Association studies of genetic polymorphisms in TLRs and leprosy.
\r\n\tIn this book we will try to cover most of the topics related to tensile testing. These topics will include the introduction to tensile testing, tensile testing equipment, tensile measuring, and data analysis software, tensile testing behavior of conventional and advanced engineering and biomaterials, tensile testing of engineering designs, the applications of tensile testing and effects of tensile loads on materials properties. It is expected that this book will be very attractive for undergraduate and graduate engineering students. Moreover, this book will be equally beneficial for design, production, processing, quality control, material testing, and safety engineers.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"db0f7fb44c3e0152c42b386df6d2f591",bookSignature:"Dr. Muhammad Tariq Saeed Chani",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9301.jpg",keywords:"Tensile Testing History, Tensile Testing Methods, Hydraulic Machines, Electromechanical Machines, Material Testing, Data Analysis, Metals and Alloys, Polymers, Composites, Strain Hardening, Loading rates, Tensile Testing Applications",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 23rd 2020",dateEndSecondStepPublish:"May 14th 2020",dateEndThirdStepPublish:"July 13th 2020",dateEndFourthStepPublish:"October 1st 2020",dateEndFifthStepPublish:"November 30th 2020",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:'Dr. Muhammad Tariq Saeed Chani has published more than 48 research papers and has 15 completed and 5 ongoing research projects as well as registered 4 US patents, among which "Composition and method of making a strain sensor and its use."',coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"308513",title:"Dr.",name:"Muhammad Tariq Saeed",middleName:null,surname:"Chani",slug:"muhammad-tariq-saeed-chani",fullName:"Muhammad Tariq Saeed Chani",profilePictureURL:"https://mts.intechopen.com/storage/users/308513/images/system/308513.png",biography:"Engineer Dr. Muhammad Tariq Saeed Chani is currently working at the Center of Excellence for Advanced Materials Research, King Abdulaziz University, Jeddah, Saudi Arabia. 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In this sense, several studies have been conducted aiming to explore the molecular basis of leprosy, and they are mainly focused on aspects of host-pathogen interaction and modulation of host immune response against
Recognition of
One family of PRRs is the Toll-like receptors (TLRs) that are expressed on cell surface of different cells from innate immune system or endocytic vesicle membrane [2]. These receptors have an extracellular domain that recognizes different bacterial agonists. The activation of TLRs mediate host immune response regulating phagocytosis and antimicrobial activity or initiating signaling cascades (through activation of transcription factors NF-κB and IRF) culminating in modulation of cytokines and chemokines release [3].
There is another family of PRR, called nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) that are cytoplasmic receptors, and they are able to recognize bacterial agonists inside the host cells. Some members of this family activates NF-κB, interferon regulatory factors (IRFs), and mitogen-activated protein kinases (MAPKs), which control the expression of mediators of immune response, such as cytokines, chemokines, and type I interferons. On the other hand, other members lead to activation of caspase-1 acting through inflammasomes [4].
Genetic variants leading to functional changes in PRRs, such as TLRs and NLRs, may be involved with differences in host immune response modulation and consecutive susceptibility or resistance to leprosy and their clinical forms as well leprosy reactions.
In this chapter, studies about genetic association of PRRs, specifically TLRs and NOD2, with leprosy will be reviewed.
TLRs were first described in the mid-1990s after genome sequencing of
The TLRs are also type 1 transmembrane receptors and may be expressed at cell surface or endocytic vesicle membrane. The receptors TLR1, TLR2, TLR4, TLR5, and TLR6 are expressed at cell surface, whereas TLR7, TLR8, and TLR9 are expressed at endosomes and lysosomes. TLR3 may be expressed either at cell surface and endosomes or lysosomes [2]. The intracellular TLRs recognize viral and bacterial nucleic acids [8].
There are ten human TLRs already described (TLR1–TLR10), and they recognize different kinds of ligands [2, 9]. TLR1, TLR2, and TLR6 are mainly stimulated by lipoproteins from Gram-positive bacteria; TLR2 also recognizes lipoteichoic acid (Gram-positive bacteria), zymosan, β-glucan from fungi, and GPI anchors; TLR3 recognizes dsRNA from viruses; TLR4 is activated by lipopolysaccharides (Gram-negative bacteria) and GPI anchors; TLR5 recognizes bacterial flagellin; TLR7 and TLR8 recognize ssRNA from viruses; TLR9 is activated by DNA; and ligands for TLR10 are still not known [10]. TLRs are also able to sense and signal tissue damage through recognition of damage-associated molecular pattern molecules (DAMPs) [11].
After recognition of ligands by the specific TLR, a dimerization of TLRs as homodimers or heterodimers may occur. Ligation of TLR and a ligand may also lead to conformational changes in dimers. The differential association in heterodimers causes a diversification in ligand recognition. For example, the heterodimer TLR1-TLR2 recognizes triacylated bacterial lipopeptides, whereas TLR2-TLR6 heterodimer recognizes diacylated lipopeptides [12, 13].
Dimerization and conformational changes will activate intracellular signaling cascade and innate immune response against the pathogen, produce acute inflammation, lead to modulation of adaptive immune response, and induce antimicrobial pathways [3, 12, 14].
The important roles played by TLR in recognizing PAMPs, especially PAMPs from
Association studies of genetic polymorphisms in TLRs and leprosy.
The heterodimer formed by TLR1 and TLR2 recognizes killed
The involvement of TLR1 in leprosy has been highlighted by several association studies.
Some polymorphisms in TLR1 were studied in different populations revealing their association with conditions related to leprosy. One of them is the SNP rs5743618 (T/G I602S) which presents a variable frequency depending on the population. Hawn et al. (2007) showed that rs5743618 in
Investigating the possible involvement of TLR1 with adaptive immune response affecting the clinical manifestations of leprosy, Misch and coll. (2008) evaluated rs5743618 polymorphism demonstrating a decrease of NF-κB activity related to the 1805G allele. In a Nepalese sample, the 1805G allele was protective against reversal reaction, which is characterized by an exacerbated Th1 cytokine response [19]. Another polymorphism able to interfere with TLR1 activity is rs4833095 (C/T N248S) as the 248 N variant impairs TLR1 functioning and sensing of microbial cell wall components [20]. In a Bangladeshi study, the homozygous 248SS genotype was associated with leprosy, but 248 N is homogeneously distributed among subjects. The 248 N allele is associated with erythema nodosum leprosum, while patients with reversal reaction are more likely to have the 248S allele [21]. These results were corroborated in a Brazilian sample, in which an association effect of 248S allele with leprosy susceptibility was found. In this same study, no association was identified between rs5743618 and leprosy diverging from previous results. However, rs4833095 was shown to be in a moderate linkage disequilibrium with rs5743618, suggesting a higher effect of the last one among Brazilians. The susceptibility allele 248S leads to a lower tumor necrosis factor (TNF)/IL-10 ratio after stimulation with
TLR2 can form heterodimer with TLR1, as mentioned before, and with TLR6. These different forms of presentation allow TLR2 to recognize different cell wall components, such as lipopeptides, peptidoglycan, glycosylphosphatidylinositol-linked proteins, and zymosan [24].
TLR2 is responsible by recognition of cell wall fractions from
In this way, variations in
Another important SNP in
In addition to SNPs, microsatellites are important polymorphisms that can be markers of disease susceptibility, and there are studies trying to identify microsatellites in
TLR4 has as main ligand LPS from Gram-negative bacteria. However, studies have already demonstrated that TLR4 also recognizes ligands from
The association of polymorphisms in
Changes in non-translated regions of
The activity of TLRs must be under tight regulation to avoid an exacerbated response that would be harmful to the host. Toll-interacting protein (TOLLIP) is an adaptor protein which impairs the NF-κB and JNK signaling induced by TLR2 and TLR4, among other signaling pathways, playing a role in immune response regulation. TOLLIP is able to suppress IRAK-1 activity facilitating IL-1R/TLR-induced cell signaling during inflammation [44, 45]. After TLR2 stimulation, TOLLIP regulates the pro-inflammatory and anti-inflammatory balance by inhibiting IL-6 and TNF and stimulating IL-10 [46].
In this way, changes in TOLLIP activity may be related to leprosy susceptibility. In a Mexican population, the possible association of TOLLIP polymorphisms with leprosy susceptibility was analyzed. The following four polymorphisms were investigated in lepromatous leprosy patients and control individuals: promoter −526 C > G (rs5743854), silent mutation rs3750920 (C/T P139P), rs5744015 (C/A A222S), and 3’UTR rs3750919 (G/A). However, no association was identified between the cited polymorphisms and lepromatous leprosy, but a trend of protective association to lepromatous leprosy was identified in homozygous CC genotype of rs3750920 [44].
Six haplotype-tagging SNPs were also analyzed regarding association with leprosy in a population from Nepal. Among the tested SNPs, the intronic rs3793964 (C/T) exhibited association with leprosy being the TT genotype associated with leprosy susceptibility. Moreover, TT genotype and T allele were associated with increased expression of TOLLIP and IL-1Ra in the skin tissue. The study also demonstrates that TOLLIP induces IL-1Ra in monocytes that inhibit IL-1R activating helping
Nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) is a family of intracellular PRR that is able to recognize bacterial molecules. Humans have 23 different NLRs that present two characteristic domains: NOD and leucine-rich repeats (LRRs). The first one is required for oligomerization, while the second one is required for ligand binding. There are different subfamilies of NLRs [48]. Some NLRs activate the protease caspase-1, so they act through inflammasomes leading to pyroptosis. The inflammasome-related NLRs are NLRP1, NLRP3, NLRP6, NLRP7, NLRP12, NLRC4, and NAIP. However, other components of NLR family (NOD1, NOD2, NLRP10, NLRX1, NLRC5, and CIITA) act through activation of NF-κB, mitogen-activated protein kinases (MAPKs), and interferon regulatory factors (IRFs) leading to innate immune response stimulation [4].
There are some PAMPs exclusively recognized by NLRs, such as muramyl dipeptide (MDP) that is a constituent of peptidoglycan cell wall present in Gram-positive and Gram-negative bacteria [49].
Due to its role in immune response modulation, variations in
In a genome-wide association study from Zhang and coll. (2009) about susceptibility to leprosy in a Chinese population, variants in six genes of innate immune response were identified as associated with the disease (
In a subsequent study, Marcinek and coll. (2013) genotyped the variant rs9302752 but now in an Indian population. However, this variant was not in Hardy-Weinberg equilibrium in this population. Some other variants in genes involved in signaling pathways mediated by NOD2 were shown to be associated with leprosy in this study. One of them is the haplotype formed by rs40457 G and rs42490 A in
Other conflicting results about genetic variants in
The previous study conducted by Zhang and coll. was realized in a Han Chinese population, which is the main ethnic group from China [57, 61]. A second study was realized in a Chinese population evaluating the genetic association of
Even with some discrepant results, the cited studies agree in associate
In addition to association with susceptibility to leprosy, variation in
Some of the previously mentioned polymorphisms in
Polymorphism rs751271 were already investigated in a Nepal population regarding its association with reactive states of leprosy showing no association with type 1 reversal reaction (RR) neither with erythema nodosum leprosum (ENL). In this study, 32 polymorphisms in
The association of polymorphisms in
Association studies of genetic polymorphisms in NOD2 and leprosy.
This work received financial support by Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes).
To release new or optimized component, it is mandatory to evaluate it under fatigue load conditions to prevent any kind of unexpected failure on the product life. To reduce the time of the development process, accelerated tests can be performed to obtain the mechanical strength feedback to improve its fatigue performance, thereby reducing the excessive material or reinforcing critical areas as stress concentrators. Nowadays, this information directly influences the component with physical optimization or analyzes it in a virtual way, in order to reduce the number of physical prototypes. The importance of implementing accelerated tests in the early stages of design is to evaluate components developed in the concept stage or to modify it, for changes in design during its production life act as facelift. Figure 1 shows the time development reduction when accelerated tests are implemented. Most improvements have to be made prior to mass production and in the early manufacturing process of the tooling [1].
Development time reduction using accelerated tests.
To perform this kind of test, it is necessary to evaluate the critical failures on the component related with the major probability of occurrence. All the load cases are evaluated; however, the target of this kind of a test is to evaluate the component in an easier way, with uniaxial test, where it is possible.
In other cases, the test is developed depending on the part or the process that has to be evaluated, for example, a new stamped part, or the weld cordon or the sequence of the welding. In those cases, a localized damage is developed with a correlation to its use in normal load conditions, but the important thing is to find the direction and load amplitude generated by use conditions, to get a correlation between the number of load repetitions and how many kilometers or time of use represent it.
The way to develop this kind of test starts with the instrumentation of a car with displacement transducers, accelerometers, force-moment transducers and strain gauges where it is necessary. The instrumented car is measured on different roads, used by different drivers in all the markets and under different weather conditions to acquire loads to measure the changes on the responses of the wheels.
These responses are acquired as signals, which are analyzed to synthetize it in one signal representing all of these driving and use conditions. The new signal used for the durability test is known as spectrum. The reproduction of this spectrum in labs reaches the same damage on the component as in the roads, but the target of this kind of test is to reduce the time of evaluation in a controlled manner to detect the location of failure, the moment of occurrence and its propagation. To accelerate the test, the spectrum is extrapolated and proving grounds are developed. These are faster than duration cars on the roads, but it is possible to reduce the evaluation time developing accelerated test on test benches through extrapolating the loads; to perform this, the component can be mounted as assembly, subassembly or component as itself. Test reduction is reached due to the loads that represent more damage by their amplitude and severity, than that applied on the component in normal-use conditions.
Test responses and desired signals have to be evaluated through statistical analysis. The correlation of results in lab and on roads is essential as the main target of accelerated tests is to reproduce the same failures as on the road so as to take steps to prevent them. The component´s load spectrum is made more aggressive by including all the variables in load conditions as in the case of drivers on roads, and the spectrum is also modified to build test requirements to include the safety factor.
In this chapter, a review of durability test has been performed, describing the process to develop a fatigue test and also the development of accelerated test. A general overview is done on the product evolution process (PEP) to define where the evaluation of the component is applied and how it affects the development process, the general process of the fatigue life evaluation of the component, a description of the finite element analysis and its application on fatigue life prediction to evaluate an automotive component and develop stiffness devices necessary for the test that are used with the modified loads to reproduce the failures.
It is important to evaluate components in experimental tests because fatigue strength has its inherent scatter due to four main factors: the loading, design, manufacturing and material (Figure 2). Experimental results under variable loads differ from analytical predictions owing to the effect of sequence loads [2, 3], Jimenez et al. [4] proposed a modification in Linear Damage Rule to include the effect of sequence in fatigue life prediction.
Parameters influencing the structural durability of components.
While manufacturing generally determines the strength and scatter, the geometry can modify the effect of mechanical properties [5, 6] due to the material that has variations on its properties. Loads have the major variability due to the diversity of drivers and factors such as number of passengers, weight on the car and its distribution, weather and its effects on the interchange of the loads between the non-suspended mass and the pave and the loads generated by bumpy ways and maneuvers.
Fatigue strength at the endurance limit is affected by the type of load and the size, reliability and surface roughness of the component [7]. The surface roughness can be improved with processes such as shot peening, which is important because fatigue cracks usually initiate at the surface in homogeneous materials [8, 9].
In durability tests, the aim is to minimize the likelihood of failure applied for the more aggressive driver using the weakest component. Figure 3 shows a strength-load interference model [10], which helps to manage the likelihood of failure of a component. As described in Figure 2, the component has different sources of scatter and its structural strength is determined on a bell-shaped curve. On the other side is evaluated the scatter for the loads applied to the component. The safety factor is defined by the difference between the central value of applied loads and its difference with the central value of the component\'s structural strength.
Failure likelihood in components subjected to cyclic loads.
Jimenez et al. [7] reported that the advantage of component testing is that the effects of the material, manufacturing process and geometry are inherently accounted for. Although with controlled process as in test laboratory, fatigue test results have scatter, the main sources of scatter are summarized in Table 1 [11].
Variable | Fatigue in components | |
---|---|---|
In laboratory | In service | |
Production and materials. | Production samples and different lots | Material from different lots and suppliers processed in different facilities. |
Quality on the specimen Surface. | Quality on Surface in critical areas as in notches. | |
Loads including environment | Type of load (CA, VA)* | Loads in service from different users |
Accuracy of test equipment | Residual fatigue life | |
Environment | Temperature, humidity in laboratory | Temperature, snow, rain. |
Human | Skills and expertise of lab staff to perform and evaluate the test. | Different users and styles of use, overloads not expected, abuse loads. Responses changed for the environment. |
Main sources of scatter in mechanical fatigue.
CA-Constant amplitude; VA-Variable Amplitude.
Fatigue evaluation is not simple to predict by analytical methods, and to perform durability assessment and to predict the component´s life, it is necessary to measure the most precise information, and to do this, the loads in service are acquired and analyzed, to reproduce them as shown in Figure 4.
Parameters influencing the structural durability of components.
To build a track to perform a durability test, it is necessary to get information from the customers through a data acquisition with strain gauges, accelerometers and displacement transducers; then this information is analyzed. The output of this analysis is to get the desired signal that is known as spectrum. The importance of getting the spectrum is to compare the loads with the S-N curve in order to predict the component life through damage accumulated rule. Every step of the development process is evaluated to improve its mechanical response, and after the design is released, tests are performed to monitor the quality of the product to prevent failures in its service life.
To reduce the time required for testing on public roads, accelerated tests are performed on proving grounds. This simulates road damage for different maneuvers, different vertical loads of frames and different longitudinal dynamics for accelerating and braking, lateral dynamics and vibrations [12], combining all the events (normal roads, rough roads, emergency braking, high speed, city and country roads). In addition, the tests can be performed in the laboratory [5]. It is possible to increase the number of repetitions at high or medium loads, avoiding inadmissible stresses that satisfy the test results. Although some proposals [13] have included the omission of low loads, they depend on the type of material and the application (Figure 5). The main objective is to develop an accelerated spectrum to get a test track.
Schematic S-N curve: (a) linear-linear, (b) semi-log, and (c) log–log.
The loads acquired are compared with S-N curves. The S-N curves are often expressed on semi-log, normal and log–log coordinates. Figure 5 shows a schematic curve on different coordinates, linear, semi-log and log–log. The most common representation is log-log since it becomes linear (Figure 5c).
The S-N curve represents the material or component fatigue strength, and is split into regions depending on its cycles. Extremely low cycle fatigue (ELCF) is defined from 0 until 100 cycles, between this limit and until 1000 cycles is low cycle fatigue (LCF), and between 1000 cycles and until 1 × 106 for steel and 5 × 107 for nodular cast iron is defined as high cycle fatigue (HCF). Anything beyond this point is defined as very high cycle fatigue (VHCF) [7].
To compare the S-N curve with loads, the time history is analyzed. Figure 6 shows a schematic waveform. The main characteristic is the stress amplitude Sa. If it has constant amplitude, the stress range SR is constant and is defined by the difference of the maximum stress (Smax) and minimum (Smin) in a cycle (Eqs. (1)–(3)).
Signal characteristics.
The mean stress Sm is defined as
A fully alternating stress Sm = Sa.
The stress ratio
The fatigue damage of a component is influenced in high cycle region by the mean stress expressed by its stress ratio. In normal
Stress ratio.
The amplitude ratio is the ratio of the stress amplitude to mean stress as show in Eq. (5).
The loads are monitored with cycle counting that is used to summarize variable amplitude time histories, providing the repetitions of the load during the time history. There are different cycle counting methods such as the Rainflow used to extract cycles from random histories in the time domain [13, 14], based on the analogy of raindrops falling on a roof. Figure 8 shows Rainflow counting process.
Rainflow counting process: (a) initial counting, (b) continue counting, and (c) residue.
The cycle counting is represented in a matrix based on Figure 5. The signal has 2 cycles from 5 to 3, 1 cycle from 6 to 3, 1 cycle from 1 to 5, 1 cycle from 2 to 4 (Figure 5a), 2 cycles from 1 to 6 (Figure 5b), and it has residue. In Figure 5c, these cycles are tabulated on a matrix, which depending on its counting can be represented by colors.
It is possible to evaluate time histories with other types of cycle counting methods, such as the level crossing method [15] where the amplitudes of the loads are split into a number of levels based on ranges, and the load is counted when it has peak at a different level, changing its slope from positive to negative or negative to positive; the cycle counting is shown in Figure 9.
Level crossing counting process: (a) time history, (b) quantity per class, (c) histogram, and (d) absolute cumulative frequency.
In the range pair counting method, the magnitude of loads is split into a number of levels. The result of the extracted number of reversals is shown tabulated in Figure 10b. Table 2 summarizes the events counted in Figure 10.
Range pair counting process, (a) time signal and (b) events.
Range (Units) | Cycle counts | Events |
---|---|---|
8 | 1.0 | G-H,H-I |
7 | 0.5 | C-D |
6 | 1.5 | F-G, I-J,K-L |
5 | 1.0 | D-E,L-M |
4 | 1.0 | B-C,J-K |
3 | 1.0 | A-B,E-F |
2 | 0 | |
1 | 0 |
Range pair counting.
The signals can be seen in time domain and frequency domain. A transfer function can be used in the frequency domain to relate the power spectral density (PSD) of the input desired load to the PSD of the output stress (Eq. (6)) [16]:
here the squaring process is required to get the transfer function in the correct units of PSD stress [17]. In this equation, σPSD (w) is the PSD of the stress at frequency
The time histories for constant amplitude test spectrum is linear (Figure 11a), and for variable amplitude, it is a curve (Figure 11b) generated by the cycle counting. Although a theoretical fatigue limit has been proposed, with the introduction of new test equipment for high frequency, the prediction has been improved at low load levels. Although this stress couldn’t damage the components by itself, the accumulated damage induced by high loads can be propagated by such small loads. The correction factors for the slope depend on the material [18].
Schematic spectrums versus components S-N curves: (a) constant amplitude, (b) variable amplitude.
Component life testing is commonly designed to validate fatigue strength of a component based on a target customer usage and is based on loads acting on components. The measurement period is usually not long enough to be used directly in a test. The main target for extrapolated signals is based on time measurement restrictions and problems such as synchronicity, spikes, drifts observed on measurement devices. For this reason, the most representative road or proving ground is determined and that is extrapolated to reach the kilometers of the total life.
The advantages of finite element simulation are mainly in the early stages of design where the prototypes are not yet available, and also to improve its design without physical components. But also in this case, the loads for variable or spectrum as well as constant amplitude are developed to correlate with the accelerated tests. All the factors are evaluated in physical tests, and the results are analyzed through statistical results.
The load measures are extrapolated to the requirement. These spectrums are evaluated to include all the behaviors, as is shown in Figure 12. Spectrum test is developed using different loads from the users and different responses are taken into account.
Spectrum development target.
Schematic spectrum development.
The repetitions of the cycles are found using the linear damage rule of Miner (Eq. (7)), and damage is evaluated using the ratio of the loads
The damage could be reached when the summation is 1 and there is an effect of sequence load. Depending on sequence effect loads, the damage can be reached too with values above or below 1 [4]. The failure is observed when there is a physical crack.
Figure 2 shows that the first point to acquire information is to install measurement devices on the component or in its vicinity, to obtain the responses of the components that induce stress. To obtain the most important information we install the measurement devices at the main stress points. To do this, it is necessary to perform a finite element simulation in order to get the point and the direction of the stresses. Figure 14 shows the typical process used to perform this kind of simulation.
General procedure for simulation.
The components evaluated can be from different materials and built with different manufacturing process. In the next figure are shown instrumented components with a point selection from finite element evaluation [7]. Then to find the point and direction of the main stresses, components are instrumented with strain gauges. Its nominal resistance is 120 or 350 ohms. Higher resistance can be used for base material with low heat conductivity and higher voltage excitation than 10 Volts can be mainly used in environments with high electrical noise [18]. Figure 15 shows chassis components instrumented, Figure 15a the rear subframe for a rigid axle, Figure 15b a front axle steering knuckle and Figure 15c a frontal axle track control arm.
Instrumented components with strain gauges, (a) rear rigid axle subframe, (b) steering knuckle, and (c) track control arm.
The accelerated tests are developed to reduce the time and complexity of the test in order to have faster results. Figure 16 shows a test stand to evaluate a frontal axle track control arm.
Durability test stand for frontal track control arm.
The information collected from the strain gauges can be used to evaluate the component, perform a correlation with virtual or analytical tools and build a spectrum. In not all the cases, can we directly measure the microstrain to validate the virtual simulation. The acceleration can be used to validate the finite element model with experimental acceleration results. With this validation, the stresses are found in a virtual way and can be used to perform the real-life prediction [4].
In the next part, the process to develop an accelerated test is shown. The time history in Figure 17 shows the raw data time history to evaluate a track control arm, its main characteristics is a range of 42,354 N, maximum value of 21,473.6 N and minimum value of −20,880.8 N and the time length is 249.9 s.
Raw data time history for uniaxial test of track control arm.
The information acquired is then analyzed to eliminate unnecessary information such a noise. To do this, we apply filters, and perform statistical analyses. For a structural analysis it is necessary to have a low pass filter of 100 Hz [19]. To evaluate the changes after applying the filter, it is necessary to perform the statistical analysis using cycle counting tools and evaluate the pseudodamage using the linear damage rule [4]. The results of this evaluation are shown in Figure 18.
Statistical analysis of the raw data compared with the filtered signal (a) cycle counting, (b) cumulative cycle count, and (c)PSD.
After applying the filter, the time history obtained has the next characteristics: range of 41,715.3 N, maximum value of 21,283.5 N and minimum of −20,433.6 N; the time length keeps its length of 249.9 s. The pseudodamage was reduced from 4.55 to 4.41, it means a reduction of 3.07% of damage, taking as a reference the raw signal.
There are many ways to accelerate the test. One of them is to eliminate the loads amplitude that do not apply a high amount of damage. To do this, in the time history, we eliminate the amplitude below 5000 N. Figure 19 shows the process to show the selected areas and the final time history.
Cutting load damage areas from the raw data time history filtered, (a) 0–249.9 s, (b) zoom between 221.7 and 233.6 s.
The cut signal and the raw data and the filtered raw data were compared using statistical analysis as it is shown in Figure 20.
Statistical analysis after cutting loads below 5000 N, (a) cycle counting, (b) cumulative cycle count, and (c) PSD.
After eliminating the amplitudes below 5kN, the new time history has the next characteristics: range of 41,717 N, maximum value of 21,283.5 N and minimum value of −20,433.6 N and the time length is 208.4 s. The pseudodamage has not been modified, while the time has been compressed from 249.9 to 208.4 s (16.6%). This is our target signal to generate the test spectrum, in order to develop the durability test.
To accelerate the test, we can increase the number of repetitions of loads with amplitude high and medium. Figure 21 shows the statistical analysis increasing the medium loads. The time history obtained has the next characteristics: range of 42,340.9 N, maximum value of 21,906.9 N and minimum value of −20,433.9 N; time length of 135.1 s. The damage was increased from 4.55 to 8.52, which means that it was increased by a factor of 1.87, reducing the time by 45.93% with respect to the raw data.
Statistical analysis increasing medium loads, (a) cycle counting, (b) cumulative cycle count, and (c) PSD.
Figure 22 shows the statistical analysis increasing the amplitude of high loads and the number of repetitions of high and medium loads. The time history obtained has the next characteristics: range of 59,124.4 N, maximum amplitude of 30,670.6 N and minimum of −28,543.8 N; time length of 167.7 s. The damage was increased from 4.55 to 39.6. This means that it was increased by a factor of 8.7, reducing the time by 32.89% with respect to the raw data.
Statistical analysis increasing high loads: (a) cycle counting, (b) cumulative cycle count, and (c) PSD.
Figure 23a summarizes the spectrums of the all strategies extrapolated to the time histories, the raw filtered data could be cut at below load levels to reduce the time; the medium and high loads in the signal can be increased, reducing the original time and increasing the damage. Figure 23b shows the schematic techniques to accelerate the test.
Spectrum of the time histories, (a) summary of time test reduction and (b) schematic accelerated test.
An alternative option to represent the spectrum instead of time history is with Matrix Rainflow. Figure 24 shows the four analyzed signals: original (Figure 24a), filtered (Figure 24b), increasing the medium loads (Figure 24c), and increasing the number of reversals in high loads inclusive of above the maximum loads of the raw data (Figure 24d). The major differences are shown in Figure 24c and d for medium and high loads, respectively, and the ranges for medium-to-high loads and its number of repetitions have been increased.
Rainflow matrix (a) raw data, (b) cut filtered signal, (c) medium loads increased, and (d) high loads increased.
For variable amplitude loads, this statistical analysis is used to monitor and guarantee that the loads have been applied correctly. Because it is necessary to build a drive used for the actuators to test the applied loads, the feedback through loads are measured and compared with the desired spectrum, and the drive to control the test actuators is developed through an iteration process [20]. Another way to perform an accelerated test based on the spectrum is using two load levels with a constant amplitude load for each load level. Then these results are plotted in an S-N component curve, and the specimen results are evaluated to predict the fatigue strength and different load levels [21]. Figure 25 shows the experimental results of uniaxial constant amplitude loads of steering knuckle.
Test results in steering knuckle analysis.
In an S-N curve, the percent replication
This value represents the portion of specimens that may be used in the variability to replicate the tests. The recommended values by Lee et al. [18] are as follows:
17–33 for preliminary and exploratory tests,
33–50 for research and development tests,
50–75 for design allowable data tests and
75–88 for reliability tests.
Steering knuckle results shown in Figure 25 have 7 level of loads, and 90 Specimens using Eq. (4) get a percent replication of 92.2. These high values obtained from these results are evaluated to analyze a proposal to estimate an S-N curve. For a component test, recommended samples used depend on the target, for research and development tests 6–12, and for reliability tests 12–24 samples. The minimum samples for two load levels are three specimens for each load level.
The median is the central value of results at each load level, and the tendency is considered at 50% of reliability and is necessary to evaluate it to know the scatter of the factors described in Figure 1 (Eq. (8)).
To evaluate the scatter of the components based on its fatigue results, the standard deviation is evaluated using Eq. (10). To take into consideration, its results have to be between 0.05 and 0.15; for samples without notches, the range is between 0.1 and 0.2, for uniaxial tests the range is between 0.2 and 0.3, while in complex tests, it can reach values between 0.3 and 0.6 [22].
Results of the slope found in tests are compared with the requirement, and changes on the slope affect the behavior at low or high load levels. Figure 26 shows the evaluation of the results with constant amplitude loads.
Evaluation results (a) slope test ktest = slope requirement kreq, (b) ktest< kreq, (c) ktest> kreq.
Accelerated tests are used to reduce cost and time in the development process. It can also be used to monitor the quality of the components during its manufacturing life. Experimental evaluation is mandatory prior to final release and start of production to analyze the scatter of the manufacturing process and prevent failures in service life. The importance of performing variable amplitude loads tests is because the prediction of fatigue life under the complex spectrum loads is not possible by any damage hypothesis. The spectrum to evaluate the components in the tests is developed with the loads from different customers and markets and use conditions. Experimental results show discrepancies even within the same batch of production, and the statistical value to evaluate the reliability of the lot under test is the standard deviation that shows the influence of the factors described in Figure 1. Although the tests are performed under controlled conditions in a laboratory, in specimens with notches, the batch of production is released if the standard deviation of its fatigue results has a maximum value of 0.2. For samples without notches in uniaxial tests, the maximum scatter allowed is 0.3 and 0.6 for complex test [22]. To evaluate the fatigue strength as well as the scatter, it is necessary to perform durability tests, to prevent failures on the service life.
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\\n\\nOut of all of the publishing options available to researchers, why choose to contribute your research to an IntechOpen Edited Volume? The reasons are simple. IntechOpen has worked exceptionally hard over the past years to fine tune the Open Access book publishing process and we continue to work hard to deliver the best for all of our contributors. The quality of published content is of utmost importance to us, followed closely by speed, and of course, availability and accessibility. To view current Open Access book projects that are Open for Submissions visit us here.
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\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
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\n\nOur Open Access book collection includes:
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\n\nCURRENT PROJECTS
\n\nTo view current Open Access book projects that are Open for Submissions visit us here.
\n\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
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A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"6",type:"subseries",title:"Viral Infectious Diseases",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11402,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null,series:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188"},editorialBoard:[{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",institutionString:null,institution:{name:"Grenoble Alpes University",institutionURL:null,country:{name:"France"}}},{id:"188219",title:"Prof.",name:"Imran",middleName:null,surname:"Shahid",slug:"imran-shahid",fullName:"Imran Shahid",profilePictureURL:"https://mts.intechopen.com/storage/users/188219/images/system/188219.jpeg",institutionString:null,institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"214235",title:"Dr.",name:"Lynn",middleName:"S.",surname:"Zijenah",slug:"lynn-zijenah",fullName:"Lynn Zijenah",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEJGQA4/Profile_Picture_1636699126852",institutionString:null,institution:{name:"University of Zimbabwe",institutionURL:null,country:{name:"Zimbabwe"}}},{id:"178641",title:"Dr.",name:"Samuel Ikwaras",middleName:null,surname:"Okware",slug:"samuel-ikwaras-okware",fullName:"Samuel Ikwaras Okware",profilePictureURL:"https://mts.intechopen.com/storage/users/178641/images/system/178641.jpg",institutionString:null,institution:{name:"Uganda Christian University",institutionURL:null,country:{name:"Uganda"}}}]},onlineFirstChapters:{},publishedBooks:{paginationCount:7,paginationItems:[{type:"book",id:"7102",title:"Pneumonia",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7102.jpg",slug:"pneumonia",publishedDate:"May 11th 2022",editedByType:"Edited by",bookSignature:"Nima Rezaei",hash:"9fd70142814192dcec58a176749f1b60",volumeInSeries:13,fullTitle:"Pneumonia",editors:[{id:"116250",title:"Dr.",name:"Nima",middleName:null,surname:"Rezaei",slug:"nima-rezaei",fullName:"Nima Rezaei",profilePictureURL:"https://mts.intechopen.com/storage/users/116250/images/system/116250.jpg",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"9615",title:"Chikungunya Virus",subtitle:"A Growing Global Public Health Threat",coverURL:"https://cdn.intechopen.com/books/images_new/9615.jpg",slug:"chikungunya-virus-a-growing-global-public-health-threat",publishedDate:"February 9th 2022",editedByType:"Edited by",bookSignature:"Jean Engohang-Ndong",hash:"c960d94a63867dd12a8ab15176a3ff06",volumeInSeries:12,fullTitle:"Chikungunya Virus - 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