**1. Introduction**

The development of chemosensors has been the subject of intensive research for potential applications in various fields covering human health, industries, and security fields. Gas chromatography, high-performance liquid chromatography, and electrochemical sensing are commonly used for the detection of small molecules and volatile organic compounds. However, most of these methods fail to meet the requirements of simple operation and are expensive. In this context, vapochromic materials that undergo color and/or fluorescence changes in response to specific gases and vapors have been a promising phenomenon. Colorimetric sensor arrays with pattern recognition capabilities have been widely used to detect and discriminate multiple chemically similar samples.

In the last decades, vapochromic materials based on organic dyes, metal complexes, metal organic frameworks, and covalent organic frameworks have attracted a lot of attention and various researches have been carried out. Various metal complexes are known to show vapochromism in the solid-states due to the significant changes in the metal-to-metal interaction and coordination bonding modes caused by the adsorption and desorption of vapor molecules. Several examples of metal-containing vapochromic materials such as Pt(II) or Au(I) have been reported and compiled in review papers [1–6]. On the other hand, metal-free vapochromic materials have been recently considered. The purpose of this mini-review is to investigate recent advances in the fields of vapochromism/vapofluorochromism of organic materials, especially focusing on host–guest compounds with distinct mechanisms and where structural identification has been achieved.


**2. Health benefits of phytochemicals**

"Phyto" in the word of phytochemicals is derived from the Greek word "phyto," which means plant. Phytochemicals are a naturally occurring group of chemicals in plants and plant-derived foods, which may function in reducing the risk of chronic diseases [8]. Although it is estimated at least more than 5000 dietary phytochemicals have been discovered, it is believed that a high percentage of phytochemicals in foods still remain unknown [8]. Critical reviews of studies available in the literature support the concept that phytochemicals (polyphenols, tocopherols, tocotrienols, carotenoids, and ascorbic acid) has been associated with the maintenance of good health as well as prevention/treatment of many health conditions including cancer, cardiovascular diseases, diabetes, hypertension, stroke, metabolic syndrome, and other degenerative diseases. It is largely accepted that the additive effects of the combinations of various phytochemicals in whole plant-based foods are shown to have stronger protective actions than single, isolated phytochemical compounds [9].

#### **3. Role of secondary metabolites**

New drug model seeks to meet on bringing compounds active toward target proteins. Even though newly pharmaceutical companies and support organizations

**183**

**Figure 1.**

*Major pathways of biosynthesis of secondary metabolites [15].*

Ocimum *Phytochemicals and Their Potential Impact on Human Health*

take influence in molecular design, combinatorial chemistry and synthetic chemistry, natural productions, and especially those of plant source, remain as a prerequisite cause of new remedies, current medicine leads and other new synthetic entities

Plants produce a diverse array of compounds that can broadly be categorized into primary and secondary metabolites. Primary metabolites are the ones which are required for the normal growth and biological processes and are produced in the pathways that are crucial for plant survival. The other class of metabolites, though generally termed secondary, is also very crucial for plants from the ecological

These secondary metabolites are classified on the basis of their biosynthetic pathway and the following types are frequently observed-terpenes, phenylpropanoids, alkaloids, saponins, and glucosinolates. The availability of carbon, nitrogen, and sulfur along with energy from the primary metabolism governs the

Metabolites are the mediators and amounts of metabolism. The term metabolite is commonly confined to narrow fragments. Metabolites have specific functions,

*DOI: http://dx.doi.org/10.5772/intechopen.88555*

biosynthesis of these compounds [12].

**4. Classification of secondary metabolites**

(NCEs) [10, 11].

perspective.

Ocimum *Phytochemicals and Their Potential Impact on Human Health DOI: http://dx.doi.org/10.5772/intechopen.88555*

*Phytochemicals in Human Health*

demand against green remedies because of their rapid-acting implements, people have been turned up to understand the benefits correlated with essential remedies. Chemically prepared drugs may act at earlier, but they side effects which influence the human body separately in the long run, because medicinal plants work in an integrated or probiotic with limited or no negative effects on the body [3].

The genus *Ocimum* includes approximately 150 species, possessing a great variation in plant morphology and biology, essential oil content, and chemical composition [4]. The economic importance and global dissemination of *Ocimum*, with its many uses in cooking and folk medicine, make it important to investigate its pharmacological and toxicological effects in order to ensure its efficacy and safety. In India, among the medicinal herbs known for their healing properties, the genus *Ocimum* (commonly known as 'Basil' or 'Tulsi') is very important for its curative potential. Basils contain a wide range of essential oils rich in phenolic compounds and a wide array of other natural products including flavonoids and anthocyanins [5] having great pharmacological importance. Nowadays, scientists are mainly focused on exploring the potential of plant antioxidants for curing several diseases. Antioxidants are compounds that can delay or inhibit the oxidation of lipids or other molecules by inhibiting the initiation or propagation of oxidative chain reactions [6]. Antioxidants counteract the harmful reactive oxygen species (ROS) and free radicals generated in the living organism during regular metabolism, especially under stress conditions. Several in vitro and in vivo studies have already proved the antioxidative property of beta-carotene, alfa-tocopherol, ascorbic acids, phenolics, flavonoids present in different plants [7]. Presently due to our regular stressful lifestyle, we are suffering from several types of diseases like aging, diabetes, degenerative disorders, etc., which mainly develop due to the development of ROS in our body. A dynamic balance is already operating in our body to reduce the harmful effects of generated ROS that is not adequate enough. Therefore, it is obvious to enrich our diet with antioxidants for developing protection. The purpose of this paper will focus on the recent research of the major nutrients and phytochemicals of *Ocimum* and their potential health

benefits related to the dietary prevention of chronic diseases.

"Phyto" in the word of phytochemicals is derived from the Greek word "phyto," which means plant. Phytochemicals are a naturally occurring group of chemicals in plants and plant-derived foods, which may function in reducing the risk of chronic diseases [8]. Although it is estimated at least more than 5000 dietary phytochemicals have been discovered, it is believed that a high percentage of phytochemicals in foods still remain unknown [8]. Critical reviews of studies available in the literature support the concept that phytochemicals (polyphenols, tocopherols, tocotrienols, carotenoids, and ascorbic acid) has been associated with the maintenance of good health as well as prevention/treatment of many health conditions including cancer, cardiovascular diseases, diabetes, hypertension, stroke, metabolic syndrome, and other degenerative diseases. It is largely accepted that the additive effects of the combinations of various phytochemicals in whole plant-based foods are shown to have stronger protective actions than single, isolated phytochemical compounds [9].

New drug model seeks to meet on bringing compounds active toward target proteins. Even though newly pharmaceutical companies and support organizations

**2. Health benefits of phytochemicals**

**3. Role of secondary metabolites**

**182**

take influence in molecular design, combinatorial chemistry and synthetic chemistry, natural productions, and especially those of plant source, remain as a prerequisite cause of new remedies, current medicine leads and other new synthetic entities (NCEs) [10, 11].

Plants produce a diverse array of compounds that can broadly be categorized into primary and secondary metabolites. Primary metabolites are the ones which are required for the normal growth and biological processes and are produced in the pathways that are crucial for plant survival. The other class of metabolites, though generally termed secondary, is also very crucial for plants from the ecological perspective.

These secondary metabolites are classified on the basis of their biosynthetic pathway and the following types are frequently observed-terpenes, phenylpropanoids, alkaloids, saponins, and glucosinolates. The availability of carbon, nitrogen, and sulfur along with energy from the primary metabolism governs the biosynthesis of these compounds [12].


**4. Lipid peroxidation metabolites as influential signaling biomarkers in asthma and airway inflammation**

Oxidative stress at molecular and cellular level can have many detrimental effects on airway function, including airway smooth muscle contraction, induction of airway hyper responsiveness, mucus hypersecretion, vascular exudation and shedding of epithelial cells. Furthermore, ROS can induce cytokine and chemokine production through induction of the oxidative stress-sensitive transcription of nuclear factor–kB in bronchial epithelial cells [53]. Recently discovered series of bioactive prostaglandin (PF)F2-like compoundswere produced independently of the cyclooxygenase enzymes via the peroxidation of arachidonic acid, catalyzed by free radicals. The pathway leads to formation of 64 isomeric structures, of which 8-iso-PGF2α is most well characterized. Evidence suggests that 8-iso-PGF 2α may act in part through the vascular thromboxane A2/PGH2 (TP) receptor [54]. 8-iso-PGF2α has been found to elicit airway hyper-responsiveness in isolated perfused mouse lungs, and cause airway obstruction and air plasma exudation in guinea pigs in *vivo* [55]. These experimental findings offerassumption about the contribution of Isoprostanes to the airway narrowing that is characteristic of asthma and in addition to being reliable signaling marker of lipid peroxidation, Isoprostane may prove to have an important biological role in the pathological of asthma. A significant elevation of reactive oxygen species, MDA formation (A product of Lipid peroxidation) and Isoprotane was estimated in the broncho-alveolar lavage (BAL) fluidwithin 24 hrs of allergeninduced asthma. This clearly indicates, Isoprotane is produce as a biomarker in respiratory tract tissues that leads to the late observed physical symptoms in allergeninduced asthma [56].

A recent study demonstrated that concentrations of exhaled ethane were increased in patients with more severe bronchoconstriction (forced expiratory volume in one second (FEV1) <60%), compared with less-constricted patients (FEV1 > 60%) and provides evidence that lipid peroxidation is related to asthma severity. These relationships between markers of oxidative stress and disease severity suggest that such tests may indicate the clinical status of asthma patients [57]. The increased level of 8-sio-PGF2α concentrations has been observed in chronic obstructive pulmonary diseases and asthma [58]. The discovery of Isoprostane has generated attention, as they provide a reliable index of oxidative stress *in vivo*. Isoprostane are structurally stable, are produced *in vivo* and are present in relatively high concentrations [59]. Traceable levels of F2-isoprostanes can be found in all normal animal and

*Lipid Peroxidation: A Signaling Mechanism in Diagnosis of Diseases DOI: http://dx.doi.org/10.5772/intechopen.99706*

**Figure 3.**

*Lipid peroxidation metabolites as influential signaling biomarkers in lung diseases.*

human biological fluids (including plasma, urine, bile, gastric juice, synovial fluid and cerebrospinal fluid)), and esterified in normal animal tissues. Thus, they overcome many of the methodological problems associated with other signaling markers. Determination of 8-iso-PGF2αas a marker of oxidative stress, of carbon tetrachloride (CCl4)-induced lipid peroxidation has been shown to be 20 times more sensitive than measurement of Thiobarbituric acid reactive substances (TBARS). Thus, the reliability of isoprostanes as in vivo markers of lipid peroxidation makes them an extremely valuable signaling biomarker for defining the potential of antioxidant agents (Vitamin C, E and β-carotene) in humans [60]. A significant amount of elevated ethane produced following lipid peroxidation has been observed in plasma and breath condensate of asthmatics as a biomarker indicator of acute airway inflammatory diseases. Moreover, measurement of auto-antibodies directed against oxidative modifications of low density lipids (LDL) is a recently developed technique that provides an in vivo marker of lipid peroxidation. Enzymes-linked immunosorbent assays are available in kit form, providing a quick and simple methodology. Thus measurement of isoprostanes in breath condensate should provide useful information concerning the degree of oxidant stress and success of antioxidant therapy in asthma (**Figure 3**).

#### **5. Lipid peroxidation: a signaling mechanism in diagnosis of liver injury**

Oxidative stress is one of the mechanisms involved in the pathogenesis of drug-induced reactive oxygen species which lead to the depletion of intracellular antioxidants, causing an imbalance in the redox status of the hepatic cells [61]. Rapid, extensive lipid peroxidation of the membrane structural lipids due to oxidative stress mechanism involved in the pathogenesis of drug-induced had seen proposed as the basis of drug-induced hepatocellular toxicity. The most of the xenobiotics such as Acetaminophen, Isoniazid and Rifampicin are well-known to

#### *Accenting Lipid Peroxidation*

induced hepatic damage directly or indirectly via lipid peroxidation [62]. However, peroxy radical attribute to lipid peroxidation, thus known for the destabilization and disintegration of the cell membrane, that further causes arteriosclerosis, hepatic and kidney damage. The increased serum markers such as MDA formation are of diagnostic importance of hepatic injury because they are released due to the damage of hepatocytes and consequently participate in endogenous enzymatic antioxidant system imbalance [63]. CCl4-ehanced lipid peroxidation has been observed in liver tissue homogenates, isolated hepatocytes and in vivo, and this has been associated with changes in endoplasmic reticular enzyme activity, in vivo fatty acid export and protein synthesis [64]. CCL4 metabolism enhances production of malondialdehyde in vitro and increase ethane production and lethality in vivo (**Figure 4**). Consequently, lipid peroxidation initiated by free radical reactions and unchecked by compromised cellular defenses, provides a possible link between

#### *Lipid Peroxidation: A Signaling Mechanism in Diagnosis of Diseases DOI: http://dx.doi.org/10.5772/intechopen.99706*

ethanol metabolism and associated liver disease [65]. The lipid peroxide content of liver is elevated by both short and long-term ethanol exposure and an enhanced rate of lipid peroxide formation following ingestion has been ascertained by MDA production, diene conjugate formation and *in vivo* ethane and pentane exhalation. Lipid peroxidation might merely be a sign of oxidative processes which occur after reduced glutathione is depleted concomitant with free radical attack on cellular protein and nucleic acid. The elevated MDA formation as a product of lipid peroxidation in drug-induced liver damage provide a significant biological signaling marker for the early detection or diagnosis of liver injury.


*Article* **Chemical Composition and Anti-Microbial Activity of Hog Plum (***Spondias mombin* **L.) Peel Oil Extracted from Different Regions of Tropical Climates**

**Md. Emrul Ahsan Plabon 1, Shakti Chandra Mondal 1, Md. Mamun Or Rashid 1, M. Kaium Chowdhury 2, Abu Saeid 3, Fayez Althobaiti 4, Eldessoky S. Dessok 5, Muhammad Ishaq Asif Rehmani 6,\*, Syed Khalid Mustafa 7 and Mohammad Sohidul Islam 8,\***

**Abstract:** The hydro-distilled essential oil of hog plum peel may be used for enhancing the flavor and taste of food products as well as for hiding the unpleasant odor of drugs. Thus, the waste peels of *Spondias mombin* appear to have economic importance. To find out the chemical composition and anti-microbial properties of hog plum peel oil, the samples were collected from different regions of Bangladesh for extraction and identification of volatile compounds by GC-MS, where dichloromethane was used as an extraction solvent. The required standard analytical methods were used to assay the anti-microbial properties of hog plums. In this study, pentenyl-3-thy-met-4-alphamethyl-alpha-ethanol-oxiranen (29.04%), (3,3.1,1)-4-dimethylethyl-1,1-phenol (8.00%), cycohexanol-3 (10.85%), 4-hydroxy-penzeneethanamine (7.09%), hydroxylamine (4.63%), dibutyl phthalate (6.85%), etc., were majorly determined. Consequently, the highest content of 75.81% volatile compounds was found in the Dinajpur district, where the lowest content of 35.00% was found in the Rajshahi district. In contrast, 33 volatile compounds were identified in hog plum peels collected from the Barishal district, whereas 22 compounds were detected in the peel samples collected from the Dinajpur district. In addition, the antimicrobial activity of the oil was analyzed by the disk diffusion method, and the results revealed that the highest Ciprocin content was recorded in the hog plums of Barishal (22.0–23.0 mm), while the lowest was recorded in the Mymensingh sample (20.67–21.63 mm), which was on par with Rajshahi sample (20.70–21.50 mm). The results of the anti-fungal activities of the peel oil showed the highest zone of inhibition against the *Aspergillus niger* (11.63 ± 0.0003 mm) and *Penicillium oxalicum* (13.67 ± 1.97 mm) content of the Rajshahi and Pabna district samples, respectively.

**Keywords:** waste peel; essential oil; GC-MS; volatile compounds; antimicrobial activity; chemical composition

**Citation:** Plabon, M.E.A.; Mondal, S.C.; Or Rashid, M.M.; Chowdhury, M.K.; Saeid, A.; Althobaiti, F.; Dessok, E.S.; Rehmani, M.I.A.; Mustafa, S.K.; Islam, M.S. Chemical Composition and Anti-Microbial Activity of Hog Plum (*Spondias mombin* L.) Peel Oil Extracted from Different Regions of Tropical Climates. *Horticulturae* **2021**, *7*, 428. https://doi.org/10.3390/ horticulturae7110428

Academic Editors: Dasha Mihaylova and Aneta Popova

Received: 19 August 2021 Accepted: 16 October 2021 Published: 22 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

https://www.mdpi.com/journal/horticulturae


**2. Conclusion**

God is the creator of all things that are in existence of which plants which are in abundance and of various diversities are among. He put plants in place to serve man especially for food and medicine to heal their diseases. It is therefore imperative to search more into medicinal plants for phytochemicals which can provide a clue in terms of lead (active) compound discovery for the cure of many so-called incurable diseases like cancer, diabetes, hypertension, ulcer, and other degenerative diseases that are just being managed by medical practitioners till the patient passes on. It is realized that many natural remedies carry their cures along with them; therefore, it could be concluded that botanists (plant scientists), pharmacists, pharmacognosists, pharmacologists, chemists, biochemists, and all other fields related to the science of drug discovery should rise up to these challenges of sourcing from plant alternative medicines which God had placed into our hands only left to be discovered.


**2. Preliminaries**

*2.1. Pythagorean Fuzzy Set (PFS)*

**Definition 1** ([18,23])**.** *Suppose X is a fixed set. A PFS takes the form of:*

$$P = \{ \langle \text{ x, } P(\mu\_P(\mathfrak{x}), \upsilon\_P(\mathfrak{x})) \rangle | \mathfrak{x} \in X \}$$

*where vp*(*x*)*: X* → [0, 1] *and up*(*x*)*: X* → [0, 1] *represent the non-membership function and membership function of x* ∈ *X, respectively, u*2*p*(*x*) + *v*2*p*(*x*) ≤ 1*. In addition, πp*(*x*) = +1 − *u*2*p*(*x*) − *ν*2*p*(*x*) *denotes the hesitation degree of x* ∈ *X*.

For the sake of simplicity, Zhang and Xu [19] named *P*(*uP*(*x*), *vP*(*x*)) the Pythagorean fuzzy number (PFN), expressed by *β* = *P*-*uβ*, *vβ*., where *up*(*x*), *vp*(*x*) ∈ [0, 1], *πp*(*x*) = +1 − *u*2*p*(*x*) − *ν*2*p*(*x*) and *u*2*p*(*x*) + *v*2*p*(*x*) ≤ 1.

**Definition 2** ([9])**.** *Assume S* = {*si*|*i* = 0, ··· , *t*, *t* ∈ *R*} *is a linguistic term set, si is the linguistic evaluation value, t is the granularity of S. Take the seven-level linguistic term as an example: S =* {*s*0 *= Extremely low, s*1 *= Very low, s*2 *= Low, s*3 *= Fair, s*4 *= High, s*5 *= Very high, s*6 *= Extremely high*}.

*S* must satisfy the following two properties:

**Definition 3** ([22])**.** *Based on the definition of linguistic term set and PFS, the Pythagorean fuzzy linguistic set* (*PFLS*) *takes the form of D* = *sτ*(*x*), *up*(*x*), *vp*(*x*)HHH*x* ∈ *X, and the Pythagorean fuzzy linguistic number* (*PFLN*) *is denoted as sτ*(*x*), *up*(*x*), *vp*(*x*)*, where sτ*(*x*) *is the linguistic evaluation value.*

When the attribute values are represented in the form of linguistic terms in MADM problems, the linguistic variable cannot be directly calculated. Therefore, Xu used the subscript of the linguistic term [24] for computation, Wang put forward a linguistic scale function [11] to convert linguistic terms into crisp numbers, and Herrera converted linguistic terms into fuzzy numbers [25]. However, all those methods still use linguistic subscript to express language variables in nature. To express the fuzziness and uncertainty of linguistic terms, we introduce shadowed set method to cope with linguistic term, and further put forward a new Pythagorean shadowed set.

## *2.2. Shadowed Set*

**Definition 4** ([13,14])**.** *A shadowed set S is a set-valued mapping as follows:*

$$S: \mathcal{U} \to \{0, [0, 1], 1\}$$

*where U is a given universe of discourse.*

> The core of the shadowed set *S* is the area where the mapping values of the elements are equal to 1.

$$core(S) = \{ \mathbf{x} \in \mathcal{U} | S(\mathbf{x}) = 1 \}$$

The elements of *U* whose mapping values are unit intervals in *S* compose the shadowed zone of the shadowed set and are expressed as follows,

$$CLI(S) = \{ \mathbf{x} \in \mathcal{U} | S(\mathbf{x}) = [0, 1] \}$$

The elements of *U* whose mapping values are equal to 0 will be excluded from the shadowed set *S*.

**Definition 5.** *A* = [*a*, *b*, *c*, *d*] *is called a shadowed number* (*SN*)*, where a*, *b are the lower and upper bound of the left-shoulder shadowed part, and c*, *d are the lower and upper bound of the right-shoulder shadowed part. Figure 1 shows an illustration of shadowed number.*

**Figure 1.** Shadowed number.

#### *2.3. Pythagorean Shadowed Set (PSS)*

In this section, we will define the Pythagorean shadowed set and give some properties for it.

**Definition 6.** *Suppose X is a fixed set, a Pythagorean shadowed set T over X takes the form of*

$$T = \{ \langle \, A, \, P(\mu\_P(\mathfrak{x}), \upsilon\_P(\mathfrak{x})) \rangle | \mathbf{x} \in X \}$$

*where A* = [*a*, *b*, *c*, *d*]*, a*, *b are the lower and upper bound of the left-shoulder shadowed part, respectively, and c*, *d are the lower and upper bound of the right-shoulder shadowed part, respectively. Function up*(*x*)*: X* → [0, 1] *and vp*(*x*)*: X* → [0, 1] *denote the membership function and non-membership function, respectively*. *u*2 *p*(*x*) + *v*2 *p*(*x*) ≤ 1*, and πp*(*x*) = +1 − *u*2*p*(*x*) − *ν*2*p*(*x*) *denotes the hesitate degree of x* ∈ *X*.

**Definition 7.** *A Pythagorean shadowed number* (*PSN*) *takes the form of:*

$$V = \langle A, P(\mu\_P(\mathfrak{x}), \upsilon\_P(\mathfrak{x})) \rangle$$

*where a*, *b are the lower and upper bound of the left-shoulder shadowed part, c*, *d are the lower and upper bound of the right-shoulder shadowed part, and up*(*x*)*: X* → [0, 1] *and vp*(*x*)*: X* → [0, 1] *represent membership function and non-membership function, respectively.*

Let *V*1 = *A*1, *P*(*uP*(*x*1), *vP*(*x*1)) and *V*2 = *A*2, *P*(*uP*(*x*2), *vP*(*x*2)) be two PSNs, where *A*1 = [*a*1, *b*1, *c*1, *d*1] and *A*2 = [*a*2, *b*2, *c*2, *d*2], then the operation rules are as follows:

$$\begin{pmatrix} \mathbf{1} \\ \end{pmatrix} \quad V\_1 + V\_2 = \left\langle \begin{bmatrix} a\_1 + a\_2, b\_1 + b\_2, c\_1 + c\_2, d\_1 + d\_2 \end{bmatrix}, P \left( \sqrt{\left( u\_p(\mathbf{x}\_1) \right)^2 + \left( u\_p(\mathbf{x}\_2) \right)^2 - \left( u\_p(\mathbf{x}\_1) \right)^2 \left( u\_p(\mathbf{x}\_2) \right)^2}, v\_p(\mathbf{x}\_1) v\_p(\mathbf{x}\_2) \right) \right\rangle\_{\mathbf{x}\_1, \mathbf{x}\_2}$$

$$\begin{pmatrix} \mathbf{\mathcal{D}} & \mathbf{V}\_1 \times \mathbf{V}\_2 \end{pmatrix} = \left\langle \begin{bmatrix} a\_1 \times a\_2 \ b\_1 \times b\_2 \times c\_1 \ c\_1 \ d\_1 \times d\_2 \end{bmatrix}, P \left( u\_p(\mathbf{x}\_1) u\_p(\mathbf{x}\_2) \right) \sqrt{\left( v\_p(\mathbf{x}\_1) \right)^2 + \left( v\_p(\mathbf{x}\_2) \right)^2 - \left( v\_p(\mathbf{x}\_1) \right)^2 \left( u\_p(\mathbf{x}\_2) \right)^2} \end{pmatrix}$$

$$\begin{aligned} \text{(2)} \qquad &V\_1 \times V\_2 = \left\langle \left[a\_1 \times a\_2, b\_1 \times b\_2, c\_1 \times c\_2, d\_1 \times d\_2\right], P\left(u\_p(\mathbf{x}\_1)u\_p(\mathbf{x}\_2), \sqrt{\left(\nu\_p(\mathbf{x}\_1)\right)^2 + \left(\nu\_p(\mathbf{x}\_2)\right)^2 - \left(\nu\_p(\mathbf{x}\_1)\right)^2 \left(\nu\_p(\mathbf{x}\_2)\right)^2}\right) \right\rangle \\ \text{(3)} \qquad &\lambda V\_1 = \left\langle \left[\lambda a\_1, \lambda b\_1, \lambda c\_1, \lambda d\_1\right], P\left(\sqrt{\left(1 - \left(\nu\_p(\mathbf{x}\_1)\right)^2\right)^\lambda}, \left(\nu\_p(\mathbf{x}\_1)\right)^\lambda}\right) \right\rangle \end{aligned}$$

$$\begin{aligned} \text{(3)} \quad \lambda V\_1 &= \left\langle \left[ \lambda a\_1, \lambda b\_1, \lambda c\_1, \lambda d\_1 \right], P \left( \sqrt{1 - \left( 1 - \left( \mu\_{\mathcal{P}}(\mathbf{x}\_1) \right)^2 \right)^{\lambda}}, \left( \nu\_{\mathcal{P}}(\mathbf{x}\_1) \right)^{\lambda} \right) \right\rangle, \lambda \geq 0 \end{aligned}$$

$$(4)\quad V\_1^{\lambda} = \left\langle \left[a\_1^{\lambda}, b\_1^{\lambda}, c\_1^{\lambda}, d\_1^{\lambda}\right], P\left(\left(\mu\_p(\mathbf{x}\_1)\right)^{\lambda}, \sqrt{1 - \left(1 - \left(\nu\_p(\mathbf{x}\_1)\right)^2\right)^{\lambda}}\right) \right\rangle, \lambda \ge 0.$$

**Theorem 1.** *For any two PSNs V*1 = *A*1, *P*(*uP*(*x*1), *vP*(*x*1)) *and V*2 = *A*2, *P*(*uP*(*x*2), *vP*(*x*2))*, whereA*1 = [*a*1, *b*1, *c*1, *d*1] *and A*2 = [*a*2, *b*2, *c*2, *d*2]*, the calculation rules satisfy the following properties:*

170

*(1) V*1 + *V*2 = *V*2 + *V*1 *(2)V*1×*V*2 = *V*2×*V*1

#### **3. Shadowed Set Model of Linguistic Terms**

We collect the interval data for each language word in the form of the seven-level linguistic term listed in Definition 2 and use the collected interval data to construct the shadowed set models for the seven-level linguistic term. The interval data are obtained by means of questionnaire survey. The main framework of our questionnaire is designed to get a proper interval value for each word of the seven-level linguistic term from those respondents according to their experience, habits and common sense. It is necessary for the filled numbers to be accurate to the first decimal place.

We handed out our questionnaires via leaflets, emails and online survey websites to people in different fields, especially to those with a bachelor degree or above. In the end, we got 1205 valid questionnaires, and the questionnaire data were processed by the following interval data preprocessing method to obtain the shadowed number of the seven-level linguistic term.

#### *3.1. Interval Data Preprocessing*

Wu and Liu [26,27] proposed an efficient method to preprocess interval data, and we preprocessed the *n* interval endpoint data [*ak*, *bk*](*k* = 1, 2, . . . , *n*) based on this method, as follows:

**Step 1:** Bad data processing. This aims to remove unreasonable results from the surveyed people, whose answers were beyond the range of the universe of discourse *U*. If the interval endpoints satisfy the following conditions, the interval data are acceptable. Otherwise, they will be rejected.

$$\begin{cases} \ 0 \le a\_k \le 10 \\\ \ 0 \le b\_k \le 10 \\\ b\_k \ge a\_k \end{cases} , k = 1, 2, \dots, m$$

By this step, some data will be abandoned, and *n*∗ < *n* interval data will be preserved.

**Step 2:** Outlier Processing. By using the Box and Whisker test [28], the data that are extremely large or small, i.e., outliers, can be eliminated. Outlier tests can be applied to process the endpoints of interval data and the lengths of interval data *Lk* = *bk* − *ak*, respectively. Consequently, only the interval endpoints and lengths satisfying the following conditions are kept:

$$\begin{cases} \begin{aligned} a\_k &\in \left[Q\_a(0.25) - 1.5IQR\_a, Q\_a(0.75) + 1.5IQR\_a\right] \\ b\_k &\in \left[Q\_b(0.25) - 1.5IQR\_b, Q\_b(0.75) + 1.5IQR\_b\right] \\ L\_k &\in \left[Q\_L(0.25) - 1.5IQR\_L, Q\_L(0.75) + 1.5IQR\_L\right] \end{aligned} \end{cases}, k = 1, 2, \dots, n^\* \end{aligned}$$

where *Qa* and *IQRa* are respectively the quartile and interquartile ranges of the left endpoints, *Qb* and *IQRb* are respectively the quartile and interquartile ranges of the right endpoints, *QL* and *IQRL* are respectively the quartile and interquartile ranges of the interval data's length. *Q*(0.25) and *Q*(0.75) are the first and third quartiles, which include 25% and 75% of the data, respectively. In addition, the interquartile range *IQR* is the difference between *Q*(0.25) and *Q*(0.75); that is to say, *IQR* contains 50% of the data between *Q*(0.25) and *Q*(0.75). The points that are more than 1.5*IQR* below the first quartile or more than 1.5*IQR* above the third quartile are regarded as outliers.

After this step, *m*∗ < *n*∗ interval data will remain.

Then, the following statistics of the *m*∗ interval data are calculated: *ml* and *σl* are mean values and standard deviations of the *m*∗ left endpoints, respectively. Similarly, *mr* and *σr* represent the mean values and standard deviations of the *m*∗ right endpoints. *mL* and *σL* denote the mean values and standard deviations of the lengths of the *m*∗ interval data.

**Step 3:** Tolerance limit processing. If the remaining intervals satisfy the following conditions, then they will be accepted; otherwise, they will be rejected.

$$\begin{cases} a\_k \in \left[ m\_l - \eta \sigma\_l, m\_l + \eta \sigma\_l \right] \\ b\_k \in \left[ m\_r - \eta \sigma\_r, m\_r + \eta \sigma\_r \right] \\ L\_k \in \left[ m\_L - \eta \sigma\_L, m\_L + \eta \sigma\_L \right] \end{cases} , k = 1, 2, \dots, m^\*$$

where *η* is the tolerance factor, which represents that we can assure the given limits at least include the proportion 1 − *α* of the measurements with 100 · (1−*γ*)% confidence level. The value of tolerance factor can be obtained from Table 1 [29].

**Table 1.** Tolerance factor *η* for several collected data.

After the processing of Step 3, *m*∗∗ < *m*∗(1 ≤ *m*∗∗ ≤ *n*) interval data will be left, and the following statistical characteristics of the *m*∗∗ data will be computed: *ml*, *σl*, *mr*, *σr*, *mL* and *σL* of the left (right) endpoints the *m*∗∗ interval data.

**Step 4:** Reasonable-interval processing. If the intervals satisfy the following conditions, they will be kept; otherwise, they will be rejected.

$$2m\_l - \phi^\* \le a\_k < \phi^\* < b\_k \le 2m\_r - \phi^\*$$

where

$$\phi^\* = \frac{\left(m\_r \sigma\_l^2 - m\_l \sigma\_r^2\right) \pm \sigma\_l \sigma\_r \left[\left(m\_l - m\_r\right)^2 + 2\left(\sigma\_l^2 - \sigma\_r^2\right) \ln\left(\sigma\_l / \sigma\_r\right)\right]^{1/2}}{\sigma\_l^2 - \sigma\_r^2}$$

After this step, there will be *m* interval data.

In a word, there will be *m* interval data after the four processing steps above, which is not greater than the *n* interval data at the beginning, as shown in Figure 2.

$$\mathcal{M} \xrightarrow{\text{Bad data}} \mathcal{M} \xrightarrow{\text{Outliers}} \mathcal{M} \xrightarrow{\text{"Iolerance"}} \mathcal{M} \xrightarrow{\text{Reasonable}} \mathcal{M}$$

**Figure 2.** The process of data preprocessing.

#### *3.2. Shadowed Set Model of Seven-Level Language Terms*

After data preprocessing, the distribution of the remaining interval data is obtained as shown in Figure 3. The intervals of the left-end points and right-end points can reflect the linguistic word's uncertainties from different surveyed persons. Therefore, it is necessary to determine the representative intervals for the left-end points and right-end points to express the uncertainties. As shown in Figure 3, the core area can be determined even if the surveyed people cannot give accurate representative intervals. As a result, the core of the shadowed set is the core area and the uncertain bound of the shadowed set is the representative intervals.

**Figure 3.** Distribution of the remaining interval data.

Next, we will estimate the representative intervals by the tolerance limit method via the following steps.

**Step 1:** Calculate the mean *ml* and standard deviation *σl* of the remaining left-end points

$$m\_l = \frac{\sum\_{k=1}^{m} \hat{I}\_k}{m} \tag{1}$$

$$
\sigma\_l = \sqrt{\frac{\sum\_{k=1}^{m} \left(\hat{l}\_k - m\_l\right)^2}{m}} \tag{2}
$$

where ˆ *lk* denotes the left-end point of each remaining interval, *m* is the number of remaining intervals.

**Step 2:** Determine the representative interval. Let [*Ll*, *Lr*] and [*Rl*, *Rr*] be the representative intervals of the left-end points and right-end points, respectively.

$$L\_l = m\_l - \eta \* \sigma\_l \tag{3}$$

$$L\_r = m\_l + \eta \* \sigma\_l \tag{4}$$

where *η* is the tolerance factor in Table 1.

> Then, the representative interval for the right-end points is calculated in the same way.

The parameters *γ* and *α* are set to 0.05 and 0.1 in this paper, respectively, and we can obtain a tolerance factor *η* of 1.709 from Table 1. Take the seven-level language terms as an example: based on the results above, the shadowed set models for seven-level language terms can be constructed as shown in Figure 4.

**Figure 4.** The shadowed set models for seven-level language terms.

#### **4. The Score Function of Pythagorean Shadowed Number**

Based on the concepts of shadowed number and Pythagorean shadowed number in Section 2, we will further present the score functions of shadowed number and Pythagorean shadowed number, respectively. Numerical examples will also be given to illustrate the specific calculation process of the two score functions.

According to the central limit theorem, the attribute value *rij* given by the decision-maker is stable and tends to be the most likely attribute value at a certain point, so it is believed that *rij* obeys the normal distribution within the fuzzy interval. From the tolerance limit method in Section 3.2, we can obtain the distribution of attribute value in the shadowed set *S* = {*Ai*|*U*}, *Ai* = [*ai*, *bi*, *ci*, *di*], as shown in Figure 5.

**Figure 5.** Normal distribution of attribute value.

**Definition 8.** *The score function of shadowed number A is defined as follows:*

$$\text{score}(A) = a + \int\_{a}^{b} f(\mathbf{x}) \, d\mathbf{x} + c - b + \int\_{c}^{d} f(\mathbf{x}) \, d\mathbf{x} + d \tag{5}$$

*where f*(*x*) = √ 12*πσ* · *e*−(*x*−*u*)2/(2·*σ*2).

> According to the 3*σ* principle of normal distribution:

$$p(r \in [\mu - 3\sigma, \mu + 3\sigma]) = 0.9974, \ p(r \in [a, d]) = 0.9974$$

Then *u* = *a*+*d* 2 , *σ* = *d*−*a* 6 .

**Example 1.** *The score function value of shadowed set A*0 *for 'High' in Figure 3 can be calculated as follows:*

$$\mu = a + d = 5.77 + 7.96 = 13.73,\\ \sigma = \frac{d - a}{6} = \frac{7.96 - 5.77}{6} = 0.37$$

*f*(*x*) = 1.08*e*−(*x*−6.87)2/0.27

> Then, we can gain the figure of shadowed number 'High' as shown in Figure 6.

**Figure 6.** The shadowed number of 'High' and its normal distribution.

In the same way, we can get the score function of shadowed sets for the other six language terms in Figure 3.

**Definition 9.** *The score function of a Pythagorean shadowed number V is denoted as:*

$$\text{score}(V) = \left( a + \int\_{a}^{b} f(\mathbf{x}) \, d\mathbf{x} + c - b + \int\_{c}^{d} f(\mathbf{x}) \, d\mathbf{x} + d \right) \cdot (u/v) \tag{6}$$

**Example 2.** *For a Pythagorean shadowed number V*1 = {[5.77, 6.48, 7.21, 7.96], *P*(0.7, 0.4)}*, the score function value is:*

$$\text{score}(V\_1) = \left(5.77 + \int\_{5.77}^{6.48} f(\mathbf{x})d\mathbf{x} + 7.21 - 6.48 + \int\_{7.21}^{7.96} f(\mathbf{x})d\mathbf{x} + 7.96\right) \cdot (0.7/0.4) = 31.33\dots$$

$$\text{score}(\dots) = \text{score}^2 \text{score}$$

*where f*(*x*) = 1.08*e*−(*x*−6.87)2/0.27.

#### **5. MADM Method Based on the Pythagorean Shadowed Set**

With the concept of PSS in mind, we can put forward a novel MADM approach under Pythagorean fuzzy linguistic term circumstances. The diagram of the proposed method is shown in Figure 7. Firstly, present a description of the MADM problem under the Pythagorean linguistic fuzzy circumstances. Secondly, transform the PFLS into PSS through a data-driven method. Thirdly, determine the ranking order of all alternatives so as to obtain the best choice(s) by means of the score function of PSNs and OWA operator. The whole decision-making process is carried out in the following steps.

**Figure 7.** Diagram of the proposed method.

**Step 1:** Standardized decision matrix. For PFLVs *Pij* = *sτij* , *P*-*up*-*xij*., *vp*-*xij*..9 For beneficial attributes, *Pij* = *Pij* = *sτij* , *P*-*up*-*xij*., *vp*-*xij*..9 For cost attributes, *Pij* = -*Pij*.−1 = *s*(*τij*)−1 , *P*-*vp*-*xij*., *up*-*xij*..9 where -*τij*.−1 = *l* + 1 − *τij* and thenumberoflanguageterm.

**Step 2:** Collect the data by questionnaire and get the shadowed set of language terms by processing the data. Transform Pythagorean fuzzy linguistic numbers into PSNs using Figure 4.

**Step 3:** Transform the PFSN decision matrix into score function matrix based on Equation (6). **Step 4:** By OWA operator, the attribute values *rij*of each alternative *ai*are aggregated to obtain

the comprehensive attribute values *zi*.

*l* is

$$z\_i = \text{OWA}\_{\text{w}}(r\_{i1}, r\_{i2}, \dots, r\_{im}) = \sum\_{j=1}^{m} w\_i r\_{ij}, i = 1, 2, \dots, m$$

where *w* = (*w*1, *w*2,..., *wm*) is the criterion weight vector, *n* is the number of alternatives, *m* is the number of attribute.

**Step 5:** Determine the order of all the alternatives in the light of the comprehensive attribute values *zi*.

## **6. Numerical Study**

The proposed algorithm will be demonstrated by solving the problem of how to select the most suitable supplier for a company under various evaluation factors. At the same time, comparisons with the linguistic term subscript method and the linguistic scale function method are performed to show the advantages of our approach.

#### *6.1. Supplier Selection Problem*

A car company needs to choose appropriate supplier of spare parts. A total of five alternative suppliers are denoted as *a*1, *a*2, *a*3, *a*4, *a*5. After synthetical consideration, four main factors are taken into account: *c*1 Supply capacity, *c*2 Delivery timeliness, *c*3 Service quality, *c*4 Scientific research ability. The criterion weight vector is *w* = (0.3, 0.2, 0.4, 0.1). Language evaluation of the four attributes adopts the form of seven-level linguistic term, *S* = {*s*0,*s*1,*s*2,*s*3,*s*4,*s*5,*s*6} = {Extremely low, Very low, Low, Fair, High, Very high, and Extremely high}. The decision matrix given by experts is shown in Table 2:

**Table 2.** Decision matrix.

**Step 1:** *c*1, *c*2, *c*3, *c*4 are beneficial attributes. Therefore, the standardized decision matrix is the same with Table 2.

**Step 2:** Transform PFLNs into Pythagorean shadowed numbers using Figure 4, and the result is shown in Table 3.

#### **Table 3.** Decision matrix with PFSN.

**Step 3:** Transform the PFSNs decision matrix into score function matrix (shown in Table 4) based on Equation (6).

**Table 4.** Score function matrix.

**Step 4:** By OWA operator, the attribute values *rij* of each alternative *ai* are aggregated to obtain the comprehensive attribute values *zi*.

*z*1 = 25.87, *z*2 = 25.15, *z*3 = 31.65, *z*4 = 27.01, *z*5 = 29.91**Step 5:** Rank the alternatives and obtain the best alternative(s) according to the comprehensive attribute values *zi* in the Step 4.

*z*3 > *z*5 > *z*4 > *z*1 > *z*2, that means, *a*3 *a*5 *a*4 *a*1 *a*2.

And the alternative *a*3 is the best choice of the supplier option problem.

## *6.2. Comparison Analysis*

To verify the superiority of our method, comparations will be made between our approach and the other two approaches, i.e., the linguistic term subscript method [22] and the linguistic scale function method [11,19].

In [22], the score function of *p* = *sτ*(*x*), *uA*(*x*), *vA*(*x*)is:

$$score(p) = \frac{\pi(\mathbf{x})}{t+1} \* \left(\mu\_{\beta}^2 - \nu\_{\beta}^2\right) \tag{7}$$

where *τ*(*x*) is the subscript of the linguistic term, and *t* is the number of linguistic terms.

We can obtain the comprehensive attribute values *zi* based on Equation (7) and the OWA operator. *z*1= 0.094, *z*2= 0.104, *z*3= 0.098, *z*4= 0.115, *z*5 = 0.147,and *z*5> *z*4> *z*2> *z*3> *z*1.

 Therefore, the alternative *a*5is the best choice.

 In [19], the score function of PFN *β* = *P*-*uβ*, *vβ*. is:

$$score(\beta) = \mu\_{\beta}^{2} - \nu\_{\beta}^{2} \tag{8}$$

In [11], the improved linguistic scale function is calculated as follows:

$$f(s\_i) = \theta\_i = \begin{cases} \frac{m^a - (m-i)^a}{2m^a} & (i = 0, 1, 2, \dots, m) \\\frac{m^b + (i-m)^b}{2m^b} & (i = m+1, m+2m, \dots, t) \end{cases} \tag{9}$$

where *α*, *β* ∈ (0, 1], *m* = *t*2, and *t* is the number of linguistic terms.

According to the improved linguistic scale Function (8) and score Function (9), we can obtain the score function of *p* = *sτ*(*x*), *uA*(*x*), *vA*(*x*) as:

$$score(p) = f(s\_i) \* \left(\mu\_{\beta}^2 - \nu\_{\beta}^2\right) \tag{10}$$

Let *α* = *β* = 0.5. We can obtain the comprehensive attribute values *zi* based on Equation (10) and the OWA operator.

*z*1 = 0.07, *z*2 = 0.15, *z*3 = 0.2, *z*4 = 0.14, *z*5 = 0.16, and *z*3 > *z*5 > *z*2 > *z*4 > *z*1. Therefore, the alternative *a*3 is the best choice.

From Table 5, it can be observed that the ranking result obtained via our algorithm is different from the other two methods. By using the linguistic term subscript method, the ranking order is *a*5 *a*4 *a*2 *a*3 *a*1, which is totally different form the results of our method and the language scale function method. The reason is that replacing linguistic words simply with linguistic subscript leads to distortion of information. In fact, the linguistic subscript cannot effectively reflect original decision information. Compared with the linguistic term subscript approach, the linguistic scale function method seems more reasonable for describing the linguistic term information with a so-called language scale function. However, the language scale function still replaces linguistic words with numbers in nature, and information loss or information distortion is still inevitable. On the other hand, different people may have different viewpoints on the same word, but the linguistic subscript and linguistic scale function can only express a single meaning for a word. Compared with the other two methods, we utilize a data-driven method to construct the shadowed set models for the linguistic terms, which cannot only maintain the original decision information as far as possible, but also take different views into account for a single word.


**1. Introduction**

336 Energy Efficiency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities

Ad Hoc Networks 3 (3) 325–349.

Actor Network Protocols and Applications,

System Modeling, pp. V3-612- V3-616, IEEE.

Applications Vol. 02, Issue: 04, pp. 745-754,

of Advanced Science and Technology. Vol.36.

communications 29, pp 2230- 2237.

March.

2528-2532.

CiSE. pp. 1-4, IEEE.

IEEE INFOCOM, 2005.

K. Akkaya, M. Younis, (2005). A survey on routing protocols for wireless sensor networks,

S. Lindsey and C. S. Raghavendra, (2002). PEGASIS: Power Efficient GAthering in Sensor Informatio Systems. Proceedings of the IEEE Aerospace Conference, Big Sky, Montana,

G. Smaragdakis, I. Matta, A. Bestavros, 2004. SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks. Second International Workshop on Sensor and

L. Qing, Q. Zhu, M. Wang, (2006). Design of a distributed energy efficient clustering algorithm for heterogeneous wireless sensor networks. ELSEVIER, Computer

X. Du, F. Lin. (2005). Improving Routing in Sensor Networks with Heterogeneous Sensor Nodes. Proceedings of IEEE 61st Vehicular Technology Conference, VTC, Vol. 4, pp.

LI Han, (2010). An Energy Efficient Routing Algorithm for Heterogeneous Wireless Sensor Networks. Proceedings of th International Conference on Computer Application and

Q. Xuegong, M. Fuchang, Ch.Yan, Y. Weizhao (2009). The Protocol of Cluster Multi-Hop Transmission Based on Heterogeneous Wireless Sensor Networks. Proceedings of International Conference on Computational Intelligence and Software Engineering,

V. Katiyar, N. Chand, S. Soni, (2011). A Survey on Clustering algorithms for Heterogeneous Wireless Sensor Networks. Internationnal journal Advanced Networking and

M. Yarvis, N. Kushalnagar and H. Singh (2005).Exploiting heterogeneity in sensor networks,

W.B.Heinzelman et al. (2002),An application-specic protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications 1 (4) 660–670. R. Sheikhpour, S. Jabbehdari, A. Khadem-Zadeh (2011). Comparaison of Energy Efficient Clustering Protocols in Heterogeneous Wireless Sensor Networks. International Journal An urban network, according to the document RFC 5548 (2009) - Routing Requirements for Urban Low-Power and Lossy Networks, is defined as:

"Sensing and actuating nodes placed outdoors in urban environments so as to improve people's living conditions as well as to monitor compliance with increasingly strict environmental laws. These field nodes are expected to measure and report a wide gamut of data (for example, the data required by applications that perform smart-metering or that monitor meteorological, pollution, and allergy conditions). The majority of these nodes are expected to communicate wirelessly over a variety of links such as IEEE 802.15.4, low-power IEEE 802.11, or IEEE 802.15.1 (Bluetooth), which given the limited radio range and the large number of nodes requires the use of suitable routing protocols".

According to Gungor et al. (2010), low-range WSNs are being widely recognized as a promising technology due to results obtained for smart metering, in particular the IEEE 802.15.4 standard, standardized by IEEE (Institute of Electrical and Electronics Engineers) in 2006 (IEEE 802.15.4), for its robustness, financial costs, low power consumption and simplicity.

Furthermore, there are two task groups that foresee the extension of IEEE 802.15.4 protocol in order to suit the requirements from smart grids. One of them (IEEE 802.15 WPAN TASK GROUP 4G, 2011) is preparing a protocol to support large networks, geographically diverse, with minimal infrastructure and millions of nodes; the other (IEEE 802.15 WPAN TASK GROUP 4E, 2011) is including characteristics extracted from CWPAN (Chinese WPAN standard – Chinese Wireless Personal Area Network), which specify power-saving methods for networks with high latency tolerance.

This paper presents an application for urban networks using the IEEE 802.15.4 standard, which is used for monitoring and control electric variables in a public lighting scenario.

© 2012 Pantoni et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This application consists of an urban network, where sensors (nodes) are coupled to the lamp posts or lighting points in order to control the control the lighting for these points, and capture important information from diagnostics, operation and failures.

Street Lighting System Based on Wireless Sensor Networks 339

projects on the "Brazilian Smart Grid Program" (ANEEL, 2010a). This program focuses on presenting project proposals in order to join efforts to coordinate and generate new technological knowledge of great relevance for the Brazilian electricity sector. More specifically, in accordance with the document generated by the mentioned public hearing (ANEEL, 2010a), this study contributes to the research of "telecommunication

In addition, the regulatory resolution nº 414, of September 9, 2010 (ANEEL, 2010b) from ANEEL establishes changes to the maintenance policy for the Brazilian public lighting structure. This document states that, from September 2012, municipal governments shall be responsible for maintaining the public lighting structure, instead of the concessionaires,

Finally, control and monitoring public lighting, through lamps or dimming ballasts, decreases light pollution, caused by poor adjustment of luminosity and number of lamps lit in specific directions, depending on the angle, intensity, etc (Figure 1). Besides, light pollution should be studied aiming to improve roads, streets and avenues conditions, in order to provide better maneuverability for drivers, and brightness for unsafe places, such

The current public lighting structure in Brazil consists of a set of lighting points arranged in a certain manner. Each point has a housing with a device called photocell relay, which is responsible for turning the lights on or off according to a brightness sensor. Then, at nightfall, the relay turns the light on at the lighting point. This limited structure may compromise consumption efficiency, quality of service to the community, and other factors,

To solve the problems mentioned above in an automatic way, allowing fast and efficient preventive and corrective maintenance, an automation system may be applied. The basis for such a system is implementing the communication between parts involved, like devices,

for example, problems in the lamp, power supply, the photocell relay, etc.

which shall be responsible only for power supplying.

as alleys and public squares (MIZON, 2001) (SCHWARZ, 2003).

infrastructure".

**Figure 1.** Light Pollution

This type of application is expected to improve quality in public lighting service and rationalize power consumption through smart sensors and supervision screens. The next section describes specific aspects involving the quality of public lighting service and the current stage of its structure in Brazil. Section 3 describes the system requirements, which its understanding is fundamental to develop the control and monitoring application and routing protocols. In section 4 the solutions regarding the control and monitoring application are detailed. Section 5 is presented the routing protocols strategies. In the last section, the conclusions are indicates the conclusions.


**Abstract**

The first heart transplantation (HTx) done by Dr. Wada in 1968 misled the general public not to allow to do brain-dead organ transplantation in Japan. It took almost 30 years to issue the Organ Transplantation Act to allow us to perform HTx in Japan. However, this Act required living written consent for brain death and organ donation. Therefore, children younger than 15 years could not donate their organs under brain-dead condition. After 6 years of struggle to revise this Act, the revised Act was issued in 2010 and brain-dead organ donation as well as HTx increased then. But the number in a year has been still extremely less than other developed countries in Japan. Then, we established a special donor management and assessment system to maximize donor organ availability as well as to improve post-transplant outcomes. During these periods, many types of mechanical circulatory supporting (MCS) have been introduced in Japan. Moreover, there have been many developments in immunosuppressive regimens and monitoring to improve outcomes after LVAD implantation and HTx. The chapter will summarize the past, current status, and future aspects of HTx as well as MCS in Japan.

**Keywords:** heart transplantation, mechanical circulatory support, donor assessment and management, immunosuppressive regimen, organ transplantation network system


**1. Introduction**

Lignocellulosic biomass, predominantly comprised of cellulose, hemicellulose and lignin, is an abundant and renewable carbon-based alternative to fossil resources [1]. Several applications with nanocomposites of cellulose and hemicelluloses have been reported, for example water-purification [2], (bio)sensing [3] and anti-microbial treatment [4]. However, lately, the transformation of materials, as well as their monomeric C6 and C5 carbohydrates, to value-added chemicals and fuels have been studied extensively [5–15]. In comparison, lignin has received much less attention as feedstock, possibly due to its complex polymeric structure and lower reactivity, even though it is a major part of lignocellulosic biomass, typically 30% by weight and 40% by energy content. However, recent developments have demonstrated lignin to be a potentially important feedstock for producing chemicals, especially aromatic compounds [16–23]. This progress is important for the future of biorefineries as valorization of the entire biomass substrate improves economic viability.

The direct transformation of lignin usually requires mechanical pretreatment and harsh reaction conditions, due to its poor solubility and complex heterogeneous structure. Thus, in order to understand the reactivity of lignin, in general, various lignin model compounds containing di fferent structural linkages, such as α-O-4 and β-O-4, have been widely used as substrates. Among the di fferent linkages, the most abundant structural unit in lignin is the β-O-4 (Figure 1), representing approximately 60% of hardwood and 45–50% of softwood [24].

**Figure 1.** Schematic representation of typical lignin fragments and the corresponding β-O-4 lignin model compound guaiacyl glycerol-β-guaiacyl ether (GGGE).

Several studies have converted lignin and simple aromatic model compounds by catalytic oxidation under typically harsh reaction conditions and afforded low yield and/or selectivity to the targeted products, whereas dimeric lignin model compounds containing β-O-4 linkages have only been scarcely studied [25,26]. In this context, the exploration of bulky β-O-4 lignin model compounds may provide valuable insight that can be transferred to the reactivity of the complex lignin molecule, especially on cleavage of β-O-4 linkages and further reactivity of the formed monomers [18].

Aerobic oxidation of the bulky lignin model compound guaiacyl glycerol-β-guaiacyl ether (GGGE) has primarily been examined with vanadium-based homogeneous catalyst systems. Hence, Son et al. reported a vanadium-based catalyst for the non-oxidative C-O bond cleavage of dimeric lignin model compounds with a conversion of 80% [27]. Furthermore, vanadium complexes showed promising catalytic activity for oxidative C–C bond cleavage of GGGE compounds, and promoted multistep reactions affording C–C and C–O cleavage products from alternative dimeric β-O-4 lignin model compounds [28–30]. Alternatively, Rahimi et al. introduced a two-step, metal-free organocatalytic method using first 4-acetamido-TEMPO as the catalyst for chemoselective aerobic oxidation of the secondary benzylic alcohols in GGGE followed by C–C cleavage using H2O2 [23]. In addition, Leitner et al. more recently introduced a highly active and selective ruthenium-complex catalyst system for C–C bond cleavage of β-O-4 lignin linkages involving a dehydrogenation-initiated retro-aldol reaction [31]. Despite these promising homogeneous catalyst systems for selective cleavage of C–C and C–O bonds, separation and recyclability of the catalysts remains cumbersome for such catalytic systems [30,32]. In contrast, solid catalysts with supported metals/metal oxides can easily be recovered from liquid reaction mixtures, and can often be recycled multiple times with preservation of the catalytic performance.

Supported ruthenium catalysts such as Ru/alumina, are an effective and reusable heterogeneous catalyst system for aerobic oxidation of both activated and non-activated alcohols in the presence of sulfur, nitrogen and carbon-carbon double bonds [33], and they are therefore interesting in the context of oxidative lignin valorization. We previously examined such catalysts for the aerobic oxidation of the lignin model compound veratryl alcohol to veratraldehyde in water and methanol with good results [34]. In the present study, analogous ruthenium supported catalysts with the different supports

γ-alumina (Ru/Al2O3), silica (Ru/SiO2), zirconia (Ru/ZrO2), spinel (Ru/MgAl2O4) and USY zeolite (Ru/HY), which were prepared, characterized and applied for aerobic oxidative cleavage of GGGE in acetonitrile to produce guaiacol, vanillin and vanillic acid under mild reaction conditions (Scheme 1). Acetonitrile was preferred as the reaction solvent to ensure dissolution of GGGE and the products, and reaction parameters such as temperature and time were optimized for the promising catalyst Ru/Al2O3 to increase the selectivity for the desirable products, and the recyclability of the catalyst examined by performing consecutive reaction runs. For comparison, other alumina-supported metal catalysts M/Al2O3 (M = Mn, Ag, Cu and Fe) were prepared and evaluated.

**Scheme 1.** Catalytic aerobic oxidation of guaiacyl glycerol-β-guaiacyl ether (GGGE) to guaiacol, vanillin and vanillic acid with supported metal catalysts.


**3. Conclusions**

With the study carried out, it was possible to construct different types of gardens for different spaces, bringing to the school spaces the plant production and the productive knowledge for children in urban areas, in the guidelines of (1) sustainability in the ecological, economic, social, cultural, political and ethical dimensions; (2) agricultural production bringing well-being and guaranteeing productivity; (3) construction with low cost and use of recyclable materials and adopting the method in educational spaces.

**103**

**Author details**

Adriana Maria dos Santos1

Romulo Wilker Neri de Andrade2

and Fabiana Terezinha Leal de Morais1

provided the original work is properly cited.

*Installation of Vegetable Based Roof Gardens in Schools From Recyclable Materials: A Study*

\*, Mariana Paiva Baracuhy1

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Federal University of Campina Grande, Campina Grande, Brazil

2 Universidade Federal da Paraíba, João Pessoa, Brazil

\*Address all correspondence to<EMAIL_ADDRESS>
, Jackson Rômulo de Sousa Leite1

, Dermeval Araújo Furtado1

,

*DOI: http://dx.doi.org/10.5772/intechopen.90721*

*Installation of Vegetable Based Roof Gardens in Schools From Recyclable Materials: A Study DOI: http://dx.doi.org/10.5772/intechopen.90721*