**2. Oxidative stress and Alzheimer's disease**

#### **2.1 Alzheimer's disease**

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases characterized as insidious, progressive, and degenerative. It accounts for 70% of all dementia cases in people aged 65 years and older [1]. The World Health Organization revealed that AD ranked as the seventh leading cause of death worldwide from 2000 to 2019. Although it is assumed that AD is triggered from genetics, environment, and dietary factors, the exact causes of AD are still not fully understood [2].

Patients with AD experience irreversible damage to the brain which leads to cognitive and behavioral deterioration, shrinkage of brain tissue, and progressive memory loss [3]. *Aβ* and NFTs are considered as the two key factors in the neurodegeneration of AD patients. The forebrain cholinergic neurons are damaged due to neurofibrillary tangles (NFTs) of P-tau and the accumulation of senile plaques composed of amyloid-*β*(*Aβ*Þ in the hippocampus, neocortex, amygdala, and basal nucleus of Meynert [4].

Although still debated, the amyloid cascade hypothesis best explains the pathology of AD. The *Aβ* protein, a 36 to 43 residue polypeptide (in several studies, 39 to 43 residues/38 to 43 residues), is generated in the process of amyloid precursor protein (APP) enzymatic proteolysis, a transmembrane protein responsible for neuron growth and repair. Among the two main pathways for disposal of APP, a nonamyloidogenic*α* -secretase-mediated pathway and an amyloidogenic *β*-and *γ* -secretase-mediated pathway, the neuropathology of AD derives from the latter, in which *Aβ* peptide is produced [5].

APP consists of both a cytoplasmic C-terminus and an extracellular glycosylated N-terminus. In the amyloidogenic pathway, APP is initially cleaved by a *β*-secretase creating a membrane bound 99-amino-acid C-terminal fragment. The C-terminal fragment, now acting as a substrate, is serially cleaved by a *γ*-secretase, resulting in a full length *Aβ*, mainly the 40-amino-acid *Aβ*40 and the 42-amino-acid *Aβ*42 [6, 7].

Due to the insolubility in AD patients, the *Aβ* monomers abnormally aggregate into higher order assemblies, oligomers, protofibrils, and fibrils, which ultimately deposit into senile plaques. Amyloid senile plaques spread throughout the brain, eventuating in the interference of intercellular communication and the activation of immune cells which provoke inflammation. Neurological brain damage induced from amyloid plaques are commonly detected in the neocortex of AD patients [7, 8].

NFTs, another factor regarded as a key contributor of AD, is linked with *Aβ* as well. Microtubules (MTs) in neurons work as directional highways between the axon and dendrites for organelle transport such as nutrients, neurotransmitters, motor proteins. The MT arrays also act as architectural elements that stabilize the structure and shape of the neuron [9]. The firmness of MTs depends on tau, a microtubuleassociated protein (MAP), which plays a vital role in regulating the dynamic network and assembly of MTs [10].

There is accumulating evidence that *Aβ* peptide induces tau hyperphosphorylation, which reduces the MT-tau affinity. Tau, no longer able to bind to MTs, start to aggregate forming tau clumps. Consequently, due to the decreased stability, MTs start to disintegrate. Separated tau cluster into tau oligomers, which eventually develop into neurofibrillary tangles (NFTs) [11]. With the breakdown of the MT system, neurons are incompetent to transmit organelles, resulting in the neurodegeneration of nerve cells which explains the memory loss and cognitive and behavioral decline of AD patients.

#### **2.2 Reactive oxygen species**

Reactive oxygen species (ROS) are unstable, highly reactive molecules and radicals which are derived from molecular oxygen. ROS production takes place in aerobic organisms that utilize mitochondrial electron transport for respiration or undergo oxidation catalyzed by metals and intracellular enzymes [12]. In normal settings, ROS play a crucial role for cell signaling such as cell cycle regulation, enzyme activation and apoptosis. Yet, under oxidative stress conditions, the immoderate production of ROS has detrimental effects on cells causing protein, DNA, and lipids damage and eventually, cell death [1].

When a molecular oxygen goes through a monovalent reduction, superoxide anion radical (*O*�� 2 ), a precursor compound of ROS, is formed [13]. *O*�� 2 , due to its unstable state, react with other radicals such as nitric oxide (*NO*Þ, forming highly reactive peroxynitrite (ONN*O*�). 0�� 2 also propagates further oxidative chain reactions, producing hydrogen peroxide (*H*2*O*2Þ with the help of superoxide dismutase (SOD). *H*2*O*2 are sequentially reduced either to hydroxyl radical (*OH*� ), one of the most reactive oxidants, or fully reduced to water [14, 15].

ROS generation, mainly in forms of *O*�� 2 , *H*2*O*2, *OH*� , are induced by both endogenous and exogenous pathways. The endogenously produced ROS are mainly byproducts of mitochondrial respiratory chain and phagocytic nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, circumstances in which the reduction of oxygen is enabled (**Figures 1** and **2**). Transition metals and numerous intracellular enzymes such as, Xanthine oxidase (XO), Lipoxygenases (LXO), and Cyclooxygenase (COX) are also principal endogenous ROS generators (**Figures 3**–**5**) [16].

ROS are produced in response to exogenous or environmental factors as well, such as radiation, air pollutants, diet, tobacco smoke, drugs and xenobiotics, chemotherapy, and pesticides [16, 17]. Exposure to UVR from solar radiation develops high concentrations of ROS, which causes an imbalance between ROS and cellular antioxidants, thus provoking oxidative stress [17]. Tobacco smoke, another notable factor of ROS production, consists of 1014-1016 free radicals per puff which can potentially produce *H*2*O*2 and *OH*� [16].

The right duration, quantity, and location of ROS production is required for normal physiological processes. In cases where the appropriate conditions are not met,

#### **Figure 1.**

*Amyloidogenicβ -and γ-secretase-mediated pathway of APP disposal In the amyloidogenic pathway of APP disposal, APP is first cleaved by a β-secretase yielding SAPPβ and CTF99/89. Subsequently, CTF99/89 is cleaved by a γ-secretase creating AICD and Aβ.*

#### **Figure 2.**

*Aβ plaques formation abnormal aggregation ofAβ monomers into oligomers, protofibrils, fibrils, and ultimately plaques can be seen in AD patients.*

#### **Figure 3.**

*NFTs formation Aβ peptide induces tau hyperphosphorylation, which reduces MT-tau affinity. Separated tau develop into oligomers and eventually NFTs.*

both insufficient and excessive ROS production, ROS-related diseases can arise [15]. Such medical conditions include glucolipotoxicity, insulin resistance, diabetes mellitus, mitochondrial dysfunction, cancer, autoimmune disorders, cardiovascular, neurological, and psychiatric disease [15, 18].

Antioxidants work as the defense mechanism against ROS induced damage. Its role is to maintain the effective functions of ROS while at the same time, regulate its level. Oxidative stress is attenuated by both endogenous antioxidant system and the exogenous intake of antioxidants [19, 20]. The former includes enzymes such as SOD, glutathione (GSH), catalase and glutathione peroxidase (GPx) [19]. Meanwhile, the essential exogenous antioxidants are absorbed through vegetables, whole grains, fruits, and omega-3 fatty acid containing diet. Vitamin C, vitamin E, *β*-carotene, selenium, carotenoids, and polyphenols represent exogenous antioxidants [19, 20].

#### **2.3 Oxidative stress and Alzheimer's disease**

Majority of current research show that oxidative stress, the imbalanced state of ROS production level and antioxidative level, is related to the pathogenesis of neurodegenerative diseases, representatively AD [21]. This chapter approaches mainly the

*Metal Ions-Mediated Oxidative Stress in Alzheimer's Disease and Chelation Therapy DOI: http://dx.doi.org/10.5772/intechopen.99690*

*ROS production pathways endogenous and exogenous pathways of ROS production include mitochondrial production, NADPH oxidase, peroxisome, and xanthine oxidase. Through such pathways, O*�� 2 *, ONNO*�*, H*2*O*2*, and OH*� *are yielded.*

**Figure 5.**

*Mitochondrial ROS production complex Iand complex III of the inner mitochondrial membrane create O*�� 2 *through oxidative phosphorylation. O*�� 2 *, through further reactions, can also yield H*2*O*2*, and OH*� *.*

association of oxidative stress with AD, mostly regarding the correlation between *Aβ* and ROS production and how it affects the neighboring neural molecules.

As previously stated above in the *Alzheimer's Disease* section, amyloid plaques and NFTs are regarded as the 'hallmarks' of AD. Overwhelming evidence show that amyloid plaques are highly concentrated in metal ions, such as copperð Þ *Cu* , ironð Þ *Fe* , zincð Þ *Zn*

and calciumð Þ *Ca* , which are present in the synaptic areas. Such metal ions are interconnected with the amyloid cascade reaction and NFT formation [22].

Metal ions imbalance induces oxidative stress which triggers ROS production. Increased production of ROS leads to secretases imbalance and phosphatases imbalance, each interconnected with the formation of *Aβ* and P-tau. Accordingly, *Aβ* and P-tau production increases, which eventually leads to neurodegenerative diseases including AD [23]. Thus, the *Aβ* toxicity, NFTs, oxidative stress, and ultimately neuronal cell death depend on the existence of redox metals [24]. This chapter mainly discusses the correlation of metal-catalyzed ROS production with A*β* (**Figure 6**).

### *2.3.1 Copper*

Among the metal ions, copper is considered the most redox reactive. The association of copper ions with *Aβ* can be described as a three-step process. First, endogenous reductants bind with the copper, followed by the reduction of *Cu*(II) to *Cu*(I). The reductive state of copper triggers the reduction of molecular oxygen as well, producing ROS [25]. Copper directly interacts with *Aβ*, promoting increased aggregation of *Aβ* and the toxicity of amyloid oligomers and plaques [22, 26].

Histidine ð Þ *His*6, *His*13, *His*14 and Tyrosine (Tyr10) amino acid residues modulate the binding of copper to *Aβ:Cu*(II) is reduced to *Cu*(I), after its chemically binding to *Aβ*(higher affinity to *Aβ*1- 42 compared to *Aβ*1- 40), generating hydrogen peroxide as a byproduct which has high potential to be reduced to hydroxyl radical. Accordingly, the complexation of copper in *Aβ* elevates the neurotoxicity, now endowed with enlarged

#### **Figure 6.**

*Metal ions imbalance, increased ROS production, and neurodegeneration imbalance of metal ions, such as copper, iron, zinc, and calcium, creates oxidative stress condition. This is followed by increased production of ROS, and consequently Aβ and NFTs, which eventually provokes neurodegenerative diseases including AD.*

reduction potential [24]. The *Cu*- *Aβ* couple correspondingly assists the further process of ROS production. The copper- *Aβ*-mediated oxidation of reductant species such as ascorbate, which are abundant in the brain, induces generation of ROS: hydrogen peroxide, hydroxyl radical and superoxide anion [25].

#### *2.3.2 Iron*

Iron, as a redox active metal, is also significantly linked with AD pathology. However, unlike copper, iron ions do not directly interact with and bind to *Aβ* [27]. Iron exists in both in redox-inactive forms *Fe*3þ and redox-active forms *Fe*2þ within the brain. They are also found in zero-oxidation-state *Fe*0 or as ionic compounds such as magnetiteð Þ *Fe*3*O*4 as well. All forms are possible inducers for *Aβ* aggregation, prompting the iron redox cycle and ROS production [28, 29]. Iron concentration and increased free radical production had been noticed in the cerebellum and glia cells of AD patients [23].

After iron's indirect interaction with *Aβ*, the redox cycle of Haber-Wiess and Fenton reaction is triggered, yielding ROS in forms of hydrogen peroxide, hydroxyl radical and superoxide anion, as in the process of copper-mediated oxidation. The resulting ROS effects *Aβ* aggregation and other oxidative damages in local organelles as well. Research results based on high-resolution transmission electron microscopy (HR-TEM) and synchrotron-based X-ray absorption studies support the storage of iron within *Aβ* and the iron-catalyzed ROS production [27, 29].

During the process of the metal-catalyzed ROS production in correlation with *Aβ*, both the *Aβ* peptide itself and the surrounding molecules undergo oxidative damages. The amino acid residues of *Aβ*, cysteine, methionine, arginine, histidine, lysine, phenylalanine, tryptophan, and tyrosine, are oxidated as well, chemically changed, and impaired. The ROS produced through metal-mediated oxidation also cause protein carbonylation and nitration, lipid peroxidation, and protein modification. The mitochondria of nearby cells also experience oxidation, leading to increased mitochondrial and nuclear DNA &RNA damages which all potentially lead to the etiology of AD [30].

#### *2.3.3 Zinc*

The impact of zinc *Zn*2þ in AD is rather controversial [23]. Some research suggests irregularly high concentration of *Zn*2þ have been investigated in AD patients' brains, inferring the linkage between imbalance of *Zn*2þ homeostasis with AD pathogenesis [31]. One study indicated that *Zn*2þ promotes both *Aβ*40 and *Aβ*42 aggregation, but only at the early stage [32]. In another study, high concentration of *Zn*2þ was shown to induce NADPH-oxidase reaction and ROS production (especially mitochondrial ROS production) in AD pathological state. Excessive zinc therefore prompted *Aβ* cascade reaction [23]. On the contrary, other research analysis show significant decrease of *Zn*2þ in AD patients [33].

#### *2.3.4 Calcium*

Calcium *Cu*2þ elevation also significantly contributes to *Aβ* production in AD patients. Sequentially, increased *Aβ* level in turn promotes an increase in *Cu*2þ level by triggering the opening of voltage-dependent *Cu*2þ channels. Moreover, high degree of *Cu*2þprovokes further influx of *Cu*2þ by enabling overexpression of L-type calcium

channel subtype (Cav1.2). Excessive *Cu*2þ consecutively stimulate *Aβ* production and aggregation [23].


**4. Conclusion and recommendations**

This study assessed soil loss using a GIS-based RUSEL equation. The GIS-baes RUSEL equation well estimated the amount of soil loss in our study areas, which resulted in comparable findings with other findings. The annual soil loss increased at LS and S factors compared with the other RUSEL factors. Compared with other land uses, barelands and croplands that found at the higher elevations generated more soil loss. It is found that lack of vegetative cover during the critical period of rainfall, expansion of croplands, and lack of support practices also increase soil erosion. The application of soil bund, area closure, contour tillage, terraces, and grass strip barriers are suggested to break the slope length into shorter distances, reducing overland flow velocity and soil erosion.


*3.2.4. Full-bridge converter with controlled at secondary side*

The configuration shown in Figure 17 corresponds to a secondary side controlled converter with a full-bridge inverter on primary side and phase-controlled rectifier on secondary side. The switches on primary side are operated by complementary gating scheme with fixed duty ratio. The switches on secondary side are controlled to produce phase difference between primary and secondary side voltages of HF transformer to control the output voltage with load and input line voltage variations.

The main characteristics of this configuration can be summarized as follows:

**1.** Control is easy and simple.

**Figure 15.** Series Resonant converter with inductive output filter.

be summarized as follows:

344 New Developments in Renewable Energy

**Figure 16.** PWM full-bridge converter.

*3.2.3. PWM full-bridge converter with inductive output filter*

**2.** High voltage stress on rectifier diodes on secondary side.

conduction losses, which decreases converter efficiency.

**3.** Parasitic ringing at the secondary side of transformer.

required to keep conduction losses lower.

The configuration shown in Figure 16 is Phase-shifted full bridge converter with inductive output filter. This is most widely used soft-switched configuration for high power applications. This constant frequency converter features ZVS of the primary switches with relatively small circulating circuit. The ZVS is achieved by filter inductance, transformer leakage inductance, snubber capacitance and parasitic junction capacitances of switches. The control of the output voltage at constant frequency is achieved by phase shift technique. Its main characteristics can

**1.** The major limitation of this configuration is loss of duty cycle on the secondary side.

**4.** Large inductor may increase ZVS range but needs a transformer turns-ratio (Np/Ns) to be decreased at the same time, which increases primary side current, causing large

**5.** A good compromise between the transformer turns-ratio and leakage inductance is

**Figure 17.** PWM full-bridge converter.

#### *3.2.5. Current-fed two-inductor boost converter*

Figure 18 shows the current-fed two-inductor boost converter, which is a dual of voltage-fed half-bridge converter configuration. This topology requires a very small turns ratio trans‐ former with only two switches used on the primary side. The turn off of the primary switches is smooth since voltage across the switches is sinusoidal. Also the same for turn on and turn off of rectifier diodes. However, the peak and average current through rectifier diodes is high and this is very difficult to achieve ZVS at variable line input and load condition. This topology is a good selection for constant-input and constant-output voltage applications.

**Figure 20.** Electrical circuit of the SRC.

low pass filter in the secondary (Lf

current, iL are assumed to be purely sinusoidal.

as represented by Figure 22 and equation 14.

the load, respectively.

*3.4.1. Voltage*

The operation of the converter can be described as follows: the voltage supplied by the fuel cell stack, which is typically low must be converted to a high and constant level, for example; 48 V or 400 VDC in order to be able to feed an electric vehicle or to be sent to the grid through an inverter. The HF transformer is a step-up voltage transformer, which also serves as galvanic isolation between the high and low voltage levels of the circuits. The waveforms of the voltage and current in the LC series resonant circuit in the primary side of the transformer are sinusoidal. Selecting appropriate values for the Lr and Cr components, the resonant frequency of the circuit is established. Then, the DC voltage of the fuel cell is firstly inverted in the primary side of the HF transformer, being rectified on the secondary side. The low pass filter in the primary side (LPEM, CPEM) allows at protecting the PEM fuel cell from the ripple current and voltage produced by the converter, and also allows the storage of energy in the DC bus. The

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347

The series-resonant inverter as part of the topology of Figure 21 is constitute by a controlled

The analysis of this circuit by sinusoidal approximation neglects the harmonics of the switch‐ ing frequency and the tank waveforms, namely the capacitor voltage, VC and the inductance

In Figure 21 the output voltage of the full-bridge inverter, which in turn is the input voltage of the series resonant circuit, is a square-waveform, wherein in the first half period [0-π] the IGBTs Q1 and Q3 conduct, while in the second half period conduct IGBTs Q2 and Q4 [π - 2π]

full-bridge and a series resonant circuit with natural frequency *fr* and impedance Zr.

) allows at reducing the ripples of current and voltage to

, Cf

**3.4. Mathematical analysis of the series resonant converter (SRC)**

**Figure 18.** Current-fed two-inductor boost converter.

#### *3.2.6. Bidirectional current–fed converter*

The configuration of Figure 19 corresponds to a soft-switched bidirectional current-fed boost converter. The active switches in the both sides of the transformer make this converter bidirectional. Normally the Mosfets are used in the low voltage side and the IGBTs are used in the high voltage side. This configuration can be in push-pull, half-bridge or full-bridge, with the full-bridge considered one of the best topology choices for fuel cell applications.

**Figure 19.** Soft-switched bidirectional current-fed boost converter.

#### **3.3. Selection of converter topology and operation**

Considering the analysis made previously a full-bridge series-resonant inverter, followed by a high frequency transformer and a rectifier, composes the topology selected. Additionally, two low-pass filters are included, one in the primary side which is used to protect the PEM of high ripple-current and another in the secondary side, to improve the quality of the energy supplied by the power system to the load or the grid. Figure 20 below represents electrical scheme of its topology, which is also called a series resonant converter (SRC).

**Figure 20.** Electrical circuit of the SRC.

**Figure 18.** Current-fed two-inductor boost converter.

**Figure 19.** Soft-switched bidirectional current-fed boost converter.

**3.3. Selection of converter topology and operation**

The configuration of Figure 19 corresponds to a soft-switched bidirectional current-fed boost converter. The active switches in the both sides of the transformer make this converter bidirectional. Normally the Mosfets are used in the low voltage side and the IGBTs are used in the high voltage side. This configuration can be in push-pull, half-bridge or full-bridge, with

Considering the analysis made previously a full-bridge series-resonant inverter, followed by a high frequency transformer and a rectifier, composes the topology selected. Additionally, two low-pass filters are included, one in the primary side which is used to protect the PEM of high ripple-current and another in the secondary side, to improve the quality of the energy supplied by the power system to the load or the grid. Figure 20 below represents electrical

scheme of its topology, which is also called a series resonant converter (SRC).

the full-bridge considered one of the best topology choices for fuel cell applications.

*3.2.6. Bidirectional current–fed converter*

346 New Developments in Renewable Energy

The operation of the converter can be described as follows: the voltage supplied by the fuel cell stack, which is typically low must be converted to a high and constant level, for example; 48 V or 400 VDC in order to be able to feed an electric vehicle or to be sent to the grid through an inverter. The HF transformer is a step-up voltage transformer, which also serves as galvanic isolation between the high and low voltage levels of the circuits. The waveforms of the voltage and current in the LC series resonant circuit in the primary side of the transformer are sinusoidal. Selecting appropriate values for the Lr and Cr components, the resonant frequency of the circuit is established. Then, the DC voltage of the fuel cell is firstly inverted in the primary side of the HF transformer, being rectified on the secondary side. The low pass filter in the primary side (LPEM, CPEM) allows at protecting the PEM fuel cell from the ripple current and voltage produced by the converter, and also allows the storage of energy in the DC bus. The low pass filter in the secondary (Lf , Cf ) allows at reducing the ripples of current and voltage to the load, respectively.

### **3.4. Mathematical analysis of the series resonant converter (SRC)**

The series-resonant inverter as part of the topology of Figure 21 is constitute by a controlled full-bridge and a series resonant circuit with natural frequency *fr* and impedance Zr.

The analysis of this circuit by sinusoidal approximation neglects the harmonics of the switch‐ ing frequency and the tank waveforms, namely the capacitor voltage, VC and the inductance current, iL are assumed to be purely sinusoidal.

### *3.4.1. Voltage*

In Figure 21 the output voltage of the full-bridge inverter, which in turn is the input voltage of the series resonant circuit, is a square-waveform, wherein in the first half period [0-π] the IGBTs Q1 and Q3 conduct, while in the second half period conduct IGBTs Q2 and Q4 [π - 2π] as represented by Figure 22 and equation 14.

tank rings sinusoidal and is(t) is well approximated by a sinusoid waveform as represented

Im sin , for 0 0, for 2

ï+ ´ -F < < í ï < < î

( )

A simple equivalent circuit similar to that represented in Figure 23, whose operation can be analyzed by a second-order differential equation as follows, can perform the analysis of the

Applying the Kirchoff loop equation (sum of voltages around loop equals zero), to the RLC

*diLr Lr VCr RI V*

circuit of Figure 23 the equation of the series resonant circuit is the follow.

*dt*

ï < < í ï- ´ -F < < î

Im sin , for 2

*ax wt wt*

0, for 0

*ax wt wt*

*wt*

*wt*

p

Methodology of Designing Power Converters for Fuel Cell Based Systems: A Resonant Approach

p

p

(15)

349

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(16)

 p

p

 p

+ += (17)

( ) 1 3

by the equations 15 and 16.

SRC.

*iQ iQ*

2 4

= = ì

*iQ iQ*

**Figure 23.** Series resonant circuit with a resistive load.

= = ì

**Figure 21.** Electrical equivalent circuit of the SRC.

**Figure 22.** Output voltage in a full-bridge inverter.

$$\begin{cases} +V\_{\mathbb{F}\epsilon^{\prime}} \text{ for } 0 < \mathsf{wt} \le \pi \\ -V\_{\mathbb{F}\epsilon^{\prime}} \text{ for } \pi < \mathsf{wt} \le 2\pi \end{cases} \tag{14}$$

#### *3.4.2. Current*

The current is (t) is equal to the output current +is (t) in the first half period [0-π], and is its inverse -is (t), in the second half period [π - 2π]. Under the conditions described above, the tank rings sinusoidal and is(t) is well approximated by a sinusoid waveform as represented by the equations 15 and 16.

$$\begin{aligned} \mathbf{iQ1} = \mathbf{iQ3} = \begin{cases} \\ + \text{Im} \, a \mathbf{x} \times \sin \left( wt - \Phi \right) , \text{ for } 0 < wt < \pi \\ 0 , \qquad \text{for } \ \pi < wt < 2\pi \end{cases} \end{aligned} \tag{15}$$

$$\begin{aligned} \text{iQ2} = \text{iQ4} = \begin{cases} 0, & \text{for } 0 < wt < \pi \\ -\text{Im}\,ax \times \sin\left(wt - \Phi\right), & \text{for } \pi < wt < 2\pi \end{cases} \end{aligned} \tag{16}$$

A simple equivalent circuit similar to that represented in Figure 23, whose operation can be analyzed by a second-order differential equation as follows, can perform the analysis of the SRC.

**Figure 23.** Series resonant circuit with a resistive load.

**Figure 22.** Output voltage in a full-bridge inverter.

**Figure 21.** Electrical equivalent circuit of the SRC.

348 New Developments in Renewable Energy

*3.4.2. Current*

, for 0 wt , for wt 2

ìï+ <£

ï- <£ î

p

The current is (t) is equal to the output current +is (t) in the first half period [0-π], and is its inverse -is (t), in the second half period [π - 2π]. Under the conditions described above, the

p

 p (14)

*Fc Fc V V*

í

Applying the Kirchoff loop equation (sum of voltages around loop equals zero), to the RLC circuit of Figure 23 the equation of the series resonant circuit is the follow.

$$Lr\frac{d\bar{u}Lr}{dt} + V\mathcal{C}r + RI = \overline{V} \tag{17}$$

And the input impedance Z of this circuit is expressed by equation 18 with*R* =*Z*cos(*θ*) and X=*Z*sin(*θ*).

$$\overline{Z} = R + j\left(w\,L\_r - \frac{1}{w\,\mathrm{Cr}}\right) \tag{18}$$

#### *3.4.3. Angular frequency and reactance*

If the reactance of the resonant circuit becomes zero with XLr = XCr then Z=R and the circuit operates at resonant conditions that is, for fs=fr. This is characterized by the angular frequency wr and characteristic impedance Zr as defined by equations 19 and 20 follow.

$$
\pi w\_r = 2\pi f\_r = \frac{1}{L\_r \mathbb{C}\_r} \tag{19}
$$

$$Z\_r = \sqrt{\frac{L\_r}{C\_r}}\tag{20}$$

**Figure 24.** Amplitude of the current in the series resonant circuit

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**Figure 25.** Amplitude of the power in the series resonant circuit

#### *3.4.4. Quality factor*

The quality factor Q measures the "goodness" or the quality of the circuit. This is the ratio between the power stored in the reactance and the power dissipated in the resistance of the resonant circuit. The quality factor Q, can be defined expressed by the equation 21 or equiva‐ lently by equation 22.

$$Q = \frac{w\_r \times Lr}{R} = \frac{1}{w\_r \times C\_r \times R} = \frac{Z\_r}{R} \tag{21}$$

$$Q = \frac{1}{R} \times \sqrt{\frac{L\_r}{C\_r}} \tag{22}$$

#### *3.4.5. Normalized amplitudes of the current and power*

Normalized amplitudes of the current and output power in the series resonant circuit as a function of fs/fr and R/Zr =1/Q are represented in figures 24 and 25, respectively. The maximum current value in the circuit occurs at the resonance frequency and with low load resistance. It is also observed that at resonant frequency fr if the load R→ 0Ω then the peak current I→∝ and the system can de destroyed. This analysis leads to the conclusion that it can't ever work with a short circuit in the load side. The maximum output power occurs at the resonance frequency and with low load resistance.

**Figure 24.** Amplitude of the current in the series resonant circuit

And the input impedance Z of this circuit is expressed by equation 18 with*R* =*Z*cos(*θ*) and

æ ö =+ - ç ÷

If the reactance of the resonant circuit becomes zero with XLr = XCr then Z=R and the circuit operates at resonant conditions that is, for fs=fr. This is characterized by the angular frequency

*r r*

*r*

*r L*

The quality factor Q measures the "goodness" or the quality of the circuit. This is the ratio between the power stored in the reactance and the power dissipated in the resistance of the resonant circuit. The quality factor Q, can be defined expressed by the equation 21 or equiva‐

> 1 *r r r r w Lr Z Q R wCR R*

> > 1 *r*

*r*

Normalized amplitudes of the current and output power in the series resonant circuit as a function of fs/fr and R/Zr =1/Q are represented in figures 24 and 25, respectively. The maximum current value in the circuit occurs at the resonance frequency and with low load resistance. It is also observed that at resonant frequency fr if the load R→ 0Ω then the peak current I→∝ and the system can de destroyed. This analysis leads to the conclusion that it can't ever work with a short circuit in the load side. The maximum output power occurs at the resonance

è ø (18)

(19)

*C* = (20)

´ == = ´ ´ (21)

*L Q R C* = ´ (22)

1 *Z R j wLr wCr*

wr and characteristic impedance Zr as defined by equations 19 and 20 follow.

1 2 *r r*

*w f L C* = = p

*r*

*Z*

X=*Z*sin(*θ*).

350 New Developments in Renewable Energy

*3.4.4. Quality factor*

lently by equation 22.

*3.4.5. Normalized amplitudes of the current and power*

frequency and with low load resistance.

*3.4.3. Angular frequency and reactance*

**Figure 25.** Amplitude of the power in the series resonant circuit


*Optimal Management of Electrified and Cooperative Bus Systems DOI: http://dx.doi.org/10.5772/intechopen.93892*

allows to manage the interplay between PT ecosystem actors (vehicles, signals, and e-infrastructure). Secondly, it enables joint optimization and coordination of actions carried out by the different actors, in order to achieve system goals.

**Figure 2** provides an overview of the eCoBus integrated system developed in this project. The core module consists of collecting *static* input, namely the location of charging stations, lines timetables, together with the characteristics of the fleet (number of e-busses and hybrid vehicles), the characteristics of the lines (trip lengths) and of the signal infrastructure. We also assume to collect in real time trip times through AVL technology, battery states from the busses, status of each charging (occupied, available) and to have a good estimate of the passenger arrivals at stops (via e.g. APC information). These are input to the *scheduling and charging optimization* module, which is presented in detail in Section 3, whereas the *driver advisory system* combining holding and C-ITS based control and TSP are used at the operational phase to manage the vehicles in real time. The integrated system is shown to provide significant benefits both for planning objectives (better use of the fleet and the charging infrastructure, lower operations costs), and management goals (lower trip time variability and passenger costs, less fuel or energy consumed, less use of TSP requests). These benefits will be showcased in simulations using realistic scenarios in the next sections.


**3. Results**

#### *3.1. Study Sample*

For the purpose of this study, subjects for whom dietary data were missing or incomplete were excluded (out of 939 subjects, 71 were excluded). Accordingly, the final study sample consisted of 868 subjects (439 boys and 429 girls), with a mean age of 13.06 years (±3.91) and a median age of 12.85 years.  The male to female ratio was of 1.02 with 50.6% boys and 49.4% females.

The proportion of parents who had attained high school level education and above was of 38.6% for fathers and 46.3% for mothers with no significant differences between age groups (Table 1). A significantly higher proportion of working mothers was reported amongst 12–19-year-old adolescents (29.6%) compared to children (19.4%). Parental obesity (mother or father) was reported amongst 31% of the study population with no significant differences between age groups. The majority of subjects (81.7%) had a crowding index SHUVRQVURRP7DEOH

The proportions of subjects reporting a daily consumption of breakfast was significantly higher amongst children compared to adolescents (86.4% *vs.* 69.5% in adolescents) while a significantly higher proportion of adolescents reported eating outside home more than once per week (58.4% in adolescents compared to 44.4% in children) (Table 1). Similarly, sedentary time was significantly higher amongst 12–19-year-old adolescents compared to children (10.09 ± 2.94 *vs.* 8.72 ± 2.77 h/day) while the proportion of subjects reporting high physical activity was significantly higher in children compared to adolescents (63.9% *vs.* 33.5%).

Mean weight (35.01 ± 12.55 in children *vs.* 60.77 ± 15.61 kg in adolescents), mean height (135.63 ± 12.26 *vs.*164.17 ± 10.07 cm), mean BMI (18.53 ± 3.99 *vs.* 22.32 ± 4.37 kg/m2 ) and mean WC (63.77 ± 10.75 *vs.* 74.93 ± 10.97 cm) were all significantly higher in 12–19-year-old adolescents compared to children aged 6–11 years (Table 1).

**Table 1.** Socio-demographic, lifestyle and anthropometric characteristics of the study sample by age group, Lebanon (*n* = 868).

**Table 1.** *Cont.*

(1) Lack of corresponding sum of frequencies with total sample size is due to missing data; (2) Differences between age groups were examined using *t*-test and chi-square test for continuous and categorical variables, respectively; (3) Total number of parents with anthropometric data was equal to 564; (4) The three categories of physical activity (Low, Moderate, High) refer to the frequency of physical activity outside the school setting (Never; 1–2 times/week; >2 times/week, respectively).

Taking both genders, 40.2% of 6–11-year-olds and 30.8% of 12–19-year-olds were found to be overweight (BMI *z* score >+1), while 17.1% and 10.3% were found to be obese (BMI *z* score >+2), respectively (Table 2). Gender-based differences were noted amongst 12–19-year-olds, with the prevalence of overweight (37.9% in boys *vs.* 23.7% in girls) and obesity (16.1% in boys *vs.* 4.4% in girls) being significantly higher in boys compared to girls. Similar gender-based differentials were noted in the total sample of 6–19-year-old subjects.

Based on WC as an indicator of central fatness, abdominal obesity was observed in 13.8% of 6–11-year-olds and 14.1% of 12–19-year-olds, with no significant differences between genders (Table 2). Elevated WHtR was observed in 22% and 20.9% of children and adolescents, respectively, with gender-based differences being observed amongst 12–19-year-old subjects (26.2% in boys *vs.* 15.6% in girls; *p* < 0.05). Similar gender-based differentials were noted in the prevalence of elevated WHtR in the total sample of 6–19-year-old subjects (Table 2).

As shown in Figure 1, the prevalence of overweight, obesity, elevated WHtR and elevated WC amongst boys reached the highest rates at the age of 10–13 years (47%, 23%, 32% and 18%, respectively), while declining afterwards (Figure 1). Amongst girls, the prevalence of overweight and obesity was the highest at 6–9 years (38% and 14%, respectively) and followed a consistent declining trajectory with age. The prevalence of elevated WC reached its highest in girls aged between 14 and 17 years (16%) and declined afterwards (Figure 1).

**Table 2.** Prevalence of overweight, obesity and abdominal adiposity amongst Lebanese children and adolescents (*n* = 868) by gender and by age group.

WC: Waist Circumference; WHtR: Waist to Height ratio; (1) Overweight and obesity defined based on sex and age specific +1 and +2 BMI *z*-scores, respectively [22]; (2) For subjects aged 6–15 years, abdominal obesity: WC > 90th percentile [26] or adult cut-off value if lower [25]; For subjects aged 16–19 years, abdominal obesity: WC > 94 cm for males and >80 cm for females [25]; (3) Elevated WHtR defined as WHtR > 0.5 [13]; a,b,c Within each age group, values with the same superscripts are significantly different by gender at *p* < 0.05 (Using Chi-square test).

#### *3.3. Dietary Intake*

As shown in Table 3, average energy intake (2255.85 *vs.* 1736.48 kcal/day) and percent contribution of fast food (17.27% *vs.* 11.35%) and legumes and nuts (3.28% *vs.* 2.05%) to daily energy intake were significantly higher among 12–19-year-old adolescents compared to 6–11-year-old children. On the other hand, the percent contribution of milk and dairies (8.90% *vs.* 6.47%) and breads and cereals (36.92% *vs.* 32.67%) were significantly higher in 6–11-year-old children compared to adolescents. No significant differences in macronutrient intake were observed between age groups.

**Table 3.** Energy, macronutrient and food group intake amongst Lebanese children and adolescents according to age group (*n* = 868).

a,b,c,d,e Values with the same superscripts are significantly different by age group at *p* < 0.05 (Using *t*-test).