2.5.1. Culture Conditions

Gram-positive (*Staphylococcus aureus* and *Bacillus subtilis*) and Gram-negative (*Escherichia coli* and *Klebsiella Pneumoniae*) microbes were used for antibacterial assays. The standard growth medium Mueller–Hinton (MH, Difco, Sparks, MD, USA) agar was used for the growth of both bacterial types and prepared by sterilization using an autoclave (15 min, 120 ◦C). These two species of microbes were evenly spread on the solid MH agar and incubated aerobically at 37 ◦C for 24 h. The bacterial suspension was measured by using Spectrophotometer BiomerieuxDensicheck Plus at 600 nm to obtain optical density at 0.5.

#### 2.5.2. Qualitative Study

The Gram-positive and Gram-negative bacteria suspensions were evenly spread on the solid MH agar in the sterile Petri plates. Using a sterile cotton swab, both bacteria were swabbed over the surface of the agar plates and dried in a laminar flow air chamber. The hydrogel sample was gently pressed on the agar with bacteria and incubated at 37 ◦C for 24 h in triplicates. The presence of any clear zone around the disc on the MH agar was recorded as an indication of inhibition against the bacteria.


Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.

**Keywords:** face recognition, linear regression classification, occlusion tendency, weighted module face recognition, texture histogram difference

### **1. Introduction**

As the progress of the computer vision and machine learning, person identification and verification for security considerations become practical and play an important role for modern smart living applications. Face recognition for security control has received lot of attentions recently. Designed with a camera, the face

recognition becomes a simple and direct way to achieve reliable identification and verification. There are numerous algorithms [1–4] made important contributions to face recognition. These approaches can be divided into two categories, holistic based and modular based methods [5]. The holistic based approaches include the principle component analysis (PCA) [6] and linear discriminant analysis (LDA) [6]. John *et al.* [7] exploiting sparsity representation to improve the recognition rate. On the other hand, the modular based approaches provide more valuable features for face recognition. Naseem *et al.* [8] proposed linear regression classification (LRC) and its modular design to solve occlusion problems. During training stage, the LRC algorithm is to form the regression surface, which has the most fit of the data distribution, of each identify. In the testing stage, the unlabeled face image vector is used to recognize the person by finding the shortest project distance to the regression surface of the identify.

In practical applications, especially in COVID-19 pandemic period, face recognition might encounter diversified challenges such as low resolution, luminous change, facial expression and partial occlusion affected by facial accessories and covering face masks and scarf. For various challenges, there are several improved LRC algorithms proposed in the literatures, such as PCA-based LRC [9], unitary LRC [10], class-specific kernel LRC [11], and sparse representation classifier [12] methods.

**Figure 1** shows some examples of partially-occluded faces, where they are partially covered by facial accessories or covering obstacles. Partially occlusion is one of the practical and difficult problems in face recognition [13, 14]. Thus, some researches have focused on partially-occluded faces [15–19] recently. Most approaches tried to detect the testing images in several modules, avoided occlusion area influence the recognition results. The module LRC (MLRC) proposed in [8] is an effective way to solve occlusion issues in facial recognition. The MLRC treats each module equally important, however the occluded modules should be excluded or reduced their contribution for the final classification. Thus, by using regression parameters (RP), our early-version weighted module linear regression classification (WMLRC-PR) [20] is first proposed to enhance the MLRC method. To secure the access of ATM machines, the occluded face detection methods are proposed [21, 22]. By using support vector machine (SVM) to detect the occlusion modules, the LDA face recognition method is performed in the non-occlusion part [23]. By using structured dictionary learning (SDL) method, the SRC face recognition can separate occluded face images [24]. The robust face recognition algorithms based on various features, such as Huber loss [25], local binary feature [26] and topology preserving structure matching (TPSM) [27] are proposed to solve occlusion problems successfully. It is noted that the state-of-the-art CNN approaches [28–31], which need pre-trained by all face images, will not be applicable for partiallyoccluded face recognition. Besides, the CNN approaches usually need to pre-train the model, any instance increase of new members becomes impossible.

**Figure 1.** *Examples of partially-occluded situations.*

*Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

In this chapter, we propose a better occlusion tendency detection by using texture histogram (TH) to distinguish occluded and non-occluded modules. With the TH occlusion tendency, the WMLRC-TH methods are then proposed. The rest of the chapter is organized as follows. In Section 2, we first review the basic LRC concept and introduce the module LRC design for face recognition. Then, Section 3 first analyzes texture histograms and discusses how to use it to determine the occlusion tendency of the module. The proposed WMLRC-TH method in use of texture histogram is the proposed. In Section 4, the proposed WMLRC-TH method, which is tested on AR [32] and FRGC2.0 [33] face databases with synthesized occlusions, is compared to the well-known and robust face recognition methods. The realization of the partially-occluded face recognition system based on the WMLRC-TH method in mobile platforms are also addressed. Finally, conclusions are addressed in Section 6.

#### **2.  Assume that we have *C* subjects, which are characterized by training facial images used for identity recognition. For *C* identities, the *i* th identity is with *N* training facial images all with the size of *p* � *q* pixels. Each pixel could be further represented with *K* color channels. Thus, the *k*th channel data of the *i* th identity is represented by *vi,j,k* ∈ *Rp* � *q* , *i* = 1, 2, … , *C*, *j* = 1, 2, … , *N* and *k* = 1, 2, … , *K*, where *K* = 3 for most RGB face images.

For the *k*th channel, we can cascade its *q* column vectors into a larger column vector, *xi,j,k*∈ *Rpq* � 1 . For the *i* th class identify, its *N* training vectors, *xi,j,k*, *j* = 1, 2, … , *N* are grouped into the *i* th class vector space as

$$\mathbf{X}\_{i,k} = [\mathbf{x}\_{i,1,k}, \mathbf{x}\_{i,2,k}, \dots, \mathbf{x}\_{i,N,k}] \in R^{pq \times N}, \quad i = \mathbf{1}, 2, \dots, \mathbf{C}. \tag{1}$$

If *νi,j,k* is reformed by linearly combining channels to one grayscale channel, we set *K* = 1 or just remove the subscript *k* as *xi,j* ∈ *Rd* � 1 and **X***i* ∈*Rpq*�*N* for simplicity. For face recognition, the linear regression classification (LRC) and module LRC are briefly reviewed as follows.

#### **2.1 Linear regression classification**

Let *uk* ∈ *Rp* � *q* be the *k*th channel of an unlabeled query image, which is formed as a column vector *yk* ∈ *Rpq* � 1 . If the query data vector *yk* belongs to the *i* th class, the prediction with the linear combination of the *i* th class vector space can be rewritten as

$$\mathbf{y}\_k = \mathbf{X}\_{i,k}\mathbf{f}\_{i,k}, \quad i = 1, 2, \dots, C,\tag{2}$$

where *βi,k* ∈ *RN* � 1 is the regression parameter vector of the *i* th class. The optimal regression parameter vector can be solved by the least square optimization and expressed in matrix operations as

$$\mathfrak{g}\_{i,k} = \left(\mathbf{X}\_{i,k}^T \mathbf{X}\_{i,k}\right)^{-1} \mathbf{X}\_{i,k}^T \mathbf{y}\_k, \quad i = 1, 2, \dots, C. \tag{3}$$

With optimized regression parameter vector, *βi,k*, the predicted response vector **y**~*i*,*k* of the *k*th channel for the *i* th class can be formed as

*Digital Image Processing Applications*

$$\tilde{\mathbf{y}}\_{i,k} = \mathbf{X}\_{i,k} \mathfrak{h}\_{i,k}, \quad i = 1, 2, \dots, \mathbf{C}. \tag{4}$$

For all *C* classes, we first compute the *k*th-channel predicted response vectors. To construct the recognition system, by combining Eq. (3) and Eq. (4) together, we can store the *pq*�*pq* projection matrix of the *i* th identity as:

$$\mathbf{H}\_{i,k} = \mathbf{X}\_{i,k} \left(\mathbf{X}\_{i,k}^T \mathbf{X}\_{i,k}\right)^{-1} \mathbf{X}\_{i,k}^T, \quad k = \mathbf{1}, \mathbf{2}, \dots, K. \tag{5}$$

The construction of the *i* th projection matrix can be treated as the training process of the *i* th identity. For the trained parameters of all *C* identities, we need to store *Cp*2 *q*2 coefficients for recognition. The computation of predicted response vector becomes the projection process as

$$
\tilde{\mathbf{y}}\_{i,k} = \mathbf{H}\_{i,k} \mathbf{y}\_k, \quad k = \mathbf{1}, 2, \dots, K,\tag{6}
$$

which takes about *Kp*2 *q*2 multiplications. For testing all *C* identities, one identity recognition process, it needs *CKp*2 *q*2 multiplications. To determine the best class, the LRC method chooses the identity, which is with the minimum prediction error of all channels. Thus, the classified identity *i*\* is determined by minimizing the L2 norm distance between the predicted response vector and the query data vector as

$$\text{i } i^\* \text{.LRC} = \text{arg}\min\_i \left( d\_i \right), \quad i = \textbf{1}, \textbf{2}, \dots, \textbf{C}. \tag{7}$$

where

$$d\_i = \sum\_{k=1}^{K} \left\| \tilde{\mathbf{y}}\_{i,k} - \mathbf{y}\_k \right\|\_2, \quad i = 1, 2, \dots, C,\tag{8}$$

for *K-*channel classifications.

#### **2.2 Module linear regression classification (MLRC)**

For real applications, if a query sample is partially corrupted or occluded, the LRC algorithm cannot handle these special situations well since the discernible messages are getting less. The module, which is in a small and clean subface, approach can efficiently overcome this problem.

For the module LRC (MLRC), each training image *νi,j,k* ∈ *Rp* � *qxK* is segmented into *M* non-overlapped partitions. **Figure 2** shows an example of segmentation of facial images with *M* = 16. The *m*th module, as the LRC approach, is formed as a column vector **x***m i*,*j*,*k*, *i* = 1, 2, … ,*C*, *j* = 1, 2, … , *N*, *m* = 1, 2, … , *M*. As Subsection 2.1,

**Figure 2.** *Segmentation of face images with* M *= 16 modules.*

*Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

in the training phase, the column vectors of all training image related to each class are grouped accordingly. Hence, for the *i* th class, we could obtain *M* groups of the *k*th channel data spaces as

$$\mathbf{X}\_{i,k}^{m} = \begin{bmatrix} \mathbf{x}\_{i,1,k}^{m}, \mathbf{x}\_{i,2,k}^{m}, \dots, \mathbf{x}\_{i,N,k}^{m} \end{bmatrix} \in \mathbb{R}^{\mathbb{R}^{N} \times N} \tag{9}$$

for *i* = 1, 2, … , *C*, *j* = 1, 2, … , *N.*

For the *k*th channel of the query image *uk*, as the training images, we first segment it to *M* modules and form them as column vectors *yk m* for *m* = 1, 2, … , *M*. If the *m*th module of the query image, *yk m* is assumed to lie on the *m*th module of the *i* th class, the least square prediction can be expressed in the training images of the *m*th module in the *i* th class as

$$\mathbf{y}\_k^m = \mathbf{X}\_{i,k}^m \mathfrak{f}\_{i,k}^m, \quad i = 1, 2, \dots, C, m = 1, 2, \dots, M,\tag{10}$$

where the optimal regression parameter vector is given as

$$\mathbf{g}\_{i,k}^{m} = \left( \left( \mathbf{X}\_{i,k}^{m} \right)^{\mathrm{T}} \mathbf{X}\_{i,k}^{m} \right)^{-1} \left( \mathbf{X}\_{i,k}^{m} \right)^{\mathrm{T}} \mathbf{y}\_{k}^{m} = \mathbf{R}\_{i,k}^{m} \mathbf{y}\_{k}^{m} \,. \tag{11}$$

and the regression matrix is defined as

$$\mathbf{R}\_{i,k}^{m} = \left( \left( \mathbf{X}\_{i,k}^{m} \right)^{\mathrm{T}} \mathbf{X}\_{i,k}^{m} \right)^{-1} \left( \mathbf{X}\_{i,k}^{m} \right)^{\mathrm{T}}.\tag{12}$$

Similar to Eq. (5), the corresponding module response vectors **y**~*m i*,*k* can be predicted as

$$
\tilde{\mathbf{y}}\_{i,k}^{m} = \mathbf{X}\_{i,k}^{m} \mathfrak{f}\_{i,k}^{m} = \mathbf{H}\_{i,k}^{m} \mathbf{y}\_{k}^{m}, \tag{13}
$$

where **H***m i*,*k*is the (*pq*/*M*) � (*pq*/*M*) projection matrix of the *m*th module of the *i* th identity as

$$\mathbf{H}\_{i,k}^{m} = \mathbf{X}\_{i,k}^{m} \left( \left( \mathbf{X}\_{i,k}^{m} \right)^{\mathrm{T}} \mathbf{X}\_{i,k}^{m} \right)^{-1} \left( \mathbf{X}\_{i,k}^{m} \right)^{\mathrm{T}}, \quad k = \mathbf{1}, 2, \ldots, K. \tag{14}$$

The project matrix, **H***m i*,*k*, for *m* = 1, 2, .., *M*, *k* = 1, 2, … , *K* can be treated as the training parameters for the *i* th identity. For the trained parameters of *M* modules of all *C* identities, we need to store *CKp*2 *q*2 /*M* coefficients for recognition process. For testing all *C* identities, we can directly use (13) to compute the project result. Thus, the MLRC recognition process, which needs to compute all **y**~*m i*,*k* for *M*-module and *K*-channel *p* � *q* facial images by using (13), will need *CKp*2 *q*2 /*M* multiplications. The computation of the MLRC is less than the LRC method, which needs *CKp*2 *q*2 multiplications.

With the above module optimization, each module is processed by the LRC computation individually. Without the knowledge of occluded modules, the minimum distance could be used as the distance metric which implicates that the occluded modules will be automatically removed. The MLRC in min-min distance measure is expressed as.

$$i^\*\_{\text{MLRC}} = \arg\min\_i \left( \min\_m d\_i^m \right), \tag{15}$$

where

$$d\_i^m = \sum\_{k=1}^K \left\| \tilde{\mathbf{y}}\_{i,k}^m - \mathbf{y}\_k^m \right\|\_2, \quad i = 1, 2, \dots, C; \quad m = 1, 2, \dots, M. \tag{16}$$

for *K*-channel classifications.

### **3. Weighted MLRC by detection of occluded modules**

The module linear regression classification (MLRC) shows better recognition performance under occlusion situations. However, the MLRC with min-min distance measure only uses the best module for classification essentially. Thus, we can further improve its classification performance if we can properly gather more reliable non-occluded modules. As shown in **Figure 2**, we should fully utilize all top 8 clear modules (#1, #5, #9, #13, #2, #6, #10, #14) modules and moderately adopt 4 partially-occluded modules (#3, #7, #11, #15) for face recognition.

Thus, in the early age, we suggested an occlusion detection measure, which can be used to infer the occlusion tendency of the module. For linear regression classification method, the occlusion detection could be obtained by regression parameters [15] by the assumption that the regression parameters of the clean modules will have large variations for in-class and out-class faces. The module with small variations of regression parameters will have high probabilities to be detected as the occluded modules. The regression parameter (RP) concept is only good for dark and less texture occlusion objects. If the face recognition algorithm is not a linear regression approach, it will need extra computation to compute the regression parameters. In this chapter, we suggest a simple detection method to find the occlusion tendency of the module. Generally, the module has its particular location, the texture distribution of the module could easily be identified by its texture histogram.

#### **3.1 Occlusion detection by texture histogram**

To detect the occluded modules, instead of regression parameters, we can also perform the occlusion detection in the texture domain. First, the D-bin texture histogram of a grayscale image *I*(*x, y*) ∈ *Rp* � *q* is expressed by

$$\mathbf{h}\_{l(x,y)} = \begin{bmatrix} t\_1, \dots, t\_i, \dots, t\_D \end{bmatrix}^T \tag{17}$$

where the count of gray level *I*(*x*, *y*) for every *b* = 256/*D* in *D* bins will be collected as.

$$t\_i = \sum\_{\mathbf{y}=1}^p \sum\_{\mathbf{x}=1}^q q\_i(I(\mathbf{x}, \mathbf{y})) \tag{18}$$

and *qi* ð Þ *p* is to quantize *p* into the *i* th bin as

$$q\_i(p) = \begin{cases} 1 & \text{if } \ (i-1) \cdot b \le p < i \cdot b \end{cases},\tag{19}$$

For finding *D*-bin texture histogram of modules of the *i* th class among *N* training samples, we first obtain the expected *vm i* as

$$\overline{\mathbf{v}}\_{i,k}^{m} = \frac{1}{N} \sum\_{j=1}^{N} \mathbf{v}\_{i,j,k}^{m}. \tag{20}$$

#### *Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

where *vm i,j,k* is the *k*th channel of the *m*th module, *νi,j,k* ∈ *Rp* � *q* . Let the *k*th channel of the query image *u* ∈ *Rp* � *q* , *uk* be segmented into *M* partitions as *uk m*. To evaluate the occlusion tendency of the modules, γi m, which denotes the distance between the *D*-bin texture histograms of the *m*th module of query image *uk m* and the averaged module **v***m i*,*k*after summing all *K* channels, becomes

$$\overline{\gamma}\_i^m = \sum\_k \left| \mathbf{h}\_{\mathbf{u}\_k^m} - \mathbf{h}\_{\overline{\mathbf{v}}\_{i,k}^m} \right|\_2. \tag{21}$$

It is obvious that the modules of the query image, which are with larger *γm i* , will have more occlusion tendency due to dissimilar intensity distribution. In AR face database, we test 200 scarf images, With respect to 100 identities, **Figure 3** shows 200x100 overlapped curves of *γm i* for all 16 modules. The results also show that *γm i* in the occluded modules with *m* = 4, 8, 12, 16 are larger than the reminding ones. Similarly, the texture histogram difference could be also used to determine the occlusion tendency.

Conceptually, the texture histogram difference (HTD) of the module is a meaningful measure to identify the location of the face. For example, the modules #5 and #6 are forehead and left-center-eye modules, respectively. The average of texture histogram for module #5 has flat texture distribution of pure skin color while that for module #6 is unevenly distributed with textures of eyeball and nose regions. These two modules have their distinct and special texture histograms. If the modules are occluded by the mask, their texture histograms become very different from the original ones. For any face recognition algorithm, the classification criterion generally can be expressed by

$$i^\*\,\_{\text{face}} = \arg\min\_i \left( f\_i(\mathbf{y}) \right), \ i = 1, 2, \dots, \mathcal{C}, \tag{22}$$

where *fi*(*y*) denotes the *i* th class score function of the face recognition algorithm. If we divide the face region into *M* modules, the adaptive weighted module face recognition algorithm generally becomes

**Figure 3.** *Plots of γm i for all 16 modules of 200x100 scarf query images in AR face database.*

$$i^\*\_{\text{ surface-HT}} = \arg\min\_i \left( \sum\_{m=1}^M \mathbf{g}\left(\tilde{\boldsymbol{\eta}}\_i^m\right) f\_i\left(\mathbf{y}^m\right) \right), \quad i = 1, 2, \dots, C,\tag{23}$$

by using the above averaged texture histogram difference stated in (21). In (23), *g γm i* � � is a function of HTD parameters should be properly designed to achieve the best face recognition performance. Without loss of generality, we apply the HTD parameters to the adaptive weighted MLRC method in the following subsection.

#### **3.2 Weighted MLRC method by texture histogram**

By using texture histogram difference (THD), we suggest a weighted MLRC method, called WMLRC-HT for robust face recognition. For minimization measure errors, it is obvious that occluded modules should give larger weights in the WMLRC such that we can improve the drawback of the MLRC and achieve better recognition performance. Similarly as the observation shown in **Figure 3**, the texture histogram distance, *γm i* has highly correlated to the occlusion tendency of the module, In other words, the occluded module has larger *γm i* than the normal one. Thus, we define the texture histogram (TH) weight for the *m*th module to be

$$\boldsymbol{w}\_{\rm TH}^{\rm m} = \mathbf{g}\left(\overline{\boldsymbol{\gamma}}\_{i}^{\rm m}\right) = \left(\overline{\boldsymbol{\gamma}}\_{i}^{\rm m}\right)^{-1}.\tag{24}$$

With (24), the TH weight will be bigger for smaller texture histogram difference. Thus, *wm TH* for the occluded module is smaller than the normal one. As the module LRC, the response vector **y**~*m i* is predicted in terms of **X***m i*,*j* as

$$
\hat{\mathbf{y}}\_i^m = \mathbf{X}\_i^m \hat{\boldsymbol{\mathfrak{b}}}^m. \tag{25}
$$

By using THD weight defined in (24), the WMLRC-TH with the weighted minimum rule can be adjudicated as

$$\mathbf{i}i^\*{}\_{TH} = \arg\min\_i \left( \sum\_m \left( ||\bar{\mathbf{y}}\_i^m - \mathbf{y}^m|| \cdot \frac{\boldsymbol{w}\_{TH}^m}{\sum\_m \boldsymbol{w}\_{TH}^m} \right) \right). \tag{26}$$

#### **4. Experimental results**

In our experiments, the recognition performance with different face recognition algorithms will be used to validate the proposed methods. In experiments, AR and FRGC 2.0 face databases are used by synthetically adding osculation. We compare recognition performances of the proposed WMLRC methods to those of PCA, LDA, LRC, MLRC, SRC, locality preserving projection (LPP) [34], neighboring preserving embedding (NPE) [35] methods. All the experiments are carried out on a personal computer, which is equipped with Intel Core2 Q9400 CPU associated a 4-GB RAM. The testing environment is on Microsoft Visual Studio 2013 with OpenCV.

#### **4.1 Experiments on AR database**

The famous face recognition algorithms and recent robust face recognition methods [23, 24, 27] all show the recognition performances on AR database, which *Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

contains more than 100 people's color face images. For experiments, the cropped facial images are normalized to 40 � 40 pixels. The first experiment is to test all the algorithms on AR database [32] under sunglasses, scarf, colored blocks, black, white, and texture occlusions, overlapped onto normal faces. The AR database consists of more than 100 people's color face images. The images also include several facial variations, such as illumination change, expression and facial disguises. We choose a subset of 100 subjects with 50 men and 50 women. In each subject, 3 normal images are used as the training set as shown in **Figure 4** while 2 images with sunglasses and 2 images with scarf are used for the testing set as shown in **Figure 5**.

Before the experiments, we should first decide suitable settings for the proposed WMLRC-TH method. For module designs, the images are divided into 16 modules, i.e., *M* = 16. For the WMLRC-RP, we choose *α*, *ε*, and *γ* to be fixed to 2, 0.3, 0.7, respectively. The number of bins in texture histogram is *D* = 64 for WMLRC-TH. For the MLRC, we further modify the min-min criterion stated in Eq. (12) to become min-min 4 criterion, the MLRC-4, which is the sum of distances of the best 4 modules as

$$\left(i^\*\right)\_{\text{MLRC-4}} = \arg\min\_i \left(\sum\_{m\*=1}^4 \min\_m d\_i^m\right),\tag{27}$$

By using original AR database, **Table 1** shows the comparison results with those normal face algorithms while **Table 2** exhibits the reported results from the recent robust face recognition methods. From **Table 1**, the results show that the proposed WMLRC-TH methods achieved with over 95% accuracy outperforms the other methods. The LDA with SVM method [23] achieves 91.5% recognition rate, the SRC with SDL method [24], the recognition rate for sunglasses plus scarf face images, the recognition rate achieves up to 92%. The topology preserving structure matching (TPSM) [27] achieve 91.7%. recognition accuracy. From **Table 2**, the proposed WMLRC-TH outperforms the WMLRC-RP [20], the LDA-SVM [23], SRC-SDL [24] and TPSM [27] methods for recognizing occlusion face images on AR database.

To further analyze the detailed performances of the algorithms with different conditions of occlusions. The synthesized occlusions with various occlusion levels overlapped to the normal faces are shown in **Figures 6**–**9**.

**Tables 3**–**6** show the recognition performances of all the face recognition methods for all synthesized occlusion cases, where WMLRC-TH1 and WMLPRC-TH3 denote the WMLRC-TH preformed with gray (*K* = 1) and RGB color (*K* = 3) images, respectively. The experimental results show that the proposed WMLRC-TH with gray and color images outperform the other methods, which can achieve over

*Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

#### **Figure 8.**

*White partial occlusion with various occlusion ratios on AR database.*

#### **Figure 9.**

*Different occlusion (a) - (e) color wall patterns with 50% occlusion ratio on AR database.*

#### **Table 3.**

*Recognition performances (%) of different methods with varying occlusion ratios of dark partial occlusion on AR database.*

95% of accuracy. The MLRC and MLRC-4 performed badly in dark occlusion because low pixel value produces low prediction error, which leads to error recognition. All results show that the proposed WMLRC-TH methods achieve the best recognition performances. The WMLRC-TH had a few benefit from RGB color information since the WMLRC-TH (*K* = 3) exhibits better recognition performance than WMLRC-TH (*K* = 1).

#### **4.2 Experiments on FRGC 2.0 database**

The second experiments are to test all the methods on the FRGC2.0 database [33, 36] under gray texture blocks, dark, white, and texture occlusions overlapped

#### *Digital Image Processing Applications*

#### **Table 4.**

*Recognition performances (%) of different methods with varying occlusion ratios of colored block occlusion on AR database.*

#### **Table 5.**

*Recognition performances (%) of different methods with varying levels of white partial occlusion on AR database.*

onto normal faces. The FRGC2.0 database consists of more than 4000 people's front-view grayscale images including non-facial expressions and slightly facial variations. We choose a subset of 100 subjects with 50 men and 50 women. In each subject, two non-facial expression images are used for the training set as shown in **Figure 10**. In additions, the synthesized occlusions with various occlusion levels overlapped to normal faces as shown in **Figures 11**–**14** are also used for the testing. As to the experiment settings, the facial images are also normalized to 40 � 40 pixels and divided into 16 modules.

**Tables 7**–**10** illustrate the recognition performances of all the methods in all test cases. On FRGC 2.0 database, the proposed WMLRC-TH methods also achieve

*Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

#### **Table 6.**

*Recognition performances (%) of different methods with different occlusion textures of 50% occlusion on AR database.*

#### **Figure 10.**

*Normal faces as the raining set on FRGC2.0 database.*

**Figure 11.**

*Added texture block with various occlusion ratios on FRGC 2.0 database.*

**Figure 12.**

*Dark partial occlusion with various occlusion ratios on FRGC 2.0 database.*

better performances than other approaches. From **Table 8**, the MLRC and MLRC-4 performed badly in dark occlusion also. In most cases, the WMLRC-TH performs better than WMLRC-RP. In flat and bright occlusions, as shown in **Table 9**, the WMLRC-RP carries out the better performance than WMLRC-TH. Since the

#### **Figure 13.**

*White partial occlusion with various occlusion ratios on FRGC 2.0 database.*

#### **Figure 14.**

*Different occlusion (a) - (e) gray wall patterns with 50% occlusion on FRGC 2.0 database.*

#### **Table 7.**

*Recognition performances (%) of different methods with varying occlusion ratios of random block occlusion on FRGC 2.0 database.*

sunglasses are the most common situation in daily life, WMLRC-RP is still useful in real applications in the cases of dark occlusions.

From above experiments, the proposed WMLRC-HT method achieves best accurate recognition rate. For the cases of light color occlusions, the WMLRC-RP performs better than the WMLRC-HT. For the other cases, the WMLRC-HT achieves the best recognition performances. The simulations show that the texture histogram feature can be used to detect occluded modules effectively. Besides, we observe that the MLRC-TH in RGB space domain performs better than that in grayscale domain because the grayscale images would waste the useful colored information.

#### **4.3 Android based system implementation**

Recently, Android-based operating system successfully supports smartphones and tablets because of its computing ability, storage capacity and its handy

#### *Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

#### **Table 8.**

*Recognition performances (%) of different methods with varying occlusion ratios of dark occlusion on FRGC 2.0 database.*

#### **Table 9.**

*Recognition performances (%) of different methods with varying occlusion ratios of white partial occlusion on FRGC 2.0 database.*

functions for image capture and image processing. Thus, we realize the proposed WMLRC-TH method on Android system to a robust face recognition system on mobile devices since it has more robust performance in general situations, especially in color image.

In general, the face recognition system can be divided into training phase and the testing phases. As shown in **Figure 15**, the user needs to select the training phase first after capturing face images of a new identity. The user will be asked to enter the name of the identity then the system will store face images with the name in database. In the test phase, the user can capture face image and then identify it. The testing phase basically consists of three steps. **Figure 16** shows the flow chart of testing phase. The first step is the face detection, which rapidly captures human faces when the human is located within a proper distance by the mobile phone camera. The second step is the pre-processing stage that is removing the noisy

#### *Digital Image Processing Applications*

#### **Table 10.**

*Recognition performances (%) of different methods with different occlusion texture of 50% occlusion on FRGC 2.0 database.*

**Figure 15.** *Proposed android face recognition system.*

#### **Figure 16.**

*Flow chart of testing phase in Andorid phones.*

*Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition DOI: http://dx.doi.org/10.5772/intechopen.100621*

#### **Figure 17.**

*User interfaces for registration and face recognition. (a) Registration and setup database (b) recognition and show name.*

pixels like background and human hairs of the face images. The third step is face recognition that is recognizing the face according to the face database collected before.

Considering the computation resources of mobile phones, we surveyed several face detection methods [37–41]. By trade-off between accuracy and computation, we finally selected the LBP-feature [37] for face detector, where the LBP operator is given as:

$$LBP\left(\mathbf{x}\_{\varepsilon}, \mathbf{y}\_{\varepsilon}\right) = \sum\_{p=0}^{p-1} \mathfrak{Z}^p s\left(i\_p - i\_{\varepsilon}\right), s(\infty) = \begin{cases} 1, \varkappa \ge 0 \\ 0, \varkappa < 0 \end{cases} \tag{28}$$

where *ic* is the intensity of (*xc*, *yc*), at the central pixel in the local image, *ip* is the intensity of neighbor pixel. *P* is the total number of neighbor pixels. After face detection, the pre-processing stage, including cropping, gray transform, elliptical mask, and resizing. It is noted that the face detection and pre-processing should be identically performed in both training and testing phases. Finally, the face image vector is then classified by the proposed WMLRC-HT algorithm. **Figure 17** shows the user interfaces of face registration and face recognition of the proposed system.

The proposed Android face recognition system is mainly developed on OpenCV SDK and Android NDK (Native Development Kit). Android NDK is used to overcome the limitations of Java, such as memory management and performance, by programming directly into Android native interface to support native development in C/C++.

#### **5. Conclusion**

In this chapter, we first review the linear regression classification algorithm as the base of face recognition. If we divide the face region into several subfaces, called modules, any face recognition algorithm will become a new module face recognition algorithm, which can avoid the serious degradation of recognition performance to solve the occlusion problem. We proposed the texture histogram difference of the module to detect the its occlusion tendency of the input face image. The concept of the texture histogram difference can be used for any face recognition algorithms if they adopt the module design. By using texture histogram (HT) concept, the weighted module linear regression classification (WMLRC-HT) method for partially-occluded face recognition is finally proposed. The proposed WMLRC-HT method with adaptive HT weights can effectively improve the shortcoming of the original LRC and MLRC algorithms. The experimental results show that the proposed WMLRC-HT method performs better than the existing linear regression classification methods and the contemporary approaches like SRC with various

occluded situations. Since the WMLRC-HT method acquires small computation cost in both training and testing, after the face detector with LBP-features, we implement the proposed method on smart phones. Even the people wear masks, we can easily train and successfully detect the identify only with a smart phone.


9. Phenol modification by oxidizing coupling

That is why it is necessary to find conditions for the perfect binding of phenols. It appears to be the method of oxidative cross-linking of phenols by their treatment with persulfates of alkali metals. The mechanism of the modification includes dimerization of phenols to form C-C bond involving unsubstituted (free) ortho- and para-positions of hydroxyl group, the mechanism is accompanied by a series of different reactions. The method of oxidative cross-linking is presented by a scheme:

And it was employed as the method of modification of SCCP to obtain the inhibitor of thermopolymerization for processing semi-products of pyrolysis [5]. As far as dimeric phenols practically are non-water soluble, it is supposed that oxidative dimerization of phenols allows to remove one of the main disadvantages of coke-chemical phenols—their high water solubility.

During the first stage of the reaction of the oxidizer and phenol, the generation of phenoxyl radical occurs (for monoatomic phenols) and then its mesomerization with the transfer of radical center from oxygen atoms to carbon atoms of aromatic circle and subsequent recombination of the formed radicals takes place [7]. During oxidation of di- and polyatomic phenols, the mechanism is more complicated, and the possibility of the generation of quinoid compounds is not excluded, as well as some other secondary inhibitors. During the oxidation reaction, quinone methides, polymers, and subsequent oxidative products of primary formed binuclear compounds could be formed. It is evident, the yields of individual product at so diversity of the products obtained in the oxidation are not high. The secondary reactions between oxidized substrates and the starting phenols are not precluded. That is why the summary effect of oxidized phenol mixture essentially depends on the structures of initial phenols and amounts of the oxidizer used (Figure 3). As it was stated above during the one-step cross-linking modification of phenols under action of persulfates of alkali metals, the dimers of phenols are formed, which are not soluble in water, but well soluble in alcohols and alkyl acetates.

Dependency of the inhibiting effectiveness from viscosity of PFR at the consumption of 0.03 %

Viscosity of resins, sec. 70 135 240 540 720 effectiveness, % 56.3 74.7 43.9 8.1 0

According to these dates, the resins having viscosity in the rage of 135 s (Viscosimeter VZ-4) are the most effective. There is dependence between amounts of residual phenols in synthetic tar-water and viscosity of the resin. For example, in the best point (74.7%) the amount of nonreacted phenols on water phase reached almost 5% of their load, and all the phenols are

That is why it is necessary to find conditions for the perfect binding of phenols. It appears to be the method of oxidative cross-linking of phenols by their treatment with persulfates of alkali metals. The mechanism of the modification includes dimerization of phenols to form C-C bond involving unsubstituted (free) ortho- and para-positions of hydroxyl group, the mechanism is accompanied by a series of different reactions. The method of oxidative cross-linking is

And it was employed as the method of modification of SCCP to obtain the inhibitor of thermopolymerization for processing semi-products of pyrolysis [5]. As far as dimeric phenols practically are non-water soluble, it is supposed that oxidative dimerization of phenols allows to remove one of the main disadvantages of coke-chemical phenols—their high water solubility. During the first stage of the reaction of the oxidizer and phenol, the generation of phenoxyl radical occurs (for monoatomic phenols) and then its mesomerization with the transfer of radical

mass is as follows:

presented by monoatomic isomers.

presented by a scheme:

9. Phenol modification by oxidizing coupling

316 Phenolic Compounds - Natural Sources, Importance and Applications

Figure 3. Inhibiting activity of SCCP after treatment with sodium persulfate; oxidizer consumption: 1—0%, 2—10%, 3— 25%, 4—50%, 5—75%mass.

According to the data of chromato-mass-spectrometry and nuclear magnetic resonance (NMR) spectroscopy 1 H and 13C for the products obtained by the oxidation of monoatomic phenol with sodium persulfate, the expected dimeric products of "ortho-ortho," "para-para," and "ortho-para" cross-linking are obtained in a combination of 9:50:19%, respectively.

The analysis of inhibiting activity of oxidative cross-linking SCCP mixtures demonstrates that the maximal effectiveness of 72–84% is manifested for oxidized mixtures with the consumption of oxidizer of 25% mol relatively to the overall phenol mass and with the inhibitor consumption of 0.03–0.04% mass.

Thus, we have developed single stage and rather simple methods of chemical modifications of CCP, which make it possible to obtain high-performance phenol inhibitors for the processing of pyrolysis liquid products of petrochemical industry.


**8.1 What did the workers suggested from the focus groups**

In two different sites, the workers approached the outcome of the focus group quite differently:

*Occupational Wellbeing*

**6**

**Table 1.**

**Static frame description**

**7. Research design**

and stepping on the ladder

ceiling, drywall in air

Carrying the drywall panel in overhead arm posture

moments generated in the ceiling installers.

v) attaching the panes to the ceiling.

Holding the drywall panel (overhead arms) to the

*The selected static frames from the videos for 3DSSPP analysis.*

**Baseline PHASE Intervention PHASE**

Lifting the drywall in overhead arm posture Sliding the drywall panel to the electrical lift, both

This intervention study was designed as a quasi-experimental study with no control group. The only experimental group consisted of the five drywall installers.

workers whether for a 60 lb., 80 lb. or 100 lb. drywall panel. The same study found that low back loading while lifting a 100 lb. drywall panel exceeds 760 lb., the maximum value recommended by NIOSH in the Work Practices Guide for Manual Lifting. All four lifting techniques also involved risk of perturbation in postural balance [5]. Yuan et. Al., [2] found that the average disc compression force during installation of drywall panels exceeded value of 760 lb. (3400 N) disc compression force, set by NIOSH as the recommended action limit. The highest value of disc compression force in this study was found to be 1721 lb. (7748.8 N) and was sustained for an

average of 8.5% of the total 8 hour work shift, or around 41 minutes.

vals to make direct observations and coded postures into a PDA.

**5. PATH (posture, activities, tools, and handling) data collection**

**6. 3DSSPP (three dimensional static strength prediction program)**

3DSSPP (3 Dimensional Static Strength Prediction Program) was utilized in order to find out the compressive forces generated at low back and shoulder

The sequence of static postures was selected while performing the task of ceiling drywall installation. The selected static postures from the task videos were not made at a defined interval of seconds or minutes. Instead, the postures were selected corresponding to the main set of activities. For example, the task of installing the drywall to the ceiling at the intervention phase comprised the following six activities, i) loading the panels to the electrical lift, ii) lifting the panels while being on the lift, iii) raising the panels to the ceiling, iv) holding the panels to the ceiling,

PATH method [6, 7], was used to code posture, activities, tools used and materials handled at every moment for at least 3–4 hours every day with a hierarchical taxonomy. Since the drywall installation task is cyclical in nature, we collected data for a week that is absolutely representative of the whole task. We used fixed inter-

One feet in air, maintaining the previous posture The arms are down, maintaining the previous

arms down

posture

by the electrical lift

and supported by 'deadman'

Keeping the hands on the drywall panel being raise

Overhead arms, drywall being lifted to the ceiling

#### *Occupational Wellbeing*

In both of these sites, the workers took the trial and error method, i.e., they would continue with the devices if they like it and they would stop the trial immediately if they do not like it.

Efficacy studies, usually, are those that proves the accuracy of an instrument or assistive devices to the degree it says it will. By that definition, both 'deadman' and 'hanger's helper' were ready to be assessed for their efficaciousness in the real field. Effectiveness studies, in construction are those that will prove whether any instrument/assistive device would be effective in making a permanent place in the construction trade. This study did not evaluate the effectiveness.


**Author details**

Jun-Sik Yoon, Jinsu Jeong, Seunghwan Lee, Junjong Lee and Rock-Hyun Baek\* Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 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.


**2. Protocol and Network Tier**

Different solutions for PN-Tier have been proposed addressing specific challenges [4,15–19]. We have identified the technological components that are grouped at the protocol and network layers. The security of the entire tier is ensured as a result of integrating the publicprivate key cryptography for locking and unlocking transactions, with the tamper-resistant data structures and consensus algorithms.


**3.3 Experimental results on simulation model with three sinks**

In this subsection, through experimental results on the simulation model with three sinks, the effectiveness of the transmission power control introduced in our scheme is evaluated. In the following experimental results, the battery capacity of each sensor node was set to 0.2J, and the range of radio wave of high power transmission in each sensor node was set to 200 m and it of low power transmission in each sensor node was set to 150m.

As the first experiment on the simulation model with three sinks, it was assumed that the evaluation node marked in Fig.4 detected an abnormal value and transmitted the data packet with this abnormal value periodically, as in the above subsection 3.2. The routes used by

Autonomous Decentralized Control Scheme for Long-Term

me is extended by early switching to low power in high node density.

*evaluation node*

*evaluation node*

Fig. 8. Routes used by applying our scheme (*Te* = *E*×0.5J )

**3.4 Discussion**

Operation of Large Scale and Dense Wireless Sensor Networks with Multiple Sinks 455

nsor nodes is shown, and the lifetime of the simulation model with three sinks, as in Figs.7, 8 and 9, is compared. From Figs.10, 11 and 12, it can be confirmed that the effect of our sche-

*evaluation node*

*evaluation node*

(c) 1 to 2000 data packets (d) 1 to 3000 data packets

To facilitate ubiquitous information environments by wireless sensor networks, their control mechanisms should be adapted to the variety of types of communication, depending on ap-plication requirements and the context. Currently, adaptive communication protocols for the long-term operation of the above ubiquitous sensor networks (Intanagonwiwat et al., 20-03; Silva et al., 2004; Heidemann et al., 2003; Krishnamachari & Heidemann, 2003; Wakabay-ashi et al., 2007) are under study. In

(a) 1 to 500 data packets (b) 1 to 1000 data packets

applying our scheme are shown in Figs.7, 8 and 9, where the number of sensor nodes is 1000. In Figs.7, 8 and 9, *Te* was set to 0.0J, *E*×0.5J, and *E*×0.9J, where *E* indicates the battery capaci-ty of each sensor node. Of the 3000 data packets transmitted from the evaluation node, the r-outes used by the first 500 data packets are illustrated in Figs.7, 8 and 9(a), those used by the 1000 data packets are in Figs.7, 8 and 9(b), those used by the 2000 data packets are in Figs.7, 8 and 9(c), and those used by a total of 3000 data packets are in Figs.7, 8 and 9(d). From Figs. 7, 8 and 9, it can be confirmed that the effect of our scheme is extended by early switching to low power.

Fig. 7. Routes used by applying our scheme (*Te* = 0.0J )

Next, it was assumed that data packets were periodically transmitted from a total of 20 sensor nodes placed in the set simulation area. In Figs.10, 11 and 12, the transition of the delivery ratio of the total number of data packets transmitted from a total of 20 randomly selected sensor nodes is shown, and the lifetime of the simulation model with three sinks, as in Figs.7, 8 and 9, is compared. From Figs.10, 11 and 12, it can be confirmed that the effect of our scheme is extended by early switching to low power in high node density.

Fig. 8. Routes used by applying our scheme (*Te* = *E*×0.5J )


*2.1.4. Innate lymphoid cells*

In recent years, studies have been published to identify innate lymphoid cells (ILCs), all members of this new family are characterized by a similar lymphocyte morphology, however, lack markers on its surface that identifies them as another immune cell type, because this is defined as lacking cells lineage markers (Lin− ) [39]. The ILCs come from two development pathways: (1) the first called cytotoxic ILCs, integrated by classic NK; (2) on the other hand, we have non-cytotoxical ILCs. The last group is subdivided into three types: ILC1s, ILC2s and ILC3s; they express T-bet, GATA-3 and ROR-γT, respectively, which is the main difference between them. ILCs can directly communicate with several varieties of cells and regulate immunity, inflammation and homeostasis in different tissues [8, 39].

ILC2s plays an important role in the regulation of glucose metabolism, lipid storage and redox balance in lean individuals. It accomplishes these by communicating with other immune cells associated with the type 2 immune axis (i.e. M2, eosinophils and invariant natural killer T) and participates in cross-talk with adipocytes [8, 39]. These cells produce cytokines associated with lymphocyte T-helper 2, cytokines that are required for immunity against helminths, allergic inflammation and tissue repair [8].

In contrast, it was found that cytotoxic ILCs (individuals and mice) and non-cytotoxic ILC1s (mice) are increased in visceral adipose tissue when obesity is present, accompanied by an increase in the production of interferon gamma, the latter contributes to change of the phenotype of macrophages to M1, thus, favours an inflammatory environment and increased recruitment of immune cells type 1 axis [8, 10, 12].


3.2.5. Distraction

In contrast, most participants reported that notifications, social media and the obligation to check in with work were a distraction, for example, "*If you've got your phone it's very easy to be distracted and to keep it constantly in use and to be distracted from doing this I'll just check Facebook."* (P2) Whilst the obligation to check in with work was also a distraction, *"You can feel obligated to check work emails, like I'm just stepping out of work for today just to watch him for a minute but you do feel obligated to be checking, too available*." (P5).

Participants highlighted a paradox between the convenience and distraction of MDU, giving reasons why they would not use their device in the playground. For example:

"*I guess that you have to be conscious that it doesn't distract you from spending this time with the kids* ... *, and giving them your full attention because you want to be interacting, you want to be there with them in the moment*". (P20) However, there was a tension between distraction and convenience, "*It's like a psychological kind of thing where I'm drawn to answer that beep and that takes me away from my kid. It's on silent now. It's just a distraction. I use it for my convenience rather than other people's convenience*." (P16).

Participants believed that brief moments of MDU, such as making a telephone call would have the least impact on supervision, whereas lengthy periods viewing the device for text messaging or social media were more likely to diminish the quality of supervision, "*I think if you're scrolling through Facebook, then that obviously requires a lot more attention than if you're communicating on a phone. I think you're much more able to supervise if you're only using it in its basic sense whereas if you're typing a text or you're writing an email, scrolling through Facebook then I think your ability to supervise is impaired. Coz obviously it takes more concentration*." (P5).

Using the camera was viewed di fferently "*Other than photos, anytime you are with your kids you should be supervising them so in public unless you've got somebody else with you who can take over that role. I always have eyes on (child) even through the camera on my phone. I think that's the main thing*." (P16).

Participants commented on the social acceptability of MDU within the playground setting with work tasks or essential telephone calls deemed more acceptable distractions than social media, along with some criticism of this behaviour. For example, "*There's not too many things that are more important than watching a kid, you know if he's close to the road you don't need to use your phone but if he's in the playground and I need to take a phone call for any particular reason. But I don't think there is any reason that you need to use Facebook or something like that if the kid needs watching*." (P5).


**7. Conclusions**

As described above, many problems regarding adjuvant therapy for endometrial cancer remain. (1) Which patients receive the highest benefit from adjuvant therapy? (2) Is there a

meeting33). These two studies were undertaken to clarify whether the sequential combination of chemotherapy and radiotherapy improves the PFS in high-risk subjects with endometrial cancer. These studies had similar designs; however, some differences existed regarding the distribution of stages and the rates of pelvic or paraaortic lymphadenectomy. Most of the enrolled cases were stage I in the former study, while all the cases were stage II or III in the latter study; in addition, the rate of pelvic or paraaortic lymphadenectomy was higher in the ILIADEIII study. In total, patients (n = 540) with surgically resected endometrial cancer stage I – III and with no residual tumor or prognostic factors implying a high -risk were randomly

allocated to an adjuvant radiotherapy group with or without sequential chemotherapy.

severe in the combined modality group.

**plus chemotherapy**

**7. Conclusions**

In the NSGO/EORTC study, patients with stage I, II, IIIA (positive peritoneal fluid cytology only), or IIIC (positive pelvic lymph nodes only) diseases were enrolled. The chemotherapy modalities included AP (doxorubicin, 50 mg/m2 + cisplatin, 50 mg/m2; 83%), EP (epirubicin, 75 mg/m2; 4%), TEC (paclitaxel, 175 mg/m2 + epirubicin, 60 mg/m2 + carboplatin, AUC 5; 3%), and TC (paclitaxel, 175 mg/m2, carboplatin, AUC 5 – 6; 10%). The radiation arm consisted of pelvic EBRT (44 Gy) with or without brachytherapy. The combined modality treatment was associated with a 36% reduction in the risk of relapse or death (HR, 0.64; 95% CI, 0.41 – 0.99; *P* = 0.04); two-sided tests were used. In the MaNGO ILIADEIII study, only the AP therapy was used as a chemotherapy regimen. The results from the MaNGO ILIADEIII study pointed in the same direction (HR, 0.61) as those of the NSGO/EORTC study, but were not significant. In both studies, adverse effects were more

In the combined analysis, the estimate of the risk for relapse or death was similar but with narrower confidence limits (HR, 0.63; 95% CI, 0.44 – 0.89; *P* = 0.009). Neither study showed significant differences in the OS. In the combined analysis, the OS approached statistical significance (HR, 0.69; 95% CI, 0.46 – 1.03; *P* = 0.07) and cancer-specific survival (CSS) was significant (HR, 0.55; 95% CI, 0.35 – 0.88; *P* = 0.01). Thus, the addition of adjuvant chemotherapy to radiation improved the PFS and CSS in surgically treated endometrial cancer patients with no residual tumor and a high-risk profile. Regarding the pathological subtypes, combined therapy offered a superior benefit to patients with endometrioid type and grade 1 or 2 diseases, but not to patients with serous or clear cell types and grade 3

diseases. Several remaining questions need to be further investigated in future trials.

is examining brachytherapy followed by three cycles of TC.

**6. Ongoing trials comparing radiation therapy alone and radiation therapy**

At present, there are several ongoing studies comparing radiation therapy alone and radiation plus chemotherapy (Table 3). The RTOG-GOG9905 study finished accrual in 2004; however, its results have not yet been presented. Accrual for the PORTECIII and GOG249 trials is ongoing. These three trials are phase III randomized trials comparing a radiation alone group and a combined radiation and chemotherapy group. Two of them are examining concurrent chemoradiotherapy followed by four cycles of TC, and the third trial

As described above, many problems regarding adjuvant therapy for endometrial cancer remain. (1) Which patients receive the highest benefit from adjuvant therapy? (2) Is there a definite consensus regarding the criteria for grouping patients according to the risk of recurrence? (3) Which chemotherapy regimen should be certified as the gold standard regimen for adjuvant therapy based on the results of phase III randomized trials? (4) Which combination of radiation therapy and chemotherapy is best? To answer these questions, before designing a trial concept, a worldwide consensus on the criteria for risk groups needs to first be obtained. In addition, to interpret the results of various adjuvant therapy trials, careful attention to the kind of surgery that the patients have received and the percentages of grade 3 endometrioid adenocarcinoma and aggressive pathological subtypes (serous, clear cell, undifferentiated, and so on) is needed. In this review, as shown in Tables 1, 2, and 3, we have collected information regarding the percentages of pelvic lymphadenectomy, paraaortic lymphadenectomy, grade 3 endometrioid adenocarcinoma or aggressive pathological subtypes, and informations about surgical stage distribution, treatment compliance, and adverse effects. Before arguing the results of clinical trials, sufficient information regarding the patient conditions after surgery and just before receiving adjuvant therapy is needed. For example, some trials with low percentages of pelvic or paraaortic lymphadenectomy, trials with high percentages of G3 or aggressive pathological subtypes, and trials with high percentages of advanced stage patients tend to favor chemotherapy, since these patient groups tend to have higher possibilities of micrometastases that cannot be identified using imaging.

The results of ongoing studies, such as GOG0237 (TAP vs. TC, advanced or recurrent disease, phase III) and JGOG2043 (AP vs. DP vs. TC, adjuvant, phase III) may provide important information regarding question (3) above, and the results of the RTOG-GOG9905, PORTECIII, and GOG249 studies may help to answer question (4). Further studies are needed to resolve question (1).

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Lundgren C, Salmi T, Andersson H, and Reed NS. A randomized phase-III study on adjuvant treatment with radiation (RT) ± chemotherapy (CT) in early-stage high-risk endometrial cancer (NSGO-EC-9501/EORTC 55991) J Clin Oncol. 2007; 25


**5. Conclusions**

The impairment of the cardiovascular system in COVID-19 comprises a wide spectrum of dysfunctions, ranging from mild to severe, or even life-threatening forms, often having an acute onset, sometimes continuing during recovery or even resulting in chronic pathologies. Individuals are affected regardless of age, race, gender, and associated cardiovascular risk factors, but those with a history of cardiovascular

pathology prior to the SARS-CoV-2 virus infection have a worse outcome. Therefore, a comprehensive cardiologic evaluation, including TTE, is justified to assess the involvement of the cardiovascular system, for initiating a proper therapy as soon as possible and to schedule a follow-up program particularly in patients at high risk.


Meet the editor

Branislav Sobota was born in 1967. In 1990 he graduated (MSc.) with honors at the Department of Computers and Informatics of the FEEI at the Technical University in Košice, Slovakia. He obtained his PhD in 1999 and habilitation thesis in 2008 in the field of virtual reality and computer graphics. He is currently working as an associate professor at the Department of Computers and Informatics at the Technical University of Košice, Slovakia. His

scientific research is focused on computer graphics, parallel computing, and especially virtual reality and related technologies.

## Contents

Preface

Human evolution is tied to the process of collecting, processing, transferring, and recording information. If people gain most of their most information visually, then computer graphics is one of the generational changes in computer technology. Creating a book on computer graphics is quite challenging given its actual range. Nevertheless, in this book, modern approaches, procedures, algorithms, as well as devices in the area of light and colors, shading and lighting, realistic and photorealistic imaging, definition of graphical scenes or objects, and security based on graphical objects are presented. The book also covers graphical transformations and projections, spatial imaging, curves and surfaces, filling and texturing, image filtering, virtual reality, color imaging and graphics in the service of light-source design, and analytical methods for reflection and refraction, as well as area of visualization. Topological visualization techniques for volume multifield data and "cosmos visualization" and usage of advanced related technologies such as virtual reality and graphics-based security can also be found in this book, whether it's topdown historical reconstruction in virtual reality or new graphical password schemes

Finally, I would like to express my gratitude to all the authors for their valuable contributions and professional efforts in providing such precious content.

**Branislav Sobota, PhD**.

Technical University of Kosice,

Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics,

Assoc. Prof. Ing.

Slovak Republic

containing questions-background-pattern.


**Acknowledgements**

It was also reported that the prenylation together with the 41

30 Current Understanding of Apoptosis - Programmed Cell Death

observed sensitivity of MCF-7 breast cancer cells to the compound.

induction of apoptosis becomes a strategy for cancer drug discovery [18].

treated breast cancer cells and the Annexin V-FITC flowcytometric assays.

cells thus, halting its progression to immortality.

induced by Artonin E.

**5. Conclusion**

, 51

E had enhanced its affinity to the human estrogen receptor α. This affinity of Artonin E to the human estrogen receptor can be said to be mostly responsible for its better growth inhibition in MCF-7 cells observed in this study. Consistent with our findings, Obiorah et al. [13] discovered that ERα was exclusively responsible for the apoptosis induction of genistein, equol, and coumestrol, compounds which are structurally similar to Artonin E. They confirmed the phenomenon by a gene knockdown of ERα which prevented growth inhibition and apoptosis induced by these phytoestrogens. These support the involvement of the estrogen receptor in the growth inhibitory potential of Artonin E. Similarly, Turner et al. [14] also reported that prenylflavones show selectivity to estrogen receptors. Thus, it is of no doubt, that the affinity of Artonin E for the estrogen receptor is a possible basis for the

Various modes of cell death include apoptosis, necrosis and autophagy. However, from the results of this study, Artonin E provoked morphological features typical of apoptosis in the Artonin E treated breast cancer cells. In support of this, Carou et al. [15] and Gerl and Vaux [16], reported that apoptosis results in unique morphological changes including cell shrinkage, membrane alteration, DNA fragmentation and nuclear condensation. These features were observed in this study after treating the breast cancer cells with Artonin E. Succinctly, agents that trigger apoptosis are very essential in the management of cancer, giving that a unique hallmark of cancer cells is apoptosis evasion which is also implicated in its pathogenesis [17]. Hence,

A compromise in the phospholipid membrane asymmetry of a cell, results in the externalization of phosphatidylserine. To further strengthen the assessment of the apoptotic mode of cell death, annexin V FITC in combination with a DNA binding fluorochrome, PI were utilized [9, 19, 20]. In this study, Artonin E has been seen to significantly reduce the population of viable breast cancer cells in a concentration and time dependent manner while increasing the population undergoing apoptosis. Thus, treatment of breast cancer cells with Artonin E distorted the integrity of the lipid bilayer of the cancer cells, exposing their phospholipid as detected in this study. These observations qualifies apoptosis as the mode of cell death

Apoptotic endonucleases in the course of apoptosis, degrades chromosomal DNA into fragments [21]. This fragmented DNA can be visualized in a gel electrophoresis. In this study, after treating the cancer cells with Artonin E, its DNA was seen to have degraded into fragments in comparison to the untreated control. This DNA fragment induction by Artonin E, confirms apoptosis as the mode of cell death [22] which was also seen in the morphology of Artonin E

Artonin E inhibited the unregulated growth of breast cancer cells and induced apoptosis in the cancer cells. It produced enhanced cytotoxicity on the estrogen receptor positive MCF-7

vicinal diol groups in Artonin

We express our gratitude to the Universiti Putra Malaysia and all the staff of MAKNA-CANCER laboratory, UPM for their immense support in the success of this research. This study was co-supported by TETFund Nigeria, University Putra Malaysia and Ministry of Science, Technology and Innovation Malaysia (Vote No. 5450742).


**5. Engineering Application of Rotary Kiln**

The rotary kiln and the entire system have been operated in a pesticide plant in Shandong, China. The incineration system is mainly composed of pretreatment, waste incineration disposal, and a flue gas treatment system. Figure 16 shows the practical engineering application of our rotary kiln.

**Figure 16.** Incineration process and flue gas treatment process.

The incineration disposal disposes the pesticide waste liquid and the solid wastes after pretreatment. Under the continuous rotation of the rotary kiln, the mixed wastes are continuously turned, heated, dried, vaporized and burned in the kiln. The combustion temperature in the rotary kiln is kept at about 850 ◦C, and the residence time of the wastes in the kiln is more than 1 h. Primary dust removal is performed in high-temperature cyclone separator to reduce the particle content in flue gas produced by combustion. After dust removal, the gas enters the secondary combustion chamber. The gas products of the rotary kiln are mixed with the combustion-supporting air of the secondary combustion chamber, and the unburned combustible gas in the rotary kiln is completely burned to remove the toxic organic component. The combustion temperature of the second combustion chamber reaches 1100 ◦C, and the residence time of the flue gas in the second combustion chamber is no more than 2 s. The discharged gas enters the flue gas treatment system.

The flue gas treatment system is composed of heat exchanger, air preheater, quench tower, activated carbon and a calcium oxide injection system, baghouse, and wet desulphurization. The flue gas discharged from the second combustion chamber initially enters the heat exchanger and air preheater, which can fully utilize the waste heat of high-temperature flue gas, save energy and reduce consumption. The outlet gas temperature is 500–550 ◦C. Thereafter, the flue gas enters the quench tower. The high-temperature flue gas contacts directly with the atomized cooling water in the quench tower, and the flue gas temperature drops rapidly to 200 ◦C within 1 s, thereby avoiding dioxin regeneration. Before the flue gas from the quenching tower enters the baghouse, calcium oxide and activated carbon are sprayed successively into the flue. The calcium oxide is used to neutralize the acidic substances in the flue gas. The activated carbon is used to absorb the heavy metal and dioxins in the flue gas. In the baghouse, the suspended particles in the flue gas are intercepted by the filter bag and discharged in the form of fly ash. The flue gas enters the wet absorption tower of the desulphurization unit and contacts with sprayed limestone slurry droplets. The acid substances in the flue gas are absorbed.

An Atmos FIR Fourier infrared multicomponent gas analyzer was used to measure the concentration of SOX, NOX and HCl at the chimney outlet according to GB/T16157-1996. In order to ensure the measurement accuracy, the gas analyzer was calibrated with the target standard gas before measuring. Similarly, automatic smoke sampler with sampling gun was used to measure dust. According to the USA EPA23a method, flue gas sampling was isokinetically collected, and dioxins

were preprocessed and measured [29]. The final flue gas concentration (Table 5) meets the emission standard of waste incineration.

**Table 5.** Measured value of pollutant concentration in flue gas.

The main power consumption equipment in the incineration process of the rotary kiln re the screw conveyor, the motor driving device of the rotary kiln, the draft fan, the ignition burner, the secondary burner, the blower, the slag discharging motor, the quench pump, the lye pump and so on. The total power of the power consumption equipment is 98.29 kW. The hourly waste disposal capacity of the rotary kiln is 1000 kg/h. It takes 353 MJ energy to dispose of a ton of waste. The low calorific value of diesel oil is 42 MJ/L. The diesel oil consumption is 200 L/h. The economic cost of the rotary kiln incineration system mainly includes equipment operation cost, diesel fuel cost, and maintenance cost. The rotary kiln works continuously for 10 h. The power consumption of a rotary kiln is 982.9 kWh per day. The diesel oil consumption per day is 2000 L. There are three workers in total. It costs 691 RMB to treat a ton of waste.

#### **6. Conclusions**

A large amount of liquid and solid wastes generated during pesticide production are urgently needed for disposal. A co-incineration scheme for mixing multi-component wastes in a rotary kiln was proposed for waste disposal from pesticide production. The co-incineration experimental results showed that the residence time of solid and liquid mixtures in the kiln is more than 1 h, and the clinker ignition loss of the slag is less than 5%. Therefore, the co-incineration of solid and liquid wastes is feasible. A numerical study of different particle diameters and different wall rotational speeds was performed to reproduce the configurations of solid flows (i.e., sliding, slumping, rolling, cascading, cataracting, and centrifuging). The rolling or cascading mode was selected as the best operating condition. The numerical simulation revealed the combustion interactions of the co-incineration of mixed wastes, and the results showed that the co-incineration of solid and liquid wastes is feasible by adding auxiliary fuel. The rotary kiln incineration can keep the kiln temperature, mass residue and pollutants within the allowable range. Finally, the rotary kiln incineration process and flue gas treatment process were operated and commissioned. The liquid and solid wastes can be completely burned, and the flue gas concentration after treatment can meet the emission standard of waste incineration.

**Author Contributions:** Investigation, B.Z. and J.H.; methodology, B.Z.; software, C.H.; data curation, W.C.; writing—original draft, J.H.; writing—review and editing, B.Z.

**Funding:** This research was funded by Key Research and Development Program of Shandong Province of China (No. 2019GHY112002).

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

#### **Nomenclature**

#### **References**

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