**3.1 Amyloid light chain (AL) protein**

*Amyloid Diseases*

**62**

**Table 1.**

*Amyloid fibril protein classification and associated disease.*

AL type amyloid is the most common amyloid in the United States. The AL chain is a clonal immunoglobulin light chain or light chain fragment, which is produced by the uncontrolled productions of antibodies by plasma cells. Lambda (λ) type (ALλ) is more common than kappa (κ) type (ALκ) light chain. This type of amyloidosis is related to monoclonal plasma cell disorders, especially multiple myeloma or other B-lymphocyte-related disorders. If we cannot determine AL type amyloidosis to a certain disease, it is categorized as primary amyloidosis. Even if we cannot demonstrate a specific disease to AL type amyloidosis, we still see monoclonal plasma cell proliferation in these patients. In either case, we can find immunoglobulin or light chain within the serum or urine. The most commonly affected organ is the kidney. The heart, peripheral nervous system, gastrointestinal tract, and respiratory tract can also be affected. A localized form of AL type amyloid can be seen in the gastrointestinal tract [2]. Amyloid heavy chain (AH) is rarely reported but also associated with monoclonal plasma cell disorder [3].


# **Wearable Movement Sensors for Rehabilitation From Technology to Clinical Practice**

Edited by G.R.H. (Ruben) Regterschot, J.B.J. (Hans) Bussmann and Gerard M Ribbers

Printed Edition of the Special Issue Published in *Sensors*

www.mdpi.com/journal/sensors


*2.2. About the Parameters*

This section provides a review and discussion of the eight testing parameters of HPC analysed in this study (see Figure 2), regardless of the barrier between the hardened mechanical and durability parametric categories (see Section 2.1.2).


**Preface to "Integration of Renewable and Distributed Energy Resources in Power Systems "**

Decarbonization, digitalization, and decentralization are transforming our power systems towards a more sustainable energy system. The integration of large amounts of renewable generation is required to achieve an almost 100% decarbonized power system. Many of the new renewable generators, wind and solar, are connected to distribution grids. Digitalization allows changing the traditional paradigm of passive grid operation and planning to smart grids with higher levels of automation and self-control, maintaining the security of supply and economic efficiency. Communications and cybersecurity are required to interconnect millions of sensors and equipment connected not only on the network but also on the customer side. In this Special Issue, we have the opportunity to delve deeply into some of the advances that are required to achieve this transformation. Microgrids are a new concept to operate, in a clever manner, thousands of new control equipment located on customer premises, such as flexible demand, renewable generation sources, and storage installations. In addition, microgrids is an alternative to create more resilient systems against extreme weather conditions or massive cyberattacks. New planning techniques for urban districts and grid development tackling uncertainty regarding load growth and flexibility are also needed. Finally, new operational and planning criteria are explored to integrate a massive penetration of distributed generation, electric vehicles, and controllable loads.

> **Tom´as G ´omez San Rom´an, Jos´e Pablo Chaves-Avila ´** *Editors*


*2.3.1 Static observations, non-pulsatile flow*

Observations on the effects of non-pulsatile arterial pressure on arterial wall diameter and thickness were made using the experimental apparatus described above omitting the perfused cannula beneath the FI. Pressure was raised in a stepwise fashion using the barostatic valve, having closed the primary return circuit valve, and measurements of the artery made in cross section using B mode ultrasound.

#### *2.3.2 Direct and derived variables, pulsatile flow*

The perfusion apparatus allowed one group of variables to be adjusted and controlled directly whilst the other parameters were held at set levels. These direct variables were: pulse rate, baseline ("diastolic") pressure, cam profile, outflow resistance and arterial wall stiffness. Outflow resistance was a directly set value achieved by adjusting the outflow barostat to a specific pressure in relation to flow. Fluid viscosity was adjusted by alternating saline as a perfusate with a viscosity of 1 mPa.s for sucrose/saline solutions at concentrations of 10, 20 and 30% providing a range of viscosities at 15 deg. C from 1.4 to 3.9 mPa.s [19]. Arterial wall stiffness was adjusted by exposing the test artery to formaldehyde vapour for periods up to 48 hours whilst another artery kept in Ringer's lactate for the same period acted as a control. The degree of increase in stiffness produced by formadelhyde exposure is similar in order of magnitude to that reported in post-mortem studies of atherosclerotic human aortas [20] and to the effects of hypertension in the test vessels. The effect of altering a single variable on CTS and FI could thus be studied, and this was done so within series of individual arteries where the other parameters could be maintained at set levels. In this way, for example, the effect of changing the pulse rate on NS and false intima separation under different conditions of diastolic and systolic pressure and fluid viscosity could be explored in artery with an established elastic properties.

Changing the cams for ones of different stroke volume and profile created two derived variables, waveform and pulse pressure. Pulse pressure showed a direct physical link to CTS with some variation between cam profiles which was dependent on the shape of the pressure wave. In exploring this, different cams were used to alter the shape of the pressure wave and a schedule of machine settings worked through (**Table 1**) to harvest groups of identical pulse pressure for comparison of the effects of waveform and vice versa. In comparison of the effect of pulse pressure on CTS independent of waveform, peak waveform shape was ranked according to the area under each peak within 40 mmHg of peak pressure. The total number of pressure waves of equivalent peak sharpness thus harvested was 122 and the number of identical pulse pressures (tested at different pulse rates and baseline pressures) was 186. The schedule of machine settings permitted the independent effects of baseline pressure, pulse rate and viscosity to be studied whilst the other variables were kept constant (**Table 1**).


**3.2. Drylaid nonwoven**

The drylaid nonwoven process consists in fiber preparation, web formation, web bonding, and stabilization (Figure 4).

The fiber preparation is close to classical textile industry staple fiber preparation including bale opening, blending, coarse, and fine opening. Then the web forming machine is fed with the help of a chute in case of short staple fibers or with the help of a hopper in case of long staple fibers. The fibers fed by a chute or hopper condensed into the form of a lap are introduced into a carding machine. The carding machine separates fibers, starts the process of individu‐ alization, and delivers the fibers in the form of a web [24,25].

The web is then formed into the desired web structure by layering the webs extracted from the carding machine. Based on the final chosen weight and web structure, the layering can be completed by using longitudinal layering, cross layering, or perpendicular layering.

Web strengthening can be done mechanically with the help of needle punching which consists in bonding nonwoven web by mechanically interlocking the fibers through the web using barbed needles (Figure 4), with the help of stitch bonding which consists in consolidating fiber webs using knitting elements or with the help of hydro-entanglement which consists in locking the fibers together using fine high pressure water jets directed across the web.

**Figure 4.** SEM photomicrograph of needle-punched nonwoven (natural and synthetic fibers)

Mechanical bonding can be replaced with web chemical bonding which consists in applying the chemical binder to the web and curing it.

In case of thermoplastic and thermostable fiber blend web, the strengthening can be achieved by thermal bonding.


*2.1. Pre-Processing*

The considered slices are presented to the system as (.dcm) files. Rescale correction is performed on all the slices which provide a 512\*512 pixel array for each image. The DICOM tags Rescale Intercept (RI) and Rescale Slope (RS) postulates the linear transformation of pixels to their memory representation. The Rescale Correction [36] of the slices is given by:

$$RC = I^\star RS + RI \tag{1}$$

where *RC* is the rescaled units, *I* is the intensity value. In MR DICOM metadata, the attributes rescale slope and rescale intercept are not available as tags. For the computational purpose, the tag values are engaged as one and 1024, respectively. The available slice location header attribute in MR allows brain slices to be added for processing sequentially.

Abnormality identification in the brain requires removal of the skull or non-brain tissues such as dura, arachnoid, pia mater for e ffective extraction of ROI. The skull portions possess a low solidity area. Solidity is the proportion of the contour area to its convex hull area. Regions having the least solidity are removed, leaving only the region occupied by the actual brain tissue. The slices which have high solidity objects were retained after rescaling correction, thereby eliminating the non-brain matter.

### *2.2. Fuzzy Clustering Based Object Extraction from DICOM Slices*

The fuzzy clustering (FC) technique is performed to extract the best segmentation in a feature space containing varying cluster intensities and shapes. Fuzzy clustering classifies a set of data points *p*1, *p*2, *p*3, ... , *pm* of a DICOM slice into *k* (≤*m*) clusters, which minimizes the total distortion. Each data point in the feature space has a degree of membership (*aij*) to its own cluster. The points closest to cluster centroids has a higher degree compared to the points in the cluster edge. For a data point *i* assigned to cluster *j*, gives *aij* coefficient value for being in the jth cluster. The sum of *aij* coefficient is always 1. The fuzzy assisted clustering algorithm based on minimization of the following objective function (*Fw*) concerning *A* (fuzzy *k* partition) and *B* (*k* set of clusters) and it is given by:

$$F\_W(A,B) = \sum\_{j=1}^{m} \sum\_{i=1}^{k} \left( a\_{ij} \right)^w d^2 \left( P\_{j'}, B\_i \right); \ k \le m \tag{2}$$

where, *w*(>1), is the weighting exponent acts as a control parameter for the fuzziness in *aij*, *Pj* is the *j*th point in the feature vector of N-dimensional space, *Bi* being the centroid for cluster i, *aij* is the degree of membership of the pixel *Pj* in cluster *i*, *d*2*Pj*, *Bi* is the distance measure between *Pj* and *Bi*, *m* and *k* represent the number of data points and the number of clusters, respectively.

The degree of membership of all feature vectors is associated with the inverse of the distance to the cluster center:

$$a\_{i\bar{j}} = \frac{\left[\frac{1}{d^2\left(P\_{\bar{j}}, B\_{\bar{i}}\right)}\right]^{1/\nu - 1}}{\sum\_{i=1}^{k} \left[\frac{1}{d^2\left(P\_{\bar{j}}, B\_{\bar{i}}\right)}\right]^{1/\nu - 1}}\tag{3}$$

The Euclidean distance measure is used to compute the degree of membership (*aij*) is given as:

$$d^2(P\_{j'}B\_i) = \left(P\_{j} - B\_i\right)^T I \left(P\_{j} - B\_i\right) \tag{4}$$

where *I* denotes the identity matrix. The new centroid positions are computed based on the mean of all the points, weighted by its corresponding degree of membership (*aij*) to the cluster:

$$\widehat{B\_i} = \frac{\sum\_{j=1}^{n} \left(a\_{ij}\right)^{w} P\_j}{\sum\_{j=1}^{n} \left(a\_{ij}\right)^{w}} \tag{5}$$

Based on new centroid positions the updated degree of membership *a ij*, is computed according to *aij* shown in Equation (3). This process is repeated until the sum of distances of each point in the slice to the centroid of the cluster is minimum, i.e., a termination criterion '∈' is reached, which ensures maximum accuracy. Other stopping criteria include no further improvement in the variance over some iterations. The structures of the brain are segmented from the set of DICOM slices for a set of '*k*' values ranging from 2 to 9.

### *2.3. Selection of the Best (k) Using Silhouette Index*

Fuzzy clustering renders the clustered image for the preferred number of clusters (k). However, optimal 'k' should be chosen in order to place cluster labels within the centroid. In literature, a well-balanced coefficient named silhouette score, presented by Rousseeuw [37], has shown higher performance in finding optimal clusters. The silhouette score pertains to the deviation between the within-class tightness and separation. Specifically, the silhouette value for a pixel in the slice pixel array is given by,

$$\text{sil}(j) = \frac{b(j) - a(j)}{\max(a(j), b(j))} \tag{6}$$

where, *a*(*j*) = 1/|*Vi*| − 1 *y*∈*Vi d*(*x*, *y*), be the mean distance of pixel point '*x*' with other pixels (*y*) within the cluster *Vi* and *b*(*j*) = *min* {1/*Vi y*∈*Vi d*(*x*, *y*)}, be the average dissimilarity of a point '*x*' to about any cluster *Vi* of which chosen point '*y*' is not associated with it.

The maximum value of *s*(*j*) reflects the optimal number of clusters. Correspondingly, the minimum of *b*(*j*) is taken for computing *s*(*j*). If *b*(*j*) is larger, then the point is very far from its next neighboring cluster. The squared Euclidean distance provides the distance metric *d*(*x*, *y*) between clusters for computing the silhouettes.

K-means clustering with silhouette analysis is executed to find out the optimal 'k' ranging from 2 to 9.

Silhouette always lies between −1 to 1, and it is defined as:

$$all(j) = \begin{cases} 1 - a(j)/b(j), \text{ if } a(j)  b(j) \end{cases} \tag{7}$$

If the silhouette values are approaching either +1 or −1, the pixel points are well clustered or misclassified, respectively. If zero, the points could be assigned to another cluster also.

Further, to validate the segmented slices, the entire pixel array of each slice is considered, and the average silhouette width is computed. The average silhouette width for every slice is calculated from mean of all the distinct cluster silhouettes is given by:

$$S\_{\text{avg}}(s\_i) = \frac{\sum\_{i}^{n} sil(j)}{k} \tag{8}$$

where, *n* denotes the number of clusters segmented. The *Savg* is used to find the best k for a slice *si*. The silhouette coefficient (*kbest*,*i*) is defined as the maximum average silhouette width which is given by,

$$k\_{\text{best},i} = \max \{ \mathbf{S}\_{\text{avg}}(\mathbf{s}\_i) \} \tag{9}$$

The algorithmic steps of incorporating fuzzy clustering and silhouette metric to the set of DICOM slices are illustrated in Algorithm 1.

### *2.4. Morphological Operations for Objects Enhancement*

Image masking is used to specify the foreground, background, or probable background /foreground. Contour masking separates the objects from the original images, and it is essential for further analysis. It is eliminating the outliers such as air, from the actual brain slices. The fuzzy clustering process discovers the best-segmented clusters. These clusters form a binary mask that overlaid on the actual slices to acquire the respective contour intensities. Mutual information (I) is computed between the contour mask with the corresponding slice for ensuring similarity [38,39]. The weighted contribution (Wi) of the contour mask (CMi) to the original slice (Si) is calculated as:

$$\mathcal{W}\_{i} = \frac{1}{\mathcal{C}M\_{i}} \varepsilon^{-\frac{l\_{i} - l\_{\min}}{l\_{\max} - l\_{\min}}} \tag{10}$$

where *Ii* represents the mutual information between *CMi* and *Si*. *Imax*, *Imin* are the maximum and minimum mutual information for the overall *CMi*. After the extraction of structures from the fuzzy clustering process, the obtained binary mask of the chosen slices may be distorted due to noise and texture. Mathematical morphology, a kind of contrast enhancement technique, assists selective enhancement of the small diagnostic contour features that are overlaid on a composite background. Hence, the binary mask representing the extracted structures is further practiced with non-linear operations such as morphological erode and morphological dilate for removing the inadequacies in order to retain the form and structure of the extracted objects. Erosion is a reverse process to dilation-erosion strips pixel layer over the edges, contradictorily dilation augments pixel layer over the edges.

Dilation adds pixels to the contour boundaries in the slices. The number of supplementary pixels integrated into the mask image is subject to the shape and size of the structuring element. Dilation process is done by:

$$\text{'CM} \oplus \text{SE} = \{\text{z} | (\text{SE})\_z \cap \text{CM} \neq q \} \tag{11}$$

where *CM* is the set of pixels representing the binary mask, *SE* be the structuring element initially reflected as *SE* then the reflected element is translated by *z*. This process enlarges the binary mask in all directions not to miss any pixels, particularly at contour edges. Similarly, erosion is performed by:

$$\text{CM} \ominus \text{SE} = \left\{ \left| z \middle| (SE)\_z \subseteq \text{CM} \right| \right\} \tag{12}$$

Stating that *z* confined in *CM* shifts the *SE*. Erosion removes pixels, thus sharpening the object boundary. The number of pixels stripped is subject to the size of the SE. Erosion strips the connected normal and abnormal contours, which aid in the effective extraction of ROI in the post-processing stages.

### *2.5. Tumor Quantization and Validation*

The extracted objects possess high solidity ventricles and the tumor region. In order to extract the ROI from the brain structures, image post-processing approaches are utilized to mine the ROI from the extracted objects. After determining the abnormal regions, the size of the tumor is quantified based on its area and perimeter.

The validation metrics are used to evaluate the spatial intersection of ground truth (GT) of the clinical slices with the extracted ROI [40]. The performance of the segmentation procedure is validated using similarity measures such as Dice, Jaccard, false positive (FPR), and false negative (FNR) rates.

These measures are mathematically conveyed as:

$$\text{Jaccard}(I\_{\text{\\$^t}}, I\_{\text{ROI}}) = (I\_{\text{\\$^t}} \cap I\_{\text{ROI}}) / (I\_{\text{\\$^t}} \cup I\_{\text{ROI}}) \tag{13}$$

$$\text{Dice}\{I\_{\mathfrak{g}t\urcorner}I\_{\text{ROI}}\} = \mathfrak{L}\left(I\_{\mathfrak{g}t\urcorner} \cap I\_{\text{ROI}}\right) / \left(I\_{\mathfrak{g}t\urcorner} \cup I\_{\text{ROI}}\right) \tag{14}$$

$$FPR\left(I\_{\mathbb{S}^{\mathsf{t}}\prime}I\_{\mathrm{ROI}}\right) = \left(I\_{\mathbb{S}^{\mathsf{t}}}\prime I\_{\mathrm{ROI}}\right) / \left(I\_{\mathbb{S}^{\mathsf{t}}}\cup I\_{\mathrm{ROI}}\right) \tag{15}$$

$$FNR\left(I\_{\mathfrak{J}^{\text{tf}}}, I\_{ROI}\right) = \left(I\_{ROI} / I\_{\mathfrak{J}^{\text{tf}}}\right) / \left(I\_{\mathfrak{J}^{\text{tf}}} \cup I\_{ROI}\right) \tag{16}$$

where, *Igt* expresses to the ground truth (GT) and *IROI* points for the segmented image with the proposed strategy. Other related works implemented on brain MRI can be found in [41–49].

### *2.6. Volume Assessment and 3D Modeling*

The clinical slices considered in this work have the cubical stack format [SC × W × H], SC signifies the number of slices (22), W, and H indicates the width and height of a slice [512 × 512] in pixels, respectively. The slices are processed in DICOM format, which holds adequate slice information. In DICOM metadata, it is identified that 'slice thickness (ST)' is 5 mm, 'pixel spacing (PSx/PSy)' is (0.4492 mm/0.4492 mm) and 'spacing between slices (SS)' is 6.5 mm. The original stack [22 × 512 × 512] migrated to a resampled stack [110 × 230 × 230] based on the spacing information presented in the DICOM header. Resampling is achieved by cubic spline interpolation function. The tumor volume is estimated as:

$$Volume = \text{Turnor Voxels} \ast \text{Voxels size} \tag{17}$$

where tumor voxels are the number of voxels that contributes to tumor and voxel size is measured based on pixel spacing and slice thickness.

Visualization of tumor voxels in the complete study is achieved by merging the tumor containing slices to form a voxel mesh in all three anatomical planes. Further, this exemplifies an interconnected set of triangular faces of tumor voxels [50].

### **3. Results and Discussions**

This section illustrates the results achieved with the proposed procedure. Figure 2 demonstrates a brief overview of the proposed procedure for brain MR examination. Initially, the DICOM slices of the patient study are pre-processed with rescaling correction. Moreover, this creates the intensity of the similar tissues in the study to confirm across the image slices.  Figure 3 shows the representative image of slice 12 before extraction (a) of brain tissue after removing (b) the non-brain portions. This procedure avoids non-brain tissues to add unnecessary information, thereby enhancing the e fficacy of extracting the ROI. The combination of Fuzzy clustering with validated silhouette index (as the criterion for choosing best k) discovers the precise number of clusters from the slices. Figure 4 shows the extracted objects of Slice-14 and its corresponding mask image for '*k*' clusters (*k* ranges from 2 to 9).

Table 1 shows the silhouette scores *Savg* for the considered slices for the chosen k values. A more substantial silhouette value gives a high split over the data points. For slice10 in Table 1, the optimal k is elected as two, since *Savg*(*si*) holds the maximum value 0.45571. Similarly, the optimal *kbest,i* is selected from the range of slices (Slice11-Slice14) based on the average silhouette width.

**Table 1.** Silhouette scores for Slices (10–14).

The maximum silhouette score (*kbest*,*i*) obtained for the cluster range are highlighted in **bold**.

In slice14 the maximum *Savg*(*s*14) = 0.58523 when *k* is six, but a minimum value resulted when *k* is two, (*Savg*(*s*14) = 0.48988). Further, this shows the loss of tumor information in Figure 4 when *k* is two, and an optimal segmentation is obtained when *k* is six.

**Figure 2.** Flowchart of the proposed brain MRI examination of a patient study.

**Figure 4.** Segmented results and Masked Objects for Slice 14.

Figure 5a shows the graph with silhouette scores versus the number of clusters '*k'* for a representative sample image 'slice14.dcm'. The average silhouette width for the slices, which are more than 0.48, confirms a good split for all *k* clusters. As shown in the graph, a reasonable peak is obtained when *k* = 6 and also possess a maximum value (*Savg*(*s*12) = 0.58523).

Figure 5b depicts the silhouette plot for slice14. The selection of an optimal *k* provides better separation in feature space with more similar thickness and sizes. This increase in *Savg*(*s*14) is due to the distinct separation from their neighboring clusters except for the background. The well-diversified information is obtained from the slices since the proper selection silhouette index was made. From the achieved outcomes, it is clear that silhouette analysis plays a significant role in the identification of best-clustered objects. After FC, the pixels representing abnormalities are spatially identified and segmented. The mask image is produced from the objects extracted from each slice, in order to distinguish from outliers/ background. The mask image holds the pixel intensity values of abnormal

and normal pixel intensities. The mask is applied to the corresponding slice for the extraction of clustered intensities from the actual slices. The objects are further enhanced by morphological processes by performing morphological open, dilate, erode and close operations in a sequential order to obtain a smoother object boundary without speckles. The experimental results of Figure 3 confirms a superior image enhancement step; as a result, it shows the best separation of objects obtained from composite backgrounds.

**Figure 5.** (**a**) No. of Clusters vs. Silhouette Scores; (**b**) Silhouette plot for Slice-14.

Each slice in the MR sequence is distinctly examined using the eminent state-of-art segmentation methods such as watershed [51], Chan-Vese [52], and fuzzy clustering (FC). The image quality measures obtained from these methods are portrayed in Table 2. From the segmented results of FC, it is perceived that FC based segmentation offers distinct separation of objects and aids a better confidant for the image post-processing stages.

**Table 2.** Segmented results for state-of-art segmentation methods.

Further, for measuring the supremacy of the segmentation approaches, the well-known image quality measures [53] such as peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), normalized absolute error (NAE) and structural similarity index (SSIM) are calculated and are presented in Table 3. From the table values, it is perceived that the FC method discovers the vital prominent structures and thus preserves the segmentation quality. Also, the average image quality measures attained through FC is superior compared to the other state-of-art approaches reflected in this work.

**Table 3.** Image quality measures for segmentation methods.

The proposed approach had been tested for its performance by validating it against the grand challenge benchmark image dataset called the BRATS (size: 236 × 216 pixels). In this dataset, ten patient studies of T2 and T1C modalities had been taken up for analysis, which contains axial brain MR image series. The sample image series of patient studies and their corresponding GT that are obtained are shown in Figure 6. The BRATs dataset that had been considered in the proposed research has a number of advantages, few of those are–The desirable amount of 2D slices of a patient study can be easily extracted from its skull stripped 3D brain MRI, modalities like Flair, T1, T1C, and T2 are easily supported, contains ground truth images for all modalities o ffered by an expert member. Due to these reasons that most researchers had adopted the BRATS images for testing their disease examination tool.

**Figure 6.** BRATS Dataset image series with Expert's Ground Truth.

For a comparative analysis in the post-processing stage, the ROI mining technique, marker controlled watershed segmentation (MCW), Seed region growing (RG) and distance regularized level sets (DRLS) are adopted and implemented to extract the anomalous section from the segmented objects (shown in Figure 4). MCW is a well-known segmentation technique associated with marker controlled morphological function and Sobel's edge detection. This procedure detects ROI with the assistance of priori provided whole alike image intensities [54].

In contrast to MCW, the level sets the implicit active contour models, uses gradient information of the image, and thus naturally handles topological deviations by merging or splitting the contours [55]. The parameters for DRLS is assigned as follows; number of iterations = 100, scale parameter = 1.5, potential function = single-well and timestamp = 5.

RG is an operator instigated semi-automated method, extensively used to extract the desired (abnormal) structures from medical images [56]. RG requires a seed point (pixel position) to be initialized somewhere within a contour or ROI. From the seed point, the RG procedure will start mining the similar intensities of possible connected neighboring pixels accessible in the ROI.

The images chosen are initially subjected to FC for objects enhancement before applying the post-processing. The image quality gets enhanced through the validated FC technique and provides an ideal platform for the post-processing that is performed using MCW, RG, and DRLS. Figure 7 depicts the brain abnormality segmentation results extracted from the 2D slices of T2 modality images through FC assisted MCW, RG, and DRLS techniques. Correspondingly, results of segmented ROI from 2D slices of T1C modality are shown in Figure 8.

The segmentation methods (MCW, RG, and DRLS) that had been implemented were assessed for their performance by carrying out a comparative analysis that was executed between the ROI and GT. The extracted ROI and GT were initially compared on T2 modality images, followed by T1C modality images. The results obtained from these comparisons were recorded in Tables 4 and 5. The recordings were made based on image similarity measures like Jaccard, Dice, FPR, and FNR. Figure 9 shows the comparative analysis of assessed similarity measures of T2 and T1C weighted images. The corresponding average scores of Tables 4 and 5 are depicted in the graph.

**Figure 7.** Segmentation results of BRATS T2 series.

**Figure 8.** Segmentation results of BRATS T1C series.

**Table 5.** Image similarity measures for BRATS T1C MRI series.

**Figure 9.** Average picture similarity measures of a patient study against expert's ground truth.

From the recorded values for the considered slices presented in Tables 4 and 5, it could be inferred that the outcome produced through FC based DRLS technique is far more superior to FC+MCW and FC+RG techniques. Also, the metrics Jaccard and Dice are computed for ten patient studies of BRATS individually. Figures 10 and 11 depicts the average scores for ten patient studies of the BRATS dataset, and 'Av.' represents the overall average score of all the patient studies from T2 and T1C weighted images, respectively.

**Figure 10.** Average Jaccard and Dice score of individual patient studies for T2 modality. 'Av.' specifies the average score of all the patient studies.

**Figure 11.** Average Jaccard and Dice score of individual patient studies for T1C modality. 'Av.' Specifies the average score of all the patient studies.

Furthermore, the suggested procedure is attempted on clinical MRI brain study of a patient [57]. The patient study considered contains axial T2 MR DICOM slices. The DICOM slices between the ranges Slice-10 to Slice-14 are considered in this approach for tumor analysis, as they hold enough tumor information. Slice15 and above are excluded from the examination as it does not contain any tumor region. Initially, the considered series are enhanced using FC, then at the post-processing stage, the mining techniques MCW, RG, and DRLS are adopted. Figure 12a,b embody the slice number with an optimal k and the original (actual) middle slices. Figure 12c represents the ground truth provided by an expert member. Figure 12d–f signify the ROI extracted from the validated fuzzy clustering-assisted MCW, RG, and DRLS procedures.

**Figure 12.** Segmented results of the clinical dataset (middle slices only).

The validation of the FC-aided mining procedures against GT images is performed using well-known image similarity measures such as Dice, Jaccard, false-negative, and false-positive rates. These parameters stay as an aid to assess the efficacy of the segmentation procedure.

From Table 6, it is observed that maximum similarity is attained between ROI and GT by the suggested FC+DRLS procedure shown on the Jaccard and Dice metric scores. FPR values indicate that typical pixels of the brain are misclassified as tumor pixels. Similarly FNR values depict pixels which contribute as tumor are misclassified as normal pixels of the brain.

**Table 6.** Image similarity measures for clinical T2 MRI series.

The minimum values of FPR and FNR guarantee the efficiency of the FC+DRLS segmentation method against MCW and RG in the set of DICOM slices. Figure 13 shows the comparative analysis of assessed similarity measures using the average scores of the slices depicted in Table 6. Therefore it is evident that in the proposed approach, the FC+DRLS based segmentation technique produces superior results for the clinical study as well.

**Figure 13.** Average picture similarity measures of clinical study with expert's ground Truth.

Further, the ROI (tumor) is extracted to acquire the geometrical properties such as area and perimeter. The captured tumor information is clearly visible from slice10 to slice14. In Table 7, the parameters, area, and perimeter are calculated based on the tumor information extracted from the ROI. The parameters gradually increase up to slice13, which holds the maximum tumor part and then decreases. The overall study of a patient is determined from the entire DICOM slices.

**Table 7.** Quantization features Area and Perimeter for best 'K'.

The slices are reconstructed to a cubical stack based on in-stack position attribute of DICOM. The extracted objects of the clinical study undergo volumetric estimates and 3D reconstruction. Table 8 shows the volume of tumor calculated for DICOM and resampled stack.

**Table 8.** Volume calculation of DICOM and Resampled stack.

Tumor volume is calculated for the DICOM grid using the physical spacing metadata available in the DICOM header. The inter-slice resolution of the considered patient study is coarse, as the slice thickness is 5 mm, which is considerably higher than the in-plane pixel size, i.e., 0.4492 mm. This anisotropic characteristic results in appalling issues for modeling 3D and image analysis. Thus resampling is often considered as a vital step to transform DICOM stack to an isotropic stack. In the resampled stack, the accuracy of integrating the contours of individual slices not degraded; also, it interpolates the z dimension with lower resolution and in-plane dimensions with higher resolution.

Figure 14 shows the 3D models of DICOM and resampled grid for the patient study. The three anatomical planes are used, and their dimensions are set in mm. For the DICOM grid in Figure 14a, it is observed that the dimensions [X × Y × Z] are set as [512 × 512 × 22]. Similarly, the resampled grid in Figure 14b the dimensions are viewed as [230 × 230 × 110]. The model permits to visualize the object interactively in all the three directions specified.

**Figure 14.** (**a**) DICOM Grid; (**b**) Resampled Grid.

From Figure 14a,b, it is clear that the resampled grid o ffers a smoother surface than the DICOM grid. Resampled grid o ffers a smoother iso-surface and better 3D visualization.

The proposed work had been validated against various modalities of the BRATS dataset and clinical slices. The performance progression is carried out at each stage of the suggested segmentation procedure. The fuzzy clustering technique had given prominent results in the enhancement phase that aids in e ffective extraction in post-processing stages. The proposed work had considered two to nine classes (*k*) that were applied on each slice of the patient under study for ascertaining the most prominent *k* that could yield the best segmentation. The silhouette score is taken as validation metric results in the optimal enhancement of slices since it considers the *kbest*,*i* measure for making up the number of required classes. This validated clustering process helps in minimizing the loss of tumor intensities over the patient study. Also, a comparative segmentation analysis had been carried out against Chan-Vese and watershed algorithms for ensuring the segmentation quality of FC. To overcome the computational complexity, the proposed work had considered *k* = 9 as the upper limit for the number of clusters. For DRLS post-processing, imparting single well potential function and Gaussian kernel value as 1.5 had yielded better extraction of tumor part than RG and MCW techniques. In the future, the proposed procedure can be pondered on brain slices containing di ffused boundaries and other image modalities in addition to magnetic resonance angiograms (MRA).


Contents

Preface

Significant socio-economic transitions are characterised by a rise in change when, from one generation to the next, people work and live differently than they used to and daily life becomes radically different [1]. These transitions are induced by different inventions, leading to major changes in how people live and relate with one another. The first industrial revolution recorded the introduction of the steam engine, which transformed industries, while the second revolution was associated with the use of electricity to operate new technologies for manufacturing. This era was based on combustion engines and the development of transport, communications, and high-tech industries. This was followed by the third revolution, which saw a shift from a society based on conventional fossil fuel to one based on renewable energy. This revolution is widely known to have been inspired by information technology. Since the beginning of the 21st century, the fourth industrial revolution has been in progress and it presents the possibilities of unprecedented inventions

Widely known as the knowledge age, the 21st century is an age where growth and progress is a function of knowledge and ideas. New patterns of work have been developed and as a result, new kinds of workers with new and different skills are required. According to Khalil and Osman ([5], p.1), "the shift in this current world economy from a manufacturing-based to a knowledge-based economy, scientific innovation, augmented globalisation and advances in communication and information technology (ICT) have changed the job market in this modernised era". This implies that this century is focused on innovations, which is largely driven by advances in Science, Technology, Engineering, and Mathematics (STEM) fields. The generation of innovation in science and technology has become key in the development in this era thereby driving the need for professionals in the fields of STEM. Foundational efforts are being made to prepare students to meet emerging 21st century realities and STEM education has been identified as a priority as it is at the heart of our fast-moving technology-driven world [3]. This preface seeks to expose the need for STEM education in our contemporary society while exploring the possibilities and opportunities that abound in its application in handling real-

Advances in the disciplines of STEM drive innovation and this explains the rationale behind the prioritization of these fields by different societies in this century. These are fields that improve human understanding of the physical environment, support research, and experimentation, in order to gain knowledge and skills needed for the real world. These fields are individually significant and can be taught in isolation, but when collectively applied, they can deepen understanding and be used to solve real-world problems [6]. STEM education blurs the boundaries amongst these disciplines, presenting an integrated approach to solving problems,

and emerging breakthroughs in technology [2–4].

**Stem and stem education in the 21st century**

**Introduction**

life situations.


**About the Editor**

#### **Charbel Darido**

Dr Charbel Darido graduated with a Master's in Endocrinology and a Master's in Physiology before completing his PhD studies on colon cancer at the University of Montpellier in France. He then conducted post-doctoral studies at the Royal Melbourne Hospital and the University of Melbourne in Australia. He initiated a cancer project that led to the identification of a critical tumour-suppressor gene against squamous cell carcinoma (SCC) of skin and head and neck tissues. Following his first postdoc, he relocated to Monash University to lead the cancer program in the "epidermal development laboratory"at the Central Clinical School (The Alfred Centre). More recently, Dr Darido was appointed to a Group Leader position within the Cancer Therapeutic Program at Peter Mac to establish a vibrant research program on head and neck cancer. The main focus of his lab is to develop model systems for the investigation of head and neck SCC with an emphasis on studying the initiating lesions and identifying the cells of origin of these cancers. His research group studies the molecular basis of cancer, with emphasis on the basic mechanisms of tumour suppression and signal transduction and their dysregulation in skin and head and neck squamous cell carcinogenesis. He is recognised for his pioneering research aimed at unravelling the complexity of the intracellular signalling circuitries by which the loss of transcription factors promotes oncogenic addiction, thereby leading to malignant transformation and cancer cell proliferation. Dr Darido's current investigations are made possible through the use of genetically engineered mouse models and carcinogenic protocols, orthotopic SCC xenografts and cultured cancer-derived cell lines. His ultimate goal is to translate molecular insights into new strategies of preventing and treating this disease, which causes significant mortality and morbidity worldwide.


**Author details**

Motunrayo Ganiyat Akande\*, Solomon Usman Abraham and Johnson Caleb Ogunnubi Faculty of Veterinary Medicine, Department of Veterinary Pharmacology and Toxicology, University of Abuja, Nigeria

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*4.1. Experimental Results and Analysis*

According to Section 2.2, we select *N* = 500 and *n* = 200. The loss value curve of the experimental results is shown in Figure 4.

**Figure 4.** Comparison diagram of loss value of training set and verification set.

The results of the stability prediction on the test set are shown in Figure 5. Taking 0.5 as the dividing line, the value that is greater than 0.5 is classified as "1", which is considered as stable. The value that is less than 0.5 is classified as "0", which is considered as unstable. The accuracy is 2294/2300.

**Figure 5.** Stability prediction results of test set (first 200 cycles).

It is shown in Table 2 that the false negative value is 6, i.e., six samples are actually stable. However, the prediction result is unstable. In order to maintain the stable operation of EI, it is acceptable to consider appropriate over-warning in real scenarios. While the false positive value is 0, i.e., the actual unstable sample is correctly predicted, which meets our requirements. In this paper, the threshold of accuracy is set to be 99%. The calculation is available with a precision of 1, a recall of 98.7%, and an f1-score of 99.4%.

**Table 2.** Stability prediction results (first 200 cycles).

The evaluation of stability prediction at this stage is to prepare for the decision optimization of subsequent reactive power compensation. In order to know in advance whether the EI would return to a stable state in the future, the characteristic time period is expected to be reduced. The data from the first 100 cycles would be intercepted, and the aforementioned CNN is still used for training. The accuracy is 2252/2300, which also meets the expected requirements.

The results of stability prediction are specifically analyzed in Table 3. Through calculating, the precision, the recall and the f1-score are 91.2%, 99.4% and 95.1%, respectively. The accuracy does not meet the requirement.

**Table 3.** Stability prediction results (first 100 cycles).

Although the precision of the prediction has achieved 97.9%, such rate is still relatively low and does not meet the requirement. Therefore, the structure of CNN is determined to be optimized. The parameters in the CNN is set to be fixed. The size of the convolution kernel is selected as 3 × 3. The depth of the network is adjusted by changing the number of convolution layers. The number of feature extractions is altered by changing the number of convolution kernels per layer, which makes the extraction e ffect much better. After twenty experiments are conducted, the average value of the training results of di fferent CNN structures with di fferent parameters is obtained. The results are shown in Table 4.

**Table 4.** Parameter settings of the convolutional neural network (CNN).

It can be seen from Table 4 that as the number of convolution kernels increases, the time for calculation increases substantially. The e ffect of increasing the number of the convolution layer on the calculation time is not obvious. The setting of the number of convolution layers and convolution kernels a ffects the prediction results. After twenty experiments are implemented, when the 6-layer convolution is set, and the number of convolution kernels per convolution layer is 64, the duration of calculation becomes shorter, and the accuracy is higher. The false positive value is smaller. More than half of the false positive values in the experimental results are all 0, which implies that the samples that are actually unstable are successfully predicted to be unstable. In this sense, the goal of stability prediction in this paper has been achieved. Thus, the model of CNN is selected. The results of the stability prediction on the test set are shown in Figure 6. The accuracy is 2236/2300, which meets the expected target.

**Figure 6.** Stability prediction results of the test set after optimization (the first 100 cycles).

The analysis of the stability prediction results is shown in Table 5. The false negative value is 64, i.e., 64 samples are actually stable. However, the prediction result is unstable, which is acceptable in the real engineering scenario. The false positive value is 0, i.e., the actual unstable samples are not predicted

to be stable, which meets the expected requirements. Compared with the result before optimization, although the false negative value has increased, such value is reduced to be 0. In real-world applications, a false negative is acceptable, and a false positive is unacceptable. Therefore, the optimized results appear to meet the expected requirements. Through computation, the precision, the recall and the f1-score are 1, 86.3% and 92.7%, respectively.

**Table 5.** Stability prediction results after optimization (first 100 cycles).

The output of existing research on stability prediction is mainly based on the analysis of single node data. Di fferently, this study selects the data of each node of the whole network as input data. Meanwhile, the data format is di fferent. In order to verify the feasibility and e ffectiveness of the study as a comparative experiment, the SVM algorithm [16] is used for training. The false negative value is 141, i.e., 141 samples are stable. However, the prediction result is unstable, which is acceptable. The false positive value is 25, i.e., 25 samples are unstable but these samples are predicted to be stable. In real engineering practice, important information about voltage instability would be missed in such results. The future unstable states are di fficult to be predicted accurately. Therefore, it is di fficult to make a judgement, which is unacceptable. The obtained precision, the recall and the f1-score are 92.9%, 69.9% and 79.7%, respectively, which are much lower than the result obtained by our algorithm.

In summary, when data for each node in the whole grid system is used as high-dimensional input feature data within a certain characteristic time period, feature extraction can be better performed by deep learning CNN than by the conventional machine learning algorithm. A satisfactory fitting effect is obtained, and the application result in transient voltage stability judgment of EI achieves the expected target. Based on the mainstream power simulation software, a data batch processing toolkit has been developed, which improves the e fficiency of data processing.

### *4.2. Simulation Example of Reactive Power Decision Optimization*

BPA is used to simulate di fferent short-circuit faults. Since only the load model and load rate can be changed in BPA, the real-time load value cannot be collected. The system recovery time (cycle) corresponding to di fferent load rates and the total SVG compensation is shown in Figure 7.

**Figure 7.** The curve of load rate, SVG compensation, and time of returning to a stable state.

It can be seen that under the same load rate, as the amount of SVG compensation increases, the system fluctuates from an unstable state to a stable state, and the time of returning to the stable state decreases gradually.

One set of SVG compensation was extracted. The load rate-time of returning to stable state (cycle) curve is drawn under the same SVG compensation. As is shown in Figure 8, with the increase in the load rate, the time of returning to a stable state increases gradually after the same SVG compensation is obtained until stability cannot be restored. In Figure 6, the system instability is represented by the time of returning to a stable state of 0.

**Figure 8.** The curve of load rate–time of returning to a stable state when the SVG compensator is the same.

A numerical example is given in the following study where site *A* and site *B* represent for two cities. The load model is set as follows: (1) 30% constant resistance load, 40% constant current load and 30% constant power load at site *A*; and (2) 70% motor and 30% constant impedance with a load rate of 115% at site *B*. The short-circuit fault of single-circuit three-phase is set on buses with different voltage grades. Eight faults are considered in this example, including fault 1 (high-voltage level bus fault of 220 kV between site *A* and site *B*), fault 2 and fault 3 (220 kV high voltage grade bus fault on site *B*, i.e., the grid location studied in the experiment), as well as fault 4 to fault 8 (low voltage bus fault of 110 kV and 66 kV on site *B*).The fault clearing time is taken as 5 cycles, i.e., 0.1 s. Five SVGs are set at two 220 kV high voltage grade substations (A1 substation and A2 substation) and three 110 kV voltage grade substations (A3 substation, A4 substation and A5 substation), respectively. The compensation values of five SVGs, which are intervals of the action in the proposed algorithm, are set as follows: [20 Mvar, 80 Mvar], [50 Mvar, 80 Mvar], [60 Mvar, 80 Mvar], [50 Mvar, 80 Mvar] and [50 Mvar, 80 Mvar]. In the proposed algorithm, the action space is discretized, and the discretized step is 10 Mvar. By this means, 1344 discrete actions are obtained. These actions are numbered from 1 to 1344. These actions are further converted into *one* − *hot* form. The network is trained by CNN. The loss value is shown in Figure 9.

**Figure 9.** Trend graph of loss value.

The optimization results based on deep reinforcement learning are shown in Table 6. The compensations value of each action in Table 5 for five SVGs are listed in Table 7. These two tables can be understood as follows. Taking the number in the first row of Table 6 as an example, when the network training times are 5000, the output action numbers of fault 1 to fault 8 are 34. According to the second line of Table 7, action 34 compensates for five SVGs with 20 Mvar, 50 Mvar, 80 Mvar, 50 Mvar and 60 Mvar, respectively.

Optimization

 Results.

**Table 6.** Decision

**Table 7.** Compensation Values.

Fault 1, fault 2 and fault 3 are in the high voltage bus. Thus, reactive compensation should be increased. It can be seen that at the initial stage of training, the differences from fault 1 to fault 3 and from fault 4 to fault 8 are not successfully identified. Similar action outputs with high compensation are given. However, the decision of reactive compensation for different grades could be given by increasing the training times. The difference can be seen in the results of later training.

The contrast curve of *Q* value and reward value are shown in Figure 10. It can be seen that the general trend of the *Q* value is consistent with the general trend of the reward value. As the training time increases, the *Q* value is constantly close to the reward value, which achieves the goal of training.

**Figure 10.** Comparison chart of Q value and reward value (25000 times of training).

All buses with high voltage are set to be stable. The total compensation of SVG is the lowest. The decision scheme of the final test is shown in Table 6.

Fault 1 occurs on the highest level of the transmission bus between site *A* and site *B*. One of the decision schemes obtained by the model is action 19, i.e., five SVGs compensate 20 Mvar, 50 Mvar, 70 Mvar, 50 Mvar and 70 Mvar, respectively. It can be seen that the SVGs are distributed. The compensation value of each SVG is smaller than that of only two SVGs. The desired distributed setting of the reactive power compensation device is achieved. The stability result is shown in Figure 11. The time of returning to a stable state is about 550 cycles.

**Figure 11.** Fault 1 Action 19: Bus positive sequence voltages after SVG compensates 20 Mvar, 50 Mvar, 70 Mvar, 50 Mvar and 70 Mvar, respectively.

Fault 2 occurs on the high-voltage bus with 220 kV at site *B*. One of the decision schemes given by the model is action 34, that is, five SVGs compensate 20 Mvar, 50 Mvar, 80 Mvar, 50 Mvar and 60 Mvar, respectively. The stability result is shown in Figure 12. The time of returning to a state of stability is about 250 cycles.

**Figure 12.** Fault 2 Action 34: Bus positive sequence voltages after SVG compensates 20 Mvar, 50 Mvar, 80 Mvar, 50 Mvar and 60 Mvar, respectively.

Fault 3 occurs on the high-voltage bus with 220 kV at site *B*. One of the decision schemes given by the model is action 389, i.e., five SVGs compensate 40 Mvar, 50 Mvar, 60 Mvar, 60 Mvar and 50 Mvar, respectively. The time of returning to a stable state is about 295 cycles.

Fault 4 occurs on the high-voltage bus with 220 kV at site *B*. One of the decision schemes given by the model is action 34, i.e., five SVGs compensate 20 Mvar, 50 Mvar, 80 Mvar, 50 Mvar and 60 Mvar, respectively. The time of returning to the state of stability is about 50 cycles.

From the above analysis, the difference between the high voltage bus with 220 kV and 110 kV in the initial stage of training is not distinguished by the model. The scheme with the same total compensation amount is selected. Although the requirement of restoring stability can be met, the whole EI is subject to the impact of smaller short-circuit faulting with lower bus voltage level. In the later stage of training, a scheme for fault 4 to fault 8 is given by the model. Five SVGs compensate 30 Mvar, 60 Mvar, 60 Mvar, 50 Mvar and 50 Mvar, respectively. At this point, the total compensation amount is 250 Mvar. Fault 4 is set, and then action 241 is executed. The stability result is shown in Figure 13. The time of returning to a stable state is about 60 cycles. A better optimal decision scheme can be given by the model though training.

**Figure 13.** Fault 4 Action 241: Bus positive sequence voltages after SVG compensates 30 Mvar, 60 Mvar, 60 Mvar, 50 Mvar and 50 Mvar, respectively.

However, in the previous setting of the reward value calculation formula, only the requirement that buses with high voltage grade finally restore stability is considered. The time of returning to a stable state is not considered. Thereby, we can see from Figure 11 that although the strategy given by the algorithm ultimately achieves system stability, it is time consuming. The stable operation of the grid and the usage of the customer side's load would also be affected. The calculation of rewards is changed by the choice. The time of returning to the stable state is taken into account. New and different strategies are given by the algorithm in the final experimental results. One of these strategies is action 673, i.e., five SVGs compensate 50 Mvar, 70 Mvar, 60 Mvar, 50 Mvar and 50 Mvar, respectively. Although the total compensation is slightly higher than the previous strategy, it can be seen that the compensation of each SVG is distributed more evenly.

Fault 1 is set, and action 673 is executed. The stable result is shown in Figure 14. When five SVGs compensate 50 Mvar, 70 Mvar, 60 Mvar, 50 Mvar and 50 Mvar, respectively, the time of returning to a stable state is about 200 cycles.

Based on the comparison of the above experimental results, it can be seen that the strategy proposed by the final algorithm meets the requirement of bus voltage stability. Meanwhile, the SVG presents distributed settings, and the output compensation is distributed uniformly. In addition, the distributed SVG conducts reactive compensation with a shorter timeframe, which greatly improves the efficiency of decision-making compared with conventional manual operations. The distributed SVG obtains stability within 200 cycles, which meets the requirement for secure and stable operation of the EI.

**Figure 14.** Fault 1 Action 673: Bus positive sequence voltages after SVG compensates 50 Mvar, 70 Mvar, 60 Mvar, 50 Mvar and 50 Mvar, respectively.


*2.3. Measurements and Stimulation of Piezoelectric Nanofiber Nanogenerators (NGs)*

The generated voltage, current, and charge of the NGs generated via dynamic mechanical stimulus from a speaker (8 Ω, 1 W) were assessed by a Tektronix Keithley electrometer 6514. The speakers were driven by sinusoidal signals includinga4V amplitude at different frequencies (2 Hz, 3 Hz, 4 Hz, and 5 Hz) generated by function generator (DS345, Stanford Research Systems, Sunnyvale, CA, USA).

To quantitatively analyze the deformation and potential distribution of P(VDF-TrFE) nanofibers based on NG by a uniaxial stress (~0.5 Mpa), we performed finite element modeling (FEM) by COMSOL multiphysics software (5.3, COMSOL Inc., Stockholm, Sweden).

From the analysis of electrical characteristics, the cell membrane can be equivalently modeled as a resistor capacitor with electrical properties such as extracellular medium resistance (Rcm), membrane resistance (Rm), membrane capacitance (Cm) and ion equilibrium potential (Vm). According to these four characteristics, an equivalent circuit can be constructed [40,41]. The external electrical stimulation is generated by a P (VDF-TrFE) piezoelectric nanofiber NG under a dynamic mechanical vibration, which can be represented as a voltage source. By using the equivalent circuit model, the expected behavior of the effective voltage and current applied to the cell membrane by NG was evaluated. The circuit was simulated by Multism software (14.0, National Instruments Co., Austin, TX, USA).


**4. Conclusions**

Recent advances in animal experimentation and molecular biology have led to significant progress in our understanding of the mechanisms underlying the pathogenesis of arthritis pain. The concept that pain is a mere byproduct of joint damage and nociceptive response has become outdated. Aside from DAMPs produced from joint destruction, various nociceptive players, such as NGF- TrkA axis, CGRP, TRPV-1, and SASP, have been proposed as molecular mediators of OA pain. For RA, pain independent of inflammation has been correlated with ATF-3, TLR-4, and FcγR1. With the advances in cutting edge molecular techniques such as single cell polymerase chain reaction (PCR) and CRISPR/Cas9 mediated gene regulation, the knowledge base for pain pathogenesis would be significantly and rapidly expanded. Despite the inherent difficulty in extrapolating data gained in animal pain behavior studies to human arthritis, the critical assessment of various molecular mediators and translational studies would help to define the biological relevance of novel therapeutic targets for the treatment of arthritis pain.

**Author Contributions:** Acquisition and interpretation of data for the work, J.-I.H.; drafting the work, I.Y.P., J.-I.H. and H.A.K.; conception or design of the work, acquisition and interpretation of data for the work, H.A.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by a grant of the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2017R1A2B2001881), and by the Hallym University Research Fund.

**Conflicts of Interest:** The authors declare no conflict of interest.


**Author details**

Elina Panahi1 , Danielle I. Stanisic1 , Christopher S. Peacock2,3 and Lara J. Herrero1 \*

1 Institute for Glycomics, Griffith University, Southport, Queensland, Australia

2 The Marshall Centre for Infectious Disease Research and Training, School of Biomedical Sciences, The University of Western Australia, Perth, Western Australia, Australia

3 Telethon Kids Institute, Perth Children's Hospital, Nedlands, Western Australia, Australia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Protective and Pathogenic Immune Responses to Cutaneous Leishmaniasis DOI: http://dx.doi.org/10.5772/intechopen.101160*