F2 Function. (c) F4 Function. (d) F8 Function.
Figure 2.
The converge graphs of compared methods on 60D benchmark functions. (a) F16 Function. (b) F21 Function. (c) F17 Function. (d) F3 Function.
5. Conclusions
In this article, a multi-strategy search with crow search algorithm is proposed for solving global optimisation problems. In the proposed approach, adaptive pursuit is used to select search strategies according to the search dynamics. Search strategies are obtained from other state-of-the-art metaheuristics and implemented into crow search algorithm. Twenty-three benchmark functions with different characteristics have been used to compare strategies’ performance. The proposed approach is demonstrated that it helps improve the performance of original crow search algorithm especially on the higher-dimensional problems. Future works of this study would be dynamic and adaptive parametric settings, especially for awareness probability and flight length distance. It may also include embedding machine learning approaches to develop high-performance adaptive strategy selection schemes into the algorithm to achieve transfer learning.
Acknowledgments
This study has been conducted under the research support scheme of 2219, namely “Post-doctorate research programme”, TUBITAK of Republic of Turkey.
Conflict of interest
The authors declare no conflict of interest.
Notes/thanks
The first author would like to show his thanks to TUBITAK for supporting his visit in UWE Bristol, UK, in the period between October, 2021 and October 2022.
\n',keywords:"swarm intelligence, crow search algorithm, global optimisation, strategy selection, operator selection schemes",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80810.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80810.xml",downloadPdfUrl:"/chapter/pdf-download/80810",previewPdfUrl:"/chapter/pdf-preview/80810",totalDownloads:30,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"January 25th 2022",datePrePublished:"March 10th 2022",datePublished:null,dateFinished:"March 10th 2022",readingETA:"0",abstract:"Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80810",risUrl:"/chapter/ris/80810",signatures:"Rafet Durgut and Mehmet Emin Aydin",book:{id:"10653",type:"book",title:"Optimization Algorithms",subtitle:null,fullTitle:"Optimization Algorithms",slug:null,publishedDate:null,bookSignature:"Prof. Nodari Vakhania",coverURL:"https://cdn.intechopen.com/books/images_new/10653.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83968-666-5",printIsbn:"978-1-83968-665-8",pdfIsbn:"978-1-83968-667-2",isAvailableForWebshopOrdering:!0,editors:[{id:"202585",title:"Prof.",name:"Nodari",middleName:null,surname:"Vakhania",slug:"nodari-vakhania",fullName:"Nodari Vakhania"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related works",level:"1"},{id:"sec_3",title:"3. Materials and methods",level:"1"},{id:"sec_3_2",title:"3.1 Crow search algorithm",level:"2"},{id:"sec_4_2",title:"3.2 Search strategies",level:"2"},{id:"sec_5_2",title:"3.3 CSA with multi-strategy search (CSA-MSS)",level:"2"},{id:"sec_6_2",title:"3.4 Benchmark functions",level:"2"},{id:"sec_8",title:"4. Experimental results",level:"1"},{id:"sec_9",title:"5. Conclusions",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"},{id:"sec_13",title:"Conflict of interest",level:"1"},{id:"sec_10",title:"Notes/thanks",level:"1"}],chapterReferences:[{id:"B1",body:'Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures. 2016;169:1-12'},{id:"B2",body:'Said GAE-NA, Mahmoud AM, El-Horbaty E-SM. A comparative study of meta-heuristic algorithms for solving quadratic assignment problem. arXiv preprint arXiv:1407.4863. 2014'},{id:"B3",body:'Helmi AM et al. Efficient and sustainable reconfiguration of distribution networks via metaheuristic optimization. In: IEEE Transactions on Automation Science and Engineering. IEEE; 2021'},{id:"B4",body:'Kalita K, Ghadai RK, Chakraborty S. A comparative study on the metaheuristic-based optimization of skew composite laminates. Engineering with Computers. 2021:1-18. (In press)'},{id:"B5",body:'Baatar N, Zhang D, Koh C-S. An improved differential evolution algorithm adopting \\𝜆-best mutation strategy for global optimization of electromagnetic devices. IEEE Transactions on Magnetics. 2013;49(5):2097-2100'},{id:"B6",body:'Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan: IEEE; 1995. pp. 39-43'},{id:"B7",body:'Wang F, Zhang H, Zhou A. A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm and Evolutionary Computation. 2021;60:100808'},{id:"B8",body:'Durgut R, Aydin ME. Adaptive binary artificial bee colony algorithm. Applied Soft Computing. 2021;101:107054'},{id:"B9",body:'Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing. 2008;8(1):687-697'},{id:"B10",body:'Garcıa J, Maureira C. A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem. Applied Soft Computing. 2021;102:107077'},{id:"B11",body:'Yang X-S, Deb S. Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE; 2009. pp. 210-214'},{id:"B12",body:'Naik A, Satapathy SC. A comparative study of social group optimization with a few recent optimization algorithms. Complex & Intelligent Systems. 2021;7(1):249-295'},{id:"B13",body:'He S, Wu QH, Saunders JR. Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation. 2009;13(5):973-990'},{id:"B14",body:'Yang X-S. Firefly algorithm, levy flights and global optimization. In: Research and Development in Intelligent Systems XXVI. Springer; 2010. pp. 209-218'},{id:"B15",body:'Yang X-S, Slowik A. Firefly algorithm. In: Swarm Intelligence Algorithms. CRC Press; 2020. pp. 163-174'},{id:"B16",body:'Morales-Castañeda B et al. A better balance in metaheuristic algorithms: Does it exist? Swarm and Evolutionary Computation. 2020;54:100671'},{id:"B17",body:'Hughes M, Goerigk M, Dokka T. Particle swarm metaheuristics for robust optimisation with implementation uncertainty. Computers & Operations Research. 2020;122:104998'},{id:"B18",body:'Majhi SK, Sahoo M, Pradhan R. A space transformational crow search algorithm for optimization problems. Evolutionary Intelligence. 2020;13(3):345-364'},{id:"B19",body:'Ozsari S et al. Adaptation of metaheuristic algorithms to improve training performance of an ESZSL model. Turkish Journal of Electrical Engineering & Computer Sciences. 2021;29:3'},{id:"B20",body:'Xiao S et al. Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation. Applied Soft Computing. 2021;100:106955'},{id:"B21",body:'Hu P et al. Multi-strategy serial cuckoo search algorithm for global optimization. Knowledge-Based Systems. 2021;214:106729'},{id:"B22",body:'Zamani H, Nadimi-Shahraki MH, Gandomi AH. CCSA: Conscious neighborhood-based crow search algorithm for solving global optimization problems. Applied Soft Computing. 2019;85:105583'},{id:"B23",body:'Sonuc E. Binary crow search algorithm for the uncapacitated facility location problem. Neural Computing and Applications. 2021;33(21):1-17'},{id:"B24",body:'Majhi SK, Sahoo M, Pradhan R. Oppositional crow search algorithm with mutation operator for global optimization and application in designing FOPID controller. Evolving Systems. 2019;12(2):1-26'},{id:"B25",body:'Rizk-Allah RM, Hassanien AE, Bhattacharyya S. Chaotic crow search algorithm for fractional optimization problems. Applied Soft Computing. 2018;71:1161-1175'},{id:"B26",body:'dos Santos Coelho L et al. Electromagnetic optimization based on Gaussian crow search approach. In: 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE; 2018. pp. 1107-1112'},{id:"B27",body:'Necira A et al. Dynamic crow search algorithm based on adaptive parameters for large-scale global optimization. Evolutionary Intelligence. 2021:1-17. (In press)'},{id:"B28",body:'Hussien AG et al. Crow search algorithm: Theory, recent advances, and applications. IEEE Access. 2020;8:173548-173565'},{id:"B29",body:'Meraihi Y et al. A comprehensive survey of crow search algorithm and its applications. Artificial Intelligence Review. 2020;54(4):1-48'},{id:"B30",body:'Prior H, Schwarz A, Güntürkün O. Mirror-induced behavior in the magpie (Pica pica): Evidence of self-recognition. PLoS Biology. 2008;6(8):e202'},{id:"B31",body:'Black H. Social skills to crow about. Scientific American Mind. 2013;24(4):12-12'},{id:"B32",body:'Abdelaziz AY, Fathy A. A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks. Engineering Science and Technology, an International Journal. 2017;20(2):391-402'},{id:"B33",body:'Kiran MS et al. Artificial bee colony algorithm with variable search strategy for continuous optimization. Information Sciences. 2015;300:140-157'},{id:"B34",body:'Peng H, Deng C, Zhijian W. Best neighbor-guided artificial bee colony algorithm for continuous optimization problems. Soft Computing. 2019;23(18):8723-8740'},{id:"B35",body:'Liang J, Xue Y. An adaptive GP-based memetic algorithm for symbolic regression. Applied Intelligence. 2020;50(11):3961-3975'},{id:"B36",body:'Aydin ME. Coordinating metaheuristic agents with swarm intelligence. Journal of Intelligent Manufacturing. 2012;23(4):991-999'},{id:"B37",body:'Abualigah L et al. A parallel hybrid krill herd algorithm for feature selection. International Journal of Machine Learning and Cybernetics. 2021;12(3):783-806'},{id:"B38",body:'Durgut R, Yavuz İ, Aydin M. Küme Birles,imli Sırt Çantası Probleminin Adaptif Yapay Arı Kolonisi Algoritması ile Çözümü. Journal of Intelligent Systems: Theory and Applications. 2021:43-54. DOI: 10.38016/jista.854584'},{id:"B39",body:'Surjanovic S, Bingham D. Virtual Library of Simulation Experiments: Test Functions and Datasets. Burnaby, BC, Canada: Simon Fraser University; 2013. p. 2015. [Accessed: May 13]'},{id:"B40",body:'Zhu G, Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation. 2010;217(7):3166-3173'},{id:"B41",body:'Trelea IC. The particle swarm optimization algorithm: Convergence analysis and parameter selection. Information Processing Letters. 2003;85(6):317-325'},{id:"B42",body:'Babaoglu I. Artificial bee colony algorithm with distribution-based update rule. Applied Soft Computing. 2015;34:851-861'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Rafet Durgut",address:"rdurgut@bandirma.edu.tr;, rafet.durgut@uwe.ac.uk",affiliation:'
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
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Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
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Choosing to publish with IntechOpen ensures the following benefits:
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Indexing and listing across major repositories, see details ...
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Long-term archiving
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Live Performance Metrics to track readership and the impact of your chapter
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Dissemination and Promotion
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Among these disasters—earthquake is the most- scary and damaging. The effects of a disaster, whether natural or human induced, are often long lasting. The Gorkha earthquake of 25 April 2015 enormously affected human, socio-economic and other multiple sectors and left deep scars mainly in the economy, livelihood and infrastructure of the country. Besides the natural factors, the damages from disasters in Nepal are in increasing trend due to the human activities and inadequate proactive legislations. Fundamentally, the weak structures have been found as the major cause of damage in earthquakes. This underlines the need for strict compliance of building codes. Thus, proactive disaster management legislation focusing on disaster preparedness is necessary. This paper analyses and shows the critical gaps and responsible factors that would contribute towards seismic risk reduction to enable various stakeholders to enhance seismic safety in Nepal. 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As research case studies, large earthquake disasters in Japan and Iran were investigated and analyzed. Normal distribution was found to be a good estimate of the magnitude distribution for earthquakes, in both the countries. In Japan, there is almost a linear correlation between magnitude of earthquakes and number of dead people. However, such correlation is not present for Iran. This lack of correlation in Iran and existence of linear correlation in Japan highlights that the magnitude of earthquakes directly affects the number of fatalities and extent of destruction in Japan, while in Iran, there is an increased complexity with regard to the factors affecting earthquake consequences. A correlation is suggested between earthquake culture and learning from large earthquake disasters in both Japan and Iran. Learning from large earthquake disasters is impacted by a multitude of factors, but the rhythm of learning in Japan is much higher if compared with Iran. For both Japan and Iran, a reactive learning approach based on past earthquake disasters needs to be constantly backed up by a proactive approach and dynamic learning.",book:{id:"6564",slug:"earthquakes-forecast-prognosis-and-earthquake-resistant-construction",title:"Earthquakes",fullTitle:"Earthquakes - Forecast, Prognosis and Earthquake Resistant Construction"},signatures:"Michaela Ibrion and Nicola Paltrinieri",authors:[{id:"209369",title:"Ph.D.",name:"Michaela",middleName:null,surname:"Ibrion",slug:"michaela-ibrion",fullName:"Michaela Ibrion"},{id:"244752",title:"Dr.",name:"Nicola",middleName:null,surname:"Paltrinieri",slug:"nicola-paltrinieri",fullName:"Nicola Paltrinieri"}]},{id:"66486",doi:"10.5772/intechopen.85557",title:"The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW) Messages Distributed via Mobile Phone Apps",slug:"the-idea-model-as-a-conceptual-framework-for-designing-earthquake-early-warning-eew-messages-distrib",totalDownloads:854,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"Short response time available in the event of a major earthquake poses unique challenges for earthquake early warning (EEW). Mobile phone apps may be one way to deliver such messages effectively. In this two-phase study, several hundred participants were first randomly assigned to one of eight experimental conditions. Results of phase one afforded researchers the ability to reduce the number of conditions to four. Phase two consisted of five experimental conditions. In each condition, a 10 second EEW was delivered via a phone app. The four treatment conditions were designed according to elements of the IDEA model. The control condition was based on the actual ShakeAlert EEW computer program message being used by emergency managers across the US west coast at the time. Results of this experiment revealed that EEW messages designed according to the IDEA model were more effective in producing desired learning outcomes than the ShakeAlert control message. Thus, the IDEA model may provide an effective content framework for those choosing to develop such apps for EEW.",book:{id:"7660",slug:"earthquakes-impact-community-vulnerability-and-resilience",title:"Earthquakes",fullTitle:"Earthquakes - Impact, Community Vulnerability and Resilience"},signatures:"Deanna D. Sellnow, Lucile M. Jones, Timothy L. Sellnow, Patric Spence, Derek R. 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Stamatopoulos",authors:[{id:"171228",title:"Dr.",name:"Constantine",middleName:null,surname:"Stamatopoulos",slug:"constantine-stamatopoulos",fullName:"Constantine Stamatopoulos"}]},{id:"67102",title:"Impacts of the 2015 Gorkha Earthquake: Lessons Learnt from Nepal",slug:"impacts-of-the-2015-gorkha-earthquake-lessons-learnt-from-nepal",totalDownloads:2212,totalCrossrefCites:5,totalDimensionsCites:9,abstract:"Nepal is highly vulnerable to a number of disasters for example: earthquakes, floods, landslides, fires, epidemics, avalanches, windstorms, hailstorms, lightning, glacier lake outburst floods, droughts and dangerous weather events. Among these disasters—earthquake is the most- scary and damaging. The effects of a disaster, whether natural or human induced, are often long lasting. The Gorkha earthquake of 25 April 2015 enormously affected human, socio-economic and other multiple sectors and left deep scars mainly in the economy, livelihood and infrastructure of the country. Besides the natural factors, the damages from disasters in Nepal are in increasing trend due to the human activities and inadequate proactive legislations. Fundamentally, the weak structures have been found as the major cause of damage in earthquakes. This underlines the need for strict compliance of building codes. Thus, proactive disaster management legislation focusing on disaster preparedness is necessary. This paper analyses and shows the critical gaps and responsible factors that would contribute towards seismic risk reduction to enable various stakeholders to enhance seismic safety in Nepal. Additionally, this chapter aims to pinpoint the deficiencies in disaster management system in Nepal with reference to the devastating Gorkha earthquake and suggest appropriate policy and advanced technical measures for improvement.",book:{id:"7660",slug:"earthquakes-impact-community-vulnerability-and-resilience",title:"Earthquakes",fullTitle:"Earthquakes - Impact, Community Vulnerability and Resilience"},signatures:"Shiva Subedi and Meen Bahadur Poudyal Chhetri",authors:[{id:"285969",title:"Mr.",name:"Shiva",middleName:null,surname:"Subedi",slug:"shiva-subedi",fullName:"Shiva Subedi"},{id:"293220",title:"Dr.",name:"Meen",middleName:null,surname:"Paudyal Chhetri",slug:"meen-paudyal-chhetri",fullName:"Meen Paudyal Chhetri"}]},{id:"63029",title:"An Estimation of “Energy” Magnitude Associated with a Possible Lithosphere-Atmosphere-Ionosphere Electromagnetic Coupling Before the Wenchuan MS8.0 Earthquake",slug:"an-estimation-of-energy-magnitude-associated-with-a-possible-lithosphere-atmosphere-ionosphere-elect",totalDownloads:1117,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"A large scale of abnormities from ground-based electromagnetic parameters to ionospheric parameters has been recorded during the Wenchuan MS8.0 earthquake. All these results present different anomalous periods, but there seems one common climax leading to a lithosphere-atmosphere-ionosphere electromagnetic coupling (LAIEC) right on May 9, 3 days prior to the Wenchuan main shock. Based on the electron-hole theory, this chapter attempts to estimate the “energy source” magnitude driving this obvious coupling with the Wenchuan focus zone parameters considered. The simulation results show that the total surface charges fall in ~107–108 C, and the related upward electric field is ~108–109 V/m. These corresponding parameters are up to 109 C and 1010 V/m when the main rupture happens, and the order of the output current is up to 107 A. The electric field increasing in the interface between the Earth’s surface and the atmosphere, on one hand, can cause electromagnetic parameter abnormities of ground-based observation, with the range beyond 1000 km. On the other hand, it can accumulate air ionization above pre-earthquake zone and lead to ionospheric anomaly recorded by some spatial seismic monitoring satellites.",book:{id:"6564",slug:"earthquakes-forecast-prognosis-and-earthquake-resistant-construction",title:"Earthquakes",fullTitle:"Earthquakes - Forecast, Prognosis and Earthquake Resistant Construction"},signatures:"Mei Li, Wenxin Kong, Chong Yue, Shu Song, Chen Yu, Tao Xie and\nXian Lu",authors:[{id:"236284",title:"Dr.",name:"Mei",middleName:null,surname:"Li",slug:"mei-li",fullName:"Mei Li"},{id:"243785",title:"MSc.",name:"Chen",middleName:null,surname:"Yu",slug:"chen-yu",fullName:"Chen Yu"},{id:"243786",title:"MSc.",name:"Chong",middleName:null,surname:"Yue",slug:"chong-yue",fullName:"Chong Yue"},{id:"243788",title:"Dr.",name:"Tao",middleName:null,surname:"Xie",slug:"tao-xie",fullName:"Tao Xie"},{id:"243789",title:"MSc.",name:"Wenxin",middleName:null,surname:"Kong",slug:"wenxin-kong",fullName:"Wenxin Kong"},{id:"243790",title:"BSc.",name:"Shu",middleName:null,surname:"Song",slug:"shu-song",fullName:"Shu Song"}]}],onlineFirstChaptersFilter:{topicId:"778",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:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,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:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"May 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,annualVolume:11410,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,annualVolume:11414,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:48,paginationItems:[{id:"81799",title:"Cross Talk of Purinergic and Immune Signaling: Implication in Inflammatory and Pathogenic Diseases",doi:"10.5772/intechopen.104978",signatures:"Richa Rai",slug:"cross-talk-of-purinergic-and-immune-signaling-implication-in-inflammatory-and-pathogenic-diseases",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"81764",title:"Involvement of the Purinergic System in Cell Death in Models of Retinopathies",doi:"10.5772/intechopen.103935",signatures:"Douglas Penaforte Cruz, Marinna Garcia Repossi and Lucianne Fragel Madeira",slug:"involvement-of-the-purinergic-system-in-cell-death-in-models-of-retinopathies",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"81756",title:"Alteration of Cytokines Level and Oxidative Stress Parameters in COVID-19",doi:"10.5772/intechopen.104950",signatures:"Marija Petrusevska, Emilija Atanasovska, Dragica Zendelovska, Aleksandar Eftimov and Katerina Spasovska",slug:"alteration-of-cytokines-level-and-oxidative-stress-parameters-in-covid-19",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",subseries:{id:"18",title:"Proteomics"}}},{id:"81681",title:"Immunomodulatory Effects of a M2-Conditioned Medium (PRS® CK STORM): Theory on the Possible Complex Mechanism of Action through Anti-Inflammatory Modulation of the TLR System and the Purinergic System",doi:"10.5772/intechopen.104486",signatures:"Juan Pedro Lapuente",slug:"immunomodulatory-effects-of-a-m2-conditioned-medium-prs-ck-storm-theory-on-the-possible-complex-mech",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}}]},overviewPagePublishedBooks:{paginationCount:27,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science and Technology from the Department of Chemistry, National University of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013. She relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the National Institute of Fundamental Studies from April 2013 to October 2016. She was a senior lecturer on a temporary basis at the Department of Food Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is currently Deputy Principal of the Australian College of Business and Technology – Kandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI) Ambassador to Sri Lanka.",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. 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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