Standard RR coefficient based on ECE R117–2.
\r\n\t
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The recent regulation mainly concerns with the reduction the source of pollution and safety, such as rolling resistance, rolling noise, and wet grip. This study discusses the finite element simulation of tyre in order to design PCR tyre having low rolling resistance coefficient that lead to a low energy consumption tyre.
The energy consumption of vehicle to some extent is contributed by tyres. According to International Council on Clean Transportation [1], improving tyre energy efficiency will reduce fuel consumption by 3 to 5% which will reduce greenhouse gas emission by more than 100 million metric ton annually. Therefore, a low rolling resistance tyre is highly required to reduce the gas emission produce by vehicles.
Tyre rolling resistance is defined as the energy consumed per unit travel distance when the tyre rolls under load [2]. Therefore, lower energy use of vehicle can be obtained by using low rolling resistance tyre.
Tyre design process includes conceptual design, benchmarking, detail design, and design review and analysis. During design review and analysis phase, a simulation was conducted to estimate the value of rolling resistance coefficient. In this simulation, a finite element model was built by using Abaqus software to simulate the tyre deformation and calculate a complete energy loss absorbed by the deformation of rolling tyre under certain load and speed. The hysteresis energy loss was calculated using a user defined subroutine written in Python and is used as Abaqus plug-in.
Tyre rolling resistance requirement was outlined in United Nation Economic Commission for Europe (UNECE) regulation No. 117 Revision 2, together with rolling sound emission and adhesion on wet surface (wet grip). The country applying this Regulation may refuse to allow the sale or entry into service of a PCR tyre (C1 Class) which does not meet the stage 1 rolling resistance requirements from 1 November 2014 and the stage 2 rolling resistance requirements from 1 November 2018 [3].
However, different countries have different policies regarding implementation standard, date and rating. European Union implements tyre labeling requirement since 2012, where the label states the rating of Rolling Resistance Coefficient, Rolling Sound Emission, and Wet Grip. Gulf Cooperation Council implement GSO standard tyre labeling starting 2014, mandatory for rolling resistance and wet grip.
Standard rolling resistance | ||
---|---|---|
Stage 1 | Stage 2 | |
12.0 | 10.5 | |
C2 | 10.5 | 9.0 |
C3 | 8 | 6.5 |
Standard RR coefficient based on ECE R117–2.
UNECE Regulation No. 117–2 defines Rolling Resistance
As stated by Tonachel [4], rolling resistance occurs as tyres deform during rotation. The load within the rubber material and rebar that construct the tyre are deformed and the loss of energy during these repeated deformations is then dissipated in the form of heat. The dissipation of energy in radial tyre occurs on crown is estimated about 70%, on sidewall 15%, and bead area 15% [5].
Therefore, the research was focused on the crown area. The simulation was done on crown initial radius and the stiffness of tread to study their effect on rolling resistance coefficient
In this research, the rolling resistance test was conducted according to ISO 28580, i.e. using force measurement method (Figure 1). In this method, the tyre and drum wheel assembly is forced toward a drum wheel with the skim load
Rolling resistance test method [
where
Tyre design consists of several phases, including conceptual design, benchmarking, detail design, design review and analysis. Design review and analysis phase is important to ensure that the final product will be in accordance with the required performance as designed. One of the processes in this phase is doing simulation by using finite element method with the following steps [7]:
Define target performance
Tyre Simulation using FEA
Validation of FEA simulation result
The tyre being designed is PCR tyre with size of 175/65 R14, should have maximum Rolling Resistance Coefficient of 8.5 N/kN and good cornering stability.
The Finite Element Method is used to analyze the rolling resistance of PCR tyre, consisting of two steps:
Tyre simulation was performed using commercial finite element software Abaqus. The simulation consists of several steps as follows:
FE tyre modeling, using axisymmetric modeling method.
Define material properties and material modeling.
Static footprint simulation and Radial stiffness.
Energy dissipation and rolling resistance were evaluated by using internally developed python code. The code extracts the strain energy results of the model and the same is post processed with viscous material data. The dissipation energy is calculated based on the strain energy function of Yeoh’s model by taking the product of elastic strain energy and the loss tangent of materials. Computation of Tyre rolling resistance with its respective compounds developed for their applications, performed by considering different crown radius and radial stiffness.
To model a tyre in Abaqus we use a cross section area of the tyre drawing and, imported as IGES file, this modeling technique is well known as axisymmetric modeling. All tyre component and their material properties are defined in this step. The tyre components that construct the tyre includes tread, base, wing, inner liner, side wall, apex, rim cushion, bead, JLB (join less belt), belt, and ply, are shown Figure 2 and made up from four different types of materials and these are rubber compounds, textile fabrics, steel cords and bead wire.
Sample of Tyre components [
The tyre model in Abaqus consists of two part partition: Carcass and Cord. Carcass and Cord partition were meshed separately, which are modeled in half axisymmetric model and then mirrored, become a complete assembly. In case the tread need to be included in the simulation, for instance to evaluate footprint, the tread is meshed separately in addition to Carcass and Cord. Tread meshing need to be carefully done so that the nodes on tread and carcass will be matched perfectly. Later on the rim part is included in the assembly. The axisymmetric model of the tyre after meshing is illustrated in Figure 3.
Tyre meshing of axisymmetric model in Abaqus [
The next steps are defining the mounting, creating constraints, defining boundary conditions, and loading (pressure) prior to running axisymetry function in Abaqus to form a full tyre. Figure 4 illustrate the axisymmetric tyre with pressure and a full round of the axisymmetric tyre model.
Pressurized axisymmetric (left) and full axisymmetric Tyre model (right).
Material properties need to be input into Abaqus during this simulation step, each component should have the following material property data including hardness, density, stress, strain, Young’s modulus, mu, Kappa, C10, and D1. Table 2 exhibits the full material properties of all tyre components under study.
No | Properties | Tread | Under tread | Wing | Inner liner | Side wall | Apex | Rim cushion | Bead | Belt | Ply |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | Hardness | 74.00 | 74.00 | 62.67 | 67 | 59 | 89 | 73 | 82 | 71 | 69 |
2 | Density | 1.16 | 1.16 | 1.09 | 1.221 | 1.088 | 1.163 | 1.162 | 1.289 | 1.182 | 1.114 |
3 | Stress, Mpa | 0.95 | 0.95 | 0.49 | 0.78 | 0.50 | 2.00 | 1.09 | 1.53 | 0.84 | 0.96 |
4 | Strain, % | 0.16 | 0.16 | 0.16 | 0.17 | 0.15 | 0.17 | 0.16 | 0.16 | 0.16 | 0.17 |
5 | Young’s modulus | 5.94 | 5.94 | 3.06 | 4.68 | 3.27 | 12.04 | 6.78 | 9.42 | 5.36 | 5.73 |
6 | Poisson ratio | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 |
7 | mu | 1.99 | 1.99 | 1.03 | 1.57 | 1.10 | 4.04 | 2.28 | 3.16 | 1.80 | 1.92 |
8 | Kappa | 99.01 | 99.01 | 50.94 | 77.97 | 54.47 | 200.60 | 113.02 | 156.95 | 89.26 | 95.53 |
9 | C10 | 1.00 | 1.00 | 0.51 | 0.78 | 0.55 | 2.02 | 1.14 | 1.58 | 0.90 | 0.96 |
10 | D1 | 0.02 | 0.02 | 0.04 | 0.03 | 0.04 | 0.01 | 0.02 | 0.01 | 0.02 | 0.02 |
Material properties.
A model that represents the stress–strain relationship of the material is needed in finite element analyses of rubber components. There are several material models available In Abaqus to describe the mechanical behavior of rubber. The model to be used in the analyses depends on several factors such as availability of experimental data, strain range, and complexity of loading.
Each tyre component shows different deformation response under external loading. Rubber exhibits non linear deformation and almost incompressible response, while fabric cords and steel wire withstand most both tension and compressions loads and therefore produce small strain. For rubber, hyper-elastic material models are used to describe high deformation. In this study, Yeoh’s model was chosen to define hyperelastic property of rubber materials and Marlow model for reinforcements such as fabric and steel cords. Bead was modeled as an elastic material.
The Yeoh material model had a cubic form with only
where
The reason for using the Yeoh’s model in the rubber material model, despite the fact that Abaqus supports other material models like Neo-Hookean and Mooney-Rivlin, because it is capable of predicting different deformation modes using data from a simple deformation mode like uni-axial tension test. A review by Wei et al. [8] found that most of material models are determined based on the polynomial expression of strain energy function. Although Mooney-Rivlin energy density function has been widely applied for tyre dynamic properties analysis, the function has a limitation that it could not be accurately applied to large deformation problems of the rubber material. Neo-Hookean material model also has a limitation that the coefficients derived from uni-axial deformation tests are not suitable to describe other deformation modes. In order to determine the parameters of rubber hyperelastic property, most of the material models need to combine three deformation tests (uni-axial, biaxial tension and pure shear), which is recognized as a complex and time consuming procedure.
After completing the axisymmetric tyre modeling, the next step is the simulation of tyre under static loading. From this simulation there are two analyses can be further performed: footprint analysis and radial stiffness analysis. For footprint analysis, the load needs to be applied on the tyre to represent the normal load according to the specified load index of the tyre. Figure 5 shows the tyre under static loading and its respective footprint result.
Footprint simulation under static loading.
For designing a new PCR tyre, there are three different tyre were taken for benchmark. The tyre being simulated is of the size 175/65 R14 and were inflated at 2.1 bar (30.5 psi) with various loads of 100 kg, 150 kg, and 200 kg using three types of tyre, called tyre A, tyre B, and tyre C. Tyre A has two grooves, tyre B has three grooves, and tyre C has four grooves.
To obtain more accurate footprint result, the full tyre with tread was modeled so that the contact pressure distribution on the tread which in contact with the road can be evaluated. Figure 6 exhibits the footprint comparison of these three tyres.
Footprint comparison of benchmark tyres [
In Abaqus, footprint simulation is performed under static loading and needs several input files for defining geometry, boundary condition, sequence and load of tyre and rim. The result of Abaqus footprint analysis as it is shown in Figure 6, suggests that the tyre having two grooves shows the largest contact area at shoulder. Large contact area on shoulder indicates better cornering stability.
The second simulation result is about radial stiffness of the tyre. The radial stiffness mainly depends on sidewall stiffness and affects the transversal bending of tyre. This transversal bending causes the tyre to lose its height by certain value, from initial radius
Tyre deformation.
The
2 Groove tyre | 3 Groove tyre | 4 Groove tyre | |
---|---|---|---|
R deflection | 269.06 mm | 268.75 mm | 268.54 mm |
R initial 291.5 mm |
Value of Tyre radius during deflection.
By looking at footprint and radial stiffness, the two groove tyre indicates a better cornering stability compared to the other tyre types.
Rolling resistance force in tyre is mainly generated by friction force, drag force, and hysteresis loss. This study will only discuss rolling resistance force generated by hysteresis loss inside the rubber and cord. The analysis was performed in two main steps, those are static tyre simulation (footprint and radial stiffness) and calculation of strain energy loss to find rolling resistance force.
With review of a number of tyre rolling resistance simulations, it is found that rolling resistance calculation is based on the strain energy loss during a traveled distance. Aldhufairi et al. [9] used a script of Abaqus to extract the 3D tyre model data as input and an analytical rigid road drum with a straight and smooth surface was added to the model, equivalent to that used in the experiment, due to the limitation of the testing machine the travel speed was limited to 30 km/h. Ghosh et al. [10] suggested a method that implements a steady state rolling simulation using Abaqus software to obtained the strain energy and principal strains, together with the loss factors (Tan d) of the material obtained separately in the laboratory, are used to estimate the energy dissipation of a rolling tyre through post processing. The internal code was developed to perform such a task.
Lind [11] suggested three sequential steps for solving the rolling resistance model; inflation, footprint and rolling. The last rolling step was performed using a dynamic solver setting where the center node was moved in the x-direction with a prescribed acceleration up to a target speed. The rolling resistance was from the FE-simulation result computed in two different ways. The first method uses the contact forces from each node multiplied with its distance from the wheel centre; the second method uses the reaction forces from the constrained middle node and computes the rolling resistance. The result presented for the material model and for the rolling resistance does not aim toward representing any specific tyre rubber compound or tyre.
While the others used FE tyre model without tread, Cho et al. [12] Included the tread in FE tyre model. The hysteretic loss during one revolution was computed with the maximum principal value of the half-amplitudes of six strain components, and the temperature distribution of tyre was obtained by the steady-state heat transfer analysis. The static tyre deformation analysis is performed by ABAQUS/Standard in the deformation module and the strain and stress results are input into the in-house dissipation module where the hysteretic loss, rolling resistance and heat generation rate are computed.
In this study, the footprint analysis was carried out with patterned tyre model and for rolling resistance simulation used tyre model without pattern for the sake of computing time. However, the accuracy is a little sacrificed but still acceptable i.e. 6.2% error as describe in the Section 3.4.
The rolling resistance analysis was based on hysteresis of rubber and cord where the phase of stress lags behind the strain as it is shown in Figure 8. The hysteretic loss ∆W per unit volume during a period Tc = 2
Stress - strain phase.
where
In engineering application, as suggested by Cho et.al [9] 3D viscoelastic bodies are subjected to more complicated multi-axial cyclic excitations, so the time histories of strains and stresses are neither one-dimensional nor sinusoidal.
Therefore, the hysteretic loss is expressed in a generalized form:
The hysteretic loss can be converted to the heat generation, and the heat generation rate Q per unit volume during a cycle is:
In order to calculate the energy loss during deformation, curve interpolation and FFT function were developed. Abaqus python contains NumPy which can do FFT. A python scripting is used to read signal curve, perform the FFT and create a new curve, i.e. amplitude vs. frequency, for plotting in Abaqus.
def interpolation(curve):
myCurve = []
i = 0
n = len(curce)
myCurve.append(0.0,curve[0])
while i < n:
myCurve.append(myAngle[i], curve[i])
i = (i + 1)
myCurve.append(360.0,curve[-1])
i = 0
n = len(myCurve)
NewCurve = []
while i < (n - 1):
angle_A = (myAngle[i + 1][1] - myAngle[i][1]) /
(myAngle[i + 1][0] - myAngle[i][0])
yo = myAngle[i][1]
xo = myAngle[i][0]
j = myAngle[i][0]
while j < myAngle[(i + 1)][0]:
newAngle.append(yo + (angle_A * (j - xo)))
j = (j + delta)
i = (i + 1)
if i == (n - 1) and newAngle.append(myAngle[i][1]):
pass
return newAngle
def fourier(sigma, epsilon):
FFT1 = 2 * abs(fft.fft(sigma)) / len(sigma)
FFT2 = 2 * abs(fft.fft(epsilon)) / len(epsilon)
k = 0
total = 0
while k < (len(FFT1) / 2):
total = total + (FFT1[k] * FFT2[k]) * k
k = k + 1
return total
The input for sigma and epsilon are the interpolated stress and the interpolated strain respectively.
In a rolling tyre, the rubber compounds exhibit the complicated 3D dynamic viscoelastic deformation. The strains and stresses are constituted in terms of the complex modulus G* = G’ + iG”. In this case, G’ is called the storage modulus and G” is the loss modulus. The complex modulus is a function of the strain amplitude
In Abaqus simulation the complex modulus G* can be obtained by extracting axisymmetric element data and therefore the heat dissipation from energy loss G” can be calculated by multiplying G and sin
Where energy is extracted from previous axisymmetric simulation element data and tand is from input data.
The rolling resistance force generated by the hysteretic loss is computed as the total hysteretic loss of the rolling tyre during one revolution divided by the traveling distance of tyre during the same period of time, hence:
where
r = effective radius of tyre
Rolling resistance coefficient Cr is the indication of how large the rolling resistance is for a given load upon which it is rolling and is calculated by:
Total force is meant the sum of force caused by hysteresis loss in each tyre component material.
To analyze the force produced by tyre component materials, a Python code was developed as plugin in Abaqus software. The process of analyzing the rolling resistance is describe in the following steps and is shown in Figure 9.
Rolling resistance analysis process using Abaqus plugin.
Prepare input files, i.e.:
Axisymmetric input file (axi.inp)
Full tyre input file (full.inp)
Running full tyre model simulation in Abaqus using the input files in step 1 in command prompt with the following command:
Abaqus job = full oldjob = axi cpus = 4
Writing axi_heat input file:
Copy axi, inp and rename it to axi_heat.inp
Change the tyre element type from cgax into dcax
Delete input of tyre_coord and rim
Delete all properties in each material and replace with:
*conductivity: 0.2
Delete all existing steps and boundary conditions and replace with steps and boundary conditions necessary for rolling resistance simulation
Copy axi.odb and rename it into axi_result.odb
Input data needed for running rolling resistance simulation:
Input files:
axi.inp
axi-heat.inp
sequence.inp
Odb files:
axi. Odb
axi_result.odb
full.odb
Tan delta data: tand.txt.
Below is tan-δ example of tread compound.
Running rolling resistance calculation using Abaqus plugin after specifying the required data as mentioned in step 5 in the pop up menu and other information needed for running the simulation such as:
Select how energy is interpolated from coordinate element to bulk elements
Define speed of tyre [km/h]
Define error limit for heat transfer [%]
Define interpolation parameter [deg]
Define parameter for tyre radius calculation.
After completing the calculation the output data will be presented in axi_RR_result file (see Figure 10), and the Rolling Resistance Coefficient (Cr) is then calculated using equation (9):
Temperature distribution of 2 groove tyre.
The example of the simulation result is shown below:
Results:
Force produced by material I40 is 2.08360116975 N
Force produced by material A02 is 1.29144915874 N
Force produced by material T61 is 18.8669933222 N
Force produced by material BW08 is 0.0 N
Force produced by material T61 is 1.67943517041 N
Force produced by material Z80 is 1.13411378677 N
Force produced by material S70 is 0.41902012456 N
Force produced by material S70 is 3.82462665603 N
Force produced by material R50 is 1.72655457713 N
Force produced by material N20 is 0.883588825178 N
Force produced by material C32 is 1.62535580812 N
Total force is 33.5347385989 N
Since load index of the tyre is 82, the maximum tyre load is equal to 475 kg. According to ETRTO standard, the tyre load for rolling resistance calculation is 80% of maximum load which is 380 kg or 3728 N, and then the rolling resistance coefficient is equal to:
Using the same calculation for tyre B and C, we obtain the following result:
tyre A produces Cr = 9 N/kN
tyre B produces Cr = 8.77 N/kN
tyre C produces Cr = 8.4 N/kN
The rolling resistance simulation result obtained from an Abaqus plugin code need to be validated by comparing the result with the actual test result. The actual test has been carried out using 14 different tyres that has been tested on RR machine conducted by certified bodies, such as TUV, and the results are compared with the RR result from simulation, as shown in Figure 11.
Rolling resistance coefficient test result.
In average, the simulation result is higher than the actual testing result by 0.46 or 6.2%.
The radial stiffness of tyre significantly affects the rolling resistance. Table 4 shows that smaller stiffness of sidewall (indicated by higher R deflection) resulted in higher rolling resistance. This phenomenon explain that to deform a higher stiffness material needs more energy, meaning that the energy loss is higher and eventually the rolling resistance is also higher.
2 groove tyre | 3 groove tyre | 4 groove tyre | |
---|---|---|---|
R deflection | 269.06 mm | 268.75 mm | 268.54 mm |
Rolling resistance coef. | 9 N/kN | 8.77 N/kN | 8.4 N/kN |
Correlation between radial stiffness and rolling resistance.
Tyre tread contour has a great influence on rolling resistance. To study this, the simulation was performed on three tyres with different crown radiuses, i.e.:
Tyre A: R1 = 250 mm and R2 = 150 mm,
Tyre B: R1 = 550 mm and R2 = 300 mm, and
Tyre C: R1 = 900 mm and R2 = 300 mm, as it is shown in Figure 12.
Crown radius relation with footprint and rolling resistance [
During the PCR tyre design and development, there is several tyre performance parameters need to be considered, including rolling resistance, wet adhesion, noise, and cornering stability. In this study, a Finite Element simulation was carried out to perform prediction of rolling resistance and cornering stability.
The simulation was performed in two stages: steady state rolling simulation using Abaqus build in function and rolling resistance calculation using internally developed Python code as Abaqus plugin.
The validation was done by comparing the simulation result and actual test on RR machine and the average discrepancy of
The simulation result suggests that the best estimated rolling resistance is four groove tyre with crown radiuses of R1 = 900 mm and R2 = 300 mm. However, two grooves tyre provides larger shoulder contact area which in turn gives better cornering stability, but has rolling resistance coefficient of 9 N/kN.
Considering that the rolling resistance coefficient (
Two grooves
Crown radius R1 = 900 mm and R2 = 300 mm
Central auditory processing (CAP) refers to the processes involved in the analysis and interpretation of auditory stimuli. It encompasses the perceptual processing of auditory information by the central auditory nervous system and the neurobiological activity underlying it that gives rise to auditory evoked potentials. It is a well-defined and consolidated entity, both from a clinical and research point of view, as well as in terms of its disorders and associated diagnosis and rehabilitation. In this chapter, we describe aspects of CAPD, including its diagnosis through behavioral and electrophysiological testing. We will cover auditory rehabilitation procedures such as auditory training, and monitoring procedures which include the analysis of electrophysiological findings.
Central auditory processing disorder (CAPD) is a deficit in “the perceptual processing of auditory information in the central nervous system (CNS) and the neurobiologic activity that underlies that processing” [1]. CAPD affects the perceptual and neural processes in the CNS which underlie sound localization and lateralization, auditory discrimination of speech and nonspeech signals, auditory performance when there is competing or degraded acoustic information, a variety of auditory temporal processing and patterning abilities, as well as others [2].
CAPD diagnosis can be performed at any age group, from childhood to adulthood. Alterations present in childhood may persist into adolescence and adulthood, may result from an acquired CNS event (traumatic brain injury, cerebrovascular accidents) and as part of the natural aging process. The estimated prevalence of CAPD may vary across age groups. In school age children the prevalence ranges from 2 to 5% [3] as a primary diagnosis. In cases where the CAPD co-runs with other difficulties such as learning disabilities (approximately 43%) and reading disorders (from 25 to 45%) [4] prevalence increases. While in adults, the prevalence increases with age to 17% at 50–54 years and may be greater than 70% after 60 years [5].
Signs and symptoms of CAPD include one or more behavioral characteristics—reading and writing difficulties, speech and language difficulties, difficulty hearing in background noise, difficulties in perceiving prosodic elements of speech as prosody, difficulty in following complex oral instructions, requesting repetitions of oral information, poor musical skills, sound localization difficulties, and others. This list is illustrative, not exhaustive, and it has to be remembered that these behavioral characteristics are not exclusive to CAPD [1, 6].
CAPD often occurs concurrently with other learning or developmental disabilities and is often associated with related cognitive, linguistic, or behavioral disorders. Sharma et al. [7] reported a high degree of comorbidity between APD and specific language and reading disorders. Individuals with autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) often have processing disorders [8], as well as dyslexia [9], and visual processing disorder [10].
The purpose of diagnosing testing is to identify presence of CAPD and delineate its characteristics and nature. Given the heterogeneity of the profiles of individuals who are referred for CAP assessment and their possible comorbidities, several auditory processing tests have been developed as tools for assessing different central auditory processing abilities. When selecting tests, the audiologist must recognize that a symptom may result from many underlying central auditory processing deficits (e.g., temporal processing, localization, spatial release of masking, performance with concurrent/degraded auditory signals), as well as language processing or cognitive problems. For that, diagnostic tests of central auditory function have been shown to be sensitive and specific for identification of CANS disorders.
The diversity of central hearing deficits supports the need for comprehensive test battery to track the various functions of the CANS. There is currently no universally accepted battery of APD tests. Currently, it is recommended that the behavioral assessment battery include non-verbal stimuli (temporal ordering, temporal resolution, and binaural interaction) and verbal stimuli (low redundancy dichotic and monaural listening). The battery should include tests representative several auditory processes as well as temporal processing, binaural separation, binaural integration, auditory closure, auditory discrimination, and sound localization [1].
Accompanying the behavioral assessment there should also be a carefully selected battery of behavioral tests with documented sensitivity and specificity. In a complementary way, there should be detailed observations of the case history and electrophysiological procedures, which together can provide a better understanding of CANS dysfunction [1, 6]. Even though (C)APD involves difficulties in the perceptual processing of auditory information in the central nervous system, electrophysiological auditory potentials reflect the neurobiologic activity that underlies that processing [6].
In individuals whose auditory processing skills are not developing normally, their auditory processing skills can improve with appropriate treatment. The principle underlying this improvement is called neuroplasticity. Neuroplasticity is the result of experience and stimulation and involves reorganization of the cortex and brainstem. Studies show that the central nervous system’s plasticity allows for reorganization and re-mapping following experience (i.e., either cortical or brainstem stimulation), and that this neural modification is reflected in behavioral changes [11, 12]. Plasticity allows the CANS to accommodate and improve central auditory processing skills [13, 14], with continual practice resulting in learning that automatically leads to better listening skills.
Some principles are fundamental and should serve as guidelines for CAPD intervention. First, the intervention must be specific to the deficit, and personalized considering the patient’s difficulties and strengths. The deficits should be clear from the results of the behavioral assessment, and the diagnosis should be given in terms of the original behavioral complaints.
The intervention should also be multidisciplinary, in most cases involving a variety of domains other than audiological, particularly if there are coexisting disorders. Checking whether CAPD is the primary disorder will help in specifying the focus of intervention and assist in prioritizing the different components and the order of their implementation.
While it is necessary to customize interventions for each individual, to maximize treatment effectiveness the treatment should incorporate both bottom-up and top-down approaches [1, 15, 16, 17]. A top-down approach focuses on auditory signal access and acquisition and includes direct auditory remediation strategies such as auditory training, as well as environmental modifications to increase signal clarity and improve the listening environment. Top-down treatments include training in core resources such as language, memory, and cognition along with environmental modifications and educational interventions [1, 16].
Intervention for CAPD should start as soon as possible after confirmation of diagnosis. It is important that the intervention is comprehensive and multidisciplinary, adding issues related to listening, academic and language, in addition to higher order processes such as attention, memory and executive control in auditory tasks, domains that are commonly affected by CAPD.
When planning an effective approach to treating CAPD, three main components should be included: (a) environmental changes, (b) compensatory strategies, and (c) direct intervention—that is, auditory training. All three areas must be addressed in any intervention plan for individuals with CAPD [1, 15, 16], regardless of the co-occurrence with other disorders or the subject’s age group.
In addition to these three components, some guidelines in the area also recommend the use of auxiliary listening systems, such as FM systems, in the process of auditory processing rehabilitation. This is especially true if the diagnostic exam shows impairment in auditory closure skills, figure-background, and selective attention [6].
Environmental modifications aim to improve the individual’s access to auditory information. This will involve increasing signal clarity and facilitating listening and learning in environments such as school, work, or social situations. Including bottom-up and top-down approaches based on acoustic aspects such as the use of assistive technology, environmental management that includes architectural interventions, removal of noise sources and consequently improvement of signal-to-noise ratios, and interventions for teachers and speakers including manner as information is transmitted and learned, highly redundant language aspects and listening and learning environments are highly recommended. The selection of modifications must be done systematically and must be based entirely on the difficulties presented by the individual and hearing deficits and the effectiveness of the implemented modifications must be continuously monitored [2].
Compensatory strategies, also known as core resource training, are designed to address secondary deficits, strengthening higher-order functions, language, cognitive skills, and academic deficits often seen in individuals with CAPD [1, 6, 15]. Through these strategies, the individual with CAPD is encouraged to take responsibility for their own success in the listening and learning processes, encouraged to paraphrase instructions to clarify misunderstandings, and advanced problem-solving techniques are taught. Involving metacognitive (thinking about thinking) and metalinguistic (thinking about language) strategies that aim to provide compensatory methods to minimize deficits in functional listening, monitor your understanding, identify your difficulties, devise alternative solutions, and be an active listener rather than a passive listener [1, 5, 17].
Auditory training as related to CAPD aims to improve the function of the affected auditory process, the goal being to minimize or eliminate the alteration in auditory processing. Auditory training consists of an intensive series of challenging tasks based on the difficulties presented by the patient during the CAPD assessment [19].
Neuroplasticity is a great ally in the auditory training process. The nervous system is plastic and its capacity for reorganization and re-mapping by experience—neuronal modification—is reflected by behavior change [11, 12]. Changes in the neural substrate are facilitated by the presentation of stimuli in an organized, frequent, and intense way that progressively challenges the patient. The level of difficulty is appropriately graded and the stimuli are integrated into everyday activities. To maximize neuroplasticity, active participation of the patient in training is required. The inclusion of immediate feedback is important, as this gives positive reinforcement. Activities should be at or near the limit of the patient’s ability [3].
Auditory training can be done formally or informally. The difference between the approaches is in the degree of control over the presentation of the stimuli and the environment. In formal training, stimuli are presented through an audiometer, allowing precise control of the level of stimulation and the types of stimuli (normally recorded). There needs to be control of intensity, frequency, stimulus duration, and inter-stimulus interval. Informal training is not concerned with stimulus control: stimuli are presented without the use of an audiometer and can be presented in person, without recordings. Informal training is carried out without the fixed “controls” needed for formal auditory training.
For auditory training to be effective, tasks must be presented systematically and graded by difficulty so that they are challenging and motivating without being exhausting. The level of difficulty is adjusted to allow the patient to achieve correct scores of approximately 70% but not less than 30% [18]. Training should be frequent and intense, considering the lengths of the sessions, the number of sessions, the intervals between sessions, and the timeframe over which the training will be performed [3, 5, 18]. To maximize motivation, performance gains, and generalization, the patient’s active participation is necessary, and should be accompanied by immediate feedback and positive reinforcement. Variation of stimuli and tasks are key factors in successful auditory training [3]. The training programs that prompt these structural and functional changes must be done with auditory material different to those used in the diagnostic tests, which must be reserved only for evaluations [18].
Recent studies suggest that auditory training can serve as a valuable intervention tool for individuals with language deficit CAPD, learning difficulties, alterations in spatial processing, and adult subjects using hearing aids [6, 20, 21, 23, 24, 25].
Software programs are increasingly being used as strategies for auditory training. Computer-based auditory training (CBAT) provides age-appropriate strategies and presentations to keep the patient engaged. Some authors who examined children with CAPD [26], learning difficulties [26], and language and reading problems [26] have demonstrated benefits of this type of training for children with CAPD and associated issues.
Electrophysiology is the branch of neuroscience that explores the electrical activity of neurons and makes it possible to investigate how molecular and cellular processes react to a given stimulus. Neuronal communication takes place through electrical and chemical signals [28].
The electrophysiology of hearing involves small electrical changes that can be collected through electrodes placed on different regions of the scalp. The responses are generated by structures located throughout the auditory pathway and their analysis allows us to understand the normal patterns existing in the processing of auditory information [29].
Electrophysiological techniques allow us to assess auditory information processing, giving us more information about the functioning of the central auditory nervous system. These assessment techniques have provided great advances in neuroaudiology—the field that studies the relationship between the ears and the brain [29]. Other researchers see the need for a whole new field of study related to cognitive auditory sciences which is able to provide information about the correlation between hearing and cognition. They emphasize that hearing disorders need to be treated in an interdisciplinary context, one which should include, depending on the case, the following professionals: speech therapist, psychologist, audiologist, and neurologist [3]. In this way, electrophysiological assessments can play an important role both in the process of assessing auditory processing and also in monitoring auditory rehabilitation programs, such as auditory training [30].
Electrophysiological assessment is a way of analyzing the central auditory nervous system both of patients who actively participate in behavioral assessments and in individuals whose responses appear to be unreliable. There is already a consensus that the assessment and monitoring of auditory processing is only complete when there is a combination of behavioral and electrophysiological methods.
Neuroplasticity is the basis of auditory training programs, and it acts on the connections between neurons and the myelination of neurons as a result of performing auditory tasks. The on-going benefits of auditory training programs can be monitored by performing electrophysiological assessments, measuring neurophysiological changes which occur in both the peripheral and central auditory nervous systems. Electrophysiological assessments are therefore a useful and effective tool in monitoring training programs.
Below are the results of some studies that correlated electrophysiology and auditory training. The results come from researchers who are engaged in studying the effects of auditory training through electrophysiology in different clinical populations.
Research on neurophysiological changes resulting from auditory-perceptual learning for adults with normal hearing suggests that, although the auditory system responds to training, there is a substantial degree of variability among individuals in their ability to make use of physiological cues [31]. Training of auditory skills, even in individuals without complaints of alterations in the processing of auditory information, shows that changes take place in cognitive potentials (notably a reduction in latency of the P300 potential) after a program of auditory intervention [32].
Learning results from the process of assimilating written and spoken language, and this process involves acoustic processing, phonemic processing, and linguistic processing. Integrated processing (acoustic, phonemic, and linguistic) must be complemented by the child’s auditory and linguistic experience, which will be decisive for the learning of reading and writing. Researchers have found that the presence of learning difficulties is often associated with hearing deficits in children, and it is possible to monitor certain electrophysiological responses after auditory training. The results have shown an increase in the amplitude and a decrease in the latency of cortical potentials, although no changes were seen in brainstem responses [27]. The frequency following response (FFR) seems to be a very promising instrument to monitor patients with school difficulties, as well as to analyze the effectiveness of treatments, and it can be used as a biological marker of these changes [33, 34].
CAPD is defined as a disorder in one or more auditory skills involving sound localization and lateralization, auditory discrimination and recognition, temporal aspects, resolution, masking, integration, and temporal ordering. Kraus et al. [35] have reported altered responses in the following assessments: (a) brainstem auditory evoked potentials (ABRs) with click stimuli; (b) middle latency auditory evoked potentials (MLAEPs); and (c) N1 and P2 components of the long latency auditory evoked potential (LLAEP). Results have shown that there seems to be an impairment in auditory discrimination that can be observed in electrophysiological tests (mismatch negativity, MLAEP, and N1 and P2 components), as well as alterations in neural synchrony evidenced by alterations in the ABR that can impact temporal coding and the perception of sounds in the presence of noise.
Hearing deprivation, derived from multiple OM episodes in childhood, can compromise the normal development and maturation of the brainstem, as well as other cerebral and cortical structures. Diminished auditory signals can lead to desynchronization in the auditory cortex both for non-verbal and verbal sounds [36, 37]. Changes in auditory evoked potentials provide objective evidence that the auditory system has been modified [38]. One auditory evoked potential that seems to be more sensitive to deprivation from OM effects is the P300 cognitive potential [39]. The use of verbal stimuli when recording long latency auditory evoked potentials also seems to be very effective, providing additional information about auditory information processing [40].
Hearing loss is a highly prevalent disability and, importantly, studies have shown a correlation between hearing loss and cognition. Typically, the use of a hearing aid is associated with an improvement in the speech perception. It has been observed that auditory training programs improve both the processing of auditory information and of cognitive information in individuals with hearing loss, especially in competitive listening environments [41]. Auditory training programs that include training which requires increased memory demand seem to improve speech perception in noise and, in the process, improve neural response time [42]. Electrophysiology can therefore be an extremely useful tool for recording these changes in neural velocity. Mismatch negativity (MMN) can also be used as an electrophysiological measure for monitoring changes resulting from auditory training, especially in the auditory rehabilitation of patients with hearing loss who use a hearing aid or cochlear implant [43].
The multisensory nature of music can have an impact on vocal production because it involves motor, auditory, and vocal mechanisms [44]. Hearing and voice are interrelated, so that the integrity of the auditory system is important for developing vocal behavior and maintaining vocal quality [45]. Individuals who sing in tune seem to have a particular pattern of responses in their FFR, showing lower latencies and stronger amplitudes than in individuals who sing out of tune. This shows that daily, long-term musical training can modify brain structures and improve the quality of auditory information processing [46].
Electrophysiological tests are important objective measures to verify the effectiveness of auditory training. In addition, it is important to emphasize that an electrophysiological evaluation plays an important role in predicting the gains to be expected from auditory training programs. Evaluation makes it possible to gauge whether the auditory training program should be continued, adjustments should be made, or a whole new intervention program begun. Thus, electrophysiological assessment is extremely important: it can indicate whether neural plasticity is possible (through improved synaptic efficiency and increased neural density) or measure the degree of functional plasticity (from behavioral changes brought about by training in auditory skills).
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Saxena",hash:"d92a4085627bab25ddc7942fbf44cf05",volumeInSeries:2,fullTitle:"Current Perspectives in Human Papillomavirus",editors:[{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",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. 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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. 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He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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