**11. Conclusions and future research directions**

The concept of strategic tillage represents a promising bridge between intensive conventional tillage and continuous no-till systems, aiming to provide targeted soil improvements while protecting long-term productivity and resilience. However, realizing the potential benefits requires further research and adaptive management.

#### **11.1 Summary of potential benefits**

When judiciously implemented, a growing body of evidence suggests that strategic tillage can provide multiple agronomic and environmental advantages compared to both intensive tillage and exclusively no-till systems. Documented benefits include reduced compaction, increased infiltration and rooting depth, incorporation of amendments, enhanced soil biological activity, increased carbon sequestration, improved nutrient availability and cycling, increased fertilizer efficiency, and higher crop yields where soil constraints are alleviated [1, 3, 4]. By remediating key limitations at critical times using minimal soil disturbance, strategic tillage offers opportunities to improve soil function and productivity without the extensive degradation associated with routine conventional tillage.

#### **11.2 Need for continued research**

However, there remain many open questions regarding long-term impacts, proper implementation, and applicability across diverse soil types, climates, and cropping

systems. Most studies on strategic tillage have evaluated relatively short-term effects over several years. More extended field research is needed to determine if periodic strategic tillage can sustain long-term soil carbon levels, biological activity, and stable aggregation compared to continuous no-till [29]. Quantifying and isolating the effects of strategic tillage frequency, depth, timing, and equipment settings requires further work. There is also a need to identify relevant indicators for when strategic tillage is warranted to address soil constraints versus conditions where no-till management alone is sufficient.


*Edited by Manar Elsayed Abdel-Raouf and Mohamed Hasan El-Keshawy*

Petroleum crude oil is the main energy source worldwide. However, global fossil fuel resources and reservoirs are rapidly and disturbingly being depleted. Thus, it is particularly important to shed light on new techniques developed for economic production and better utilization of crude oil. In addition, the processes involved in the production, refining, and transportation of crude oil are environmentally hazardous. It is essential to develop cleaner technologies and to find innovative solutions to overcome these problems. Over four sections, this book discusses materials used in cracking crude oil and improving its specifications, methods for reducing or eliminating the hazardous effects of petroleum pollution, and the environmental effects of crude oil, as well as presents case studies from different countries.

ISBN 978-1-83969-532-2

Crude Oil - New Technologies and Recent Approaches

Published in London, UK © 2022 IntechOpen © Malekas85 / iStock

## Crude Oil New Technologies and Recent Approaches

*Edited by Manar Elsayed Abdel-Raouf and Mohamed Hasan El-Keshawy*


*3.2. Process Optimization*/*Characterization*

#### 3.2.1. Characteristics of Feedstock

The density and the specific gravity of feedstock were 0.918 ± 0.050 g/cm3 and 0.934 ± 0.051 respectively, while the acid value was calculated to be 1.68 ± 0.12 mg/KOHg, the peroxide value was 15.80 ± 0.32 mEq/Kg, iodine value was 96.20 ± 1.00 and the saponification value of waste cooking oil feedstock was 182.0 ± 1.0 mg KOH/g.

#### 3.2.2. Composition Analysis of Produced Biodiesel by GC-MS

The product (biodiesel) was analysed by GC-MS. Major fatty acid methyl esters consisted of methyl esters of palmitic acid (16:0), lenoleic acid (18:2), stearic acid (18:0), gonodic acid (20:1) and arachidic acid (20:0) were identified by NIST library of GC-MS. Noshadi et al. [32] reported waste cooking oil with myristic acid, palmitoleic acid, palmitic acid, stearic acid, oleic acid, linoleic acid and linolenic acid. Uzun et al. [33] used waste oils for biodiesel production with a following fatty acid content of palmitic acid, stearic acid, oleic acid, linoleic acid and docasanoic acid. Comparable results have also been reported by [34–36], who found that the variations in fatty acid content might be due to presence of di fferent edible oils and their varying amounts in cooking oils.

#### 3.2.3. FTIR Monitoring of Biodiesel Production

Conversion of triglycerides into the biodiesel was confirmed by the FTIR spectral comparison of feedstock oil with the synthesized biodiesel. Characteristic peaks of waste cooking oil were observed at 723, 1700–1800 and 2800–3000 cm−1, which corresponds to –CH2 rocking, C=O starching and symmetric C-H stretching vibrations, respectively. While in the case of biodiesel, adsorption peak at 1437 cm−1 corresponding to methyl ester and peak appearing at 1196 cm−1 for C-O ester bond are the characteristic biodiesel adsorption peaks. The absence of 1380 cm−1 peak in biodiesel that represents the O–CH2 bonds in glycerol site of triglycerides further proves the formation of methyl esters (Figure 7). The results obtained in the present work are comparable to previous research [37–39].

**Figure 7.** FTIR of (**a**) WCO and (**b**) WCO based biodiesel.

## 3.2.4. Optimized Reaction Parameters

Table 1 describes the optimum reaction conditions for the optimized yield of waste cooking oil-based biodiesel. By carrying out 50 experiments on various conditions of selected parameters, it has been observed that Fe3O4\_PDA\_Lipase catalysed the transesterification of feedstock oil giving a maximum yield of 92%, under the given set of reaction conditions/parameters viz., biocatalyst concentration (10%), CH3OH to oil molar ratio (6:1), reaction time (30 h), reaction temperature (37 ◦C) and water content (0.6%). While biocatalyst concentration (1%), methanol to oil molar ratio (3:1), reaction time (12 h), reaction temperature (20 ◦C) and water content (1%) gave the lowest response (41.5%).

Stoichiometrically, methanol reacts with triglycerides in 3:1 molar ratio, but excess amount of methanol was provided to increase the rate of forward reaction because transesterification is equilibrium limited reaction [40]. Moreover, the viscosity of the oils is high, which hinders the mass transfer. The addition of excess methanol lessens the reaction mixture viscosity, so the rate of reaction is increased by the improved mass transfer [41]. However, excess of methanol can lead to the emulsification of glycerol, which can recombine with the esters to reduce the biodiesel yield. In addition, excess amount of methanol can deactivate the lipase by changing its globular structure [42,43]. In the current study, a 6:1 methanol to oil ratio was observed to be the optimum CH3OH concentration for the biocatalytic transesterification of waste cooking oil.

Water presence influences the activity of the immobilized enzyme and stability of the free enzyme. Water/oil interface may also be required for better catalytic activity of lipase. Furthermore, the enzyme deactivation due to small chain alcohols can be prevented if the lipase is dispersed in water [43]. However, in the presence of higher content of water hydrolysis may compete with the methanolysis [44]. Therefore, water content is required to be adjusted with minimal content as per experimental requirements. Highest triglyceride conversion into biodiesel was obtained while using 0.6% H2O content in present research. Temperature significantly effects the enzymatic transesterification. The activity of lipase increases with temperature till the optimum point, i.e., 37 ◦C, and a further increase in temperature can lead to denaturisation of lipase.

Similar conditions have also been investigated by other researchers. Ying and Dong obtained the maximum biodiesel yield using immobilized lipase with 31.3% nano-biocatalyst, 38 ◦C temperature and 4.7:1 methanol to oil ratio [27]. Thangaraj et al. reported the maximum biodiesel yield of 89% at an optimum condition i.e., 1:3 methanol/oil ratio, 12 h of reaction period and 45 ◦C temp., using the immobilized NS81006 lipase as the catalyst [45]. Iso et al. reported the highest enzyme activity at 0.3% water content using immobilized lipase [46]. Mumtaz et al., using enzymatic transesterification, reported 95.9% biodiesel yield with 0.75% NOVOZYME-435, 6:1 methanol to oil ratio and 60 h of reaction at 32.50 ◦C [47]. Arumugam and Ponnusami used lipase immobilized on activated carbon for biodiesel production; optimum parameters were 1:9 oil to methanol ratio, 10% water content and 30 ◦C temperature, achieving a yield of 94.5% biodiesel with these conditions [41]. Xia also used RSM to optimize the enzymatic transesterification process, the optimum conditions reported are 4:1 methanol to oil ratio, 6.8% of biocatalyst and 42.2 ◦C reaction temperature [26]. Andrade et al. used nano-biocatalyst prepared by immobilization of lipase on magnetic nano-particles and achieved the highest biodiesel yield at 4:1 methanol/oil raio, 20% enzyme conc., 37 ◦C temp., and 12 h of time period [25]. Andrade et al. attained 95.5% methyl ester yield at 8:1 molar ratio of alcohol to oil, 8% biocatalyst amount and 45 ◦C temperature [48].

**Table 1.** Optimum reaction conditions for biodiesel production from Fe3O4-PDA-Liapase catalysed transesterification.


**Appendix A.**

*Appendix A.1. Derivation of Bounds to Average Trace Distance*

Using Equations (2) and (4) in the main text and that *ρ*O 0 = *ρ*A 0 , we find

$$\begin{split} \left\langle 1 - \text{Tr} \left( \rho\_{T}^{\mathcal{O}} \rho\_{T}^{\mathcal{A}} \right) \right\rangle &= - \left\langle \int\_{\text{Tr} \left( \rho\_{0}^{\mathcal{O}} \rho\_{0}^{\mathcal{A}} \right)} d \, \text{Tr} \left( \rho\_{t}^{\mathcal{O}} \rho\_{t}^{\mathcal{A}} \right) \right\rangle \\ &= - \int\_{F\_{0}}^{F\_{\text{f}}} d \, \text{Tr} \left( \rho\_{t}^{\mathcal{A}} \rho\_{t}^{\mathcal{A}} \right) \\ &= -2 \int\_{0}^{T} \text{Tr} \left( \rho\_{t}^{\mathcal{A}} A \left[ \rho\_{t}^{\mathcal{A}} \right] \right) dt \\ &= + 2 \sum\_{a} \frac{1}{8 \pi\_{m}^{\mathcal{A}}} \int\_{0}^{T} \text{Tr} \left( \left[ A\_{a'} \left[ A\_{a'} \rho\_{t}^{\mathcal{A}} \right] \right] \rho\_{t}^{\mathcal{A}} \right) dt \\ &= \sum\_{a} \frac{1}{4 \pi\_{m}^{\mathcal{A}}} \int\_{0}^{T} \text{Tr} \left( \left[ \rho\_{t}^{\mathcal{A}}, A\_{a} \right] \left[ A\_{\mathcal{A}}, \rho\_{t}^{\mathcal{A}} \right] \right) dt. \end{split} \tag{14}$$

This identity can be conveniently expressed in terms of the 2-norm of the commutator [*ρ*A *t* , *A*] as

$$\left\langle 1 - \text{Tr} \left( \rho\_T^{\mathcal{Q}} \rho\_T^A \right) \right\rangle = \sum\_a \frac{1}{4 \tau\_m^a} \int\_0^T \left\| \left[ \rho\_t^A \, \big|\, A\_a \right] \right\|\_2^2 dt = \sum\_a \frac{T}{4 \tau\_m^a} \overline{\left\| \left[ \rho\_t^A \, \big|\, A\_a \right] \right\|\_{2^\*}^2} \tag{A2}$$

where we denote the time-average of a function *f* by *f* ≡ *T* 0 *f*(*t*)*dt*/*T*. Note that the expression ∑*α* 1 4*τα m* ( ( *ρ*A *t* , *Aα* ( (2 2 plays the role of a time-averaged decoherence time [15,16], generalizing Equation (11) in the main text.

This sets alternative bounds on the average distance between the state *ρ*A*t* assigned by A and the actual state of the system *ρ*O*t* , in terms of the effect of the Lindblad dephasing term acting on the incomplete-knowledge state *ρ*A*t* ,

$$\left\| T \sum\_{a} \frac{1}{4\pi\_{n}^{4}} \overline{\left\| \left[ \rho\_{l}^{A}, A\_{a} \right] \right\|} \right\|\_{2}^{2} \leq \left\langle \mathcal{D} \left( \rho\_{T}^{\mathcal{O}}, \rho\_{T}^{A} \right) \right\rangle \leq \sqrt{T \sum\_{a} \frac{1}{4\pi\_{n}^{4}}} \overline{\left\| \left[ \rho\_{l}^{A}, A\_{a} \right] \right\|}\_{2}^{2} .$$

A short time analysis provides a sense of the evolution of the upper and lower bounds on the trace distance and how they compare to its variance. To leading order in a Taylor series expansion,

$$\mathcal{P}\left(\rho\_{\tau}^{\mathcal{A}}\right) \approx 1 + 2 \operatorname{Tr}\left(\rho\_{0}^{\mathcal{A}} \Lambda \left[\rho\_{0}^{\mathcal{A}}\right]\right) \tau = 1 - \sum\_{a} \frac{1}{4\tau\_{m}^{a}} \operatorname{Tr}\left(\left[\rho\_{0}^{\mathcal{A}}, A\_{a}\right] \left[A\_{a}, \rho\_{0}^{\mathcal{A}}\right]\right) \tau,\tag{A3}$$

and one finds

$$\left|\tau \sum\_{\mathfrak{a}} \frac{1}{4\tau\_{\mathfrak{m}}^{4}}\right| \left|\left[\rho\_{0}^{A}, A\_{\mathfrak{a}}\right]\right|\Big|\_{2}^{2} \leq \left<\mathcal{D}\left(\rho\_{\tau}^{\mathcal{D}}, \rho\_{\tau}^{\mathcal{A}}\right)\right> \leq \sqrt{\tau \sum\_{\mathfrak{a}} \frac{1}{4\tau\_{\mathfrak{m}}^{4}} \left\|\left[\rho\_{0}^{A}, A\_{\mathfrak{a}}\right]\right\|\_{2}^{2}}.\tag{A4}$$

Note that the behavior of the trace distance is determined by the timescale in which decoherence occurs.

Using Equation (9) in the main text and Jensen's inequality, one obtains

$$\left\langle \mathcal{D}^2 \left( \rho\_{\overline{\mathcal{T}}'}^{\mathcal{O}} \rho\_{\overline{\mathcal{T}}}^A \right) \right\rangle \le 1 - \mathcal{P} \left( \rho\_{\overline{\mathcal{T}}}^A \right),\tag{A5}$$

which implies that the variance ΔD2*T* ≡ D2*ρ*O*T* , *ρ*A*T* − D*ρ*O*T* , *ρ*A*T* 2 satisfies

$$
\Delta \mathcal{D}\_T^2 \le \mathcal{P}\left(\rho\_T^A\right) - \mathcal{P}\left(\rho\_T^A\right)^2. \tag{A6}
$$

In the short time limit this becomes

$$
\Delta \mathcal{D}\_{\tau}^{2} \leq -2 \operatorname{Tr} \left( \rho\_{0}^{4} \Lambda \left[ \rho\_{0}^{4} \right] \right) \tau. \tag{A7}
$$

#### *Appendix A.2. Derivation of the Average and Variance of the Quantum Relative Entropy*

Using that *ρ*O*t* is pure, and that the von Neumann entropy is given by *S*(*ρ*) ≡ − Tr(*ρ* log *ρ*), we obtain that the average over the results unknown to agent A satisfy

$$
\begin{split}
\left< \mathcal{S} \left( \rho\_t^{\mathcal{O}} || \rho\_t^A \right) \right> &= \left< \text{Tr} \left( \rho\_t^{\mathcal{O}} \log \rho\_t^{\mathcal{O}} \right) \right> - \left< \text{Tr} \left( \rho\_t^{\mathcal{O}} \log \rho\_t^A \right) \right> \\
&= 0 - \text{Tr} \left( \rho\_t^A \log \rho\_t^A \right) = \mathcal{S} \left( \rho\_t^A \right).
\end{split}
\tag{A8}
$$

This sets a direct connection between the average error induced by assigning state *ρ*A*t* instead of the exact state *ρ*O*t* , as quantified by the relative entropy, in terms of the von Neumann entropy of the state accessible to agent A.

In turn, the variance of the relative entropy satisfies

$$\begin{split} \Delta S^2 \left( \rho\_t^{\mathcal{O}} || \rho\_t^A \right) &= \left< S^2 \left( \rho\_t^{\mathcal{O}} || \rho\_t^A \right) \right> - \left< S \left( \rho\_t^{\mathcal{O}} || \rho\_t^A \right) \right>^2 \\ &= \left< \text{Tr} \left( \rho\_t^{\mathcal{O}} \log \rho\_t^A \right)^2 \right> - S^2 \left( \rho\_t^A \right) \\ &\le \left< \text{Tr} \left( \rho\_t^{\mathcal{O}} \right) \text{Tr} \left( \rho\_t^{\mathcal{O}} \log^2 \rho\_t^A \right) \right> - S^2 \left( \rho\_t^A \right) \\ &= \text{Tr} \left( \rho\_t^{\mathcal{A}} \log^2 \rho\_t^A \right) - S^2 \left( \rho\_t^A \right), \end{split} \tag{4.9}$$

using the Cauchy–Schwarz inequality in the third line. Note that this expression is identical to the variance of the operator − log *ρ*A*t* , which can be thought of as the quantum extension to the notion of the "information content" or "surprisal" (− log *p*) in classical information theory.

#### *Appendix A.3. Bounds to the Difference between Perceptions of Multiple Agents*

Consider two agents A and B who simultaneously monitor different observables on a system. Each one has access to the measurement outcomes of their devices, but not to the results obtained by the other agent. The states *ρ*A*T* and *ρ*B*T* that A and B assign to the system differ from the actual pure state *ρ*O*T* that corresponds to the complete description of the system. For simplicity let us consider that A monitors a single observable *A* and B monitors a single observable *B*. The complete-description state of the system assigned by all-knowing agent O evolves according to

$$d\rho\_t^{\mathcal{O}} = L\left[\rho\_t^{\mathcal{O}}\right]dt + I\_{\mathcal{A}}\left[\rho\_t^{\mathcal{O}}\right]dW\_t^{\mathcal{A}} + I\_{\mathcal{B}}\left[\rho\_t^{\mathcal{O}}\right]dW\_t^{\mathcal{B}},\tag{A10}$$

with the Lindbladian *Lρ*O*t* ≡ −*iH*, *ρ*O*t* + ΛA*ρ*O*t* + ΛB*ρ*O*t* , with corresponding dephasing terms on observables *A* and *B*. The innovation terms *IA* and *IB* are defined as in Equation (3) in the main text, and *dW*A*t* and *dW*B*t* are independent noise terms.

The states of both observers satisfy

$$d\rho\_t^A = L\left[\rho\_t^A\right]dt + I\_A\left[\rho\_t^A\right]dV\_t^A\tag{A11}$$

$$d\rho\_t^{\mathcal{B}} = L\left[\rho\_t^{\mathcal{B}}\right]dt + I\_B\left[\rho\_t^{\mathcal{B}}\right]dV\_t^{\mathcal{B}}.\tag{A12}$$

Consistency between observers implies that their noises are related to the ones appearing in Equation (A10) by [1,3]:

$$dW\_{l}^{A} = \left(\text{Tr}\left(\rho\_{l}^{A}A\right) - \text{Tr}\left(\rho\_{l}^{\mathcal{O}}A\right)\right)\frac{dt}{\tau\_{\mathcal{M}}} + dV\_{l}^{A}$$

$$dW\_{l}^{B} = \left(\text{Tr}\left(\rho\_{l}^{B}B\right) - \text{Tr}\left(\rho\_{l}^{\mathcal{O}}B\right)\right)\frac{dt}{\tau\_{\mathcal{M}}} + dV\_{l}^{B}.\tag{A13}$$

As the state of each observer satisfies Equation (9), the triangle inequality provides the upper bound

$$\langle \mathcal{D}\left(\rho\_T^A, \rho\_T^B\right)\rangle\_{\mathcal{AB}} \le \sqrt{1 - \text{Tr}\left(\rho\_T^{A^2}\right)} + \sqrt{1 - \text{Tr}\left(\rho\_T^{B^2}\right)}.\tag{A14}$$

and the lower bound

$$\left| \left< \mathcal{D} \left( \rho\_T^{\mathcal{A}}, \rho\_T^{\mathcal{B}} \right) \right> \right|\_{\mathcal{A}\mathcal{B}} \ge \left| \text{Tr} \left( \rho\_T^{\mathcal{A}^2} \right) - \text{Tr} \left( \rho\_T^{\mathcal{B}^2} \right) \right|. \tag{A15}$$

#### *Appendix A.4. Illustration—Evolution of Limits to Perception*

We consider the case of observer O monitoring the spin components *σzj* on a 1D transverse field Ising model, with the Hamiltonian defined in Equation (18) of the main text. Figure A1 shows the evolution of the average trace distance D*ρ*O*T* , *ρ*B*T* between the complete description and B's partial one, along with the bounds (16), for different values of the monitoring efficiency *η*. Figure A2 shows the evolution of the average relative entropy *Sρ*O*T* ||*ρ*B*T* . The dynamics are simulated by implementation of the monitoring process as a sequence of weak measurements modeled by Kraus operators acting on the state of the system. Specifically, the evolution of *ρ*O*t* and corresponding state *ρ*B*t* with partial measurements is numerically obtained from assuming two independent measurement processes, as in [1].

**Figure A1. Evolution of the average trace distance and its bounds.** Simulated evolution of the average trace distance D*ρ*O*T* , *ρ*B*T* between complete and incomplete descriptions for a spin chain initially in a paramagnetic state on which individual spin components *σzj* are monitored. The simulation corresponds to *N* = 6 spins, with couplings *Jτm* = *hτm* = 1/2. The upper and lower bounds (16) on the average trace distance is depicted by dashed lines, while the shaded area represents the (one standard deviation) confidence region obtained from the upper bound (13) on the standard deviation in the main text, calculated with respect to the mean distance. For *η* = 0 (**left**), agent A, without any access to the measurement outcomes, has the most incomplete description of the system. After gaining access to partial measurement results, with *η* = 0.5 (**center**) B gets closer to the complete description of the state of the system. Finally, when *η* = 0.9 (**right**), access to enough information provides B with an almost complete description of the state. Importantly, in all cases the agent can bound how far the description possessed is from the complete one solely in terms solely of the purity P*ρ*B*T*.

**Figure A2. Evolution of the average relative entropy and its bounds.** Simulated evolution of the average relative entropy *Sρ*O*T* ||*ρ*B*T* between complete and incomplete descriptions for a spin chain on which the *z* components of individual spins are monitored. The shaded area represents the (one standard deviation) confidence region obtained from the upper bound on the standard deviation of the relative entropy, Equation (14) in the main text. As in the case of the trace distance, access to more information leads to a more accurate state assigned by the agent.

#### *Appendix A.5. Illustration—Transition to Complete Descriptions*

Consider the case of a one-dimensional harmonic oscillator with position and momentum operators *X* and *P*, respectively. We assume agent B is monitoring the position of the harmonic oscillator, with an efficiency *η*. The dynamics of state *ρ*B*t* is dictated by Equation (15) in the main text for the case of a single monitored observable, with

$$\Lambda\left[\rho\_t^{\mathcal{S}}\right] = \frac{1}{8\pi\_m} \left[X\_\prime \left[X\_\prime \rho\_t^{\mathcal{S}}\right]\right]; \qquad I\_X\left[\rho\_t^{\mathcal{S}}\right] = \frac{1}{\sqrt{4\pi\_m}} \left(\left\{X\_\prime \rho\_t^{\mathcal{S}}\right\} - 2\operatorname{Tr}\left(X \rho\_t^{\mathcal{S}}\right)\rho\_t^{\mathcal{S}}\right). \tag{A16}$$

Such a dynamics preserves the Gaussian property of states. For these, the variances

$$w\_{\mathbf{X}} \equiv \text{Tr}\left(\rho\_l^{\mathcal{B}} \mathbf{X}^2\right) - \text{Tr}\left(\rho\_l^{\mathcal{B}} \mathbf{X}\right)^2\_{\dots} \tag{A17}$$

$$w\_p \equiv \text{Tr}\left(\rho\_t^B P^2\right) - \text{Tr}\left(\rho\_t^B P\right)^2,\tag{A18}$$

and covariance

$$\mathcal{L}\_{xp} \equiv \text{Tr}\left(\rho\_t^{\mathcal{B}} \frac{\{X, P\}}{2}\right) - \text{Tr}\left(\rho\_t^{\mathcal{B}} X\right) \text{Tr}\left(\rho\_t^{\mathcal{B}} P\right),\tag{A19}$$

satisfy the following set of differential equations (in natural units) [1,21]:

$$\frac{d}{dt}\upsilon\_x = 2\omega\upsilon\_{xp} - \frac{\eta}{\tau\_m}\upsilon\_{x'}^2\tag{A20a}$$

$$\frac{d}{dt}\upsilon\_p = -2\omega\upsilon\_{xp} + \frac{1}{4\tau\_m} - \frac{\eta}{\tau\_m}c\_{xp\prime}^2\tag{A20b}$$

$$\frac{d}{dt}\mathbf{c}\_{xp} = \omega \mathbf{v}\_p - \omega \mathbf{v}\_x - \frac{\eta}{\tau\_m} \mathbf{v}\_x \mathbf{c}\_{xp}.\tag{A20c}$$

While the first moments do evolve stochastically, the second moments above satisfy a set of deterministic coupled differential equations. This in turn implies that the purity of the state, which can be obtained from the covariance matrix [22–26]

$$
\sigma(t) \equiv \begin{bmatrix} \upsilon\_x & \upsilon\_{xp} \\ \upsilon\_{xp} & \upsilon\_p \end{bmatrix} \tag{A21}
$$

as

$$\mathcal{P}\left(\rho\_T^{\mathcal{B}}\right) = \frac{1}{2\sqrt{\det\left[\sigma(t)\right]}},\tag{A22}$$

evolves deterministically as well.

> The solution for long times can be derived from Equations (A20), giving

$$
\omega\_{xp}^{\rm sc} = -\frac{\omega \tau\_{\rm m} \pm \sqrt{\omega^2 \tau\_{\rm m}^2 + \eta/4}}{\eta},
\tag{A23a}
$$

$$
\sigma\_x^{ss} = \sqrt{\frac{2\omega \tau\_m}{\eta} \epsilon\_{xp}^{ss}}\tag{A23b}
$$

$$
\sigma\_p^{\rm ss} = \upsilon\_x^{\rm ss} \left( 1 + \frac{\eta}{\omega \tau\_m} \sigma\_{xp}^{\rm ss} \right) \tag{A23c}
$$

which provides the long-time asymptotic value of the purity as a function of the measurement efficiency. The latter turns out to have the following simple expression

$$\begin{split} \mathcal{P}\left(\boldsymbol{\rho}\_{\mathcal{T}}^{\mathcal{B}}\right) &= \frac{1}{2\sqrt{\sigma\_{\boldsymbol{x}}^{\mathcal{S}\boldsymbol{x}}\boldsymbol{v}\_{\mathcal{P}}^{\mathcal{S}\boldsymbol{x}} - (\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{x}})^{2}}} \\ &= \frac{1}{2\sqrt{\frac{2\omega\tau\_{\boldsymbol{m}}}{\eta}\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}}\left(1 + \frac{\eta}{\omega\tau\_{\boldsymbol{m}}}\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}}\right) - (\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}})^{2}}} \\ &= \frac{1}{2\sqrt{\frac{2\omega\tau\_{\boldsymbol{m}}}{\eta}\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}} + (\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}})^{2}}} \\ &= \frac{1}{2\sqrt{\frac{\tau\_{\boldsymbol{m}}}{\eta}\left(\frac{1}{4\overline{\tau}\_{\boldsymbol{m}}} - \frac{\eta}{\tau\_{\boldsymbol{m}}}(\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}})^{2}\right) + (\boldsymbol{c}\_{\boldsymbol{x}\boldsymbol{p}}^{\mathcal{S}\boldsymbol{s}})^{2}} = \frac{1}{2\sqrt{\frac{1}{4\eta}}} = \sqrt{\eta}. \end{split} \tag{A25}$$

Using that

$$1 - \mathcal{P}\left(\rho\_T^{\mathbb{E}}\right) \le \left\langle \mathcal{D}\left(\rho\_T^{\mathcal{O}}, \rho\_T^{\mathbb{E}}\right) \right\rangle\_{\mathcal{B}} \le \sqrt{1 - \mathcal{P}\left(\rho\_T^{\mathbb{E}}\right)},\tag{A26}$$

then implies

$$1 - \sqrt{\eta} \le \left< \mathcal{D} \left( \rho\_{T'}^{\mathcal{O}} \rho\_T^{\mathcal{B}} \right) \right>\_{\mathcal{B}} \le \sqrt{1 - \sqrt{\eta}}.\tag{A27}$$

The entropy of a 1-mode Gaussian state can be expressed in terms of the purity of the state as

$$\begin{split} S\left(\rho\_{\overline{T}}^{\mathbb{S}}\right) &= \left(\frac{1}{2\mathcal{P}\left(\rho\_{\overline{T}}^{\mathbb{S}}\right)} + 1/2\right) \log\left(\frac{1}{2\mathcal{P}\left(\rho\_{\overline{T}}^{\mathbb{S}}\right)} + 1/2\right) \\ &\quad - \left(\frac{1}{2\mathcal{P}\left(\rho\_{\overline{T}}^{\mathbb{S}}\right)} - 1/2\right) \log\left(\frac{1}{2\mathcal{P}\left(\rho\_{\overline{T}}^{\mathbb{S}}\right)} - 1/2\right). \end{split} \tag{A28}$$

Then, using that *Sρ*O*t* ||*ρ*B*t* B = *Sρ*B*t* and Equation (A25), we obtain that for long times,

$$\begin{split} \left< S\left(\rho\_t^{\mathcal{O}}||\rho\_t^{\mathcal{S}}| \right) \right>\_{\mathcal{S}} &= S\left(\rho\_T^{\mathcal{S}}\right) \\ &= \left(\frac{1}{2\sqrt{\eta}} + \frac{1}{2}\right) \log\left(\frac{1}{2\sqrt{\eta}} + \frac{1}{2}\right) \\ &\quad - \left(\frac{1}{2\sqrt{\eta}} - \frac{1}{2}\right) \log\left(\frac{1}{2\sqrt{\eta}} - \frac{1}{2}\right). \end{split} \tag{A29}$$


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### Key Features:

	- Ethical and Privacy Considerations: Delve into the ethical challenges and privacy concerns inherent to sentiment analysis, with discussions on responsible AI usage.
	- Future Directions: Get a glimpse into the future of sentiment analysis, with discussions on emerging trends and unresolved challenges.

This book is an essential resource for researchers, practitioners, and students in fields like natural language processing, machine learning, and data science. Whether you're interested in understanding customer sentiment, monitoring social media trends, or advancing the state of the art, this book will equip you with the knowledge and tools you need to navigate the complex landscape of sentiment analysis.

## *Andries Engelbrecht, Artificial Intelligence Series Editor*

Published in London, UK © 2024 IntechOpen © your\_photo / iStock

Advances in Sentiment Analysis - Techniques, Applications, and Challenges

IntechOpen Series

Artificial Intelligence, Volume 22

Advances in

Sentiment Analysis

Techniques, Applications, and Challenges

*Edited by Jinfeng Li*


**Chapter 6**

*The Universe of Escherichia coli*

2015;**15**(1):2010-2013

[35] Kanayama A, Yahata Y, Arima Y, Takahashi T, Saitoh T, Kanou K, et al. Enterohemorrhagic *Escherichia coli* outbreaks related to childcare facilities in Japan. BMC Infectious Diseases.

[43] Cálix-Lara TF, Rajendran M, Talcott ST, Smith SB, Miller RK, Castillo A, et al. Inhibition of *Escherichia coli* O157: H7 and *Salmonella enterica* on spinach and identification of antimicrobial substances produced by a commercial lactic acid bacteria food safety intervention. Food Microbiology.

[44] Tzouros NE, Arvanitoyannis IS. Implementation of hazard analysis critical control point (HACCP) system to the fish/seafood industry: A review. Food Review International.

[45] Cusato S, Gameiro AH, Sant'Ana AS, Corassin CH, Cruz AG, Oliveira CAF. Assessing the costs involved in the implementation of GMP and HACCP in a small dairy factory. Quality Assurance and Safety of Crops & Foods.

[46] Saerekui P. Good Manufacturing Practices. ISBT Science Series.

2014;**38**:192-200

2000;**16**(3):273-325

2014;**6**(2):135-139

2009;**4**(531):6-10

[36] Newitt S, MacGregor V, Robbins V, Bayliss L, Anne Chattaway M, Dallman T, et al. Two linked enteroinvasive *Escherichia coli* outbreaks, Nottingham, UK, June 2014. Emerging Infectious Diseases. 2016;**22**(7):1178-1184

[37] Frank C, Werber D, Cramer JP, Askar M, Faber M, Heiden M, et al. Epidemic profile of Shigatoxin-producing *Escherichia coli* O104:H4 outbreak in Germany. The New England Journal of Medicine.

[38] Buchholz U, Bernard H, Werber D, Böhmer MM, Remschmidt

C. German outbreak of *Escherichia coli* O104:H4 associated with sprouts. The New England Journal of Medicine.

[39] Pennington H. *Escherichia coli* O157. Lancet. 2010;**376**(9750):1428-1435

[40] Hurd HS, Malladi S. An outcomes model to evaluate risks and benefits of *Escherichia coli* vaccination in beef cattle. Foodborne Pathogens and Disease. 2012;**9**(10):952-961

[41] Harewood P, Rippey S, Montesalvo M. Effect of gamma irradiation on shelf life and bacterial and viral loads in hardshelled clams (*Mercenaria mercenaria*).

Microbiology. 1994;**60**(7):2666-2670

[42] Clavero MR, Monk JD, Beuchat LR, Doyle MP, Brackett RE. Inactivation of *Escherichia coli* O157:H7, *Salmonella*, and *Campylobacter jejuni* in raw ground beef by gamma irradiation. Applied and Environmental Microbiology.

Applied and Environmental

1994;**60**(6):2069-2075

2011;**365**(19):1771-1780

2011;**365**(19):701-709

**100**


*Edited by Rachael Morton*

Surgery continues to be the mainstay treatment for melanoma localized to the primary tumor and/or lymph nodes. Results from randomized controlled trials indicate that sentinel node biopsy for the treatment of cutaneous melanoma of intermediate thickness has a beneficial effect on recurrence rates, and adjuvant radiotherapy to regional lymph node fields following surgical resection reduces loco-regional recurrence in patients at high risk of relapse. Isolated limb perfusion, electrochemotherapy, and photodynamic therapy continue to be evaluated for treatment of stage IV disease. However, the greatest excitement in new treatment has been with targeted therapies for genetic mutations. In particular, the promising results of partial and complete tumor response in stage IV disease from early phase trials of the B-RAF kinase inhibitors. This book provides a contemporary insight into the therapeutic treatment options for patients with metastatic melanoma and is relevant to clinicians and researchers worldwide. In addition, an update on current clinical trials for melanoma treatment has been included, and two chapters have been reserved to discuss the treatment of oral and uveal melanoma.

ISBN 978-953-307-574-7

Treatment of Metastatic Melanoma

Photo by Ssviluppo / iStock


*2.6. Statistical Analysis*

Statistical analysis was performed by Student's *t*-test. The values were found to significantly differ when *p* < 0.05. The data were presented as mean ± SEM. GraphPad Prism 5.0 (GraphPad Software, Inc., San Diego, CA, USA) was used for the statistical analyses.


**4.2.4 Biological fluids and tissues**

Odink et al. (Odink et al., 1988) presented a simple method for the routine analysis of iodide in urine. Iodide was separated by means of ion-pair reversed phase chromatography (RP-HPLC) and detected electrochemically with a silver electrode after a one-step sample cleanup. The coefficient of variation of a single analysis of iodide in a pooled urine sample (530 nmol/l) was 7.6%. The detection limit was 3 pmol (S/N 3), corresponding to 0.06 μmol/l. The recovery of iodide added to urine was 96±7 %.

There are also studies that compare spectrophotometric and RP-HPLC determinations of iodine concentrations in urine (Bier et al., 1998). In the first one ammonium persulfate was used as an oxidant in the modified ceric arsenite method. With the use of this sensitive method iodine concentrations can be determined in very small specimens (50 μL). A Technicon Autoanalyzer II and a paired-ion-RP HPLC were the basic analytical equipment. The authors found that the precision of this optimized ammonium persulfate method yielded inter assay CVs of <10% for urinary iodine concentrations >10 μg/dL. The detection limit was 0.0029 μg iodine. There was a high correlation between all three methods (r > 0.94 in any case) and the interpretation of the results was consistent. The authors suggested that the manual ammonium persulfate method could be performed in any routine clinical laboratory for urinary iodine analysis. Another benefit of the described methods is a possibility to process a large number of samples with high accuracy and minimal technician`s time.

When using the HPLC assay method, contaminations from the protein bound iodine do not interfere with the determination of the serum inorganic iodide (SII), making it the method of choice for detection in the serum. Although the clinical relevance of the measurement of SII is limited, it allows calculation of the absolute iodine uptake , which has a great value in certain pathophysiological studies *(*Rendl et al., 1998).

Błażewicz et al. (Błażewicz et al., 2011) examined correlations between the content of iodides in 66 nodular goitres and 100 healthy human thyroid tissues (50 - frozen and 50 formalin - fixed). A fast, accurate and precise ion chromatography method on the IonPac AS11 chromatographic column (Dionex, USA) with a pulsed amperometric detection (IC-PAD) followed by alkaline digestion with tetramethylammonium hydroxide (TMAH) in a closed system and with the assistance of microwaves was developed and used for the comparative analysis of two types of human thyroid samples (healthy and pathological). A good correspondence (for 10 additional determinations) between the certified (3.38 ± 0.02 ppm with variation coefficient /V.C. / of 0.59 % for Standard Reference Material (SRM) NIST 1549- non-fat milk powder) and the measured iodine concentrations (3.52 ± 0.29 ppm; V.C. = 10 %) was achieved. Suitability of the developed IC method was supported by validation results.

Ion chromatography coupled with electrospray ionization tandem mass spectrometry was applied for quantifying iodide, as well as perchlorate and other sodium-iodide symporter (NIS) inhibitors in the human amniotic fluid*.* The use of selective chromatography and tandem mass spectrometry decreased the need to clean up samples, leading to a quick and rugged method that is capable of the routine analysis of 75 samples per day. Along the physiologically relevant concentration range for the analytes, the analytical response was linear. The analysis of a set of 48 samples of amniotic fluid identified the range and median levels for iodide as: 1.7–170, 8.1μg/l (Blount & Valentin-Blasini, 2006).


**Appendix A**

**Table A1.** Data of thermal units for Test Systems 1 and 2.

**Table A2.** The data of hydropower plants of Test Systems 1 and 2.

**Table A3.** Load demand and water inflows of Test Systems 1 and 2, and wind speed of Test System 2.

**Table A4.** Optimal generations obtained by MASCSA for Test System 1.

**Table A5.** Optimal generations obtained by MASCSA for Test System 2.


*Edited by Ajit Kumar Passari and Sergio Sánchez*

The mushroom has a wide number of medicinal properties such as being an antioxidant, antimicrobial, anticancer, antidiabetic, immune enhancer, and also used for the treatment of various diseases such as anthelmintic, anti-inflammatory, antipyretics, etc. According to current information, there are approximately twelvethousand species in the world, and out of them, 2000 species are reported as being edible. Around 35 edible mushroom varieties are cultivated commercially, whereas almost 200 wild species could be used for medicinal purposes. This book also covers the diversity of edible mushrooms and describes several applications as an alternative source for food production and clinical approach. This book includes: • the diverse types of mushroom and their enzymatic activity • importance of nutritional properties along with their food product development • industrial and clinical applications of macro fungi, i.e., degradation of dyes, anticancer, antimicrobial, antioxidant, etc.

ISBN 978-1-78985-955-3

An Introduction to Mushroom

Published in London, UK © 2020 IntechOpen © Andreas\_Zerndl / iStock


**3. Results**

*3.1. Results of Calculating the Occupational and Background Risks for the Arctic Zone*

The results of the *R*F.N.t., *R*RF.av. *R*M.I.t., and *R*I.A. risk values were determined on the basis of statistical data for each region of the Arctic zone over the past 10 years, which were then subjected to correlation and regression analysis—see Table 1.

**Table 1.** Background risks of occupational injury for the Arctic zone (103).

Table 1 provides the background risk values for each Arctic zone territory, calculated using Equation (3).

The data from Table 1 show that the background risk levels have remained practically the same in the past decade. It should be noted that the Krasnoyarsk Territory displays the lowest average background risk values, while the Nenets Autonomous Area has the highest ones.

Figure 8 illustrates the relationship between background risk and injury risk for each region, indicating that background risk has an impact on the magnitude of overall injury risk. The results indicate that the background risks can have significant impact on the overall injury rates. As such, the background risk accounts for 35% of the overall risk in the Nenets Autonomous Area and for over 30% in the Chukotka Autonomous Region and the Sakha Republic (Yakutia).

Thus, by determining the risk structure, we can elucidate the relations between its determining factors, and whether they are controllable or uncontrollable [26].

**Figure 8.** Share of the background risks and occupational risks in the overall injury risks.

The controllable factors in this case are the factors linked to the enterprises' operations, and the uncontrollable ones comprise the external conditions determined by the environment (such as air pollution), placement of workers' habitations, length of the polar day and night, intensity of the UV radiation, location of the facilities, weather conditions, etc. The external conditions affect the mental and physical states of the workers.

#### *3.2. The Specifics of Determining the Priority Directions for Reducing Injury Rates at the Kirov Branch of "Apatit", JSC*

We use the vertically integrated Kirov branch of "Apatit" (based in Murmansk) as an example subject for our methodology. The company incorporates the following: the United Kirovsky mine, the Rasvumchorr mine, the East mine, and the Central mine. The original data used in the study were taken from the reports by the State Committee for Supervision of Industrial and Mining Practices (Rosgortekhnadzor), the internal reports by the Kirov branch of "Apatit", and the reports collected by other authors [27]. The calculated occupational risk values are presented as linear functions in Figure 9.

**Figure 9.** Occupational injury rates at the mines of Apatit.

Figure 9 illustrates that all the mines of the company have seen a decrease in injury rates over the 10-year period studied. The risk trend is described by a linear function with the correlation coefficient exceeding 0.7, with the confidence interval being 0.95.

At the East mine, the occupational injury rate of the first few years is largely different from the other mines' and the company as a whole. However, by 2018, the injury rates have become comparable to other mines'. For each linear correlation dependence of the injury risk for the mines of the Kirov branch of Apatit, the mean value (M) and standard error (SEM) were calculated.

The occupational risk situation at the mines is characterized by the following two indicators: the average risk of injury ( *R*) and the average rate of change - *V* in the risk of injury. The average rate of change in the injury risks corresponds to the linear correlation's regression coefficient, and the average injury risk represents the mean between the injury risks at the beginning and at the end of the period in question [28,29]. The relative changes in values ( Δ *R*, Δ *V*) are calculated as the ratio of the average injury risk and the average injury risk of change at a specific mine to the company-wide values.

On the basis of the data on occupational injury rates at the Apatit company from 2008 to 2018, we have calculated the occupational injury risks for the four mines [30,31].

Table 2 shows the relative changes in occupational injury risks and the rate of changes in the injury risks ( Δ *R* and Δ *V*) respectively) for the four company mines.

**Table 2.** Values of the relative change in injury risks (Δ*R*) and the relative change in their rates -Δ*V* and their reciprocals 1/Δ*V* for the company mines.

The relative change in the injury risk rate and the relative injury risk value serve as the basis for the "basic injury rate matrix", where the reciprocal of the relative change in the injury risk rate is shown on the X axis, and the relative value of risk, on the Y axis.

For the sake of clarity, the segments of the matrix that correspond to various occupational injury risks are colored differently: green means acceptable labor safety; yellow— satisfactory; red—unsatisfactory; dark red—critical.

The matrix allows for the results of comparative assessment of occupational injury risks at different company enterprises to be visualised and primary measures for their reduction to be determined [32–34].

Figure 10 shows the matrix of relative change in injury risk rates for the mines of the Kirov branch of the "Apatit" company. The results of the analysis and the calculated average values of the changes in injury risk rates over the decade make it possible to assess the occupational safety measures in their entirety.

**Figure 10.** Matrix of the relative change in injury risk rates at the mines of the Kirov branch of "Apatit", JSC.

> Figure 11 shows the "Matrix of changes in occupational injury risk levels over a 10-year period at the mines of the Kirov branch of "Apatit", JSC".

**Figure 11.** Matrix of changes in occupational injury risk levels over a 10-year period at the mines of the Kirov branch of "Apatit", JSC.

As shown in Figure 11, over the last decade, labor safety conditions pertaining to the occupational injury risks at the Kirov mine were satisfactory; at the Rasvumchorr mine—unsatisfactory; at the East mine—critical; at the Central mine—between satisfactory and unsatisfactory.

The analysis provided allows for the assessment of labor safety conditions pertaining to occupational injury risks both over the last 10 years and during the current period of enterprise operation [35,36].

The assessment makes it possible to determine the priority avenues for reducing injury risk and improving occupational safety.

In conclusion, it should be noted that the article advocates the methodology of identifying the enterprise with higher occupational injury rate in comparison with others within the company as a whole based on the comparative analysis of the occupational injury rates at specific enterprises and companies including these enterprises. The proposed methodology allows companies to identify the priority directions for improving occupational safety, which will be targeted at specific enterprises.

For further research, it is useful to develop software to implement the proposed algorithm for identifying the priority areas for reducing occupational injury rates.


**1. Introduction**

Recurrent pneumothorax which is associated with menstruation is named as "catamenial pneumothorax" (CPX). It was first reported by Maurer et al. [1] and was presented to be a form of ectopic endometriosis and the term CPX was stated by Lillington et al. [2].

"Catamenial" is a name from Greek meaning "monthly." CPX is most commonly associated with endometriosis, but other etiological mechanisms of this disease exist [3–6].

In the literature, CPX is defined to be a recurrent pneumothorax occurring up to 24 h before or within 72 h after the onset of menstruation [4, 6], and on the other hand, not necessarily appearing every month [7]. Symptoms and signs of CPX are mostly unspecific so much clinical suspicion has to be maintained [8]. CPX is a rare entity; however, regarding literature, about one-third of all surgically treated cases of pneumothorax in women are diagnosed to be CPX [9–12].

Therefore, thoracic endometriosis should always be suspected in reproductiveage woman who suffer chest pain from spontaneous pneumothorax.

Thoracic endometriosis syndrome may be associated with other causes than pelvic endometriosis. In the first 24–48 h of menstruation, symptoms begin to appear and are usually seen on the right side of the chest. In 90% of the patients, chest pain is the most common symptom, and in one-third of the patients, shortness of breath is rarely seen, but hemoptysis is also added to the clinical picture [13]. In the light of these findings, the diagnosis of the disease is made clinically.

From 3 to 6% of spontaneous pneumothorax cases are catamenial pneumothorax, about one-third of all surgically treated cases of pneumothorax in affected women.

## *Pneumothorax*

The mean age of onset is reported to be 32–35 years [3, 4, 12, 14–17]. CPX may also develop as late as at 39 years of age [18, 19]. CPX occurs most often (85–95%) unilaterally, usually occurring on the right side of the chest, but there are cases on which pneumothorax also occurs on the left side or bilaterally [11, 15–21].


**2. Theory**

According to the Joule effect and the magnetization theory of ferromagnetic material, there is a functional relationship between the stress of rebar and the change in magnetic permeability [30,31]. In Equation (1), *μ* is the permeability of rebar, *μ*0 is the vacuum permeability, *λ*s is the axial deformation constant, *M*s is the saturation magnetization, *K*u is the uniaxial magnetic anisotropy constant, *H*R is the excitation magnetic field, and *θ*0 is the angle between the magnetic field and the easy magnetization axis [32].

$$\sigma = E \frac{3\lambda\_{\text{sf}}M\_{\text{S}}}{2K\_{\text{u}}} (\mu - \mu\_0) H\_{\text{R}} \sin^2 \theta\_0 \cos \theta\_0 \tag{1}$$

A magnetic resonance sensor [28] was used to monitor the working stress of the rebar. The sensor's two coils are the excitation and induction coils. The coil is wound on the PVC skeleton, as shown in Figure 1. The equivalent circuit diagram [28] of the magnetic resonance sensor is shown in Figure 1. *L*T and *L*R are the inductance of the excitation coil and induction coil, respectively. *C*T and *C*R are the excitation and induction coil's compensation capacitors, respectively. *u*CT and *u*CR are the voltage of the compensation capacitor of the excitation coil and the induction coil. *R*T and *R*R are the internal resistance of the excitation coil and the induction coil, respectively. The voltage source is AC power, and the input voltage is *u*in. The millivoltmeter is regarded as a load connected in series with an induction coil, and its equivalent resistance is *R*L.

**Figure 1.** The magnetic resonance sensor and equivalent circuit diagram schematic.

According to Kirchhoff's voltage law [33], the self-impedance of the excitation coil and the induction coil is *Z*T and *Z*R, respectively, as shown in Equations (2) and (3). The loop current *I*R of the induction coil is shown in Equation (4), where *j* is the imaginary part of the complex number, *U*in is the effective value of *u*in, *ω* is the angular frequency of *u*in, and *M* is the mutual inductance between the excitation coil and the induction coil. According to the coupled mode equation of LC coupled circuit [34], the relationship between coupling coefficient *κ* and mutual inductance *M* can be expressed as Equation (5); *ω*0 is the resonant frequency.

$$Z\_{\rm T} = R\_{\rm T} + j\omega L\_{\rm T} + \frac{1}{j\omega C\_{\rm T}} \tag{2}$$

$$Z\_{\rm R} = R\_{\rm R} + R\_{\rm L} + j\omega L\_{\rm R} + \frac{1}{j\omega C\_{\rm R}} \tag{3}$$

$$\dot{I}\_{\rm R} = \frac{-j\omega M \dot{U}\_{\rm in}}{Z\_{\rm T} Z\_{\rm R} + \left(\omega M\right)^2} \tag{4}$$

$$M = \frac{2\kappa\sqrt{L\_{\text{R}}L\_{\text{T}}}}{\omega\_0} \tag{5}$$

A rebar with a cross-sectional area of *A*iron is placed in a magnetic resonance sensor. *A*air is the cross-sectional area of the nonmagnetic material between the coil and the rebar. The voltage source provides alternating current for the excitation coil. Under the action of alternating current, the excitation coil generates an excitation magnetic field [35,36]. The excitation coil and the induction coil are resonantly coupled. An excitation magnetic field of the magnetized rebar is generated in the induction coil. The magnetic field is expressed as *H*R, which has a functional relationship with the coupling coefficient *κ*, as shown in Equation (6). *N*R is the number of turns of the induction coil. *l*R is the effective magnetic circuit length of the induction coil. According to electromagnetic induction law, the induced voltage of the induction coil can be obtained by the magnetic flux in the area around the coil [37], as shown in Equation (7), where *Φ* is the magnetic flux around the area of the induction coil, and *t* is the time.

$$H\_{\rm R} = \frac{N\_{\rm R} \dot{l}\_{\rm R}}{l\_{\rm R}} = \frac{-j\omega N\_{\rm R} 2\kappa \sqrt{L\_1 L\_2} l I\_{\rm in}}{\left[ Z\_{\rm T} Z\_{\rm R} + \left( \frac{2\omega \kappa \sqrt{L\_1 L\_2}}{\omega\_0} \right)^2 \right] l\_{\rm R} \omega\_0} \tag{6}$$

$$
\mu\_{\rm CR} = N\_{\rm R} \frac{d\Phi}{dt} = N\_{\rm R} \frac{d(\mu H\_{\rm R} A\_{\rm iron} + \mu\_0 H\_{\rm R} A\_{\rm air})}{dt} \tag{7}
$$

Combined with the electric power calculation formula, the excitation coil's input power *P*in and the millivoltmeter's output power *P*o as the load can be calculated, respectively. The results are shown in Equations (8) and (9). The transmission efficiency can be obtained as shown in Equation (10). *X*T = *ωL*T−1/*ωC*T, *X*T = *ωL*R−1/*ωC*R.

$$P\_{\rm in} = \frac{\boldsymbol{\mathcal{U}\_{\rm in}}}{R\_{\rm R}} = \frac{\left\{\boldsymbol{R}\_{\rm T} \left[\left(\boldsymbol{R}\_{\rm R} + \boldsymbol{R}\_{\rm L}\right)^{2} + \boldsymbol{X}\_{\rm R}\right] + \omega^{2}\boldsymbol{M}^{2} \left(\boldsymbol{R}\_{\rm R} + \boldsymbol{R}\_{\rm L}\right)\right\} \boldsymbol{U}\_{\rm in}\boldsymbol{\mathcal{Z}}}{\left[\left.\boldsymbol{R}\_{\rm T} \left(\boldsymbol{R}\_{\rm R} + \boldsymbol{R}\_{\rm L}\right) - \boldsymbol{X}\_{\rm T}\boldsymbol{X}\_{\rm R} + \omega^{2}\boldsymbol{M}^{2}\right] + \left[\left.\boldsymbol{R}\_{\rm T}\boldsymbol{X}\_{\rm R} + \left(\boldsymbol{R}\_{\rm R} + \boldsymbol{R}\_{\rm L}\right)\boldsymbol{X}\_{\rm T}\right]^{2}}\right.\tag{8}$$

$$P\_0 = I\_{\mathbb{R}} \,^2 R\_L = \frac{\omega^2 M^2 R\_{\mathbb{L}} \, ^2 \mathrm{L} \, ^2 \mathrm{L}}{\left[ R\_{\mathbb{T}} (R\_{\mathbb{R}} + R\_{\mathbb{L}}) - X\_{\mathbb{T}} X\_{\mathbb{R}} + \omega^2 M^2 \right] + \left[ R\_{\mathbb{T}} X\_{\mathbb{R}} + (R\_{\mathbb{R}} + R\_{\mathbb{L}}) X\_{\mathbb{T}} \right]^2} \tag{9}$$

 $\eta \quad = \frac{P\_{\text{in}}}{P\_{\text{in}}} \times 100\%$
$$= \frac{\omega^2 M^2 R\_{\text{L}}}{R\_{\text{T}} \left[ \left( R\_{\text{R}} + R\_{\text{L}} \right)^2 + \chi\_{\text{R}}^2 \right] + \omega^2 M^2 \left( R\_{\text{R}} + R\_{\text{L}} \right)} \times 100\% \text{ } \tag{10}$$

When the induction coil resonates, *X*R = 0, the transmission efficiency reaches the maximum, and the measured induced voltage is the highest. In the working stress monitoring

experiment, the rebar is used as the core of the induction coil. The change of permeability of rebar caused by working stress also causes the induction coil's inductance change. After the inductance changes, the resonant frequency of the induction coil changes, as shown in Equation (11). When the resonant frequency of the induction coil deviates from the initial resonant frequency, the coil coupling coefficient *κ* and the sensor induced voltage are significantly reduced, thereby improving the sensitivity of the rebar working stress monitoring.

$$\omega\_0 = \frac{1}{2\pi\sqrt{L\_\text{R}C\_\text{R}}} = \frac{1}{2\pi\sqrt{C\_\text{R}}} \frac{1}{\sqrt{(\mu A\_{\text{iron}} + \mu\_0 A\_{\text{air}})\frac{N\_\text{R}^2}{I\_\text{R}}}} \tag{11}$$

The above relationship is solved simultaneously to explore the internal relationship among stress, magnetism, and electricity. The change in stress will lead to the change of permeability of the rebar. The relationship between induced voltage and permeability can be simplified from Equations (7)–(12), where *f(u*CR*)* is the function of induced voltage *u*CR representing permeability *μ*. The induced voltage *u*CR is related to the coupling coefficient *κ*. For a specific rebar and sensor, the relationship between the sensor's induced voltage and the rebar's working stress can be expressed as Equation (13); *g(u*CR*)* is the function of the induced voltage *u*CR representing the working stress *σ*.

$$\mu = \frac{\int \mu\_{\rm CR} dt - N\_{\rm R} H\_{\rm R} \mu\_{0} A\_{\rm air}}{N\_{\rm R} H\_{\rm R} A\_{\rm iron}} = f(\mu\_{\rm CR}) \tag{12}$$

$$
\sigma = h[f(\mu\_{\text{CR}}) - \mu\_0] = \lg(\mu\_{\text{CR}}) \tag{13}
$$

Through the above derivation, it can be found that the rebar working stress is related to the sensor's induced voltage. Therefore, the induced voltage of the magnetic resonance sensor can be used to evaluate the working stress of the rebar. To verify the feasibility of the magnetic resonance monitoring method (REME) for rebar working stress monitoring, rebar working stress monitoring experiments were carried out.


**2. Configurations of sustained dual apprenticeship systems in Austria, Germany, and Switzerland**

The plenty of elements necessary to establish a "quality apprenticeship" [6, 7] have been identified through various steps and discourses since the 2010s. These discourses have taken different starting points for the identification of apprenticeship provisions that can be ordered at a continuum between a modular and a holistic approach. A modular approach identifies apprenticeship as a conglomerate of elements around forms of work-based learning (WBL), whereas the holistic approach identifies complex configurations of interconnected elements necessary for the proper functioning of the system. The various approaches taken over time have been condensed in the International Labour Organization (ILO) concept of "quality apprenticeship" [6] that encompasses ten key features (a tripartite system of governance, remuneration, a written agreement, social security coverage, a legal framework, a program of learning, duration, both on-the-job and off-the-job learning components, a formal assessment process, a recognized qualification) ordered by six building blocks (meaningful social dialog, inclusiveness, robust regulation, strong labor market, clear roles and responsibilities, equitable funding). This demanding systemic approach has been widely adopted in political discourses in the meantime.

A definition of apprenticeship given for the United States (US) draws a basic distinction to internships. "Apprenticeships provide long-term paid work-based learning opportunities and structured educational curricula that ensure the learner gains education and hands-on experience in an occupation, similar to how we train medical doctors with a mix of classes and residency experience. … Short-term workbased learning programs such as internships or staff development programs are not apprenticeships" [14]. In European Union (EU) research the different approaches of holistic and modular structures have been applied. Based on a modular approach an inventory of apprenticeships and work-based learning in the European Union has identified a widespread use of such practices [15].

The full definition of quality apprenticeships follows a good deal of the established structures in the paradigmatic countries analyzed in this chapter. Nevertheless, these structures differ in many aspects. Previous research and documentation have shown that apprenticeship is differently embedded in the overall education structures, in connection with compulsory education on the one hand, and with tertiary education on the other. Germany and Switzerland differ from Austria in both respects. On the side of compulsory education both former countries have gradually increased the educational and competence input before the transition into apprenticeship, whereas Austria has not changed the structure of this transition with the criteria of only reaching age fifteen as the minimum age requirement of fulfilling nine years of compulsory education, without any credential required. Austrian apprentices are therefore persistently mainly of the age 15-to-18-years old age group, and the original idea of building the Fachhochschule sector on top of apprenticeship failed; a vocational baccalaureate was established not before the late 2000s.

In Germany, a medium-level school credential (*Mittlere Reife*) was established with additional schooling time between the minimum compulsory school and the upperlevel academic school that included partly the access criteria for tertiary education. The main access to dual apprenticeship shifted gradually to this medium-level credential from *Realschule*, which provided the choice of prolonged compulsory school time; in addition, graduates from academic schools holding credentials for access to tertiary education also increasingly have chosen access to apprenticeship. Consequently, the age of apprentices shifted increasingly upward towards the age groups for tertiary education; in the early 2000s the average age of German apprentices was already almost twenty ([16], p. 163), and currently, almost half of apprenticeship beginners own the medium level credential, and an additional quarter own the credential from academic school with access to tertiary education ([17], p. 44). In Switzerland, the structure of compulsory education varies by regional units (Cantons) with a range of duration and broader access to academic schools with flexible and mixed access to dual apprenticeship [18–20]. The connection between vocational and academic education was increased through the vocational baccalaureate (*Berufsmaturität*) established in the mid-1990s, and subsequently the creation of the universities of applied science (*Fachhochschule*) in the late 1990s with exclusive access from the apprenticeship system. Consequently, the apprenticeship system is situated at the lowest end of the overall education structure in Austria [21, 22], whereas this system has increasingly shifted upward towards tertiary education in Germany and Switzerland; this creates substantial cracks in the trilogy. The further analysis of comparative data tries to find empirical representations of this crack, in order to explore its depth.

### **2.1 Apprenticeship in overall aggregate education structures: indications about its relationship to low-level and higher education**

This section explores to what extent the educational structures in the three countries with strong paradigmatic dual apprenticeship systems at the upper secondary


**4. Limbal incisions**

crystalline lens, a structure that is normally opticaly clear. Hardening and clouding of the lens may result in a progressive loss of vision depending on its size, location and density. It is typically bilateral, compromises visual acuity and contrast sensitivity and increases glare. Cataract may form at any age due to a number of different etiologies such as systemic metabolic disease, use of different medications, ocular trauma, but most often is age related. As the lens ages, it increases in weight and thickness and decreases in accommodative power. In crosssectional studies, the prevalence of cataracts is 50% in people between the ages of 65 and 74 and it increases to 70% in those over the age of 75 [1]. The pathogenesis of age-related cataracts is multifactorial and not completely understood. Different methods have been developed for cataract surgery, such as intracapsular and extracapsular extraction procedures, but the most common and widely accepted procedure is phacoemulsification, since its development in 1967,

The procedure of phacoemulsification has gained increasing popularity worldwide, since the introduction of sutureless clear corneal cataract incisions, due to several advantages over the traditional sutured scleral tunnels and limbal incisions [3]. Several surgical approaches have been suggested to allow for a faster and easier phacoemulsification technique. The introduc‐ tion of clear corneal incisions to enter the anterior chamber and remove the cataract using phacoemulsification revolutionized cataract surgery [4]. This approach is the most popular and widely accepted. Clear corneal wounds have transformed cataract surgery by dramatically reducing surgical time, offering faster postoperative recovery, and lowering the induced

Scleral incisions in phacoemulsification were firstly introduced by Girard and Hoffman [5]. The incision size is usually 3-7 mm in chord length [6]. Smaller incisions may be sutureless while larger tunnels often are sutured and periotomy is performed in both cases. Scleral tunnel construction could lead to several problems. An initial scleral tunnel that is too deep will create scleral disinsertion with exposure to the ciliary body leading to different problems with hemostasis, poor wound stability, early or posterior entry to the anterior chamber, and iris prolapse. On the other hand, an incision that is too shallow may result in tear of the tunnel roof and problems with water tightness of the wound [3]. Another thing that is of great importance is the length of scleral tunnel. A dissection that is too far into the cornea creates an anterior entry into the anterior chamber resulting in decreased maneuverability and corneal striae that interfere with visibility during subsequent steps. An incision that is too short would create problems with wound closure and iris prolapse [3]. Other complicaions include induced

astigmatism, filtration, hyphema and Descemets membrane detachment.

by Charles Kelman [2].

186 Advances in Eye Surgery

**2. Phacoemulsification**

**3. Scleral tunnel incisions**

astigmatism in comparison to scleral tunnel incisions [3].

The differences in the healing effects of incisions at the limbus and the cornea are widely discussed in the literature [7-9]. Limbal incisions are likely to heal more quickly and are more resistant to deformation pressure than those in the cornea. Reasons for that are significant anatomical and physiological differences between them. Regular lamellar structure of collagen fibrils of the cornea stretch from limbus to limbus. It is arranged in a lattice formation and provides the primary structural support of the cornea and its transparency. The cornea is avascular.

On the other hand, at the limbus, this regular structure is no longer present. Vascular arcades are evident, providing a potential source of fibroblasts [10] in the effects of clear corneal and scleral or limbal incisions, related to differences in the respective structures where the incisions are made.

In comparison to limbal incisions, clear corneal incisions also appear to increase the likelihood of endophthalmitis [11, 12].


**Abstract**

As the global burden of diabetes is increasing there is a corresponding increase in the complications associated with the same. Diabetic retinopathy is a sight threatening complication of diabetes mellitus which was considered to be a microvasculopathy. Recent evidence however, has brought to light that inflammation may be a key player in the pathogenesis of this condition. Levels of inflammatory mediators like Hypoxia inducible factor, TNF-α, IL-6 and IL-1B amongst others have been noted to be elevated in the diabetic vitreous gel. The concept of the neurovascular unit better explains the changes that take place resulting in the breakdown of the blood retinal barriers and how these inflammatory mediators affect the morphology of the retina at a cellular level. Glial cells form a key instrument of this neurovascular structure and are also the cells from where the inflammatory response is initiated. Understanding of the pathogenesis of diabetic retinopathy will help us in finding targeted therapies which may provide long term benefits and possible cure. Few anti-inflammatory medications have shown promise albeit in a small clinical or experimental laboratory setting. However, future research may lead to better understanding of the disease and a better pharmacological intervention.

**Keywords:** pathogenesis of diabetic retinopathy, glial cells in diabetic retinopathy, retina inflammation, steroids, cytokines

#### **1. Introduction**

In the past few years, unhealthy lifestyle coupled with obesity has led to a rampant increase in the global burden of diabetes mellitus. As per WHO the global prevalence of the condition was 422 million in the year (2014) with 8.5% of adults aged more than 18 years suffering from this condition. Approximately 1.6 million deaths yearly are supposed to be caused by diabetes alone, this number excludes the mortality associated with cardiovascular events, renal disease and tuberculosis secondary to chronic hyperglycemia [1]. The global burden of diabetes is expected to swell to 642 million adults by the year 2040 with 75% of the affected individuals belonging to low and middle income countries. Diabetic retinopathy affects 1 in 3 adults with diabetes and is one of the major causes of blindness in the working-age population [2].

Diabetic retinopathy (DR) is a major microvascular complication of diabetes and it is categorised into a non-proliferative stage (NPDR) or proliferative stage (PDR) depending on the presence of retinal microvascular changes. The nonproliferative stage is characterised by the presence of microaneurysms, cotton wool spots, vascular tortuosity, retinal haemorrhage and lipid exudation while in the

proliferative stage aberrant new blood vessels develop which are fragile and can extend into the posterior cortical vitreous [3]. Another important element in the vast conundrum of DR is diabetic macular edema (DME). It can occur across all levels of DR changes and compromises central vision. DME is the most common cause of diminished vision in an individual with DR [4].

### **2. The neurovascular unit in diabetic retinopathy**

In recent years a concept of neurovascular unit in diabetic retinopathy has emerged. This is based on the findings that neurodegeneration is one of the earliest changes in a case of diabetic retinopathy. Indeed, a reduction in oscillatory potential in the electroretinogram is the first measurable change in retinal function, being recorded even in cases wherein there is no clinical change suggestive of DR. This indicates that neurodegeneration precedes microvascular abnormalities [5].

The retinal neurovascular unit includes the physical and biochemical interactions amongst the neurons, the vascular beds and the supporting cells of the retinal framework. The neural unit includes the ganglion cells and the glial cells while the vascular component of the unit is made up of the endothelial cells and the pericytes. The neurovascular unit reflects the inter-dependance of the vascular barrier and blood flow regulation on the glial cells, pericytes and neurons as well as their reciprocal dependance on vascular support. Together with the neurovascular unit the retinal pigment epithelium contributes to the formation of the blood retinal barrier [6]. The inner blood retinal barrier (iBRB) encompasses the endothelial cells of the retinal microvasculature which are covered by astrocytes, pericytes and muller cell end-feet. It regulates the transport across the retinal capillaries and maintain the micro-environment of the inner retina. The outer blood retinal barrier (oBRB) comprises the tight junctions of the neighbouring RPE cells and it serves as a filter for nutrients and solutes from the blood [7].

#### **3. Histopathological changes in diabetic retinopathy**

Diabetic retinopathy has been traditionally described as a microvasculopathy, however newer evidence has suggested that inflammation may provide a substantial role in the histopathological changes that are noted in the disease.

#### **3.1 Microvascular changes**

Retinal ischemia is the initiator that propels the plethora of changes that occur in DR. In the initial stages, prior to even the development of visually significant vascular alterations, there is a disruption of vascular auto-regulation which leads to oxygen and nutrient deprivation in the inner retinal layers [8].

Retinal vascular basement membrane thickening is present in the early stages of the disease and is mediated by hyperglycemia. Impaired sugar levels lead to an up-regulation of extracellular matrix proteins, collagen and fibronectin [9]. This thickening of the basement membrane may lead to an impairment in cell to cell communication between the endothelium, pericytes, glial cells and the retinal immune cells, ultimately leading to a loss of function [10]. Loss of pericytes leads to a weakening of the capillary wall; this ultimately results in the development of areas of out-pouching labelled as microaneurysms (MA), which are amongst the first clinically detectable signs of DR [11].

#### *Role of Inflammation in Diabetic Retinopathy DOI: http://dx.doi.org/10.5772/intechopen.100175*

A progressive occlusion of the retinal capillaries is noted on histopathological evaluation of post-mortem specimens. Stitt et al. [10] evaluated trypsin digest preparations of these capillaries and noted them to be acellular tubes of naked basement membrane without endothelial cells. The loss of endothelial cells can be attributed to pericyte death which ultimately culminates into breakdown of the inner blood retinal barrier.

The breakdown of the inner blood retinal barrier would lead accumulation of fluid and exudates in the retinal layers contributing to diabetic macular edema (DME). Fluid exuding out from the superficial capillary plexus leads to accumulation of fluid in the inner nuclear layer while that exuding out from the deep capillary plexus is believed to collect in the outer plexiform layer. Cystoid spaces noted in the macula appear due to liquefaction and necrosis of the muller cells and the production of prostaglandins and inflammatory cytokines [12].

#### **3.2 Neuroglial changes**

The muller cells, astrocytes and microglial cells are present in close vicinity of the retinal blood vessels and help to maintain retina homeostasis. Muller cells play a central role in the retinal metabolism and hence are susceptible to the metabolic alterations of diabetes. An increased production of glial fibrillary acidic protein (GFAP) by the muller cells is noted in the early part of the disease, which plays a key role in gliosis. This is indicative of a state of glial hypertrophy [13].

Microglial cells are the resident inflammatory cells of the retina which get activated in DR. In the early stages of mild DR a hypertrophy of the microglia is noted and the cells are settled along the retinal plexiform layers. When the DR progresses into a proliferative stage the microglial cells are found to be extensively distributed in areas of retinal ischemia and neovascularisation [14]. A strong tendency of the microglial cells to invade the outer retinal layers is also noted in prolonged DR [15].

### **3.3 Neuronal changes**

Overt degeneration of retinal neurons during diabetes is a concept that was first described in the 1960s in post-mortem patient samples. By the late 1990s experimental evidence reinforced this finding by demonstrating that depletion of some neuronal populations occurred in diabetic rodent models, possibly even prior to appearance of obvious microvascular lesions. Apoptosis of the retinal ganglion cells (RGCs) is histologically noted in diabetic retinas. This also accounts for the diminished thickness of the retinal nerve fibre layer on optical coherence tomography (OCT). It is believed that hyperglycemia induces a down-regulation of neuronal growth factors thereby contributing to programmed cell death [16].