Acknowledgements

Financial support from Patrimonio Autónomo, Fondo Nacional de Financiamiento para la Ciencia, Francisco José de Caldas, grants RC-432-2004, RC-0572-2012, and

Study of Essential Oils Obtained from Tropical Plants Grown in Colombia DOI: http://dx.doi.org/10.5772/intechopen.87199

FP44842-212-2018, is gratefully acknowledged. The authors thank Ministerio de Ambiente y Desarrollo Sostenible, through its Dirección de Bosques, Biodiversidad y Servicios Ecosistémicos for their permission of access to genetic resources, and derived products for the program ran by the Unión Temporal Bio-Red-CO-CENIVAM (Resolution 0812, June 4, 2014).


3.2. Verification

In the case of verification, the percentage of error is divided into two: false acceptance and false rejection. False acceptance occurs when an individual claims to be the person who is not and

Figure 6. Examples of (a) images from the AR(A) set. (b) Images from the AR(B) set.

Figure 7. The identification rate obtained using different numbers of training images.

this is mistakenly accepted by the system. False rejection occurs when an individual provides their identity and the system erroneously rejects this statement.

The performance of most face verification systems depends on a suitable selection of the threshold value to diminish both the false acceptance and the false rejection. After many tests, it was concluded that this relationship, this threshold, is used to decide if the person is who she/he claims to be; next, it is explained, how the threshold can be obtained with an exponential function as shown in Figure 12, where you can select the point that has the lowest value of both false acceptance and false rejection. Thus, we can assume that the false acceptance

Figure 8. Recognition performance of the evaluating approach using: SVM, Euclidean distance, and cosine distance in the classification stage. The performance of hLBPI, eigenphases, eigenfaces, Laplacianfaces, and Fisherfaces are also shown

Pfað Þ¼ Th e

�αTh (4)

Face Recognition Based on Texture Descriptors http://dx.doi.org/10.5772/intechopen.76722 119

probability is given by:

Figure 9. Ranking evaluation of image set AR(A).

for comparison.

Figure 11 shows the receiver operating characteristics (ROC) when the hLBPI and WBP algorithms are used for the verification task using the set AR(A). Figure 11 shows that both algorithms have a similar performance with a false rejection and false acceptance, and they are very low.

Figure 8. Recognition performance of the evaluating approach using: SVM, Euclidean distance, and cosine distance in the classification stage. The performance of hLBPI, eigenphases, eigenfaces, Laplacianfaces, and Fisherfaces are also shown for comparison.

Figure 9. Ranking evaluation of image set AR(A).

this is mistakenly accepted by the system. False rejection occurs when an individual provides

Figure 11 shows the receiver operating characteristics (ROC) when the hLBPI and WBP algorithms are used for the verification task using the set AR(A). Figure 11 shows that both algorithms have a similar performance with a false rejection and false acceptance, and they are

their identity and the system erroneously rejects this statement.

Figure 7. The identification rate obtained using different numbers of training images.

118 From Natural to Artificial Intelligence - Algorithms and Applications

very low.

The performance of most face verification systems depends on a suitable selection of the threshold value to diminish both the false acceptance and the false rejection. After many tests, it was concluded that this relationship, this threshold, is used to decide if the person is who she/he claims to be; next, it is explained, how the threshold can be obtained with an exponential function as shown in Figure 12, where you can select the point that has the lowest value of both false acceptance and false rejection. Thus, we can assume that the false acceptance probability is given by:

$$P\_{\hat{f}^\hbar}(Th) = e^{-\alpha \hat{T} \hbar} \tag{4}$$

and

$$P\_{\sharp^\*} = e^{-\beta P\_{\sharp^\*}} \tag{5}$$

Then from Eqs. (4) and (5) it follows that α ¼ �Ln Pfað Þ Th =Th where Pfað Þ Th is the false acceptance percentage, calculated from a threshold Th. Hence, for a desired false acceptance probability Po the suitable value of Th is given.

$$\text{Th} = -\text{Ln}(P\_o)/\alpha \tag{6}$$

4. Conclusions

This chapter presented the application of different texture descriptors for tasks ranging from feature extraction to face recognition, which are based on the LBP algorithm, specifically the hLBPI and WBP. The evaluation results demonstrate that these algorithms provide good recognition rates. In most situations, the accuracy of recognition with the WBP is slightly lower than the accuracy with the hLBPI because the feature vector estimation of the WBP does not require PCA and uses non-overlapping blocks. This fact results in an important computational complexity reduction of approximately 2NML=9 relative to hLBPI, where L is the feature vector size. If an L with a big value is used, it produces a more exact feature vector, although this causes the computational complexity to increase. Also, the evaluation results were compared with other

Face Recognition Based on Texture Descriptors http://dx.doi.org/10.5772/intechopen.76722 121

Figure 12. Relationship between the false acceptance rate and the threshold value.

Figure 13. Relationship between the false acceptance rate and false rejection rate.

Figure 13 shows the results of an exponential function and some experimental results of the relationship between the false acceptance rate and false rejection rate in this chapter.

Figure 10. Ranking evaluation of image set AR(B).

Figure 11. ROC of proposed and conventional hLBPI algorithms.

Figure 12. Relationship between the false acceptance rate and the threshold value.

Figure 13. Relationship between the false acceptance rate and false rejection rate.


**3.4 Sterility assay and ethylene oxide residues**

The samples were classified according to their position (0 to 5a; 0 to 5b) during exposure to EtO, for the penetration analysis. The outermost or surface position corresponds to the number 5, and the innermost position to the number 0. (Figure 6)

Sterility tests were carried out through the analyses of two biological indicators (bi 3M - ATTEST –TM Bacillus atrophaeus and Terragene – Bionova BT40 - Bacillus atrophaeus). Incubation time was 48 h at a temperature of 35°C ± 1.5.

Samples of packaging were also submitted to direct incubation for 7 days with TSB liquid culture at a temperature of 35°C ± 1.5. The methodology used is in accordance with the Brazilian Pharmacopoeia. The analyses of the three sterility tests (biological indicator Bionova BT40, 3Mattest and direct incubation) confirm the sterility of the packaging material after being submitted to ethylene oxide gas.

Validation of Primary Packaging for Cryopreserved Musculoskeletal Tissues 87

The analysis of ethylene oxide residues was performed by the Gas Chromatography test, determining the levels of Ethylene chlorohydrin and Ethylene glycol. The data are shown in

ETO

ETCH

ETG

Note. Maximum limits according to the Brazilian legislation. [ ETO up to 25/ ETCH up to 25/ETG up to

Analyses of the packaging used in this study demonstrated that it is a good option for

Our experience with assays to validate coextruded polyethylene and polyamide plastic film shows that the mechanical properties of this material are not altered by cryopreservation and sterilization. Penetration resistance of the thermal seal remained unaltered after all the

We found a loss of barrier due to increased oxygen permeability of around 10% after sterilization and cryopreservation, which can be explained by the humidification of the polyamide. However, this slight alteration in oxygen permeability does not compromise the

In relation to total migration, we did not observe any alterations in the assays, i.e. once again, sterilization and cryopreservation did not lead to monomer migration at levels above

Fig. 9. Residues (ETO, ETCH, ETG) found in the samples submitted to sterilization.

5A 4A 3A 2A 1A 0 1B 2B 3B 4B 5B

processes carried out in a tissue bank, such as sterilization and cryopreservation.

inner vacuum of the packaging, and does not place at risk the tissue packaged in it.

250]

0

5

10

15

20

25

30

35

40

**4. Final considerations**

cryopreservation of tissues at a temperature of – 80ºC.

**Figure 9** and show that the levels are within the limits accepted by our legislation.

Fig. 6. Samples of packaging positioned from 0 to 5 for EtO penetration analysis.

Fig. 7 and 8. Performance of the sterility test by direct incubation in samples submitted to ethylene oxide.

The analysis of ethylene oxide residues was performed by the Gas Chromatography test, determining the levels of Ethylene chlorohydrin and Ethylene glycol. The data are shown in **Figure 9** and show that the levels are within the limits accepted by our legislation.

Note. Maximum limits according to the Brazilian legislation. [ ETO up to 25/ ETCH up to 25/ETG up to 250]

Fig. 9. Residues (ETO, ETCH, ETG) found in the samples submitted to sterilization.


*2.2. Governing Equation and Meshing*

The continuity, momentum and energy equations are solved using the computational fluid dynamics approach to analyze the thermodynamic attributes of the microplate heat exchanger with single-particle and hybrid nanofluids [44–46]. The continuity Equation (1) is expressed per unit of surface area (m2 ). The governing equations are considered for the single pair microplates in the heat exchanger based on symmetrical heat transfer. While solving the equations, it is assumed that the flow is three-dimensional, steady, turbulent and incompressible. Additionally, the working fluids are assumed to be Newtonian [47].

Continuity equation

$$\nabla \cdot (\rho \mathcal{U}) = 0 \tag{1}$$

Momentum equation

$$\nabla \cdot (\rho L U) = -\nabla p + \nabla \cdot (\mu \nabla \mathbf{U}) \tag{2}$$

Energy equation for fluid domains

$$\nabla \cdot (\rho \mathcal{U} \mathcal{H}) = \nabla \cdot (\lambda \nabla T) \tag{3}$$

Energy equation for solid domains

$$
\nabla^2 T = 0 \tag{4}
$$

The working fluids in the microplate heat exchanger are exposed to heat exchange which results in entropy generation. The volumetric total entropy generation rate is the sum of the volumetric thermal entropy generation rate and the volumetric friction entropy generation rate as presented by Equation (5) [48].

$$
\dot{S}\_T = \dot{S}\_{Th} + \dot{S}\_{Fr} \tag{5}
$$

The volumetric thermal entropy generation rate is calculated using Equation (6) as the summation of volumetric thermal entropy generations due to average and fluctuating temperature gradients [48].

$$\dot{S}\_{Th} = \frac{\lambda}{\overline{T}^2} [ (\frac{\partial \overline{T}}{\partial x})^2 + (\frac{\partial \overline{T}}{\partial y})^2 + (\frac{\partial \overline{T}}{\partial z})^2] + \frac{\lambda}{\overline{T}^2} [ (\frac{\partial T'}{\partial x})^2 + (\frac{\overline{\partial T'}}{\partial y})^2 + (\frac{\overline{\partial T'}}{\partial z})^2 ] \tag{6}$$

The first term at the right side of Equation (6) presents the volumetric thermal entropy generation rate due to time–mean temperature gradients. Whereas, the second term at right side of Equation (6) presents the volumetric thermal entropy generation rate due to fluctuating temperature gradients, which could also be expressed as Equation (7) [48].

$$\dot{S}\_{Th'} = \frac{\lambda\_t}{\overline{T}^2} [(\frac{\partial \overline{T}}{\partial x})^2 + (\frac{\partial \overline{T}}{\partial y})^2 + (\frac{\partial \overline{T}}{\partial z})^2] \tag{7}$$

Therefore, the volumetric thermal entropy generation rate could be reduced to Equation (8) after manipulating *λe f f* = *λ* + *λt* .

$$\dot{S}\_{Th} = \frac{\lambda\_{eff}}{\overline{T}^2} [(\frac{\partial \overline{T}}{\partial \mathbf{x}})^2 + (\frac{\partial \overline{T}}{\partial y})^2 + (\frac{\partial \overline{T}}{\partial z})^2] \tag{8}$$

The volumetric friction entropy generation rate is evaluated using Equation (9) as the summation of direct dissipation due to the average velocity gradient and indirect dissipation due to the fluctuating velocity gradient [48].

$$\begin{array}{c} \dot{S}\_{F} = \frac{\mu}{T} \{ 2[(\frac{\partial \overline{v}}{\partial x})^{2} + (\frac{\partial \overline{v}}{\partial y})^{2} + (\frac{\partial \overline{v}}{\partial z})^{2}] + (\frac{\partial \overline{v}\_{x}}{\partial y} + \frac{\partial \overline{v}\_{y}}{\partial x})^{2} + (\frac{\partial \overline{v}\_{x}}{\partial z} + \frac{\partial \overline{v}\_{z}}{\partial x})^{2} + (\frac{\partial \overline{v}\_{y}}{\partial z} + \frac{\partial \overline{v}\_{z}}{\partial y})^{2} \} \\ \qquad + \frac{\mu}{T} \mathbf{2}[(\frac{\partial \overline{v}\_{x}}{\partial x})^{2} + (\frac{\overline{v} \overline{v}\_{y}}{\partial y})^{2} + (\frac{\partial \overline{v}\_{z}}{\partial z})^{2}] + (\frac{\partial \overline{v}\_{x}}{\partial y} + \frac{\partial \overline{v}\_{y}}{\partial x})^{2} \\ \qquad + (\frac{\partial \overline{v}\_{x}}{\partial z} + \frac{\partial \overline{v}\_{z}}{\partial x})^{2} + (\frac{\partial \overline{v}\_{y}}{\partial z} + \frac{\partial \overline{v}\_{z}}{\partial y})^{2} \end{array} \tag{9}$$

The first term on right side of Equation (9) indicates the direct entropy generation due to dissipation in the mean flow field, which is commonly denoted as direct dissipation. Whereas, the second term on the right side of Equation (9) indicates turbulent or indirect dissipation due to fluctuating velocity gradients, which is also expressed as Equation (10) [48].

$$
\dot{S}\_{Fr'} = \frac{\rho \beta^\* kO}{\overline{T}} \tag{10}
$$

Therefore, the volumetric friction entropy generation rate could be reduced to Equation (11) [48].

$$\dot{S}\_{Fr} = \frac{\mu}{\overline{T}} \left\{ 2[(\frac{\partial \overline{w}}{\partial x})^2 + (\frac{\partial \overline{w}}{\partial y})^2 + (\frac{\partial \overline{w}}{\partial y})^2] + (\frac{\partial \overline{w}\_x}{\partial y} + \frac{\partial \overline{w}\_y}{\partial x})^2 + (\frac{\partial \overline{w}\_x}{\partial z} + \frac{\partial \overline{w}\_z}{\partial x})^2 + (\frac{\partial \overline{w}\_y}{\partial z} + \frac{\partial \overline{w}\_z}{\partial y})^2 \right\} + \frac{\rho \beta^\* k O}{\overline{T}} \tag{11}$$

Here, *β* ∗ is the model constant with value of 0.09.

The Bejan number is evaluated to quantify the contribution of the volumetric thermal entropy generation rate in the volumetric total entropy generation rate. The Bejan number (Be) is defined as the ratio of the volumetric thermal entropy generation rate to the volumetric total entropy generation rate, as presented by Equation (12) [48].

$$Be = \frac{\dot{\mathcal{S}}\_{Th}}{\dot{\mathcal{S}}\_T} \tag{12}$$

The governing equations are solved using the finite volume method and second order approach. The tetrahedron mesh elements are considered for the computational geometry of the heat exchanger and fluid domains. The inflation layers are provided on the fluid domains to consider the effect of boundary layers at the walls of the heat exchanger. The mesh independency test is carried out by generating five different mesh element numbers on the computational geometry. The results of performance index are evaluated for different mesh element numbers by considering the flow of water as hot and cold fluids. The inlet temperature and mass flow rate on the hot side are 90 ◦C and 20 kg/h, and those on the cold side are 20 ◦C and 20 kg/h. The mesh independency results for five different mesh elements are presented in Table 1. The temperature and pressure drop results for hot and cold fluids are significantly varying when the mesh element numbers are ranging from 157,649 to 732,993. However, beyond the mesh element number of 732,993, the simulated results of outlet temperatures and pressure drops of hot and cold fluids vary within ±1% [49]. Therefore, the computational geometry with a mesh element number of 732,993 is considered for the further numerical investigations. The mesh configuration of computational geometry with selected final mesh elements is depicted in Figure 2. The SIMPLE scheme with velocity-pressure coupling and the convergence criteria of 10−8 are considered for solving the governing equations.

**Table 4.** Parameters for calculating viscosity of blade, platelet, cylinder and brick nanoparticle

732,993, the simulated results of outlet temperatures and pressure drops of hot and cold fluids vary within ±1% [49]. Therefore, the computational geometry with a mesh element number of 732,993 is considered for the further numerical investigations. The mesh configuration of computational geometry with selected final mesh elements is depicted in Figure 2. The SIMPLE scheme with velocity-pressure coupling and the convergence

> **Cold Fluid-Temperature (°C)**

157,649 88.669 29.841 2.413 1.321 489,478 86.295 26.877 2.637 1.444 732,993 85.180 24.888 2.738 1.502 1,142,485 85.182 24.086 2.742 1.505 1,588,899 85.181 23.865 2.751 1.507

**Hot Fluid-Pressure Drop (bar)**

**Cold Fluid-Pressure Drop (bar)**

(13)

(15)

criteria of 10−8 are considered for solving the governing equations.

**Hot Fluid-Temperature (°C)**

*Symmetry* **2021**, *13*, x FOR PEER REVIEW 10 of 33

**Table 1.** Mesh independency test results.

**Mesh Elements**

shape-based nanofluids

**Figure 2.** Mesh configuration for computational geometry. **Figure 2.** Mesh configuration for computational geometry. **Table 5.** Properties of base fluid and nanoparticles

#### **3. Thermophysical Properties of Nanofluids with Nanoparticle Shapes 3. Thermophysical Properties of Nanofluids with Nanoparticle Shapes Property Water Alumina Copper**

The thermophysical properties of single-particle and hybrid nanofluids with different nanoparticle shapes are evaluated using the models presented in Sections 3.1 and 3.2, respectively. The different nanoparticle shapes considered are Sp, OS, PS1, PS2, PS3, PS4, BL, PL, CY and BR, as depicted in Figure 3. The thermophysical properties of single-particle and hybrid nanofluids with different nanoparticle shapes are evaluated using the models presented in Sections 3.1 and 3.2, respectively. The different nanoparticle shapes considered are Sp, OS, PS1, PS2, PS3, PS4, BL, PL, CY and BR, as depicted in Figure 3. Density (kg/m3) 997.1 3050 8933 Specific heat (J/kg∙K) 4179 618.3 385 Thermal conductivity (W/m∙K) 0.613 30 400 Viscosity (Pa∙s) 0.001003 - -

**Figure 3.** Different nanoparticle shapes considered in the present study. **Figure 3.** Different nanoparticle shapes considered in the present study.


**Simulation and Implementation of Predictive Speed Controller and Position Observer for Sensorless Synchronous Reluctance Motors**

## **Tian-Hua Liu \*, Seerin Ahmad, Muhammad Syahril Mubarok and Jia-You Chen**

Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan<EMAIL_ADDRESS>(S.A<EMAIL_ADDRESS>(M.S.M.);

<EMAIL_ADDRESS>(J.-Y.C.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 18 April 2020; Accepted: 26 May 2020; Published: 28 May 2020

**Abstract:** A position observer and a predictive controller for sensorless synchronous-reluctance-motor (SynRM) drive systems are investigated in this paper. The rotor position observer, based on motor parameters, and stator currents and voltages, was designed and implemented to compute the rotor position. A pole-assignment technique was used to provide similar converging rates of the position observer, even when operated at different speeds. Furthermore, a predictive controller was designed to enhance performance. A digital-signal processor (DSP), TMS-320F-28335, was used as a computation tool. Several simulated results are provided and compared with the measured results. The measured results showed that the implemented predictive controller sensorless SynRM drive system could be adjusted from 30 to 1800 rpm with satisfactory performance, including quicker and better tracking responses, and a lower speed drop than that of a proportional-integral (PI) controller.

**Keywords:** rotor position observer; predictive speed controller; digital-signal processor; synchronous reluctance motor

## **1. Introduction**

Synchronous reluctance motors (SynRMs) that have no magnetic materials mounted on their rotors have a rugged and simple structure, and are easy to control. These motors have been applied in vacuum cleaners, sewing machines, and electric vehicles. Several major sensorless techniques have been proposed for SynRM drive systems, including extended back electromotive force (back-EMF) modelling [1], correct-slope-estimating [2], adaptive-projection-vector [3], and search-coil-detection methods [4].

Previous research also found that a position observer is a very effective way to obtain rotor position because of its simple computation and hardware circuit. For example, Senjyu et al. investigated the flux-linkage observer for a SynRM. By suitably choosing a time constant of first-order lag compensation, a precise rotor-position observer was achieved [5]. Lim et al. proposed an online position observer of a SynRM by using a finite-element method [6]. Vagati investigated a flux-based observer to obtain an accurate magnetic model [7]. Tuovinen et al. implemented a back-EMF position observer method for a SynRM drive system that accounted for cross-saturation in the motor [8]. Tuovinen et al. analyzed a position observer with resistance adaption for SynRM drive systems [9,10]. Awon et al. investigated a discrete-time observer for sensorless SynRM drives [11]. Tuovinen et al. implemented a full-order observer for sensorless SynRM drive systems [12].

Moreover, some research studied predictive controllers for SynRM drives. Lin et al. used a predictive method to design a current controller for a SynRM drive system [13]. Hadla et al. proposed a predictive stator flux that did not employ any controller calibrations or weighting factors for a SynRM [14]. Carlet et al. designed a predictive current control that was based on predictive methods that did not require knowledge of the system model [15]. Antonello et al. investigated a hierarchical scaled-state direct predictive control of a SynRM drive system that kept the current ripple limited even though the motor was operated at a low switching frequency [16]. Lin et al. studied a dual-vector modeless predictive current control on the basis of predictive methods for SynRM drives without using any system models, parameters, or DC link capacitor voltages [17].

Predictive controllers were successfully applied for SynRM drives with an attached encoder or resolver [13–17]. However, to the authors' best knowledge, in previous research, predictive controllers were not employed or were rarely employed in sensorless-synchronous-reluctance-drive systems. In addition, previous predictive-control research only focused on current-loop controllers and not speed-loop controllers for synchronous-reluctance-drive systems [1–17]. To fill these gaps in research, this paper focuses on using a predictive speed controller and rotor-position observer to enhance the performance of a sensorless SynRM drive. A pole-assignment technique was employed to provide similar q-axis current responses. Due to smooth q-axis responses, the estimated position error was bounded in four electrical degrees. Figure 1 demonstrates the structure of the SynRM, which included an a-b-c phase stator, a rotor, and an air gap. Take the a-phase as an example. The "." means the a-phase-current flows out from this paper, and the "x" means the a-phase current flows into this paper. Compared to other AC motors, this synchronous reluctance motor uses its saliency characteristics to produce its output torque.

**Figure 1.** Synchronous-reluctance-motor (SynRM) structure.

## **2. Mathematical Model**

In a synchronous frame, the differential equations of the d-q axis currents of a SynRM can be expressed as follows.

$$\frac{d\dot{i}\_d}{dt} = \frac{1}{L\_d} [v\_d - r\_s \dot{i}\_d + \omega\_{re} L\_q \dot{i}\_q] \tag{1}$$

$$\frac{d\dot{i}\_q}{dt} = \frac{1}{L\_q} [v\_d - r\_{\rm s}i\_q - \omega v\_{\rm re} L\_d i\_d] \tag{2}$$

where *id* and *iq* are d-q axis currents, *d dt* is the differential operator, *Ld* and *Lq* are d-q axis inductances, *vd* and *vq* are d-q axis voltages, *rs* s the stator resistance, and ω*re* is the electrical-rotor speed. When the d-axis current remained constant, the reluctance torque of the SynRM was proportional to *iq*, which can be shown as follows:

$$\begin{array}{rcl} T\_{\varepsilon} &=& \frac{3}{2} \frac{p}{2} (L\_{md} - L\_{mq}) i\_d i\_{q\prime} \\ &=& K\_l i\_q \end{array} \tag{3}$$

where *Te* is the reluctance torque, and *P* is the pole number. The differential equation of the speed is

$$\frac{d\omega\_{rm}}{dt} = \frac{1}{f\_t}(T\_\varepsilon - T\_L - B\_t \omega\_{rm})\,. \tag{4}$$

where ω*m* is the mechanical-rotor speed, *Jt* is the inertia of the motor and load, *TL* is the external load, and *Bt* is the viscous frictional coefficient of the motor and load. The differential equation of the rotor position is expressed as follows:

$$\frac{d\Theta\_{rm}}{dt} = \omega\_{rm\nu} \tag{5}$$

where θ*rm* is the mechanical-rotor position. The simplified block diagram of the SynRM is shown in Figure 2. By fixing the d-axis current command and controlling the q-axis current, torque *Te* was easily controlled, and speed could then be easily adjusted.

**Figure 2.** Block diagram of simplified SynRM.

## **3. Rotor-Position-Observer Design**

In this paper, a closed-loop state estimator was derived on the basis of the d-q axis synchronous frame. From Equations (1) and (2), the following differential equation of the *d-q* axis currents could be derived as follows:

$$\frac{d\dot{q}\_s}{dt} = \dot{F}\dot{q}\_s + Gv\_s\tag{6}$$

In Equation (6), the related vectors and matrices are defined as

$$F = \begin{bmatrix} -\frac{r\_s}{L\_d} & \omega\_{r\varepsilon} \frac{L\_q}{L\_d} \\ -\omega\_{r\varepsilon} \frac{L\_d}{L\_q} & -\frac{r\_s}{L\_q} \end{bmatrix} \tag{7}$$

$$G = \begin{bmatrix} \frac{1}{L\_d} & 0\\ 0 & \frac{1}{L\_q} \end{bmatrix} \tag{8}$$

and

$$v\_s = \begin{bmatrix} v\_d \\ v\_q \end{bmatrix} \tag{9}$$

where *F* and *G* are matrices, and *is* and *vs* are current and voltage vectors. Figure 3 shows the proposed closed-loop observer that can be described as follows [11,12]:

$$\frac{d\hat{\mathbf{l}}\_{\mathbf{s}}}{dt} = F\hat{\mathbf{i}}\_{\mathbf{s}} + G\mathbf{v}\_{\mathbf{s}} - \mathcal{K}(\hat{\mathbf{i}}\_{\mathbf{s}} - \mathbf{i}\_{\mathbf{s}}) = F\hat{\mathbf{i}}\_{\mathbf{s}} + G\mathbf{v}\_{\mathbf{s}} - \widetilde{\mathbf{K}}\hat{\mathbf{i}}\_{\mathbf{s}}.\tag{10}$$

From Equation (10), one can define the related vectors and matrix as follows:

$$K = \begin{bmatrix} k\_1 & 0 \\ 0 & k\_2 \end{bmatrix} \tag{11}$$

and

$$
\hat{\mathbf{i}}\_s = \begin{bmatrix} \hat{\mathbf{i}}\_{\rm ds} \\ \hat{\mathbf{i}}\_{\rm qs} \end{bmatrix} \tag{12}
$$

where ˆ*ids* and ˆ*iqs* are the estimated d-q axis currents,*is* is the estimated current vector, and *K*, including *k*1 and *k*2, is the gain matrix of the proposed observer. From Equation (10) and Figure 3, one can understand that a different matrix *K* can be designed to determine the converging rate of estimated current vector, ˆ*is* for different speeds. Detailed analysis is shown as follows.

First, from Equations (10) and (6), one can obtain the current vector error as follows:

$$\frac{d\widetilde{\mathbf{i}\_s}}{dt} = \widetilde{F}\widetilde{\mathbf{i}\_s} - \widetilde{\mathbf{K}\widetilde{\mathbf{i}\_s}}.\tag{13}$$

From Equation (13), the equation can be rewritten as follows:

$$\frac{d\overline{\dot{\mathbf{i}}\_s}}{dt} = \mathbf{M} \overline{\dot{\mathbf{i}}\_s} \tag{14}$$

and

$$M = F - K = \begin{bmatrix} -\frac{r\_s}{L\_{\text{ds}}} & \alpha\_{\text{\ell\ell}} \frac{L\_{\text{qs}}}{L\_{\text{ds}}}\\ -\omega\_{\text{\ell\ell}} \frac{L\_{\text{ds}}}{L\_{\text{qs}}} & -\frac{r\_s}{L\_{\text{qs}}} \end{bmatrix} - \begin{bmatrix} k\_1 & 0\\ 0 & k\_2 \end{bmatrix} = \begin{bmatrix} -\frac{r\_s}{L\_{\text{ds}}} - k\_1 & \alpha\_{\text{\ell\ell}} \frac{L\_{\text{qs}}}{L\_{\text{ds}}}\\ -\omega\_{\text{\ell\ell}} \frac{L\_{\text{ds}}}{L\_{\text{qs}}} & -\frac{r\_s}{L\_{\text{qs}}} - k\_2 \end{bmatrix} \tag{15}$$

The eigenvalues of matrix *M* can be derived as follows:

$$\det(\lambda I - M) = \lambda^2 + (\frac{r\_s}{L\_{\rm qs}} + \frac{r\_s}{L\_{\rm ds}} + k\_1 + k\_2)\lambda + \frac{r\_s^2}{L\_{\rm dk}L\_{\rm qs}} + \frac{r\_s}{L\_{\rm dk}}k\_2 + \frac{r\_s}{L\_{\rm qs}}k\_1 + k\_1k\_2 + \omega\_{\rm re}^2 \tag{16}$$

$$= 0$$

The two eigenvalues α and β of the det(λ*I* − *M*) are expressed as

$$\alpha, \beta = -\frac{(\frac{r\_b}{L\_{\text{sp}}} + \frac{r\_s}{L\_{\text{ds}}} + k\_1 + k\_2)}{2} \pm \frac{\sqrt{\left(\frac{r\_s}{L\_{\text{qs}}} + \frac{r\_s}{L\_{\text{ds}}} + k\_1 + k\_2\right)^2 - 4\left(\frac{r\_s^2}{L\_{\text{ds}}L\_{\text{qs}}} + \frac{r\_s}{L\_{\text{ds}}}k\_2 + \frac{r\_s}{L\_{\text{qs}}}k\_1 + k\_1k\_2 + \omega\nu\_{\text{tr}}^2\right)}{2} \tag{17}$$

Assuming estimated current vector*is* has two poles, which are −α and −β, the following equation exists and can be described as

$$(\lambda + \alpha)(\lambda + \beta) = 0.\tag{18}$$

From Equation (18), one can obtain

$$
\lambda^2 + (\alpha + \beta)\lambda + \alpha\beta = 0.\tag{19}
$$

Comparing Equations (16) and (19), one can derive

$$(\alpha + \beta) = \frac{r\_s}{L\_{\rm qs}} + \frac{r\_s}{L\_{\rm ds}} + k\_1 + k\_2 \tag{20}$$

and

$$
\alpha \beta = \frac{r\_{\rm s}^2}{L\_{\rm ds} L\_{\rm qs}} + \frac{r\_{\rm s}}{L\_{\rm ds}} k\_2 + \frac{r\_{\rm s}}{L\_{\rm qs}} k\_1 + k\_1 k\_2 + \omega\_{\rm r\varepsilon} \tag{21}
$$

One can assign the two different negative poles,−α and −β, in Equations (20) and (21), at different operating speeds. As a result, the proposed SynRM drive system had similar dynamic responses for q-axis current errors at different operating speeds. Moreover, a Lyapunov function candidate *V* can be defined as

$$V = \widetilde{i\_s}^T \widetilde{i\_s}.\tag{22}$$

From Equation (22), *V* has a positive definite value. Because *MT* and *M* are negative definite matrices, the differential value of the Lyapunov is expressed as follows [18–20]:

$$V = \widetilde{\dot{i}\_s}^T (M^T + M)\widetilde{\dot{i}\_s} < 0 \tag{23}$$

Equation (23) shows that current error vector*is* asymptotically converges and reaches zero at a steady state. The ω*re* is defined as

$$
\widetilde{\omega}\_{\text{re}} = \widehat{\omega}\_{\text{re}} - \omega\_{\text{re}} \tag{24}
$$

where ωˆ*re* is the estimated speed, and ω*re* is the estimated speed error. From Equation (24) and [7,8], one can use*iq* to replace current vector error*is* and obtain the following equation:

$$
\hat{\omega}\_{\text{re}}(t) = K\_{\text{P}} \overline{\dot{i}\_{\text{q}}} + K\_{\text{I}} \int\_{t\_0}^{t} \overline{\dot{i}\_{\text{q}}} d\tau + \hat{\omega}\_{\text{re}}(t\_0) \tag{25}
$$

where ωˆ*re*(*t*0) is the initial estimated speed, *t*0 is the initial value of time, *t* is the value of the current time, and τ is the variable. Finally, estimated rotor position θˆ*re*(*t*) is

$$
\hat{\theta}\_{n\epsilon}(t) = \int\_{t\_0}^{t} \hat{\omega}\_{n\epsilon} d\tau + \hat{\theta}\_{n\epsilon}(t\_0) \tag{26}
$$

After digitizing Equations (25) and (26), one can obtain estimated electrical-rotor speed ωˆ*re*(*m*) and rotor position θˆ*re*(*m*) as follows:

$$
\hat{\omega}\_{n\epsilon}(m) = \mathbf{K}\_{\boldsymbol{P}} \overline{\hat{i}\_{\boldsymbol{q}}}(m) + \mathbf{K}\_{\boldsymbol{I}} \sum\_{n=1}^{m} \overline{\hat{i}\_{\boldsymbol{q}}}(n) \Delta t \tag{27}
$$

and

$$\hat{\theta}\_{\text{re}}(m) = \sum\_{n=1}^{m} \hat{\omega}\_{\text{re}}(n)\Delta t + \hat{\theta}\_{\text{re}}(0),\tag{28}$$

where θˆ*re*(0) is the initial estimated position and *n* is the iterative number. The proposed position observer is illustrated in Figure 3. First, the current vector observer of the ˆ*is* was constructed. Next, the error of the current vector observer,*is*, was computed. After that, motor-speed observer ωˆ*re* was obtained via proportional-integral (PI) controller. Finally, motor-position observer θˆ*re* was computed via an integral controller.

**Figure 3.** Proposed position observer.

## **4. Predictive-Controller Design**

Predictive control is a new control technique that has had strong and widespread impact on industrial applications [20]. It has been used in process control, transportation control, and robotic control for more than three decades. Because of the powerful computation ability of digital-signal processors (DSPs), predictive control has been widely utilized for power converters and motor drives [21,22]. In our paper, predictive control was used for the first time for a sensorless SynRM drive.

The continuous-time dynamic equation of the speed of the SynRM without an external load is

$$\frac{d\omega\_{rm}}{dt} = \frac{1}{f\_t}(T\_\varepsilon - B\_t \omega\_{rm})\tag{29}$$

Combining Equations (3) and (29), one can obtain:

$$\frac{\omega\_{rm}(s)}{i\_q(s)} = \mathcal{G}\_\mathbf{c}(s) = \frac{\mathcal{K}\_\mathbf{t}}{J\_\mathbf{t}s + \mathcal{B}\_\mathbf{t}}\,'\tag{30}$$

where ω*rm*(*s*) is mechanical-motor speed, *Gc*(*s*) is the transfer function of the uncontrolled plant, and *Jt* and *Bt* are the inertia of the motor and the viscous frictional coefficient of the motor, respectively. After inserting a zero-order-hold device in front of transfer function *Gc*(*s*), transfer function *Gd*(*s*) can be rewritten as

$$\mathbf{G}\_d(\mathbf{s}) = H(\mathbf{s}) \mathbf{G}\_c(\mathbf{s}) = \frac{1 - e^{-sT\_z}}{s} \cdot \frac{\mathbf{K}\_t / I\_t}{s + (\mathbf{B}\_t / I\_t)}. \tag{31}$$

By converting the continuous into the discrete domain, discrete transfer function *Gd*(*z*) is

$$\begin{array}{l} G\_d(z) = \frac{\omega\_r(z)}{i\_q(z)}\\ = \frac{K}{B\_t} \cdot \frac{(1 - e^{-\frac{T}{T\_t}T\_z})z^{-1}}{(1 - e^{-\frac{B\_t}{T\_t}T\_z}z^{-1})} \end{array} \tag{32}$$

From Equation (32), one can obtain the discrete-time dynamic speed equation as

$$
\omega\_{rm}(m+1) = a\omega\_{rm}(m) + bi\_q(m), \tag{33}
$$

where m is the sampling step of the digital control system. In Equation (33), parameters are shown as

$$a = e^{-\frac{B\_t}{\tau\_t}T\_z} \tag{34}$$

and

$$b = \frac{K\_t}{B\_t} \Big| 1 - e^{-\frac{B\_t}{T\_t} T\_z} \Big| \,\tag{35}$$

To simplify the computations of the DSP in this paper, the predictive horizon and control horizon were selected as 1. Performance index *Jp*(*m*) was defined in previous research as follows [21,22]:

$$J\_p(m) = q[\omega\_{rm}(m+1) - \omega\_{rm}^\*(m+1)]^2 + \left[\Delta i\_q(m)\right]^2,\tag{36}$$

where *q* is a weighting factor. To prevent the positive speed error from canceling the negative speed error, or the positive control input from canceling the negative control input, we selected the square of the speed error and control input as the performance index. By taking the derivative of *Jp*(*m*) to Δ*iq*(*m*), one can obtain the following equation:

$$\frac{\partial l\_p(m)}{\partial \Delta i\_q(m)} = 2qb[\omega\_{rm}(m+1) - \omega\_{rm}^\*(m+1)] + 2\left[\Delta i\_q(m)\right] = 0\tag{37}$$

From Equation (37), the q-axis difference is obtained as

$$
\Delta i\_q(m) = \frac{qb}{qb^2 + 1} \left[ \omega\_{rm}^\*(m+1) - a\omega\_{rm}(m) - b i\_q(m-1) \right] \tag{38}
$$

Ultimately, the optimal q-axis current is derived as follows:

$$i\_q(m) = \Delta i\_q(m) + i\_q(m-1) \tag{39}$$

From Equations (38) and (39), one can construct a block diagram as demonstrated in Figure 4.

**Figure 4.** Block diagram of predictive speed control.

### **5. Implementation**

To evaluate the theoretical analysis and simulated results, a SynRM sensorless drive system was implemented. The synchronous reluctance motor, type P56H5012, manufactured in the United States, is shown in Figure 5a. Figure 5b demonstrates the implemented hardware circuit board. The circuit was a three-phase inverter using six IGBTs, type PS 21265-P/AP, as its power devices. The power devices were used for providing voltages and currents to the motor. The inverter employed space-vector modulation with a 10 kHz switching frequency. In addition, the circuit consisted of voltage- and current-sensing circuits, 12-bit A/D converters, which converted analog signals to digital signals, an interfacing circuit, and a DSP that was a control center. Two Hall effect sensors, type LTS25-NP, manufactured in Switzerland, were used as current sensors. A protecting circuit was utilized to prevent an overload of the SynRM drive system. All hardware circuits were designed by us. The block diagram of the implemented drive system is demonstrated in Figure 5c. First, the voltages and currents were read by the DSP via A/D converters. Next, the DSP executed the algorithms of the position observer to obtain the estimated rotor position and speed. The DSP compared the speed command and the estimated speed to compute the q-axis command. After that, the DSP used the PI control to obtain the voltage commands. Then, the DSP used the d-q to an a-b-c axis for coordinate transformation and to compute the three-phase voltage commands. Finally, the DSP sent triggering signals *T*1 to *T*3 to the inverter.

**Figure 5.** Implemented system. (**a**) Motor; (**b**) circuits; (**c**) block diagram.

A DSP, type TMS-320F-28335 [23], manufactured in the USA, was used as a computation tool to execute the position-estimating algorithm and predictive-control algorithm. The sampling time of the current-loop was 100 μ*s*, and the speed loop was 1 ms. The d-axis current command was set at 0.5 A to provide the required flux linkage. A larger d-axis current command increased the output torque; however, it also created flux saturation. The q-axis was also adjusted to obtain a quick torque response. Then, the proposed position observer was used to compute the rotor position.

A dynamometer, type TM-5MT(DS), made by Neiwa Company, was used for adding an external load to the SynRM. A power supply was utilized to provide 320 V to the inverter. The motor used in this paper was a three-phase, four-pole, 220 V, 560 W, 3.4 A, 60 Hz, 1800 rpm, Y-connected SynRM. Its parameters were as follows:

Stator resistance *rs* was 2 Ω; d-axis inductance *Ld* was 148 mH; q-axis inductance *Lq* was 67.2 mH; interia, *Jt* was 0.0024 kg.m2; and friction coefficient *Bt* was 0.0015 N.m/(rad/sec).

#### **6. Experiment Results**

To evaluate the proposed methods, several results are illustrated in this paper. Input DC voltage was 320 V, the sampling time of the speed-loop control was 1 ms, and the current loop was 100 μ*s*. The predictive speed control chose one step as the prediction and control horizons. PI speed controller parameters were determined by pole assignment, which were near *P*1 = −2 + *j*0.5 and *P*2 = −2 − *j*0.5. After that, one could obtain *KP* = 45 and *KI* = 4, and the d-axis current command was 0.5 A. The parameters of the PI controller of the current loop were determined by pole assignment, which uses a similar method as the PI controller of the speed loop.

Figure 6a demonstrates simulated line voltage *vab*. The line voltage was a pulse-width modulation (PWM) modulated voltage waveform with a 10 kHz switching frequency. Figure 6b shows measured line–line voltage *vab*. Both simulated and measured voltages were very close. In addition, the line–line voltage had positive and negative voltages sequentially because the space-vector pulse-width modulation (SVPWM) was applied. Figure 7a displays the simulated a-phase current, Figure 7b displays the measured a-phase current, and both currents were near-sinusoidal waveforms. Figure 7c displays the measured a-phase current harmonics. Figure 8a illustrates the comparison of the simulated results of the real and estimated rotor positions. Figure 8b illustrates the comparison of the measured results of the rotor positions. Figure 8c illustrates the measured estimated position error, which was between 0 and 4 electrical degrees. The estimated position error had a nonrepetitive waveform due to the influence of the PI controllers, which is shown in Figure 3. Figure 9a,b shows the q-axis current errors at operating speeds of 1100 and 1800 rpm, and the q-axis current errors had similar dynamic responses. The main reason was that similar poles were assigned for different speeds in this paper. In addition, the q-axis estimated current,*iq*, could reach zero at both operating speeds. Figure 10a,b illustrates the simulated and measured lowest operating speeds, which were 30 rpm. The SynRM drive system had obvious torque pulsations when it was operated below 30 rpm. Figure 11a,b illustrates the highest speeds at 1800 rpm, and they had similar transient responses. Figure 12a shows the simulated disturbance responses at 500 rpm and 2 N.m, and Figure 12b shows the measured load-disturbance responses in the same situation. The simulated and measured results were similar. The predictive speed controller performed better, including quicker recovery time and lower speed drop, than the PI controller. Figure 13a demonstrates the simulated responses, which reversed repeatedly between 200 and −200 rpm. Figure 13b demonstrates the measured responses under the same circumstances, and they had similar responses. However, the PI controller had an obvious time delay due to the influence of the integral parameter. Figure 14a demonstrates the simulated results of the triangular tracking responses, and Figure 14b demonstrates the measured results of the triangular tracking responses. The speed of the predictive controller could follow the speed command well. The PI controller, however, caused an obvious phase leg between its commands and speed responses. By choosing proper PI value coefficients, error in waveforms for Figures 12–14 could be minimized. However, the tuning of PI controllers relied on trial and error. In addition, each operation point required one parameter set of PI controller. Once the operation point changed, the PI controller could not work well. In this paper, the predictive controller provides a systemic design procedure. By suitably choosing weighting factor *q,* a systematic controller, which is illustrated in Equations (38) and (39), could be uniquely obtained, and the predictive controller parameters were related to the motor parameters, sampling time, and feedback speed.

**Figure 6.** Line–line voltage of motor. (**a**) Simulated; (**b**) measured.

**Figure 7.** Phase currents. (**a**) Simulated; (**b**) measured; (**c**) harmonics.

**Figure 8.** Responses at 500 rpm. (**a**) Simulated position; (**b**)measured position; (**c**)measured estimated error.

**Figure 9.** Measured dynamic q-axis current errors at (**a**) 1100 and (**b**) 1800 rpm.

**Figure 10.** *Cont.*

**Figure 10.** Lowest step-input speed at 30 rpm. (**a**) Simulated; (**b**) measured.

**Figure 11.** Highest step-input speed responses at 1800 rpm. (**a**) Simulated; (**b**) measured.

**Figure 12.** 2 N.m disturbance responses (**a**) simulated (**b**) measured.

**Figure 13.** *Cont.*

**Figure 13.** Square-wave tracking responses using different controllers. (**a**) Simulated; (**b**) measured.

**Figure 14.** Triangular tracking responses using different controllers. (**a**) Simulated; (**b**) measured.

## **7. Conclusions**

In this study, a position observer and a predictive controller for a SynRM were simulated and implemented. On the basis of the pole-assignment method, the error dynamics of the q-axis currents had similar poles and close dynamic responses. Both q-axis current errors and rotor-position observer errors could be converged in 2 s. In addition, the measured position errors of the position observer could be within four electrical degrees. Compared to previous research [11], which required a full-order observer to obtain the motor position, this paper provided a simplified method with satisfactory performance, including quick converging time and small steady-state estimation errors. In addition, the DSP computation time of the position observer in this paper was shorter than that in previous research. Furthermore, this paper proposed an original idea that combines the advanced position observer and speed-loop predictive controller for a SynRM drive system.

Experiment results demonstrated that the adjustable range of the SynRM was between 30 and 1800 rpm. Furthermore, the measured results demonstrated that the proposed rotor observer had errors below four electrical degrees, and the predictive controller had better dynamic responses than those of PI controllers. Most of the functions of the predictive controller and position observer were implemented by the DSP; therefore, the proposed synRM drive system uses simple hardware circuits that can easily be applied in industry.

**Author Contributions:** Conceptualization: T.-H.L. Methodology: T.-H.L. and J.–Y.C.; Software: J.-Y.C.; Validation: T.-H.L., J.-Y.C., M.S.M. and S.A.; Investigation: T.-H.L. and J.-Y.C.; Resource: T.-H.L.; Formal Analysis: T.-H.L. and J.–Y.C.; Data curation: J.–Y.C., M.S.M., and S.A.; Writing-original English draft preparation: T.-H.L. and J.–Y.C.; Writing-review & editing: J.–Y.C, M.S.M. and S.A.; Visualization: M.S.M. and S.A.; Supervision: T.-H.L.; Funding Acquisition: T.-H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The paper was supported by Ministry of Science and Technology, Taiwan under grants MOST 108-2221-E-011-085 and -086.

**Conflicts of Interest:** The authors declare no conflicts of interest

## **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**1. Introduction**

Many authors have studied disordered eating behaviors in individuals with overweight and obesity through the lens of addictions. Indeed, they found similarities between addictive behaviors and compulsive overeating, namely behavioural and neurobiological [1–4]. These similarities led to the concept of food addiction (FA) [5]. Although there is no universal definition of FA, it is known as an excessive and abnormal intake of highly palatable foods [2,3]. Since then, the study of FA has multiplied, using the Yale Food Addiction Scale (YFAS), an instrument that consists in an adaptation of the diagnostic criteria for substance dependence, to food [2,6].

In order to deepen our understanding of the development of an addictive-like pattern of eating, it is necessary to target potential risk factors of this condition. A current hypothesis on the development of obesity suggests that deficits in executive functioning could contribute to problematic eating behaviors, attitudes towards food, and weight gain [7]. Similarly, deficits in executive functioning are considered as a central component in the development of addictive behaviors [8]. Executive function is an

umbrella term including a series of cognitive processes needed to adapt to new situations, allowing to behave appropriately to the context and to produce future-oriented behaviors [9,10]. It includes central functions, like inhibition, working memory, and cognitive flexibility, which underlie many higher-order functions (e.g., reasoning, problem solving, and planning) [11,12]. Recently, a few studies have focused on the cognitive factors that could be involved in FA, mainly executive functions.

Up until now, some authors examined the cognitive factors underlying FA in adults, using neuropsychological tasks and questionnaires. In order to do so, they all compared groups of individuals endorsing higher levels of FA symptoms to individuals who endorsed fewer or none. Overall, studies conducted within adults from the general population revealed that individuals with more FA symptoms, showed faster reaction times in response to food cues among neutral pictures [13], as well as attentional biases to unhealthy food cues following a sad mood induction [14]; a poorer performance monitoring, and more difficulties to detect and process errors during the task [15]. Only one study failed to find differences between groups, on a specific task assessing inhibitory control [16]. Regarding studies in adults suffering from obesity, they essentially showed that FA symptoms were accompanied with a poorer performance on decision-making, significant deficits in sustained attention [17]; and more difficulties to detect and process errors, as well as more self-reported metacognitive difficulties [18]. Nevertheless, a very recent study failed to show specific neuropsychological impairments or difficulties in participants with FA [19]. Taken together, most of the previous studies tend to reveal that cognitive difficulties could be associated to FA symptoms. Thus, it is possible to think that these cognitive difficulties could represent risk factors and contribute to the development of FA.

Adolescence is also considered as a high-risk period to develop addictive-like behaviors, regarding the combination between less efficient emotional regulation processes and the immature impulse control, that characterize it [20]. A range of studies aimed to assess the prevalence of FA in adolescents, resulting in rates of 2 to 16% in the general population [21–25]; almost 17% in psychiatric inpatients and 10 to 38% in adolescents with overweight or obesity engaged in a weight-loss program [26–28]. These rates were similar to those observed in studies with adults [29]. Since then, a growing body of literature on the study of FA in adolescents has been observed, offering a broader understanding of this condition. For example, FA symptoms have already been found as highly correlated with more disrupted eating behaviors, as well as more impulsivity, depressive and anxiety symptoms in this population [21,22,25,26,30,31].

A recent large-scale study in adolescents aged from 12 to 18 years also showed that those with more FA symptoms also reported significantly more executive functioning difficulties on a self-reported scale (BRIEF). More precisely, they reported significantly more difficulties on both indexes, assessing behavioural regulation (inhibition, shifting, emotional control, monitoring) and metacognitive (working memory, planning/organize, organization of materials, and task completion) difficulties. It indicates that they reported more self-regulatory weakness in their everyday life, in comparison to adolescents with lower levels of FA symptoms [31]. So far, only one study has included neuropsychological tasks to assess executive functions in adolescents, according to FA symptoms. Hardee and her colleagues examined the relationships between FA symptoms and cerebral activity during an inhibitory task (Go/No-go), in a sample of adolescents [32]. Their results showed that, in participants with a higher level of FA symptoms, a hypoactivation in some brain areas was observed during the inhibitory phase of the task. They suggested that it could be associated to a poorer inhibitory control. However, no significant difference was observed when they were compared on their performances at the task. More studies are needed according to FA and cognitive functioning in adolescents, in order to identify if cognitive mechanisms are involved in addictive-like eating.

The main aim of the present study was to examine cognitive factors associated to FA symptoms in adolescents. More precisely, the objective was to assess sustained attention, as well as executive functions with neuropsychological tasks, in adolescents with a significant level of FA symptoms (two symptoms and more), and to compare them with a control group. Since very few studies have examined cognitive factors related to FA symptoms in adolescents, no formal hypothesis will be proposed.


**7. Poor prognostic factors**

Presence of macular detachment, vitreopapillary traction, thinned out retina, sclerosed vessels, pale disc, neovascularisation of iris or NVG, macular ischemia, poor initial VA (<5/200) and loss of photoreceptor layers, ELM and ellipsoid zone on OCT are poor visual prognostic factors despite a successful anatomical outcome [27].

For a successful anatomic outcome, it is very important to understand the anatomy and surgical principles in a case of diabetic vitrectomy. One has to counsel the patient that visual outcomes may not always correlate with anatomical outcomes. Importance of good systemic control should always be emphasised.


**5. Discussion**

In spring, summer, and autumn, heat energy was transferred into Ningwu ice cave through air and wall rock by thermal conduction, resulting in increasing the air temperature of the ice cave limitedly, because the efficiency of conduction is relatively low. But in winter, the air temperature of the ice cave decreases promptly, caused by air natural thermal convection. The water-ice phase change buffers the energy exchange. Considering these physical mechanisms, the modeling results present that (1) starting from a normal ground temperature distribution, a year-round ice deposit will be produced in the cave within a decade, about 5 years (**Figure 6a**), and the air temperature of the cave will drop gradually for more than a century, and also that (2) the air temperature of the ice cave will finally reach a quasi-stable cyclic state and will vary within a certain range (less than 1.0°C, from −3.9 to −2.9°C). At this stage, the annual total heat energy conducted into the cave and the heat energy removed from the cave by air natural convection are balanced.

Installing an airtight door at a cave entrance is what the Wudalianchi National Geological Park has done to "prevent" the ice deposit from melting every night during the tourist season and the entire winter. When the cave airtight door is closed, in fact, the air convection in winter is blocked. Consequently, cold air cannot enter the cave and cannot remove heat energy from the cave. Accumulation of heat energy conducted by air and rock will eventually cause the ice deposit to melt in Wudalianchi cave. Our modeling results present that it takes less than 40 years to completely melt the whole ice deposit in the cave. This means that Ningwu ice cave is probably not undergoing melting of the ice deposit at present. This study

also suggests that scientific management is significant for sustainable application of natural resources. Otherwise, well-meaning acts such as installing a door to completely seal the entrance will actually destroy the ice cave in a few decades.

We expect that no airtight door will be fixed at Zibaishan ice cave entrance and few people enter the ice cave before comprehensive and detailed studies, avoiding affecting its natural conditions.


**2. Fabrication of suspended 2D materials**

Generally, to measure the mechanical properties of 2D materials experimentally, a suspended structure needs to be fabricated. There are mainly two ways of fabricating a structure sus‐ pended with 2D materials. One approach is to transfer the 2D materials directly onto the prepatterned substrates [3, 7, 18–23]. The other approach is to transfer the 2D materials on the substrate first and then remove the sacrificial layer beneath the transferred 2D materials [24– 31]. **Figure 1(a)** and **(b)** shows the two schematics of the first fabrication approach, while **Figure 1(c)** shows the second approach.

In the first approach, taking SiO2/Si substrate for example, holes or cavities are patterned in the SiO2 layer with lithography and wet/dry etching techniques, as shown in the first step of **Figure 1(a)**. Then, 2D materials are transferred onto the prepatterned SiO2/Si substrate by the exfoliation method, forming suspended 2D materials structure (second step of **Figure 1(a)**). With this method, the suspended structure can be fabricated theoretically on various kinds of substrates. However, with the conventional mechanical exfoliation method, since the 2D Mechanical Properties and Applications of Two-Dimensional Materials http://dx.doi.org/10.5772/104209 221

**1. Introduction**

advanced nanoelectromechanical systems (NEMS).

220 Two-dimensional Materials - Synthesis, Characterization and Potential Applications

**2. Fabrication of suspended 2D materials**

**1(c)** shows the second approach.

mechanical as well as mechanoelectric transduction properties.

Since the first successful preparation of graphene by mechanical exfoliation from graphite crystals in 2004 [1], two-dimensional (2D) materials have attracted dramatic attention due to their extraordinary physical properties (ultralow weight, high Young's modulus, and high strength) [2–7] and outstanding electrical properties [1] compared with conventional bulk materials. In the past few years, graphene, with the highest measured Young's modulus (~1 TPa) [3], is the most widely studied 2D material. Studies have shown that graphene filled into the polymer matrices can reinforce the mechanical properties of the composites significantly [8]. However, pristine graphene does not have a bandgap [9], which limits its applications in certain fields requiring a semiconducting material. As a potential substitute material of graphene, the transition metal dichalcogenides (TMDCs, e.g., MoS2 and WSe2) and black phosphorus (BP) with an intrinsic bandgap [10, 11] possess the potential for electronics and optoelectronics applications [12–15] and open a new field for 2D materials study. Moreover, the existence of piezoelectricity and the more sensitive piezoresistive effect in TMDCs compared with graphene under mechanical deformation make them more interesting for innovative applications including tactile strain sensors [16], nanogenerators [17], and

In this review, first, we introduce the common approaches used for fabricating suspended 2D material structures. Then, characterization methods for extracting the in-plane and out-ofplane mechanical properties of 2D materials are presented. A summary of the experimental results is given. In the last section, we introduce the electrical output change of 2D materials induced by mechanical deformation—piezoresistive and piezoelectric effects. In addition, we provide some example applications of 2D materials that make use of their extraordinary

Generally, to measure the mechanical properties of 2D materials experimentally, a suspended structure needs to be fabricated. There are mainly two ways of fabricating a structure sus‐ pended with 2D materials. One approach is to transfer the 2D materials directly onto the prepatterned substrates [3, 7, 18–23]. The other approach is to transfer the 2D materials on the substrate first and then remove the sacrificial layer beneath the transferred 2D materials [24– 31]. **Figure 1(a)** and **(b)** shows the two schematics of the first fabrication approach, while **Figure**

In the first approach, taking SiO2/Si substrate for example, holes or cavities are patterned in the SiO2 layer with lithography and wet/dry etching techniques, as shown in the first step of **Figure 1(a)**. Then, 2D materials are transferred onto the prepatterned SiO2/Si substrate by the exfoliation method, forming suspended 2D materials structure (second step of **Figure 1(a)**). With this method, the suspended structure can be fabricated theoretically on various kinds of substrates. However, with the conventional mechanical exfoliation method, since the 2D

**Figure 1.** Schematic of two representative approaches of suspended structures fabrication. (a, b) Transfer of the 2D ma‐ terials directly onto the prepatterned substrates; (c) Suspension of the 2D materials by etching the sacrificial layer un‐ derneath.

material sheets distribute randomly onto the substrates, the 2D material sheets may not cover necessarily the specific hole in the SiO2 layers, bringing the challenge of improving the production rate. Normally, two methods can be employed to address this problem. One method is to fabricate repeatable patterns (e.g., hole arrays) in the substrate [3, 7, 22] to enhance the probability of producing the suspended structures, as shown in **Figure 2(a)**. Another method is to employ a modified exfoliation method (transfer printing/stamping [20, 32]), with a transparent viscoelastic material as the carrier for the 2D materials, which enables a precise transfer of 2D materials to the desirable location [19, 21].

**Figure 2.** (a) Optical image of graphene suspended over hole arrays [35]. (b) SEM images of a MoS2 bridge supported on Au electrodes [23]. (c) SEM image of suspended graphene stripe under Au electrodes [25].

In most cases, electrical signal needs to be applied to the suspended 2D materials; therefore, metal contacts need to be made to contact the suspended 2D materials, as shown in the third step of **Figure 1(a)**. In order to avoid the common wet process inducing the collapse of 2D membranes, the shadow mask method [27] instead of lithography should be used. In addition, by combining conventional lithography, lift-off of deposited metal and transfer printing/ stamping of 2D materials, one can realize the fabrication of suspended 2D materials supported on patterned metal contacts, as depicted in **Figures 1(b)** and **2(b)**.

The schematic of the second approach of fabricating a suspended 2D material structure is presented in **Figure 1(c)**. After the transfer of 2D material onto substrate and metal contacts deposition, the 2D material is suspended by etching the underlying sacrificial layer with the predeposited metal contacts acting as the etching mask and clamping of the 2D materials. In this method, SiO2 has been used widely as the sacrificial layer, which is removed commonly by anisotropic wet etching with buffered hydrofluoric acid (BOE). In order to prevent the 2D materials from collapsing due to the surface tension between the 2D materials and BOE, the drying process is operated normally in a critical point dryer (CPD) [25, 29, 30]. In addition, the etching time needs to be adjusted carefully to control the undercut of SiO2 beneath the metal contacts to prevent the metal from collapsing. Although a more complex structure can be fabricated with this approach, as shown in **Figure 2(b)**, the wet etching involved in the fabrication process may introduce some contamination in the 2D materials, which may degrade the performance of the devices. Moreover, the acids used in this process are not suitable for some 2D materials, such as Bi2Se3 [33] and metals (e.g., Ti, Al). Thus, to avoid acid etching in the fabrication process, photoresist can be used as the sacrificial layer instead of SiO2 which can be removed with photoresist developers [33, 34].


**3. Chronic infection of** *P. aeruginosa*

Upon infection, the host decreases iron levels in the blood [99]; this iron deficiency regulates a great number of bacterial virulent genes like alginate, the most relevant virulence factor, for *P. aeruginosa* survival [100]. In the lung, iron deficiency turns on AlgQ, the bacterial biofilm production gene, also known as AlgR2 [101, 102], under the Pfr A regulation that assists to the formation of two kinds of cytoplasmic aggregates: large vacuole-like bodies and smaller granules containing iron in association with oxygen or phosphate, very likely polyPi [103]. This leads to the RSCV type of *P. aeruginosa.* Under these conditions, the bacteria secrete alginate, a linear polysaccharide of d-mannuronic acid linked to l-guluronic acid [104].

The first gene described for the biosynthesis of alginate was the phosphomannose isomerase and GDP-mannose dehydrogenase (AlgD) that catalyze the conversion of GDP-mannose to GDP-mannuronic acid [105]. Upon oxygen limitation, *P. aeruginosa* utilizes nitrate or arginine as electron acceptors, via the succinylarginine pathway [106, 107]. The AlgD expression is tightly regulated by several environmental sources including nitrogen, O2, Pi, NaCl, etc. Although the regulation of AlgD has been extensively studied, it is not completely understood, and eradication of chronic infection greatly depends on control of alginate production.

Several authors have studied the AlgD regulation, **Figure 6** shows a 20-years breach in the finding of AlgD regulators. More positive regulators have been identified, such as AlgR that is upregulated by NaCl and also by the nitrogen source [108]. AlgD is also under the same promoter than PLC, which is sensitive to the nitrogen source [109] that regulates the anaerobiosis genes. These genes detect the ratio

**Figure 6.** *Regulators of AlgD in alginate production. Negative and positive regulators found up-today.*

of glutamine to 2-ketoglutarate, which is dependent on O2 availability [108, 110]. Another positive regulator of AlgD is AlgU [111], but the only negative regulator known is the RpoN, a sigma factor, that regulates nitrogen metabolism. RpoN is increased by disruption of pyrimidine synthesis and decreased by the supplementation with uracil, showing that a high level of RpoN, in the RSCV form, may block the alginate biosynthesis [110, 112].

Studies on the biosynthetic pathway of biofilms show that chelation of iron by lactoferrin destabilizes the bacterial membrane [113], which combined with xylitol hinders the ability of the bacteria to respond to iron deficiency [101], showing some promise for CF treatment.

### **4. Conclusions**

*P. aeruginosa* is a relevant pathogen given its widespread prevalence across different organs. The latent menace it poses for inpatients is a liability for institutions. For this, and the negative prognosis that *P. aeruginosa* infections in CF patients has, it is one of the subjects more researched for the last 40 years. The efforts have resulted in understanding the process of invasion, immune response, and bacterial tactics to achieve chronic infection. The complexity of the metabolic changes caused by the contact between the host and the bacteria is so extensive that the selection of variables for in vitro studies is difficult since the production of biofilm by *P. aeruginosa* seems to be regulated by everything, O2, N2, Fe2+, Pi, and NaCl. This multiregulatory network is still a puzzle to be resolved.

Scientists agree that suppression of alginate production is vital to treat CF patients, but in 40 years of research, little has been achieved in suppressing its production *in vivo.*

## **5. Perspectives**

The advancement of techniques with high output data like microarrays, proteomes, and mass spectrometry are closing the breach among the different approaches that have been used to tackle *P. aeruginosa* infections. For example, mass-spectrometry has verified through metabolite detection the metabolic pathways studied by molecular biologists and enzymologists. The integration of these studies with the physicians is needed to assess the areas that show more promises to control alginate production and *P. aeruginosa* eradication after it became a chronic infection.

**41**

**Author details**

Sandra Grumelli

Córdoba, Argentina

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 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,

Independent Researcher, Centro de Investigaciones en Medicina Respiratoria,

*Host-Pathogen Interaction in the Lung of Patients Infected with Pseudomonas aeruginosa*

*DOI: http://dx.doi.org/10.5772/intechopen.84657*

*Host-Pathogen Interaction in the Lung of Patients Infected with Pseudomonas aeruginosa DOI: http://dx.doi.org/10.5772/intechopen.84657*


**4. Discussion**

To evaluate the reliability of the WOAPSO, the proposed hybrid algorithm is compared with six well established metaheuristics algorithms, i.e., GSA [56], SCA [57], GWO [58], PSO [59], WOA [37], PSOGSA [60] as well as other algorithms existing in the literature. It is observed that the estimated parameters based on the optimization process are highly consistent with the experimental data for SDM, DDM, and SS2018P PV module.

For SDM, the hybrid WOAPSO algorithm generates the lowest RMSE values (7.1700 × 10 −4 ) compared to the GSA, SCA, GWO, PSO, and WOA, PSOGSA algorithms (Table 2). The RMSE of the proposed WOAPSO algorithm is also compared with previously studied algorithms (Table S2). It is noted that the hybrid WOAPSO algorithm provides the lowest RMSE values than that of others. Table S3 represents the absolute IAE for SDM analysis. The magnitude of IAE for different observations is less than 0.0018 (Table S3), which indicates that the parameters optimized by the WOAPSO are very precise.

7.1700 × 10 −4 In the case of DDM analysis, the MLBSA, EHHO, IJAYA, and GOTLBO algorithms produce the best value of RMSE (Table S4). However, WOAPSO generates the third-best value of RMSE (9.8412 × 10 −4 ), which is very close to MLBSA (9.8249 × 10 −4 ), EHHO (9.8360 × 10 −4 ), IJAYA (9.8293 × 10 −4 ), and GOTLBO (9.8317 × 10 −4 ). However, the computational cost in terms of function evaluation is 1/3 of MLBSA, EHHO, IJAYA, and GOTLBO. Moreover, WOAPSO shows superiority over other algorithms in terms of RMSE (Table 3). For DDM, the magnitude of IAE for different observations is depicted in Table S5. It is noticed that the IAE values are less than 0.0097, which demonstrates the accuracy of optimized parameters produced by WOAPSO.

9.8412 × 10 −4 9.8249 × 10 −4 For the SS2018P PV module, the hybrid WOAPSO algorithm produces the lowest RMSE values compared to the GSA, SCA, GWO, PSO, WOA, and PSOGSA algorithms. The IAE magnitudes for different observations (at 1000 W/m2 ) are less than 0.0018 (Table S6). More importantly, the computational time for WOAPSO is less than other algorithms (Table 4). The average execution time of each algorithm on the three PV models is calculated and illustrated in Figure 9. The WOAPSO algorithm requires less time (about 26.1 s)

−4 9.8317 × 10

−4

−4 9.8293 × 10

9.8360 × 10

than GWO, PSO, SCA, WOA, and PSOGSA, while GSA has the worst execution time of approximately 52 s.

**Figure 9.** Comparison of the execution time.

Furthermore, the Friedman ranking test is also performed for all algorithms and depicted in Table 6. Table 6 shows that the proposed WOAPSO algorithm significantly outperforms the GSA, SCA, GWO, PSO, WOA, PSOGSA algorithms for all three models, i.e., single-diode, double-diode, and PV module models.


**3.1 Liver toxicity in dog**

In a dog toleration study at the lowest dose tested (10 mg/kg), slight, acute central-lobular and portal inflammation with individual hepatocyte necrosis was observed. Therefore, no NOAEL could be assigned in this study which markedly complicated the further development of this drug candidate.


**1. Introduction**

Mycotoxins are toxic, secondary metabolites produced by certain filamentous fungi, mainly belonging to the genera of *Fusarium, Aspergillus, Penicillium* and *Alternaria.* Contaminating food and feed pre- and postharvest, mycotoxins pose a potential risk to food safety and thus to human and animal health. After ingestion, the intestinal tract represents the first barrier of the host against food contaminants such as mycotoxins and is therefore the first line for many defense mechanisms. In order to protect the entire organism from the entrance of unwanted solutes, microorganisms and luminal antigens, a proper function of the intestinal barrier and its innate immune response is crucial. As a defense mechanism against external stressors, intestinal epithelial cells are able to secrete cytokines and chemokines, including transforming growth factorα (TGFα), interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-8 (IL-8) or interleukin-10 (IL-10) in order to activate the immune response and to

recruit respective immune cells [1]. However, if not controlled properly, excessive immune response and cytokine release can lead to chronic intestinal inflammation and contribute to the progression of inflammatory disorders, such as inflammatory bowel diseases (IBDs) [2].

One of the most prevalent mycotoxins in temperate climate regions is the trichothecene mycotoxin deoxynivalenol (DON, vomitoxin, Figure 1A), which frequently contaminates grain- and cereal-based products [3–5]. Human biomonitoring studies revealed that, due to its ubiquitous occurrence, consumers are chronically exposed to low levels of DON [6–8]. The epoxide moiety at position C12-C13 is considered a key factor in the toxin's main mechanism of action, the inhibition of eukaryotic protein synthesis. By binding to the 60S ribosomal subunit, DON causes a ribotoxic stress response [9,10], resulting in the activation of mitogen-activated protein kinases (MAPKs) and furthermore in the induction of apoptosis and inflammation [11]. Depending on dose, exposure frequency and duration, DON can induce both immunostimulatory and immunosuppressive e ffects [11–13]. Low dose exposure caused transcriptionally and post-transcriptionally upregulation of immunostimulating cytokines and chemokines, whereas high dose exposure was shown to promote apoptosis with concomitant immunosuppressive e ffects [11,13]. Modulatory e ffects of DON on cytokine production in intestinal tissue and intestinal epithelial cancer cell lines have been studied and reviewed extensively during the last decades [11,14,15]. DON was reported to induce the secretion of IL-8 via a nuclear factor "kappa-light-chain-enhancer of activated B cells"-mediated (NF-κB) mechanism in various human intestinal epithelial cells such as Caco-2 or HT-29 [16–18]. When mimicking an inflamed intestinal epithelium, co-exposure experiments with various pro-inflammatory stimuli including interleukin-1β (IL-1β), the tumor necrosis factorα (TNFα) or lipopolysaccharide (LPS) resulted in additive and synergistic e ffects regarding cytokine secretion and NF-κB activation [16]. However, the e ffects of DON on the inflammatory response have so far only been reported in cancer cell models, but not in noncancer intestinal epithelial cells.

**Figure 1.** Chemical structures of the investigated *Fusarium* secondary metabolites: ( **A**) deoxynivalenol (DON), (**B**) type A trichothecene (NX-3) and ( **C**) aurofusarin (AURO).

Moreover, no toxicological characterization of the recently discovered type A trichothecene NX-3 (Figure 1B) regarding immunomodulatory e ffects has been performed to date. Structurally identified by Varga et al. [19] as an analogue of DON lacking the carbonyl moiety at the C8-position, this toxin was found to be produced by *F. graminearum* [20,21]. In vitro translation assays showed a similar inhibitory potency on protein biosynthesis as DON [19]. Likewise, cytotoxicity studies revealed comparable effects of both trichothecenes in human liver cancer (HepG2), nontransformed colon cells (HCEC-1CT) and colon cancer cells (HT-29) [22,23]. However, potential inflammatory properties of NX-3 have not been investigated yet.

Among many other aspects, oxidative stress plays an essential role in the pathogenesis and progression of IBDs [24]. Another *Fusarium* secondary metabolite reported to induce oxidative stress in human colon adenocarcinoma cells HT-29, is the coloring pigment aurofusarin (AURO, Figure 1C) [25]. Even though AURO was already isolated in 1937 from the mycelium of *Fusarium culmorum* [26], toxicological studies are still limited. In HT-29 and HCEC-1CT cells, Jarolim, Wolters, Woelflingseder, Pahlke, Beisl, Puntscher, Braun, Sulyok, Warth and Marko [25] reported AURO to be cytotoxic at concentrations ≥ 1 μM. Although AURO is frequently found at high concentrations in various food commodities [27–29], immunomodulatory effects have not been assessed yet. As *Fusarium* fungi can produce several mycotoxins simultaneously, infested food and feed may be contaminated by a high number of toxins. In recent years, human biomonitoring studies confirmed that humans are exposed to a variety of toxins [30–32]. Thus, the assessment of potential interactions of mycotoxins is crucial for proper risk assessment.

In the present study, we addressed the question whether the *Fusarium* metabolites NX-3 and AURO affect the NF-κB signaling pathway as individual compounds and as binary mixtures. Furthermore, we characterized alterations in the gene expression profiles of the pro-inflammatory cytokines IL-8, IL-6, TNF-α and IL-1β in a cancer and a noncancer colon cell line, mimicking an inflamed intestinal epithelium by IL-1β stimulation.


*4.1. Quantitative Assessment Based on Lesion Depths*

During the demineralization process, the OCT image contrast of caries is formed as the mineral content (MC) of the enamel decreases and the optical properties change [48]. Therefore, lesion depths determined by OCT images can be used to quantify early enamel caries. First, the filters are applied to remove speckle noise and enhance contrast for the OCT B-scan image, then the A-scan in the region of interest (ROI) is searched for the first pixel that exceeds the intensity threshold point, and the distance from the enamel surface to this pixel is taken as the lesion depth. The mean lesion depth is obtained by calculating the lesion depth for each A-scan in the ROI. It has been suggested to select *e*−2 times the peak intensity as the signal intensity threshold. The lesion depth measured by OCT is proportional to the demineralization time [49–53]. Le et al. [49] investigated bovine enamel caries lesions with 1310-nm PS-OCT. Figure 1 shows the PS-OCT B-scan of one of the bovine enamel blocks in the perpendicular axis. It can be seen that the lesion depth increases with the enhancement of the back-scattered intensity around the enamel surface, and the lesion severity increases. The results show the mean lesion depths varies significantly from 10 to 75 μm for demineralization over 0–4 days. Moreover, there is strong correlation (r = 0.85) between the mean depth of lesion measured by PS-OCT and polarized light microscopy (PLM). Jones et al. [50] prepared artificial caries on the occlusal surfaces by using a 14-day pH cycle model and detected them with PS-OCT and digital microradiography (DM). It was found that the image contrast between the sound and lesion area in the perpendicular axis was stronger than that in the parallel axis. The mean lesion depth of caries lesions calculated from the perpendicular axis images of the ten teeth was 150 ± 30 μm, which was highly

correlated with the lesion depths obtained from DM (r = 0.811). Meng [51] and Yao et al. [52] observed an approximately linear relationship between lesion depth and demineralization time using TD-OCT. Park et al. [53] established assessment criteria for OCT using lesion depths, and conducted a concordance study between OCT and light microscopy using ex vivo teeth, which showed moderate concordance (k = 0.54, *p* ≤ 0.001) with no significant difference (*p* = 0.25). Then, smooth surface in vitro and in vivo evaluations were performed using OCT and the International Caries Detection and Assessment System (ICDAS). The extent of caries was seen to vary considerably within each ICDAS category using OCT, which could effectively complement the visual assessment with ICDAS. Yavuz et al. [54] utilized 840-nm SD-OCT to assess the remineralization of artificial enamel caries. The results showed there was a significant reduction in the lesion depth after remineralization, 311.80 (344.38), 320.10 (244.36) and 312.70 (203.80) μm for the three remineralization agents, respectively. The measured lesion depth was also compared with a surface microhardness analysis but there was no correlation between the two.

**Figure 1.** PS-OCT B-scan of bovine enamel in the perpendicular axis. D0 represents the sound area and D1–D4 represent areas demineralized for 1–4 days. A red-white-blue color chart was used, with red indicating strong reflectivity and blue indicating low reflectivity. This figure was adapted from [49].

Lesion depth is a common quantitative index used in early caries studies. The main challenge for its calculation is the difficulty in selecting an intensity threshold as the end point of a lesion, as the range of OCT images is relatively high. The method of selecting *e*−2 times the peak intensity as the signal intensity threshold does not always work effectively [55]. Le et al. [49] used edge-finding algorithms based on this method to measure lesion depth. Other studies designed algorithms to determine the lesion boundary in the image and obtained lesion depth [51,56,57]. A summary of the above research results is shown in Table 1.

**Table 1.** Results of quantifying enamel caries with lesion depths.

**Table 1.** *Cont.*

**Table 1.** *Cont.*

Lesion depths of caries can visually indicate the severity of caries to some extent. However, they can only reflect part of the characteristics of the initial stage of enamel lesions, as the amount of mineral loss may be different in a certain depth range [58]. Furthermore, there is a lack of solid criteria for determining the cut-off point to define lesion depth.

#### *4.2. Quantitative Assessment Based on Reflectivity*

Many studies have used reflectivity for the quantitative assessment of early caries, since the reflectivity of caries lesions can be obtained directly from OCT signals. A commonly used quantitative index related to reflectivity is the integrated reflectivity for caries detection.

A line profile for each lesion depth is taken from B-scans, and the integrated reflectivity (Δ*R*, dB × μm) can be calculated by integrating the reflectivity from the enamel surface to various depths [50]. The observed optical depth should be divided by the enamel refraction index (n = 1.63) to determine the real lesion depth when using the line profile.

Most studies have confirmed that integrated reflectivity increases after demineralization and decreases after remineralization [49,50,57,59,60]. Le et al. [49] utilized a fixed depth algorithm and an edge detection algorithm to calculate integrated reflectivity. In the first algorithm the integration was performed to a fixed depth that needed to be greater than the maximum lesion depth, while the second algorithm could obtain the depth of the lesion. The results showed both algorithms were able to detect the difference in demineralization from 0 to 4 days, except that the fixed depth algorithm yielded a higher integrated reflectivity. Jones et al. [50] calculated the mean integrated reflectivity of artificial caries prepared by applying a 14-day pH cycle model based on the perpendicular axis PS-OCT images, and the result was 450 ± 110 arbitrary units. Meanwhile, it was demonstrated that the integrated reflectivity calculated by PS-OCT was linearly correlated with the relative mineral loss determined by DM (r = 0.755). Nee et al. [59] detected demineralization around adhesive-bound orthodontic brackets in vivo using CP-OCT for a period of 1 year and acquired 2D projection images of Δ*R* with automated algorithms. The results indicated Δ*R* for both adhesives increased remarkably with time, varying in the range from 10.2 (10.5) to 29.7 (9.4) dB × μm. PS-OCT was applied to monitor the process of remineralization of caries using an acid remineralization model by Kang et al. [60]. There were significant alterations in the integrated reflectivity of the lesion region after remineralization, from 257 ± 60.2 to 168 ± 58.5 dB × μm.

Amaechi et al. [61] scanned demineralized bovine teeth using 850-nm OCT and demonstrated that Δ*R* of the enamel reduced with the time of demineralization. They proposed the percentage reflectivity loss (*R*%) as a quantitative index as follows:

$$R\_{\%} (\text{dB} \times \text{mm}) = \frac{(R\_{\text{sound}} - R\_{\text{deminalized}})}{R\_{\text{sound}}} \times 100\% \tag{1}$$

where *Rsound* is the reflectivity of sound enamel and *Rde*min*eralized* is the reflectivity of enamel lesion.

The results showed that *R*% increased from 54.0 ± 11.27 to 86.64 ± 7.57 dB × μm with demineralization time. In a follow-on study it was demonstrated that *R*% was linearly correlated with both the mineral loss determined by transverse microradiography (TMR) (*r* = 1.00) [62] and the percentage of fluorescence loss calculated (Δ*Q*) by QLF (*R*% = 45.56 + Δ*Q*,*r* = 0.963) [63]. However, *R*% is rarely applied. A summary of the above research results is shown in Table 2.

**Table 2.** Results of quantifyin0p-[g enamel caries with reflectivity.

three 4-day periods

**Table 2.** *Cont.*

In addition, other researchers have used the mean relative reflectivity (mRR) proposed in retinal OCT imaging to assess fissure caries by using 1325-nm SS-OCT [64]. The mRR is calculated from the difference between the fissure area signal and sound enamel signal. Although the mRR of demineralized fissures were at least 6 times higher than those of sound fissures at 250, 500 and 1000 μm depths beneath the surface, the mRR was unable to accurately describe lesion mineral density (*rs* = −0.31).

In summary, the integrated reflectivity of enamel lesion calculated by OCT is linearly correlated with mineral loss. Most results confirm that the integrated reflectivity of enamel increases after demineralization, but individual studies show the opposite. Researchers often use the integrated reflectivity in combination with lesion depth to evaluate the lesion severity. However, the calculation of the integrated reflectivity suffers from similar problems to the lesion depth calculation.


**Author details**

Gabriela Valentina Caracostea1 , Alexandru Bucur2 , Iulia Cristina Micu3 \*, Andrada Soanca4 , Andreea Ciurea3 , Adriana Objelean5 , Ada Gabriela Delean6 , Corina Violeta Ionescu6 , Radu Marcel Chisnoiu6 , Marius Negucioiu7 , Mircea Viorel Ciurea8 , Dragos Alexandru Termure8 and Alexandra Roman4

1 Department of Obstetrics and Gynecology, Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Clinical Hospital, Cluj-Napoca, Romania

2 Department of Oro-Maxillo-Facial Surgery, Faculty of Dental Medicine, "Carol Davila" University of Medicine and Pharmacy, "Prof. Dr. Dan Theodorescu" Oro-Maxillo-Facial Clinical Hospital, Bucharest, Romania

3 Department of Periodontology, Faculty of Dental Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania

4 Department of Periodontology, Faculty of Dental Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Clinical Hospital, Cluj-Napoca, Romania

5 Department of Dental Materials and Ergonomics, Faculty of Dental Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania

6 Department of Conservative Odontology, Faculty of Dental Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania

7 Department of Prosthetic Dentistry, Faculty of Dental Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Clinical Hospital, Cluj-Napoca, Romania

8 Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania

\*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.

*Periodontal Medicine: Impact of Periodontal Status on Pregnancy Outcomes and Carcinogenesis DOI: http://dx.doi.org/10.5772/intechopen.96147*


**6. Bleeding**

Primicero et al (1999) reported one lesion of the external iliac vein in a patient with TVT which had to be repaired through laparotomy; Zilbert and Farrel (2001) reported one patient with laceration of the external iliac artery and a neurovascular bundle lesion of the obturator ramus when the TVT was applied. Kuuva and Nilssons (2002) reported in 1455 patients a lesion incidence of large vessels and nerves in 2 patients (0.1%). Flock et al (2004) reported in 7 patients with TVT, blood loss quantified at 250 to 400 ml (2.1%); Abouassaly et al (2004) reported major bleeding of 500 ml in 16 women (2.5%) (16/421) with TVT. Kristensen et al (2010) in 778 patients with TVT reported hemorrhage that needed transfusion in 5 patients (0.6%), Barry et al (2011) observed that bleeding is minor in the group of patients with Monarc, 49 ml; in the TVT group it was 64 ml (p < 0.05), likewise surgical time was 14.6 min with Monarc and 18.5 min with TVT (p < 0.001). Rajendra et al (2011) reported that of 419 patients with TVT-O, 3 patients (0.8%) had a blood loss over 200 ml. Dunn et al (2004) reported 30 cases with vascular injury including 2 fatalities.

Brink (2000) reported one case of intestinal lesion that was repaired with a good outcome for the patient. Although we have minimally invasive procedures, these also pose arterial complications as Jung et al (2010) reported in a patient with TVT-secur who presented a

Surgical Complications with Synthetic Materials 249

frequency of 6.8% with MONARC (5/73). Latthe et al (2010) in a systematic review of voiding disorder it is slightly lower in the TOT group than in the TVT-O group, but it wasn't statistically significant; for TOT the OR was 0.61 (IC 95% 0.35-1.07) and for TVT-O the OR

Abouassaly et al (2004) reported infection of the supra pubic wound in one patient (0.4%).

Flam et al (2009) had a patient with necrotizing fasciitis after placement of the TVT-O; they performed extensive debridement of the affected site, a colostomy, antibiotic therapy and 8

Fig. 1. 65-year-old with urinary incontinence, underwent a multifilament transobturator sling. At 14 months follow up, she experienced severe pelvic pain and vaginal discharge. Clinical examination revealed hyperthermia to 400C, sling exposure at right vaginal sulcus and severe cellulitis in the genital-crural fold: Classification: 3C T4 S 2 and 6C T4 S3

Lee et al(2011) in four of the five patients presented with symptom of chronic vaginal discharge and these patients have a chronic infection forming a sinus tract into the vagina or

other viscus, causing symptoms years after its mesh placement.

Kristensen et al (2010) reported infection of the urinary tract in 3.1%.

was 0.81 (IC 95% 0.48-1.31

sessions of hyperbaric oxygen. Fig. 1

**9. Infection**

lesion of the internal pudendal artery. The complication was resolved with embolization of the artery guided with angiography, the treatment was successful. Jabureck et al (2011) documented that retropubic access surgery has a high lesion incidence of paraurethral, bladder and paravesical plexus vessels and even external iliac vessels.